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2111.03534
Paul Krogmeier
Paul Krogmeier, P. Madhusudan
Learning Formulas in Finite Variable Logics
null
null
null
null
cs.LO cs.FL cs.PL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We consider grammar-restricted exact learning of formulas and terms in finite variable logics. We propose a novel and versatile automata-theoretic technique for solving such problems. We first show results for learning formulas that classify a set of positively- and negatively-labeled structures. We give algorithms for realizability and synthesis of such formulas along with upper and lower bounds. We also establish positive results using our technique for other logics and variants of the learning problem, including first-order logic with least fixed point definitions, higher-order logics, and synthesis of queries and terms with recursively-defined functions.
2021-11-15T00:00:00
no_new_dataset
false
0.708244
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.05283
Paul Kirkland
Paul Kirkland, Davide L. Manna, Alex Vicente-Sola and Gaetano Di Caterina
Unsupervised Spiking Instance Segmentation on Event Data using STDP
20 Pages, 13 Figures
null
null
null
cs.CV cs.LG eess.IV
http://creativecommons.org/licenses/by/4.0/
Spiking Neural Networks (SNN) and the field of Neuromorphic Engineering has brought about a paradigm shift in how to approach Machine Learning (ML) and Computer Vision (CV) problem. This paradigm shift comes from the adaption of event-based sensing and processing. An event-based vision sensor allows for sparse and asynchronous events to be produced that are dynamically related to the scene. Allowing not only the spatial information but a high-fidelity of temporal information to be captured. Meanwhile avoiding the extra overhead and redundancy of conventional high frame rate approaches. However, with this change in paradigm, many techniques from traditional CV and ML are not applicable to these event-based spatial-temporal visual streams. As such a limited number of recognition, detection and segmentation approaches exist. In this paper, we present a novel approach that can perform instance segmentation using just the weights of a Spike Time Dependent Plasticity trained Spiking Convolutional Neural Network that was trained for object recognition. This exploits the spatial and temporal aspects of the network's internal feature representations adding this new discriminative capability. We highlight the new capability by successfully transforming a single class unsupervised network for face detection into a multi-person face recognition and instance segmentation network.
2021-11-15T00:00:00
no_new_dataset
false
0.710446
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06397
Onofrio M. Marag\`o
P. Polimeno, A. Magazzu, M. A. Iati, R. Saija, L. Folco, D. Bronte Ciriza, M. G. Donato, A. Foti, P. G. Gucciardi, A. Saidi, C. Cecchi-Pestellini, A. Jimenez Escobar, E. Ammannito, G. Sindoni, I. Bertini, V. Della Corte, L. Inno, A. Ciaravella, A. Rotundi, O. M. Marago
Optical tweezers in a dusty universe
18 pages, 4 figures, 1 table. Part of EPJ plus Focus Point Issues on "Light Pressure across All Scales"
Eur. Phys. J. Plus (2021) 136:339
10.1140/epjp/s13360-021-01316-z
null
physics.space-ph astro-ph.EP astro-ph.IM physics.optics
http://creativecommons.org/licenses/by/4.0/
Optical tweezers are powerful tools based on focused laser beams. They are able to trap, manipulate and investigate a wide range of microscopic and nanoscopic particles in different media, such as liquids, air, and vacuum. Key applications of this contactless technique have been developed in many fields. Despite this progress, optical trapping applications to planetary exploration is still to be developed. Here we describe how optical tweezers can be used to trap and characterize extraterrestrial particulate matter. In particular, we exploit light scattering theory in the T-matrix formalism to calculate radiation pressure and optical trapping properties of a variety of complex particles of astrophysical interest. Our results open perspectives in the investigation of extraterrestrial particles on our planet, in controlled laboratory experiments, aiming for space tweezers applications: optical tweezers used to trap and characterize dust particles in space or on planetary bodies surface.
2021-11-15T00:00:00
no_new_dataset
false
0.710051
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06398
Yuan Xue
Jiarong Ye, Yuan Xue, Peter Liu, Richard Zaino, Keith Cheng, Xiaolei Huang
A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis
MICCAI 2021
null
10.1007/978-3-030-87237-3_59
null
eess.IV cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Generative models have been applied in the medical imaging domain for various image recognition and synthesis tasks. However, a more controllable and interpretable image synthesis model is still lacking yet necessary for important applications such as assisting in medical training. In this work, we leverage the efficient self-attention and contrastive learning modules and build upon state-of-the-art generative adversarial networks (GANs) to achieve an attribute-aware image synthesis model, termed AttributeGAN, which can generate high-quality histopathology images based on multi-attribute inputs. In comparison to existing single-attribute conditional generative models, our proposed model better reflects input attributes and enables smoother interpolation among attribute values. We conduct experiments on a histopathology dataset containing stained H&E images of urothelial carcinoma and demonstrate the effectiveness of our proposed model via comprehensive quantitative and qualitative comparisons with state-of-the-art models as well as different variants of our model. Code is available at https://github.com/karenyyy/MICCAI2021AttributeGAN.
2021-11-15T00:00:00
no_new_dataset
false
0.695105
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06399
Yuan Xue
Yuan Xue, Jiarong Ye, Qianying Zhou, Rodney Long, Sameer Antani, Zhiyun Xue, Carl Cornwell, Richard Zaino, Keith Cheng, Xiaolei Huang
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification
Elsevier Medical Image Analysis Best Paper Award runner up. arXiv admin note: substantial text overlap with arXiv:1912.03837
Medical Image Analysis 67 (2021): 101816
10.1016/j.media.2020.101816
null
eess.IV cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Histopathological analysis is the present gold standard for precancerous lesion diagnosis. The goal of automated histopathological classification from digital images requires supervised training, which requires a large number of expert annotations that can be expensive and time-consuming to collect. Meanwhile, accurate classification of image patches cropped from whole-slide images is essential for standard sliding window based histopathology slide classification methods. To mitigate these issues, we propose a carefully designed conditional GAN model, namely HistoGAN, for synthesizing realistic histopathology image patches conditioned on class labels. We also investigate a novel synthetic augmentation framework that selectively adds new synthetic image patches generated by our proposed HistoGAN, rather than expanding directly the training set with synthetic images. By selecting synthetic images based on the confidence of their assigned labels and their feature similarity to real labeled images, our framework provides quality assurance to synthetic augmentation. Our models are evaluated on two datasets: a cervical histopathology image dataset with limited annotations, and another dataset of lymph node histopathology images with metastatic cancer. Here, we show that leveraging HistoGAN generated images with selective augmentation results in significant and consistent improvements of classification performance (6.7% and 2.8% higher accuracy, respectively) for cervical histopathology and metastatic cancer datasets.
2021-11-15T00:00:00
no_new_dataset
false
0.707424
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06400
Junwei Yang
Junwei Yang, Xiao-Xin Li, Feihong Liu, Dong Nie, Pietro Lio, Haikun Qi, Dinggang Shen
Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI
null
null
null
null
eess.IV cs.CV physics.med-ph
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recent studies on T1-assisted MRI reconstruction for under-sampled images of other modalities have demonstrated the potential of further accelerating MRI acquisition of other modalities. Most of the state-of-the-art approaches have achieved improvement through the development of network architectures for fixed under-sampling patterns, without fully exploiting the complementary information between modalities. Although existing under-sampling pattern learning algorithms can be simply modified to allow the fully-sampled T1-weighted MR image to assist the pattern learning, no significant improvement on the reconstruction task can be achieved. To this end, we propose an iterative framework to optimize the under-sampling pattern for MRI acquisition of another modality that can complement the fully-sampled T1-weighted MR image at different under-sampling factors, while jointly optimizing the T1-assisted MRI reconstruction model. Specifically, our proposed method exploits the difference of latent information between the two modalities for determining the sampling patterns that can maximize the assistance power of T1-weighted MR image in improving the MRI reconstruction. We have demonstrated superior performance of our learned under-sampling patterns on a public dataset, compared to commonly used under-sampling patterns and state-of-the-art methods that can jointly optimize both the reconstruction network and the under-sampling pattern, up to 8-fold under-sampling factor.
2021-11-15T00:00:00
no_new_dataset
false
0.710823
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06401
Mohammed A. Al-Masni Dr.
Mohammed A. Al-masni, Seul Lee, Jaeuk Yi, Sewook Kim, Sung-Min Gho, Young Hun Choi, and Dong-Hyun Kim
Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRI
24 pages, 10 figures, 3 tables
null
null
null
eess.IV cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we develop an efficient retrospective deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving of the image spatial details and hence improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans.
2021-11-15T00:00:00
no_new_dataset
false
0.711262
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06402
Vittorio Del Tatto
Vittorio Del Tatto
A Fully Anisotropic Formulation of Stochastic Cell Rescaling
Thesis for the MSc in Theoretical and Computational Physics at University of Trento (Academic Year 2020/2021)
null
null
null
physics.chem-ph
http://creativecommons.org/licenses/by-nc-nd/4.0/
Anisotropic barostats are employed to carry out Molecular Dynamics simulations where the volume is allowed to fluctuate with no constraints on the shape of the simulation cell. Most of these algorithms are based on second-order differential equations and share some common drawbacks, namely they can lead to slowly damped oscillations in the equilibration phase, and they do not allow to control efficiently the volume autocorrelation time. This work develops the anisotropic version of stochastic cell rescaling, a first-order stochastic barostat that overcomes these limits and can also be employed in the production phase, resulting in the correct physical fluctuations of the cell. The algorithm can be easily implemented in the existing codes on top of the anisotropic Berendsen barostat. The validation tests, performed on a number of crystal systems, show that the method is robust against wide variations of the input parameter, which allows an efficient control of the volume autocorrelation time.
2021-11-15T00:00:00
no_new_dataset
false
0.710176
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06420
Waddah Saeed
Waddah Saeed, Christian Omlin
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities
29 pages, 2 figures, 4 tables
null
null
null
cs.LG cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems. However, this success has been met by increasing model complexity and employing black-box AI models that lack transparency. In response to this need, Explainable AI (XAI) has been proposed to make AI more transparent and thus advance the adoption of AI in critical domains. Although there are several reviews of XAI topics in the literature that identified challenges and potential research directions in XAI, these challenges and research directions are scattered. This study, hence, presents a systematic meta-survey for challenges and future research directions in XAI organized in two themes: (1) general challenges and research directions in XAI and (2) challenges and research directions in XAI based on machine learning life cycle's phases: design, development, and deployment. We believe that our meta-survey contributes to XAI literature by providing a guide for future exploration in the XAI area.
2021-11-15T00:00:00
no_new_dataset
false
0.711387
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06435
Ruhui Jin
Ruhui Jin, Francesco Rizzi and Eric Parish
Space-time reduced-order modeling for uncertainty quantification
null
null
null
null
math.NA cs.NA
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This work focuses on the space-time reduced-order modeling (ROM) method for solving large-scale uncertainty quantification (UQ) problems with multiple random coefficients. In contrast with the traditional space ROM approach, which performs dimension reduction in the spatial dimension, the space-time ROM approach performs dimension reduction on both the spatial and temporal domains, and thus enables accurate approximate solutions at a low cost. We incorporate the space-time ROM strategy with various classical stochastic UQ propagation methods such as stochastic Galerkin and Monte Carlo. Numerical results demonstrate that our methodology has significant computational advantages compared to state-of-the-art ROM approaches. By testing the approximation errors, we show that there is no obvious loss of simulation accuracy for space-time ROM given its high computational efficiency.
2021-11-15T00:00:00
no_new_dataset
false
0.711418
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06437
Abhinav Dahiya
Abhinav Dahiya, Nima Akbarzadeh, Aditya Mahajan and Stephen L. Smith
Scalable Operator Allocation for Multi-Robot Assistance: A Restless Bandit Approach
11 pages + 4 page Appendix, 7 Figures
null
null
null
cs.RO cs.SY eess.SY
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we consider the problem of allocating human operators in a system with multiple semi-autonomous robots. Each robot is required to perform an independent sequence of tasks, subjected to a chance of failing and getting stuck in a fault state at every task. If and when required, a human operator can assist or teleoperate a robot. Conventional MDP techniques used to solve such problems face scalability issues due to exponential growth of state and action spaces with the number of robots and operators. In this paper we derive conditions under which the operator allocation problem is indexable, enabling the use of the Whittle index heuristic. The conditions can be easily checked to verify indexability, and we show that they hold for a wide range of problems of interest. Our key insight is to leverage the structure of the value function of individual robots, resulting in conditions that can be verified separately for each state of each robot. We apply these conditions to two types of transitions commonly seen in remote robot supervision systems. Through numerical simulations, we demonstrate the efficacy of Whittle index policy as a near-optimal and scalable approach that outperforms existing scalable methods.
2021-11-15T00:00:00
no_new_dataset
false
0.706431
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06438
Chandralekha Singh
Chandralekha Singh, Abraham Asfaw and Jeremy Levy
Preparing students to be leaders of the quantum information revolution
Physics Today, 2021
null
10.1063/PT.6.5.20210927a
null
physics.ed-ph physics.soc-ph quant-ph
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This Physics Today article discusses why the physics community needs to embrace the challenge of educating students with diverse educational backgrounds to meet future research and workforce demands and outlines some of the efforts underway.
2021-11-15T00:00:00
no_new_dataset
false
0.70638
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06440
Gaurav Sahu
Alexandre Parmentier, Robin Cohen, Xueguang Ma, Gaurav Sahu and Queenie Chen
Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management
28 pages
null
null
null
cs.SI cs.AI
http://creativecommons.org/licenses/by/4.0/
In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modeling. In particular, we propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. We focus on demonstrating how clustering of similar users enables a critical new functionality: supporting more personalized, and thus more accurate predictions for users. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset. We then discuss how improving the detection of trusted relationships in social media can assist in supporting online users in their battle against the spread of misinformation and rumours, within a social networking environment which has recently exploded in popularity. We conclude with a reflection on a particularly vulnerable user base, older adults, in order to illustrate the value of reasoning about groups of users, looking to some future directions for integrating known preferences with insights gained through data analysis.
2021-11-15T00:00:00
no_new_dataset
false
0.710377
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06444
Mohsen Abedi
Pouria Nezhadmohammad, Mohsen Abedi, Mohammad Javad Emadi, Risto Wichman
SWIPT-Enabled Multiple Access Channel: Effects of Decoding Cost and Non-linear EH Model
IEEE Transactions on Communications
null
10.1109/TCOMM.2021.3121035
null
cs.IT math.IT
http://creativecommons.org/publicdomain/zero/1.0/
We studied power splitting-based simultaneous wireless information and power transfer (PS-SWIPT) in multiple access channels (MAC), considering the decoding cost and non-linear energy harvesting (EH) constraints at the receiving nodes to study practical limitations of an EH communication system. Under these restrictions, we formulated and analyzed the achievable rate and maximum departure regions in two well-studied scenarios, i.e., a classical PS-SWIPT MAC and a PS-SWIPT MAC with user cooperation. In the classical PS-SWIPT MAC setting, closed-form expressions for the optimal values of the PS factors are derived for two fundamental decoding schemes: simultaneous decoding and successive interference cancellation. In the PS-SWIPT MAC with user cooperation, the joint optimal power allocation for users as well as the optimal PS factor are derived. This reveals that one decoding scheme outperforms the other in the classical PS-SWIPT MAC, depending on the function type of the decoding cost. Finally, it is shown that the cooperation between users can potentially boost the performance of a PS-SWIPT MAC under decoding cost and non-linear EH constraints. Moreover, effects of the decoding cost functions, non-linear EH model and channel quality between the users are studied, and performance characteristics of the system are discussed.
2021-11-15T00:00:00
no_new_dataset
false
0.710616
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06447
Mingming Qiu
Sibo Cheng, Mingming Qiu
Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks
The manuscript is accepted for publication in Neural computing and applications
null
null
null
cs.LG cs.AI cs.NA math.NA
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Data assimilation techniques are widely used to predict complex dynamical systems with uncertainties, based on time-series observation data. Error covariance matrices modelling is an important element in data assimilation algorithms which can considerably impact the forecasting accuracy. The estimation of these covariances, which usually relies on empirical assumptions and physical constraints, is often imprecise and computationally expensive especially for systems of large dimension. In this work, we propose a data-driven approach based on long short term memory (LSTM) recurrent neural networks (RNN) to improve both the accuracy and the efficiency of observation covariance specification in data assimilation for dynamical systems. Learning the covariance matrix from observed/simulated time-series data, the proposed approach does not require any knowledge or assumption about prior error distribution, unlike classical posterior tuning methods. We have compared the novel approach with two state-of-the-art covariance tuning algorithms, namely DI01 and D05, first in a Lorenz dynamical system and then in a 2D shallow water twin experiments framework with different covariance parameterization using ensemble assimilation. This novel method shows significant advantages in observation covariance specification, assimilation accuracy and computational efficiency.
2021-11-15T00:00:00
no_new_dataset
false
0.710829
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06449
Ryuji Imamura
Ryuji Imamura, Takuma Seno, Kenta Kawamoto, Michael Spranger
Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation
Accepted at Deep Reinforcement Learning Workshop at Neural Information Processing Systems 2021
null
null
null
cs.AI cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
When humans play virtual racing games, they use visual environmental information on the game screen to understand the rules within the environments. In contrast, a state-of-the-art realistic racing game AI agent that outperforms human players does not use image-based environmental information but the compact and precise measurements provided by the environment. In this paper, a vision-based control algorithm is proposed and compared with human player performances under the same conditions in realistic racing scenarios using Gran Turismo Sport (GTS), which is known as a high-fidelity realistic racing simulator. In the proposed method, the environmental information that constitutes part of the observations in conventional state-of-the-art methods is replaced with feature representations extracted from game screen images. We demonstrate that the proposed method performs expert human-level vehicle control under high-speed driving scenarios even with game screen images as high-dimensional inputs. Additionally, it outperforms the built-in AI in GTS in a time trial task, and its score places it among the top 10% approximately 28,000 human players.
2021-11-15T00:00:00
no_new_dataset
false
0.7076
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06457
Zihao Deng
Zihao Deng and Michael Orshansky
Variability-Aware Training and Self-Tuning of Highly Quantized DNNs for Analog PIM
This is the preprint version of our paper accepted in DATE 2022
null
null
null
cs.LG cs.ET
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
DNNs deployed on analog processing in memory (PIM) architectures are subject to fabrication-time variability. We developed a new joint variability- and quantization-aware DNN training algorithm for highly quantized analog PIM-based models that is significantly more effective than prior work. It outperforms variability-oblivious and post-training quantized models on multiple computer vision datasets/models. For low-bitwidth models and high variation, the gain in accuracy is up to 35.7% for ResNet-18 over the best alternative. We demonstrate that, under a realistic pattern of within- and between-chip components of variability, training alone is unable to prevent large DNN accuracy loss (of up to 54% on CIFAR-100/ResNet-18). We introduce a self-tuning DNN architecture that dynamically adjusts layer-wise activations during inference and is effective in reducing accuracy loss to below 10%.
2021-11-15T00:00:00
no_new_dataset
false
0.708187
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06458
Ladislav Mo\v{s}ner
Ladislav Mo\v{s}ner, Old\v{r}ich Plchot, Luk\'a\v{s} Burget, Jan \v{C}ernock\'y
MultiSV: Dataset for Far-Field Multi-Channel Speaker Verification
Submitted to ICASSP 2022
null
null
null
eess.AS cs.LG cs.SD
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Motivated by unconsolidated data situation and the lack of a standard benchmark in the field, we complement our previous efforts and present a comprehensive corpus designed for training and evaluating text-independent multi-channel speaker verification systems. It can be readily used also for experiments with dereverberation, denoising, and speech enhancement. We tackled the ever-present problem of the lack of multi-channel training data by utilizing data simulation on top of clean parts of the Voxceleb dataset. The development and evaluation trials are based on a retransmitted Voices Obscured in Complex Environmental Settings (VOiCES) corpus, which we modified to provide multi-channel trials. We publish full recipes that create the dataset from public sources as the MultiSV corpus, and we provide results with two of our multi-channel speaker verification systems with neural network-based beamforming based either on predicting ideal binary masks or the more recent Conv-TasNet.
2021-11-15T00:00:00
new_dataset
true
0.708231
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06466
Vikram Jadhao
Prateek Sharma and Vikram Jadhao
Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms
4 pages, position paper appearing in the Proceedings of the 2021 IEEE 14th International Conference on Cloud Computing (CLOUD)
null
null
null
cs.DC cond-mat.soft cs.LG physics.comp-ph
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Scientific computing applications have benefited greatly from high performance computing infrastructure such as supercomputers. However, we are seeing a paradigm shift in the computational structure, design, and requirements of these applications. Increasingly, data-driven and machine learning approaches are being used to support, speed-up, and enhance scientific computing applications, especially molecular dynamics simulations. Concurrently, cloud computing platforms are increasingly appealing for scientific computing, providing "infinite" computing powers, easier programming and deployment models, and access to computing accelerators such as TPUs (Tensor Processing Units). This confluence of machine learning (ML) and cloud computing represents exciting opportunities for cloud and systems researchers. ML-assisted molecular dynamics simulations are a new class of workload, and exhibit unique computational patterns. These simulations present new challenges for low-cost and high-performance execution. We argue that transient cloud resources, such as low-cost preemptible cloud VMs, can be a viable platform for this new workload. Finally, we present some low-hanging fruits and long-term challenges in cloud resource management, and the integration of molecular dynamics simulations into ML platforms (such as TensorFlow).
2021-11-15T00:00:00
no_new_dataset
false
0.710597
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06469
Andrew Litteken
Jonathan M. Baker, Andrew Litteken, Casey Duckering, Henry Hoffman, Hannes Bernien, Frederic T. Chong
Exploiting Long-Distance Interactions and Tolerating Atom Loss in Neutral Atom Quantum Architectures
14 pages, 14 figures, In ISCA '21: The 48th International Symposium on Computer Architecture
null
null
null
quant-ph cs.AR cs.ET
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Quantum technologies currently struggle to scale beyond moderate scale prototypes and are unable to execute even reasonably sized programs due to prohibitive gate error rates or coherence times. Many software approaches rely on heavy compiler optimization to squeeze extra value from noisy machines but are fundamentally limited by hardware. Alone, these software approaches help to maximize the use of available hardware but cannot overcome the inherent limitations posed by the underlying technology. An alternative approach is to explore the use of new, though potentially less developed, technology as a path towards scalability. In this work we evaluate the advantages and disadvantages of a Neutral Atom (NA) architecture. NA systems offer several promising advantages such as long range interactions and native multiqubit gates which reduce communication overhead, overall gate count, and depth for compiled programs. Long range interactions, however, impede parallelism with restriction zones surrounding interacting qubit pairs. We extend current compiler methods to maximize the benefit of these advantages and minimize the cost. Furthermore, atoms in an NA device have the possibility to randomly be lost over the course of program execution which is extremely detrimental to total program execution time as atom arrays are slow to load. When the compiled program is no longer compatible with the underlying topology, we need a fast and efficient coping mechanism. We propose hardware and compiler methods to increase system resilience to atom loss dramatically reducing total computation time by circumventing complete reloads or full recompilation every cycle.
2021-11-15T00:00:00
no_new_dataset
false
0.710258
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06470
Gerardo Vega
Gerardo Vega and F\'elix Hern\'andez
The complete weight enumerator of a subclass of optimal three-weight cyclic codes
arXiv admin note: text overlap with arXiv:1508.05077
null
null
null
cs.IT math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
A class of optimal three-weight cyclic codes of dimension 3 over any finite field was presented by Vega [Finite Fields Appl., 42 (2016) 23-38]. Shortly thereafter, Heng and Yue [IEEE Trans. Inf. Theory, 62(8) (2016) 4501-4513] generalized this result by presenting several classes of cyclic codes with either optimal three weights or a few weights. On the other hand, a class of optimal five-weight cyclic codes of dimension 4 over a prime field was recently presented by Li, et al. [Adv. Math. Commun., 13(1) (2019) 137-156]. One of the purposes of this work is to present a more general description for these optimal five-weight cyclic codes, which gives place to an enlarged class of optimal five-weight cyclic codes of dimension 4 over any finite field. As an application of this enlarged class, we present the complete weight enumerator of a subclass of the optimal three-weight cyclic codes over any finite field that were studied by Vega [Finite Fields Appl., 42 (2016) 23-38]. In addition, we study the dual codes in this enlarged class of optimal five-weight cyclic codes, and show that they are cyclic codes of length $q^2-1$, dimension $q^2-5$, and minimum Hamming distance 4. In fact, through several examples, we see that those parameters are the best known parameters for linear codes.
2021-11-15T00:00:00
no_new_dataset
false
0.711418
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06486
Negar Hassanpour
Negar Hassanpour, Russell Greiner
Variational Auto-Encoder Architectures that Excel at Causal Inference
null
null
null
null
cs.LG cs.AI stat.ML
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Estimating causal effects from observational data (at either an individual -- or a population -- level) is critical for making many types of decisions. One approach to address this task is to learn decomposed representations of the underlying factors of data; this becomes significantly more challenging when there are confounding factors (which influence both the cause and the effect). In this paper, we take a generative approach that builds on the recent advances in Variational Auto-Encoders to simultaneously learn those underlying factors as well as the causal effects. We propose a progressive sequence of models, where each improves over the previous one, culminating in the Hybrid model. Our empirical results demonstrate that the performance of all three proposed models are superior to both state-of-the-art discriminative as well as other generative approaches in the literature.
2021-11-15T00:00:00
no_new_dataset
false
0.710666
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06489
Nguyen Phuong Dang
Nguyen Phuong Dang, Victor Gabriel Leandro Alves, Mahmoud Ahmed and Jeffrey Siebers
Gross patient error detection via cine transmission dosimetry
null
null
null
null
physics.med-ph
http://creativecommons.org/licenses/by-sa/4.0/
$\textbf{Purpose:}$ To quantify the effectiveness of EPID-based cine transmission dosimetry to detect gross patient anatomic errors. $\textbf{Method and Materials:}$ EPID image frames resulting from fluence transmitted through multiple patients anatomies are simulated for 100 msec delivery intervals for hypothetical 6 MV VMAT deliveries. Frames simulated through 10 head-and-neck CTs and 19 prostate CTs with and without 1-3 mm shift and 1-3 degree rotations were used to quantify expected in-tolerance clinical setup variations. Per-frame analysis methods to determine if simulated gross errors of (a) 10-20 mm patient miss alignment offsets and (b) 15-20 degree patient rotations could be reliably distinguished from the above baseline variations. For the prostate image sets, frames simulated through the reference CT are intercompared with (c) frames through 8-13 different CT's for the same patient to quantify expected inter-treatment frame variation. ROC analysis of per-frame error discrimination based upon (i) frame image differences, (ii) frame histogram comparisons, (iii) image feature matching, and (iv) image distance were used to quantify error detectability. $\textbf{Results:}$ Each error detection method was able to distinguish gross patient miss-alignment and gross rotations from in-tolerance levels for both H&N and prostate datasets. The image distance algorithm is the best method based on AUC. $\textbf{Conclusion:}$ In-field gross error detection was possible for gross patient miss-alignments and incorrect patients. For prostate cases, the methods used were able to distinguish different patients from daily patient variations.
2021-11-15T00:00:00
no_new_dataset
false
0.708036
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06494
Pavel Surynek
Martin \v{C}apek and Pavel Surynek
DPLL(MAPF): an Integration of Multi-Agent Path Finding and SAT Solving Technologies
null
null
null
null
cs.AI cs.MA
http://creativecommons.org/licenses/by-nc-nd/4.0/
In multi-agent path finding (MAPF), the task is to find non-conflicting paths for multiple agents from their initial positions to given individual goal positions. MAPF represents a classical artificial intelligence problem often addressed by heuristic-search. An important alternative to search-based techniques is compilation of MAPF to a different formalism such as Boolean satisfiability (SAT). Contemporary SAT-based approaches to MAPF regard the SAT solver as an external tool whose task is to return an assignment of all decision variables of a Boolean model of input MAPF. We present in this short paper a novel compilation scheme called DPLL(MAPF) in which the consistency checking of partial assignments of decision variables with respect to the MAPF rules is integrated directly into the SAT solver. This scheme allows for far more automated compilation where the SAT solver and the consistency checking procedure work together simultaneously to create the Boolean model and to search for its satisfying assignment.
2021-11-15T00:00:00
no_new_dataset
false
0.707777
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06500
John Yang
John Yang, Yash Bhalgat, Simyung Chang, Fatih Porikli, Nojun Kwak
Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
While hand pose estimation is a critical component of most interactive extended reality and gesture recognition systems, contemporary approaches are not optimized for computational and memory efficiency. In this paper, we propose a tiny deep neural network of which partial layers are recursively exploited for refining its previous estimations. During its iterative refinements, we employ learned gating criteria to decide whether to exit from the weight-sharing loop, allowing per-sample adaptation in our model. Our network is trained to be aware of the uncertainty in its current predictions to efficiently gate at each iteration, estimating variances after each loop for its keypoint estimates. Additionally, we investigate the effectiveness of end-to-end and progressive training protocols for our recursive structure on maximizing the model capacity. With the proposed setting, our method consistently outperforms state-of-the-art 2D/3D hand pose estimation approaches in terms of both accuracy and efficiency for widely used benchmarks.
2021-11-15T00:00:00
no_new_dataset
false
0.708792
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06502
Stefan Kollmannsberger
Frank Hartmann, Stefan Kollmannsberger
Enforcing essential boundary conditions on domains defined by point clouds
null
null
null
null
math.NA cs.NA
http://creativecommons.org/licenses/by/4.0/
This paper develops and investigates a new method for the application of Dirichlet boundary conditions for computational models defined by point clouds. Point cloud models often stem from laser or structured-light scanners which are used to scan existing mechanical structures for which CAD models either do not exist or from which the artifact under investigation deviates in shape or topology. Instead of reconstructing a CAD model from point clouds via surface reconstruction and a subsequent boundary conforming mesh generation, a direct analysis without pre-processing is possible using embedded domain finite element methods. These methods use non-boundary conforming meshes which calls for a weak enforcement of Dirichlet boundary conditions. For point cloud based models, Dirichlet boundary conditions are usually imposed using a diffuse interface approach. This leads to a significant computational overhead due to the necessary computation of domain integrals. Additionally, undesired side effects on the gradients of the solution arise which can only be controlled to some extent. This paper develops a new sharp interface approach for point cloud based models which avoids both issues. The computation of domain integrals is circumvented by an implicit approximation of corresponding Voronoi diagrams of higher order and the resulting sharp approximation avoids the side-effects of diffuse approaches. Benchmark examples from the graphics as well as the computational mechanics community are used to verify the algorithm. All algorithms are implemented in the FCMLab framework and provided at https://gitlab.lrz.de/cie_sam_public/fcmlab/. Further, we discuss challenges and limitations of point cloud based analysis w.r.t. application of Dirichlet boundary conditions.
2021-11-15T00:00:00
no_new_dataset
false
0.711481
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06503
Chuteng Zhou
Chuteng Zhou, Fernando Garcia Redondo, Julian B\"uchel, Irem Boybat, Xavier Timoneda Comas, S. R. Nandakumar, Shidhartha Das, Abu Sebastian, Manuel Le Gallo, Paul N. Whatmough
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator
null
null
null
null
cs.AR cs.ET cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Always-on TinyML perception tasks in IoT applications require very high energy efficiency. Analog compute-in-memory (CiM) using non-volatile memory (NVM) promises high efficiency and also provides self-contained on-chip model storage. However, analog CiM introduces new practical considerations, including conductance drift, read/write noise, fixed analog-to-digital (ADC) converter gain, etc. These additional constraints must be addressed to achieve models that can be deployed on analog CiM with acceptable accuracy loss. This work describes $\textit{AnalogNets}$: TinyML models for the popular always-on applications of keyword spotting (KWS) and visual wake words (VWW). The model architectures are specifically designed for analog CiM, and we detail a comprehensive training methodology, to retain accuracy in the face of analog non-idealities, and low-precision data converters at inference time. We also describe AON-CiM, a programmable, minimal-area phase-change memory (PCM) analog CiM accelerator, with a novel layer-serial approach to remove the cost of complex interconnects associated with a fully-pipelined design. We evaluate the AnalogNets on a calibrated simulator, as well as real hardware, and find that accuracy degradation is limited to 0.8$\%$/1.2$\%$ after 24 hours of PCM drift (8-bit) for KWS/VWW. AnalogNets running on the 14nm AON-CiM accelerator demonstrate 8.58/4.37 TOPS/W for KWS/VWW workloads using 8-bit activations, respectively, and increasing to 57.39/25.69 TOPS/W with $4$-bit activations.
2021-11-15T00:00:00
no_new_dataset
false
0.710007
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06505
Noah Kaufmann
Noah Kaufmann
Classifying All Degrees Below $N^3$
null
null
null
null
cs.FL math.LO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We answer an open question in the theory of transducer degrees initially posed in [3], on the structure of polynomial transducer degrees, in particular the question of what degrees, if any, lie below the degree of $n^3$. Transducer degrees are the equivalence classes formed by word transformations which can be realized by a finite-state transducer. While there are no general techniques to tell if a word $w_1$ can be transformed into $w_2$ via an FST, the work of Endrullis et al. in [2] provides a test for the class of spiralling functions, which includes all polynomials. We classify fully the degrees of all cubic polynomials which are below $n^3$, and many of the methods can also be used to classify the degrees of polynomials of higher orders.
2021-11-15T00:00:00
no_new_dataset
false
0.710377
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06517
Xiao Zeng
Xiao S. Zeng, Surya Dwarakanath, Wuyue Lu, Masaki Nakada, Demetri Terzopoulos
Neuromuscular Control of the Face-Head-Neck Biomechanical Complex With Learning-Based Expression Transfer From Images and Videos
12 pages, 7 figures, 2 tables
null
null
null
cs.GR cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The transfer of facial expressions from people to 3D face models is a classic computer graphics problem. In this paper, we present a novel, learning-based approach to transferring facial expressions and head movements from images and videos to a biomechanical model of the face-head-neck complex. Leveraging the Facial Action Coding System (FACS) as an intermediate representation of the expression space, we train a deep neural network to take in FACS Action Units (AUs) and output suitable facial muscle and jaw activation signals for the musculoskeletal model. Through biomechanical simulation, the activations deform the facial soft tissues, thereby transferring the expression to the model. Our approach has advantages over previous approaches. First, the facial expressions are anatomically consistent as our biomechanical model emulates the relevant anatomy of the face, head, and neck. Second, by training the neural network using data generated from the biomechanical model itself, we eliminate the manual effort of data collection for expression transfer. The success of our approach is demonstrated through experiments involving the transfer onto our face-head-neck model of facial expressions and head poses from a range of facial images and videos.
2021-11-15T00:00:00
no_new_dataset
false
0.710057
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06524
Kaijie Xu
Kaijie Xu
An Enhanced Adaptive Bi-clustering Algorithm through Building a Shielding Complex Sub-Matrix
null
null
null
null
cs.LG stat.ME
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Bi-clustering refers to the task of finding sub-matrices (indexed by a group of columns and a group of rows) within a matrix of data such that the elements of each sub-matrix (data and features) are related in a particular way, for instance, that they are similar with respect to some metric. In this paper, after analyzing the well-known Cheng and Church (CC) bi-clustering algorithm which has been proved to be an effective tool for mining co-expressed genes. However, Cheng and Church bi-clustering algorithm and summarizing its limitations (such as interference of random numbers in the greedy strategy; ignoring overlapping bi-clusters), we propose a novel enhancement of the adaptive bi-clustering algorithm, where a shielding complex sub-matrix is constructed to shield the bi-clusters that have been obtained and to discover the overlapping bi-clusters. In the shielding complex sub-matrix, the imaginary and the real parts are used to shield and extend the new bi-clusters, respectively, and to form a series of optimal bi-clusters. To assure that the obtained bi-clusters have no effect on the bi-clusters already produced, a unit impulse signal is introduced to adaptively detect and shield the constructed bi-clusters. Meanwhile, to effectively shield the null data (zero-size data), another unit impulse signal is set for adaptive detecting and shielding. In addition, we add a shielding factor to adjust the mean squared residue score of the rows (or columns), which contains the shielded data of the sub-matrix, to decide whether to retain them or not. We offer a thorough analysis of the developed scheme. The experimental results are in agreement with the theoretical analysis. The results obtained on a publicly available real microarray dataset show the enhancement of the bi-clusters performance thanks to the proposed method.
2021-11-15T00:00:00
no_new_dataset
false
0.711625
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06526
Akira Furui D.Eng.
Akira Furui, Tomoyuki Akiyama, and Toshio Tsuji
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection
Accepted at EMBC2021
null
null
null
eess.SP cs.LG stat.ML
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we propose a time-series stochastic model based on a scale mixture distribution with Markov transitions to detect epileptic seizures in electroencephalography (EEG). In the proposed model, an EEG signal at each time point is assumed to be a random variable following a Gaussian distribution. The covariance matrix of the Gaussian distribution is weighted with a latent scale parameter, which is also a random variable, resulting in the stochastic fluctuations of covariances. By introducing a latent state variable with a Markov chain in the background of this stochastic relationship, time-series changes in the distribution of latent scale parameters can be represented according to the state of epileptic seizures. In an experiment, we evaluated the performance of the proposed model for seizure detection using EEGs with multiple frequency bands decomposed from a clinical dataset. The results demonstrated that the proposed model can detect seizures with high sensitivity and outperformed several baselines.
2021-11-15T00:00:00
no_new_dataset
false
0.710239
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06527
Qian Li
Kun He, Qian Li, and Xiaoming Sun
Moser-Tardos Algorithm: Beyond Shearer's Bound
32 pages
null
null
null
cs.DS
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In a seminal paper (Moser and Tardos, JACM'10), Moser and Tardos developed a simple and powerful algorithm to find solutions to combinatorial problems in the variable Lov{\'a}sz Local Lemma (LLL) setting. Kolipaka and Szegedy (STOC'11) proved that the Moser-Tardos algorithm is efficient up to the tight condition of the abstract Lov{\'a}sz Local Lemma, known as Shearer's bound. A fundamental problem around LLL is whether the efficient region of the Moser-Tardos algorithm can be further extended. In this paper, we give a positive answer to this problem. We show that the efficient region of the Moser-Tardos algorithm goes beyond the Shearer's bound of the underlying dependency graph, if the graph is not chordal. Otherwise, the dependency graph is chordal, and it has been shown that Shearer's bound exactly characterizes the efficient region for such graphs (Kolipaka and Szegedy, STOC'11; He, Li, Liu, Wang and Xia, FOCS'17). Moreover, we demonstrate that the efficient region can exceed Shearer's bound by a constant by explicitly calculating the gaps on several infinite lattices. The core of our proof is a new criterion on the efficiency of the Moser-Tardos algorithm which takes the intersection between dependent events into consideration. Our criterion is strictly better than Shearer's bound whenever the intersection exists between dependent events. Meanwhile, if any two dependent events are mutually exclusive, our criterion becomes the Shearer's bound, which is known to be tight in this situation for the Moser-Tardos algorithm (Kolipaka and Szegedy, STOC'11; Guo, Jerrum and Liu, JACM'19).
2021-11-15T00:00:00
no_new_dataset
false
0.712589
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06531
Byeonggeun Kim
Byeonggeun Kim, Seunghan Yang, Jangho Kim, Simyung Chang
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization
Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021)
null
null
null
cs.SD cs.LG eess.AS
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
It is a practical research topic how to deal with multi-device audio inputs by a single acoustic scene classification system with efficient design. In this work, we propose Residual Normalization, a novel feature normalization method that uses frequency-wise normalization % instance normalization with a shortcut path to discard unnecessary device-specific information without losing useful information for classification. Moreover, we introduce an efficient architecture, BC-ResNet-ASC, a modified version of the baseline architecture with a limited receptive field. BC-ResNet-ASC outperforms the baseline architecture even though it contains the small number of parameters. Through three model compression schemes: pruning, quantization, and knowledge distillation, we can reduce model complexity further while mitigating the performance degradation. The proposed system achieves an average test accuracy of 76.3% in TAU Urban Acoustic Scenes 2020 Mobile, development dataset with 315k parameters, and average test accuracy of 75.3% after compression to 61.0KB of non-zero parameters. The proposed method won the 1st place in DCASE 2021 challenge, TASK1A.
2021-11-15T00:00:00
no_new_dataset
false
0.71027
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06532
Xiaopeng Li
Xiao Peng Li, Qi Liu and Hing Cheung So
Nonlinear Tensor Ring Network
null
null
null
null
cs.LG cs.AI eess.IV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The state-of-the-art deep neural networks (DNNs) have been widely applied for various real-world applications, and achieved significant performance for cognitive problems. However, the increment of DNNs' width and depth in architecture results in a huge amount of parameters to challenge the storage and memory cost, limiting to the usage of DNNs on resource-constrained platforms, such as portable devices. By converting redundant models into compact ones, compression technique appears to be a practical solution to reducing the storage and memory consumption. In this paper, we develop a nonlinear tensor ring network (NTRN) in which both fullyconnected and convolutional layers are compressed via tensor ring decomposition. Furthermore, to mitigate the accuracy loss caused by compression, a nonlinear activation function is embedded into the tensor contraction and convolution operations inside the compressed layer. Experimental results demonstrate the effectiveness and superiority of the proposed NTRN for image classification using two basic neural networks, LeNet-5 and VGG-11 on three datasets, viz. MNIST, Fashion MNIST and Cifar-10.
2021-11-15T00:00:00
no_new_dataset
false
0.710057
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06537
Raul Astudillo
Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
In Advances in Neural Information Processing Systems, 2021
null
null
null
cs.LG math.OC stat.ML
http://creativecommons.org/licenses/by/4.0/
Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-to-evaluate black-box functions. Most BO methods ignore how evaluation costs may vary over the optimization domain. However, these costs can be highly heterogeneous and are often unknown in advance. This occurs in many practical settings, such as hyperparameter tuning of machine learning algorithms or physics-based simulation optimization. Moreover, those few existing methods that acknowledge cost heterogeneity do not naturally accommodate a budget constraint on the total evaluation cost. This combination of unknown costs and a budget constraint introduces a new dimension to the exploration-exploitation trade-off, where learning about the cost incurs the cost itself. Existing methods do not reason about the various trade-offs of this problem in a principled way, leading often to poor performance. We formalize this claim by proving that the expected improvement and the expected improvement per unit of cost, arguably the two most widely used acquisition functions in practice, can be arbitrarily inferior with respect to the optimal non-myopic policy. To overcome the shortcomings of existing approaches, we propose the budgeted multi-step expected improvement, a non-myopic acquisition function that generalizes classical expected improvement to the setting of heterogeneous and unknown evaluation costs. Finally, we show that our acquisition function outperforms existing methods in a variety of synthetic and real problems.
2021-11-15T00:00:00
no_new_dataset
false
0.709416
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06539
Kota Dohi
Kota Dohi, Takashi Endo, Yohei Kawaguchi
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts
4 pages, 4 figures
null
null
null
eess.AS cs.SD
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
To develop a sound-monitoring system for machines, a method for detecting anomalous sound under domain shifts is proposed. A domain shift occurs when a machine's physical parameters change. Because a domain shift changes the distribution of normal sound data, conventional unsupervised anomaly detection methods can output false positives. To solve this problem, the proposed method constrains some latent variables of a normalizing flows (NF) model to represent physical parameters, which enables disentanglement of the factors of domain shifts and learning of a latent space that is invariant with respect to these domain shifts. Anomaly scores calculated from this domain-shift-invariant latent space are unaffected by such shifts, which reduces false positives and improves the detection performance. Experiments were conducted with sound data from a slide rail under different operation velocities. The results show that the proposed method disentangled the velocity to obtain a latent space that was invariant with respect to domain shifts, which improved the AUC by 13.2% for Glow with a single block and 2.6% for Glow with multiple blocks.
2021-11-15T00:00:00
no_new_dataset
false
0.711644
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06540
Ali Akhavan-Safaei
Ali Akhavan-Safaei, Mohsen Zayernouri
A Nonlocal Spectral Transfer Model and New Scaling Law for Scalar Turbulence
18 pages, 9 figures
null
null
null
physics.flu-dyn
http://creativecommons.org/licenses/by/4.0/
In this study, we revisit the spectral transfer model for the turbulent intensity in the passive scalar transport (under large-scale anisotropic forcing), and a subsequent modification to the scaling of scalar variance cascade is presented. From the modified spectral transfer model, we obtain a revised scalar transport model using fractional-order Laplacian operator that facilitates the robust inclusion of the nonlocal effects originated from large-scale anisotropy transferred across the multitude of scales in the turbulent cascade. We provide an $\textit{a priori}$ estimate for the nonlocal model based on the scaling analysis of scalar spectrum, and later examine our developed model through direct numerical simulation. We present a detailed analysis on the evolution of the scalar variance, high-order statistics of scalar gradient, and important two-point statistical metrics of the turbulent transport to make a comprehensive comparison between the nonlocal model and its standard version. Finally, we present an analysis that seamlessly reconciles the similarities between the developed model with the fractional-order subgrid-scale scalar flux model for the large-eddy simulation (Akhavan-Safaei et al. 2021) when the filter scale approaches the dissipative scales of turbulent transport. In order to perform this task, we employ a Gaussian process regression model to predict the model coefficient for the fractional-order subgrid model.
2021-11-15T00:00:00
no_new_dataset
false
0.711619
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06546
Weijie Liu
Weijie Liu, Chao Zhang, Nenggan Zheng, Hui Qian
Approximating Optimal Transport via Low-rank and Sparse Factorization
null
null
null
null
cs.LG
http://creativecommons.org/licenses/by-sa/4.0/
Optimal transport (OT) naturally arises in a wide range of machine learning applications but may often become the computational bottleneck. Recently, one line of works propose to solve OT approximately by searching the \emph{transport plan} in a low-rank subspace. However, the optimal transport plan is often not low-rank, which tends to yield large approximation errors. For example, when Monge's \emph{transport map} exists, the transport plan is full rank. This paper concerns the computation of the OT distance with adequate accuracy and efficiency. A novel approximation for OT is proposed, in which the transport plan can be decomposed into the sum of a low-rank matrix and a sparse one. We theoretically analyze the approximation error. An augmented Lagrangian method is then designed to efficiently calculate the transport plan.
2021-11-15T00:00:00
no_new_dataset
false
0.709799
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06548
Samuel Jackson
Daniel Keable, Alistair Jones, Samuel Krevor, Ann Muggeridge, Samuel J. Jackson
The effect of viscosity ratio and Peclet number on miscible viscous fingering in a Hele-Shaw cell: A combined numerical and experimental study
24 pages, 9 figures
null
null
null
physics.flu-dyn
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The results from a series of well characterised, unstable, miscible displacement experiments in a Hele Shaw cell with a quarter five-spot source-sink geometry are presented, with comparisons to detailed numerical simulation. We perform repeated experiments at adverse viscosity ratios from 1 - 20 and Peclet numbers from 10$^4$ - 10$^6$ capturing the transition from 2D to 3D radial fingering and experimental uncertainty. The open-access dataset provides time-lapse images of the fingering patterns, transient effluent profiles, and meta-information for use in model validation. We find the complexity of the fingering pattern increases with viscosity ratio and Peclet number, and the onset of fingering is delayed compared to linear displacements, likely due to Taylor dispersion stabilisation. The transition from 2D to 3D fingering occurs at a critical Peclet number that is consistent with recent experiments in the literature. 2D numerical simulations with hydrodynamic dispersion and different mesh orientations provide good predictions of breakthrough times and sweep efficiency obtained at intermediate Peclet numbers across the range of viscosity ratios tested, generally within the experimental uncertainty. Specific finger wavelengths, tip shapes, and growth are hard to replicate; model predictions using velocity dependent longitudinal dispersion or simple molecular diffusion bound the fingering evolution seen in the experiments, but neither fully capture both fine-scale and macroscopic measures. In both cases simulations predict sharper fingers than the experiment. A weaker dispersion stabilisation seems necessary to capture the experimental fingering at high viscosity ratio, which may also require anisotropic components. 3D models with varying dispersion formulations should be explored in future developments to capture the full range of effects at high viscosity ratio and Peclet number.
2021-11-15T00:00:00
new_dataset
true
0.500671
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06552
Yu Li
Yu Li, Zijing Wang, Hehu Xie
GCGE: A Package for Solving Large Scale Eigenvalue Problems by Parallel Block Damping Inverse Power Method
28 pages
null
null
GCGE-21-11
math.NA cs.NA
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We propose an eigensolver and the corresponding package, GCGE, for solving large scale eigenvalue problems. This method is the combination of damping idea, subspace projection method and inverse power method with dynamic shifts. To reduce the dimensions of projection subspaces, a moving mechanism is developed when the number of desired eigenpairs is large. The numerical methods, implementing techniques and the structure of the package are presented. Plenty of numerical results are provided to demonstrate the efficiency, stability and scalability of the concerned eigensolver and the package GCGE for computing many eigenpairs of large symmetric matrices arising from applications.
2021-11-15T00:00:00
no_new_dataset
false
0.707632
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06553
Fei Tong
Ziyan Zhu and Fei Tong
The Distance Distribution between Mobile Node and Reference Node in Regular Hexagon
null
null
null
null
cs.IT math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper presents a new method to obtain the distance distribution between the mobile node and any reference node in a regular hexagon. The existing distance distribution research mainly focuses on static network deployment and ignores node mobility. This paper studies the distribution of node distances between mobile node and any reference node. A random waypoint (RWP) migration model is adopted for mobile node. The Cumulative Distribution Function (CDF) of the distance between any reference node (inside or outside the regular hexagon) and the mobile node (inside the regular hexagon) is derived. The validity of the results is verified by simulation.
2021-11-15T00:00:00
no_new_dataset
false
0.710025
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06554
Yanhui Li
Y. H. Li, Y. X. Ren, Y. T. Su
A fourth-order finite difference scheme with accurate dispersion and adaptive dissipation for computational aeroacoustics
null
null
null
null
physics.comp-ph
http://creativecommons.org/licenses/by/4.0/
For computational acoustics, schemes need to have low-dispersion and low-dissipation properties in order to capture the amplitude and phase of the wave correctly. To improve the spectral properties of the scheme, the authors have previously proposed a scale sensor to automatically adjust the numerical dissipation. In consequence, a fourth-order finite difference scheme with minimized dispersion and adaptive dissipation (MDAD) has been proposed [1]. In this study, we further investigate this method for the high-fidelity numerical simulation of the acoustic problems and a new dispersion control method is proposed which is different from the traditional dispersion relation preserving (DRP) approach. Firstly, the scale sensor, which quantifies the local length scale of the solution as the effective scaled wavenumber, is modified for better performance on composite waves. Then the scale sensor is applied to control both the dispersion and dissipation of the scheme. The relationships between the dispersion/dissipation parameter and the effective scaled wavenumber are analytically and artificially constructed respectively. Thus, a fourth-order finite difference scheme with accurate dispersion and adaptive dissipation (ADAD) is constructed. The approximate dispersion relation (ADR) shows that the ADAD scheme achieves accurate dispersion property at k < 2.5. The dissipation is negligible at low wave number and gradually increases after k = 1 to suppress non-physical oscillations. Several benchmark cases of computational acoustics are presented to verify the high resolution of the proposed scheme compared with the conventional spectral optimized schemes.
2021-11-15T00:00:00
no_new_dataset
false
0.71086
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06555
Wangyang Xu
Wangyang Xu, Lu Gan, and Chongwen Huang
A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints
31 pages, 13 figures
null
null
null
cs.IT cs.LG eess.SP math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Reconfigurable intelligent surface (RIS) has become a promising technology to improve wireless communication in recent years. It steers the incident signals to create a favorable propagation environment by controlling the reconfigurable passive elements with less hardware cost and lower power consumption. In this paper, we consider a RIS-aided multiuser multiple-input single-output downlink communication system. We aim to maximize the weighted sum-rate of all users by joint optimizing the active beamforming at the access point and the passive beamforming vector of the RIS elements. Unlike most existing works, we consider the more practical situation with the discrete phase shifts and imperfect channel state information (CSI). Specifically, for the situation that the discrete phase shifts and perfect CSI are considered, we first develop a deep quantization neural network (DQNN) to simultaneously design the active and passive beamforming while most reported works design them alternatively. Then, we propose an improved structure (I-DQNN) based on DQNN to simplify the parameters decision process when the control bits of each RIS element are greater than 1 bit. Finally, we extend the two proposed DQNN-based algorithms to the case that the discrete phase shifts and imperfect CSI are considered simultaneously. Our simulation results show that the two DQNN-based algorithms have better performance than traditional algorithms in the perfect CSI case, and are also more robust in the imperfect CSI case.
2021-11-15T00:00:00
no_new_dataset
false
0.710019
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06562
Zhaozhuo Xu
Zhaozhuo Xu, Alan Baonan Ji, Andrew Woods, Beidi Chen and Anshumali Shrivastava
Satellite Images and Deep Learning to Identify Discrepancy in Mailing Addresses with Applications to Census 2020 in Houston
null
null
null
null
cs.CY
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The accuracy and completeness of population estimation would significantly impact the allocation of public resources. However, the current census paradigm experiences a non-negligible level of under-counting. Existing solutions to this problem by the Census Bureau is to increase canvassing efforts, which leads to expensive and inefficient usage of human resources. In this work, we argue that the existence of hidden multi-family households is a significant cause of under-counting. Accordingly, we introduce a low-cost but high-accuracy method that combines satellite imagery and deep learning technologies to identify hidden multi-family (HMF) households. With comprehensive knowledge of the HMF households, the efficiency and effectiveness of the decennial census could be vastly improved. An extensive experiment demonstrates that our approach can discover over 1800 undetected HMF in a single zipcode of the Houston area.
2021-11-15T00:00:00
no_new_dataset
false
0.709629
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06563
Adel Nadjaran Toosi
Hamza Javed, Adel N. Toosi, Mohammad S. Aslanpour
Serverless Platforms on the Edge: A Performance Analysis
null
null
null
null
cs.DC
http://creativecommons.org/licenses/by/4.0/
The exponential growth of Internet of Things (IoT) has given rise to a new wave of edge computing due to the need to process data on the edge, closer to where it is being produced and attempting to move away from a cloud-centric architecture. This provides its own opportunity to decrease latency and address data privacy concerns along with the ability to reduce public cloud costs. The serverless computing model provides a potential solution with its event-driven architecture to reduce the need for ever-running servers and convert the backend services to an as-used model. This model is an attractive prospect in edge computing environments with varying workloads and limited resources. Furthermore, its setup on the edge of the network promises reduced latency to the edge devices communicating with it and eliminates the need to manage the underlying infrastructure. In this book chapter, first, we introduce the novel concept of serverless edge computing, then, we analyze the performance of multiple serverless platforms, namely, OpenFaaS, AWS Greengrass, Apache OpenWhisk, when set up on the single-board computers (SBCs) on the edge and compare it with public cloud serverless offerings, namely, AWS Lambda and Azure Functions, to deduce the suitability of serverless architectures on the network edge. These serverless platforms are set up on a cluster of Raspberry Pis and we evaluate their performance by simulating different types of edge workloads. The evaluation results show that OpenFaaS achieves the lowest response time on the SBC edge computing infrastructure while serverless cloud offerings are the most reliable with the highest success rate.
2021-11-15T00:00:00
no_new_dataset
false
0.710829
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06574
Milton Kumar Kundu
Md. Ibrahim, A. S. M. Badrudduza, Md. Shakhawat Hossen, M. K. Kundu, Imran Shafique Ansari
On Effective Secrecy Throughput of Underlay Spectrum Sharing $\alpha$-$\mu$/ M\'alaga Hybrid Model under Interference-and-Transmit Power Constraints
null
null
null
null
cs.IT eess.SP math.IT
http://creativecommons.org/licenses/by-sa/4.0/
The underlay cognitive radio-based hybrid radio frequency / free-space optical (RF / FSO) systems have been emerged as a promising technology due to its ability to eliminate spectrum scarcity and spectrum under-utilization problems. Consequently, this work analyzes the physical layer security aspects of a cognitive RF / FSO hybrid network that includes a primary user, a secondary source, a secondary receiver, and an eavesdropper where the secret communication takes place between two legitimate secondary peers over the RF and FSO links simultaneously, and the eavesdropper can overhear the RF link only. In particular, the maximum transmit power limitation at the secondary user as well as the permissible interference power restriction at the primary user are also taken into consideration. All the RF links are modeled with $\alpha$-$\mu$ fading whereas the FSO link undergoes M\'alaga (M) turbulence with link blockage and pointing error impairments. At the receiver, the selection combining diversity technique is utilized to select the signal with the best electrical signal-to-ratio (SNR). Moreover, the closed-form expressions for the secrecy outage probability, probability of strictly positive secrecy capacity, and effective secrecy throughput are derived to analyze the secrecy performance. Besides, the impacts of fading, primary-secondary interference, detection techniques, link blockage probability, atmospheric turbulence, and pointing error are examined. Finally, Monte-Carlo simulations are performed to corroborate the derived expressions.
2021-11-15T00:00:00
no_new_dataset
false
0.710622
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06578
Xiyang Liu
Xiyang Liu, Weihao Kong, Sewoong Oh
Differential privacy and robust statistics in high dimensions
null
null
null
null
math.ST cs.CR cs.IT cs.LG math.IT stat.ML stat.TH
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We introduce a universal framework for characterizing the statistical efficiency of a statistical estimation problem with differential privacy guarantees. Our framework, which we call High-dimensional Propose-Test-Release (HPTR), builds upon three crucial components: the exponential mechanism, robust statistics, and the Propose-Test-Release mechanism. Gluing all these together is the concept of resilience, which is central to robust statistical estimation. Resilience guides the design of the algorithm, the sensitivity analysis, and the success probability analysis of the test step in Propose-Test-Release. The key insight is that if we design an exponential mechanism that accesses the data only via one-dimensional robust statistics, then the resulting local sensitivity can be dramatically reduced. Using resilience, we can provide tight local sensitivity bounds. These tight bounds readily translate into near-optimal utility guarantees in several cases. We give a general recipe for applying HPTR to a given instance of a statistical estimation problem and demonstrate it on canonical problems of mean estimation, linear regression, covariance estimation, and principal component analysis. We introduce a general utility analysis technique that proves that HPTR nearly achieves the optimal sample complexity under several scenarios studied in the literature.
2021-11-15T00:00:00
no_new_dataset
false
0.709617
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06580
Moontae Lee
Moontae Lee, Sungjun Cho, Kun Dong, David Mimno, David Bindel
On-the-Fly Rectification for Robust Large-Vocabulary Topic Inference
null
null
null
null
cs.CL cs.AI cs.LG
http://creativecommons.org/licenses/by-nc-nd/4.0/
Across many data domains, co-occurrence statistics about the joint appearance of objects are powerfully informative. By transforming unsupervised learning problems into decompositions of co-occurrence statistics, spectral algorithms provide transparent and efficient algorithms for posterior inference such as latent topic analysis and community detection. As object vocabularies grow, however, it becomes rapidly more expensive to store and run inference algorithms on co-occurrence statistics. Rectifying co-occurrence, the key process to uphold model assumptions, becomes increasingly more vital in the presence of rare terms, but current techniques cannot scale to large vocabularies. We propose novel methods that simultaneously compress and rectify co-occurrence statistics, scaling gracefully with the size of vocabulary and the dimension of latent space. We also present new algorithms learning latent variables from the compressed statistics, and verify that our methods perform comparably to previous approaches on both textual and non-textual data.
2021-11-15T00:00:00
no_new_dataset
false
0.71
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06582
Hongping Liu
M. H. Cai, Z. S. Xu, S. H. You, H. P. Liu
Sensitivity improvement of Rydberg atom-based microwave sensing via electromagnetically induced transparency
null
null
null
null
physics.atom-ph
http://creativecommons.org/licenses/by/4.0/
A highly excited Rydberg atom via electromagnetically induced transparency with two color cascading lasers has extreme sensitivity to electric fields of microwave ranging from 100 MHz to over 1 THz. It can be used as susceptible atom-based microwave communication antennas where the carrier wave usually works exactly resonant to the transition between a pair of adjacent Rydberg states with large electric dipole moment. A technique of superheterodyne with a strong on-resonant local microwave oscillator is employed to induce considerable Autler-Townes splitting where the antennas has a highest dynamic response to another weak target signal microwave carrier. To further improve the sensitivity of atomic antenna in communication, we detune the carrier microwave frequency off resonance forming an asymmetrically optical splitting and fix the coupling laser frequency at the shoulder of the stronger one, and optimize the local field strength simultaneously. It gives a sensitivity of 12.50(04) $\rm{nVcm^{-1}\cdot Hz^{-1/2}}$. Its enhancement mechanism of sensitivity is also proved by a theoretical simulation.
2021-11-15T00:00:00
no_new_dataset
false
0.711262
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06584
John Demme PhD
John Demme (Microsoft)
Elastic Silicon Interconnects: Abstracting Communication in Accelerator Design
null
null
null
null
cs.AR cs.PL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Communication is an important part of accelerator design, though it is under researched and under developed. Today, designers often face relatively low-level communication tools requiring them to design straightforward but error-prone plumbing. In this paper, we argue that raising the level of abstraction could yield correctness, productivity, and performance benefits not only for RTL-level designers but also for high level language developers.
2021-11-15T00:00:00
no_new_dataset
false
0.71287
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06590
Sayak Mukherjee
Sayak Mukherjee, Ramij R. Hossain
Data-Driven Pole Placement in LMI Regions with Robustness Constraints
This version contains 12 pages
null
null
null
eess.SY cs.SY
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper proposes a robust learning methodology to place the closed-loop poles in desired convex regions in the complex plane. We considered the system state and input matrices to be unknown and can only use the measurements of the system trajectories. The closed-loop pole placement problem in the linear matrix inequality (LMI) regions is considered a classic robust control problem; however, that requires knowledge about the state and input matrices of the linear system. We bring in ideas from the behavioral system theory and persistency of excitation condition-based fundamental lemma to develop a data-driven counterpart that satisfies multiple closed-loop robustness specifications, such as $\mathcal{D}$-stability and mixed $H_2/H_{\infty}$ performance specifications. Our formulations lead to data-driven semi-definite programs (SDPs) that are coupled with sufficient theoretical guarantees. We validate the theoretical results with numerical simulations on a third-order dynamic system.
2021-11-15T00:00:00
no_new_dataset
false
0.708799
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06593
Yanyi Ding
Yanyi Ding, Zhiyi Kuang, Yuxin Pei, Jeff Tan, Ziyu Zhang, Joseph Konan
Using Deep Learning Sequence Models to Identify SARS-CoV-2 Divergence
null
null
null
null
q-bio.QM cs.LG
http://creativecommons.org/licenses/by-nc-nd/4.0/
SARS-CoV-2 is an upper respiratory system RNA virus that has caused over 3 million deaths and infecting over 150 million worldwide as of May 2021. With thousands of strains sequenced to date, SARS-CoV-2 mutations pose significant challenges to scientists on keeping pace with vaccine development and public health measures. Therefore, an efficient method of identifying the divergence of lab samples from patients would greatly aid the documentation of SARS-CoV-2 genomics. In this study, we propose a neural network model that leverages recurrent and convolutional units to directly take in amino acid sequences of spike proteins and classify corresponding clades. We also compared our model's performance with Bidirectional Encoder Representations from Transformers (BERT) pre-trained on protein database. Our approach has the potential of providing a more computationally efficient alternative to current homology based intra-species differentiation.
2021-11-15T00:00:00
no_new_dataset
false
0.710208
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06596
Nasir Saeed
Zakria Qadir, Hafiz Suliman Munawar, Nasir Saeed and Khoa Le
Towards 6G Internet of Things: Recent Advances, Use Cases, and Open Challenges
Submitted to IEEE IoT Journal
null
null
null
eess.SY cs.NI cs.SY
http://creativecommons.org/licenses/by/4.0/
Smart services based on the Internet of Everything (IoE) are gaining considerable popularity due to the ever-increasing demands of wireless networks. This demands the appraisal of the wireless networks with enhanced properties as next-generation communication systems. Although 5G networks show great potential to support numerous IoE based services, it is not adequate to meet the complete requirements of the new smart applications. Therefore, there is an increased demand for envisioning the 6G wireless communication systems to overcome the major limitations in the existing 5G networks. Moreover, incorporating artificial intelligence in 6G will provide solutions for very complex problems relevant to network optimization. Furthermore, to add further value to the future 6G networks, researchers are investigating new technologies, such as THz and quantum communications. The requirements of future 6G wireless communications demand to support massive data-driven applications and the increasing number of users. This paper presents recent advances in the 6G wireless networks, including the evolution from 1G to 5G communications, the research trends for 6G, enabling technologies, and state-of-the-art 6G projects.
2021-11-15T00:00:00
no_new_dataset
false
0.709617
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06599
Parth Patwa
Mohsin Ali, Kandukuri Sai Teja, Sumanth Manduru, Parth Patwa, Amitava Das
PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages
Accepted as Student Abstract at AAAI 2022
null
null
null
cs.CL cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
NLP applications for code-mixed (CM) or mix-lingual text have gained a significant momentum recently, the main reason being the prevalence of language mixing in social media communications in multi-lingual societies like India, Mexico, Europe, parts of USA etc. Word embeddings are basic build-ing blocks of any NLP system today, yet, word embedding for CM languages is an unexplored territory. The major bottleneck for CM word embeddings is switching points, where the language switches. These locations lack in contextually and statistical systems fail to model this phenomena due to high variance in the seen examples. In this paper we present our initial observations on applying switching point based positional encoding techniques for CM language, specifically Hinglish (Hindi - English). Results are only marginally better than SOTA, but it is evident that positional encoding could bean effective way to train position sensitive language models for CM text.
2021-11-15T00:00:00
no_new_dataset
false
0.709233
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06601
Milos Cernak
Damien Ronssin, Milos Cernak
AC-VC: Non-parallel Low Latency Phonetic Posteriorgrams Based Voice Conversion
ASRU 2021
null
null
null
eess.AS cs.SD
http://creativecommons.org/licenses/by/4.0/
This paper presents AC-VC (Almost Causal Voice Conversion), a phonetic posteriorgrams based voice conversion system that can perform any-to-many voice conversion while having only 57.5 ms future look-ahead. The complete system is composed of three neural networks trained separately with non-parallel data. While most of the current voice conversion systems focus primarily on quality irrespective of algorithmic latency, this work elaborates on designing a method using a minimal amount of future context thus allowing a future real-time implementation. According to a subjective listening test organized in this work, the proposed AC-VC system achieves parity with the non-causal ASR-TTS baseline of the Voice Conversion Challenge 2020 in naturalness with a MOS of 3.5. In contrast, the results indicate that missing future context impacts speaker similarity. Obtained similarity percentage of 65% is lower than the similarity of current best voice conversion systems.
2021-11-15T00:00:00
no_new_dataset
false
0.709416
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06609
Mohammad Abu Hamed
Mohammad Abu Hamed and Alexander A. Nepomnyashchy
Phase field model for phagocytosis dynamics
arXiv admin note: text overlap with arXiv:2106.12799
null
null
null
physics.bio-ph
http://creativecommons.org/licenses/by-nc-sa/4.0/
The basic process of the innate immune system when phagocyte (white blood cell) engulf or swallow a target particle (bacterium or dead cell), is called phagocytosis. We apply the phase field approach in the spirit of [1], that couples the order parameter $u$ with 3D polarization (orientation) vector field $\textbf{P}$ of the actin network of the phagocyte cytoskeleton. We derive a single closed scalar integro-differential equation governing the 3D phagocyte membrane dynamics during bead engulfment, which includes the normal velocity of the membrane, curvature, volume relaxation rate, a function determined by the molecular effects of the subcell level, and the adhesion effect of the motionless rigid spherical bead. This equation is easily solved numerically. The simulation manifests the pedestal and the cup phases but not the final complete bead internalization.
2021-11-15T00:00:00
no_new_dataset
false
0.710998
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06623
Iurii Timrov
Iurii Timrov, Nathalie Vast, Ralph Gebauer, Stefano Baroni
turboEELS -- A code for the simulation of the electron energy loss and inelastic X-ray scattering spectra using the Liouville-Lanczos approach to time-dependent density-functional perturbation theory
null
Comput. Phys. Commun. 196, 460 (2015)
10.1016/j.cpc.2015.05.021
null
cond-mat.mtrl-sci physics.comp-ph
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We introduce turboEELS, an implementation of the Liouville-Lanczos approach to linearized time-dependent density-functional theory, designed to simulate electron energy loss and inelastic X-ray scattering spectra in periodic solids. turboEELS is open-source software distributed under the terms of the GPL as a component of Quantum ESPRESSO. As with other components, turboEELS is optimized to run on a variety of different platforms, from laptops to massively parallel architectures, using native mathematical libraries (LAPACK and FFTW) and a hierarchy of custom parallelization layers built on top of MPI.
2021-11-15T00:00:00
no_new_dataset
false
0.710208
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06625
Ovishake Sen
Ovishake Sen, Al-Mahmud and Pias Roy
A Convolutional Neural Network Based Approach to Recognize Bangla Spoken Digits from Speech Signal
4 pages, 5 figures, 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), 14 to 16 September 2021, Khulna, Bangladesh
null
null
null
cs.SD cs.AI cs.CL cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Speech recognition is a technique that converts human speech signals into text or words or in any form that can be easily understood by computers or other machines. There have been a few studies on Bangla digit recognition systems, the majority of which used small datasets with few variations in genders, ages, dialects, and other variables. Audio recordings of Bangladeshi people of various genders, ages, and dialects were used to create a large speech dataset of spoken '0-9' Bangla digits in this study. Here, 400 noisy and noise-free samples per digit have been recorded for creating the dataset. Mel Frequency Cepstrum Coefficients (MFCCs) have been utilized for extracting meaningful features from the raw speech data. Then, to detect Bangla numeral digits, Convolutional Neural Networks (CNNs) were utilized. The suggested technique recognizes '0-9' Bangla spoken digits with 97.1% accuracy throughout the whole dataset. The efficiency of the model was also assessed using 10-fold crossvalidation, which yielded a 96.7% accuracy.
2021-11-15T00:00:00
new_dataset
true
0.706418
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06626
Serhat Tonkul
Serhat Tonkul, Alper Baba, Mustafa M. Demir, Simona Regenspurg
Characterization of Sb scaling and fluids in saline geothermal power plants: A case study for Germencik Region (B\"uy\"uk Menderes Graben, Turkey)
null
Geothermics 96 (2021) 102227
10.1016/j.geothermics.2021.102227
null
physics.geo-ph
http://creativecommons.org/licenses/by/4.0/
Turkey is located on the seismically active Alpine-Himalayan belt. Although tectonic activity causes seismicity in the Anatolian plate, it also constitutes an important geothermal energy resource. Today, geothermal energy production is heavily concentrated in Turkey's Western Anatolia region. Graben systems in this region are very suitable for geothermal resources. The B\"uy\"uk Menderes Graben (BMG) is an area of complex geology with active tectonics and high geothermal potential power. Germencik (Aydin) is located in the BMG, where the geothermal waters include mainly Na-Cl-HCO3 water types. This study examined the stibnite scaling formed in the preheater system of the Germencik Geothermal Field (GGF). The formation of the stibnite scaling on the preheater system dramatically reduces the energy harvesting of the GGF. Considering the stibnite scaling in the surface equipment, the optimum reinjection temperature was determined as 95 $^\circ$C to prevent stibnite scaling in the GGF.
2021-11-15T00:00:00
no_new_dataset
false
0.709208
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06638
Yunxiao Qin
Yunxiao Qin, Zitong Yu, Longbin Yan, Zezheng Wang, Chenxu Zhao, Zhen Lei
Meta-Teacher For Face Anti-Spoofing
Accepted by IEEE TPAMI-2021
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Face anti-spoofing (FAS) secures face recognition from presentation attacks (PAs). Existing FAS methods usually supervise PA detectors with handcrafted binary or pixel-wise labels. However, handcrafted labels may are not the most adequate way to supervise PA detectors learning sufficient and intrinsic spoofing cues. Instead of using the handcrafted labels, we propose a novel Meta-Teacher FAS (MT-FAS) method to train a meta-teacher for supervising PA detectors more effectively. The meta-teacher is trained in a bi-level optimization manner to learn the ability to supervise the PA detectors learning rich spoofing cues. The bi-level optimization contains two key components: 1) a lower-level training in which the meta-teacher supervises the detector's learning process on the training set; and 2) a higher-level training in which the meta-teacher's teaching performance is optimized by minimizing the detector's validation loss. Our meta-teacher differs significantly from existing teacher-student models because the meta-teacher is explicitly trained for better teaching the detector (student), whereas existing teachers are trained for outstanding accuracy neglecting teaching ability. Extensive experiments on five FAS benchmarks show that with the proposed MT-FAS, the trained meta-teacher 1) provides better-suited supervision than both handcrafted labels and existing teacher-student models; and 2) significantly improves the performances of PA detectors.
2021-11-15T00:00:00
no_new_dataset
false
0.712038
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06639
Anay Majee
Ashutosh Agarwal and Anay Majee and Anbumani Subramanian and Chetan Arora
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object Detection
8 pages, 4 figures
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Few-shot object detection (FSOD) localizes and classifies objects in an image given only a few data samples. Recent trends in FSOD research show the adoption of metric and meta-learning techniques, which are prone to catastrophic forgetting and class confusion. To overcome these pitfalls in metric learning based FSOD techniques, we introduce Attention Guided Cosine Margin (AGCM) that facilitates the creation of tighter and well separated class-specific feature clusters in the classification head of the object detector. Our novel Attentive Proposal Fusion (APF) module minimizes catastrophic forgetting by reducing the intra-class variance among co-occurring classes. At the same time, the proposed Cosine Margin Cross-Entropy loss increases the angular margin between confusing classes to overcome the challenge of class confusion between already learned (base) and newly added (novel) classes. We conduct our experiments on the challenging India Driving Dataset (IDD), which presents a real-world class-imbalanced setting alongside popular FSOD benchmark PASCAL-VOC. Our method outperforms State-of-the-Art (SoTA) approaches by up to 6.4 mAP points on the IDD-OS and up to 2.0 mAP points on the IDD-10 splits for the 10-shot setting. On the PASCAL-VOC dataset, we outperform existing SoTA approaches by up to 4.9 mAP points.
2021-11-15T00:00:00
no_new_dataset
false
0.709025
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06643
Florian Henkel
Florian Henkel, Stephanie Schwaiger, Gerhard Widmer
Fully Automatic Page Turning on Real Scores
ISMIR 2021 Late Breaking/Demo
null
null
null
cs.SD cs.CV cs.LG eess.AS
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present a prototype of an automatic page turning system that works directly on real scores, i.e., sheet images, without any symbolic representation. Our system is based on a multi-modal neural network architecture that observes a complete sheet image page as input, listens to an incoming musical performance, and predicts the corresponding position in the image. Using the position estimation of our system, we use a simple heuristic to trigger a page turning event once a certain location within the sheet image is reached. As a proof of concept we further combine our system with an actual machine that will physically turn the page on command.
2021-11-15T00:00:00
no_new_dataset
false
0.703957
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06644
Qinxuan Wu
Qinxuan Wu and Allyson Ettinger
Variation and generality in encoding of syntactic anomaly information in sentence embeddings
BlackBoxNLP, EMNLP
null
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
While sentence anomalies have been applied periodically for testing in NLP, we have yet to establish a picture of the precise status of anomaly information in representations from NLP models. In this paper we aim to fill two primary gaps, focusing on the domain of syntactic anomalies. First, we explore fine-grained differences in anomaly encoding by designing probing tasks that vary the hierarchical level at which anomalies occur in a sentence. Second, we test not only models' ability to detect a given anomaly, but also the generality of the detected anomaly signal, by examining transfer between distinct anomaly types. Results suggest that all models encode some information supporting anomaly detection, but detection performance varies between anomalies, and only representations from more recent transformer models show signs of generalized knowledge of anomalies. Follow-up analyses support the notion that these models pick up on a legitimate, general notion of sentence oddity, while coarser-grained word position information is likely also a contributor to the observed anomaly detection.
2021-11-15T00:00:00
no_new_dataset
false
0.708377
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06647
Aseem Srivastava
Ganeshan Malhotra, Abdul Waheed, Aseem Srivastava, Md Shad Akhtar, Tanmoy Chakraborty
Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations
9 pages; Accepted to WSDM 2022
null
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
The onset of the COVID-19 pandemic has brought the mental health of people under risk. Social counselling has gained remarkable significance in this environment. Unlike general goal-oriented dialogues, a conversation between a patient and a therapist is considerably implicit, though the objective of the conversation is quite apparent. In such a case, understanding the intent of the patient is imperative in providing effective counselling in therapy sessions, and the same applies to a dialogue system as well. In this work, we take forward a small but an important step in the development of an automated dialogue system for mental-health counselling. We develop a novel dataset, named HOPE, to provide a platform for the dialogue-act classification in counselling conversations. We identify the requirement of such conversation and propose twelve domain-specific dialogue-act (DAC) labels. We collect 12.9K utterances from publicly-available counselling session videos on YouTube, extract their transcripts, clean, and annotate them with DAC labels. Further, we propose SPARTA, a transformer-based architecture with a novel speaker- and time-aware contextual learning for the dialogue-act classification. Our evaluation shows convincing performance over several baselines, achieving state-of-the-art on HOPE. We also supplement our experiments with extensive empirical and qualitative analyses of SPARTA.
2021-11-15T00:00:00
new_dataset
true
0.712038
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06649
John Paul Miranda
John Paul P. Miranda
Dataset of Philippine Presidents Speeches from 1935 to 2016
11 pages, 4 figures, 4 tables, dataset
International Journal of Computing Sciences Research 5 (2021) 1-11
10.25147/ijcsr.2017.001.1.72
null
cs.CY cs.IR
http://creativecommons.org/licenses/by/4.0/
The dataset was collected to examine and identify possible key topics within these texts. Data preparation such as data cleaning, transformation, tokenization, removal of stop words from both English and Filipino, and word stemming was employed in the dataset before feeding it to sentiment analysis and the LDA model. The topmost occurring word within the dataset is "development" and there are three (3) likely topics from the speeches of Philippine presidents: economic development, enhancement of public services, and addressing challenges. The dataset was able to provide valuable insights contained among official documents. While the study showed that presidents have used their annual address to express their visions for the country. It also presented that the presidents from 1935 to 2016 faced the same problems during their term. Future researchers may collect other speeches made by presidents during their term; combine them to the dataset used in this study to further investigate these important texts by subjecting them to the same methodology used in this study. The dataset may be requested from the authors and it is recommended for further analysis. For example, determine how the speeches of the president reflect the preamble or foundations of the Philippine constitution.
2021-11-15T00:00:00
new_dataset
true
0.686987
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06650
Yonggang Jiang
Yonggang Jiang, Chaodong Zheng
Robust and Optimal Contention Resolution without Collision Detection
null
null
null
null
cs.DC cs.DS
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We consider the classical contention resolution problem where nodes arrive over time, each with a message to send. In each synchronous slot, each node can send or remain idle. If in a slot one node sends alone, it succeeds; otherwise, if multiple nodes send simultaneously, messages collide and none succeeds. Nodes can differentiate collision and silence only if collision detection is available. Ideally, a contention resolution algorithm should satisfy three criteria: low time complexity (or high throughput); low energy complexity, meaning each node does not make too many broadcast attempts; strong robustness, meaning the algorithm can maintain good performance even if slots can be jammed. Previous work has shown, with collision detection, there are "perfect" contention resolution algorithms satisfying all three criteria. On the other hand, without collision detection, it was not until 2020 that an algorithm was discovered which can achieve optimal time complexity and low energy cost, assuming there is no jamming. More recently, the trade-off between throughput and robustness was studied. However, an intriguing and important question remains unknown: without collision detection, are there robust algorithms achieving both low total time complexity and low per-node energy cost? In this paper, we answer the above question affirmatively. Specifically, we develop a new randomized algorithm for robust contention resolution without collision detection. Lower bounds show that it has both optimal time and energy complexity. If all nodes start execution simultaneously, we design another algorithm that is even faster, with similar energy complexity as the first algorithm. The separation on time complexity suggests for robust contention resolution without collision detection, ``batch'' instances (nodes start simultaneously) are inherently easier than ``scattered'' ones (nodes arrive over time).
2021-11-15T00:00:00
no_new_dataset
false
0.711469
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06660
Silvia-Laura Pintea
Nikhil Saldanha, Silvia L. Pintea, Jan C. van Gemert, Nergis Tomen
Frequency learning for structured CNN filters with Gaussian fractional derivatives
Accepted at BMVC 2021
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Frequency information lies at the base of discriminating between textures, and therefore between different objects. Classical CNN architectures limit the frequency learning through fixed filter sizes, and lack a way of explicitly controlling it. Here, we build on the structured receptive field filters with Gaussian derivative basis. Yet, rather than using predetermined derivative orders, which typically result in fixed frequency responses for the basis functions, we learn these. We show that by learning the order of the basis we can accurately learn the frequency of the filters, and hence adapt to the optimal frequencies for the underlying learning task. We investigate the well-founded mathematical formulation of fractional derivatives to adapt the filter frequencies during training. Our formulation leads to parameter savings and data efficiency when compared to the standard CNNs and the Gaussian derivative CNN filter networks that we build upon.
2021-11-15T00:00:00
no_new_dataset
false
0.709265
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06661
Viola Wenz
Viola Wenz, Arno Kesper, Gabriele Taentzer
Detecting Quality Problems in Data Models by Clustering Heterogeneous Data Values
17 pages. This paper is an extended version of a paper to be published in "MoDELS '21: ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings". It was presented at the 3rd Workshop on Artificial Intelligence and Model-driven Engineering
null
null
null
cs.LG cs.DB
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Data is of high quality if it is fit for its intended use. The quality of data is influenced by the underlying data model and its quality. One major quality problem is the heterogeneity of data as quality aspects such as understandability and interoperability are impaired. This heterogeneity may be caused by quality problems in the data model. Data heterogeneity can occur in particular when the information given is not structured enough and just captured in data values, often due to missing or non-suitable structure in the underlying data model. We propose a bottom-up approach to detecting quality problems in data models that manifest in heterogeneous data values. It supports an explorative analysis of the existing data and can be configured by domain experts according to their domain knowledge. All values of a selected data field are clustered by syntactic similarity. Thereby an overview of the data values' diversity in syntax is provided. It shall help domain experts to understand how the data model is used in practice and to derive potential quality problems of the data model. We outline a proof-of-concept implementation and evaluate our approach using cultural heritage data.
2021-11-15T00:00:00
no_new_dataset
false
0.7116
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06662
Monika Syga
Agnieszka Kaliszewska and Monika Syga
A comprehensive study of clustering a class of 2D shapes
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The paper concerns clustering with respect to the shape and size of 2D contours that are boundaries of cross-sections of 3D objects of revolution. We propose a number of similarity measures based on combined disparate Procrustes analysis (PA) and Dynamic Time Warping (DTW) distances. Motivation and the main application for this study comes from archaeology. The performed computational experiments refer to the clustering of archaeological pottery.
2021-11-15T00:00:00
no_new_dataset
false
0.706222
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06664
Yi Huang
Igor Kulev, Berkay K\"opr\"u, Raul Rodriguez-Esteban, Diego Saldana, Yi Huang, Alessandro La Torraca, Elif Ozkirimli
Extraction of Medication Names from Twitter Using Augmentation and an Ensemble of Language Models
Proceedings of the BioCreative VII Challenge Evaluation Workshop
null
null
null
cs.CL cs.LG
http://creativecommons.org/licenses/by/4.0/
The BioCreative VII Track 3 challenge focused on the identification of medication names in Twitter user timelines. For our submission to this challenge, we expanded the available training data by using several data augmentation techniques. The augmented data was then used to fine-tune an ensemble of language models that had been pre-trained on general-domain Twitter content. The proposed approach outperformed the prior state-of-the-art algorithm Kusuri and ranked high in the competition for our selected objective function, overlapping F1 score.
2021-11-15T00:00:00
no_new_dataset
false
0.712238
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06667
Anja Thieme Dr
Anja Thieme
Understanding the Information Needs and Practices of Human Supporters of an Online Mental Health Intervention to Inform Machine Learning Applications
41 pages, 3 figures, 3 tables
null
null
null
cs.HC cs.CY cs.LG
http://creativecommons.org/licenses/by-nc-sa/4.0/
In the context of digital therapy interventions, such as internet-delivered Cognitive Behavioral Therapy (iCBT) for the treatment of depression and anxiety, extensive research has shown how the involvement of a human supporter or coach, who assists the person undergoing treatment, improves user engagement in therapy and leads to more effective health outcomes than unsupported interventions. Seeking to maximize the effects and outcomes of this human support, the research investigates how new opportunities provided through recent advances in the field of AI and machine learning (ML) can contribute useful data insights to effectively support the work practices of iCBT supporters. This paper reports detailed findings of an interview study with 15 iCBT supporters that deepens understanding of their existing work practices and information needs with the aim to meaningfully inform the development of useful, implementable ML applications particularly in the context of iCBT treatment for depression and anxiety. The analysis contributes (1) a set of six themes that summarize the strategies and challenges that iCBT supporters encounter in providing effective, personalized feedback to their mental health clients; and in response to these learnings, (2) presents for each theme concrete opportunities for how methods of ML could help support and address identified challenges and information needs. It closes with reflections on potential social, emotional and pragmatic implications of introducing new machine-generated data insights within supporter-led client review practices.
2021-11-15T00:00:00
no_new_dataset
false
0.711844
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06670
Ebenezer Isaac
Ebenezer R.H.P. Isaac
Robust Analytics for Video-Based Gait Biometrics
Ph.D. Thesis, Anna University, Chennai, Feb. 2018
null
null
null
cs.CV eess.IV
http://creativecommons.org/licenses/by/4.0/
Gait analysis is the study of the systematic methods that assess and quantify animal locomotion. Gait finds a unique importance among the many state-of-the-art biometric systems since it does not require the subject's cooperation to the extent required by other modalities. Hence by nature, it is an unobtrusive biometric. This thesis discusses both hard and soft biometric characteristics of gait. It shows how to identify gender based on gait alone through the Posed-Based Voting scheme. It then describes improving gait recognition accuracy using Genetic Template Segmentation. Members of a wide population can be authenticated using Multiperson Signature Mapping. Finally, the mapping can be improved in a smaller population using Bayesian Thresholding. All methods proposed in this thesis have outperformed their existing state of the art with adequate experimentation and results.
2021-11-15T00:00:00
no_new_dataset
false
0.709233
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06676
Rick Fritschek
Gerhard Wunder, Benedikt Gro{\ss}, Rick Fritschek, Rafael F. Schaefer
A Reverse Jensen Inequality Result with Application to Mutual Information Estimation
6 pages, ITW 2021
null
null
null
cs.IT cs.LG math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning. It can be also used to derive other standard inequalities such as the inequality of arithmetic and geometric means or the H\"older inequality. In a probabilistic setting, the Jensen inequality describes the relationship between a convex function and the expected value. In this work, we want to look at the probabilistic setting from the reverse direction of the inequality. We show that under minimal constraints and with a proper scaling, the Jensen inequality can be reversed. We believe that the resulting tool can be helpful for many applications and provide a variational estimation of mutual information, where the reverse inequality leads to a new estimator with superior training behavior compared to current estimators.
2021-11-15T00:00:00
no_new_dataset
false
0.709849
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06677
Xue Yang
Xue Yang, Yue Zhou, Junchi Yan
AlphaRotate: A Rotation Detection Benchmark using TensorFlow
7 pages, 1 figure, 1 table
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
AlphaRotate is an open-source Tensorflow benchmark for performing scalable rotation detection on various datasets. It currently provides more than 18 popular rotation detection models under a single, well-documented API designed for use by both practitioners and researchers. AlphaRotate regards high performance, robustness, sustainability and scalability as the core concept of design, and all models are covered by unit testing, continuous integration, code coverage, maintainability checks, and visual monitoring and analysis. AlphaRotate can be installed from PyPI and is released under the Apache-2.0 License. Source code is available at https://github.com/yangxue0827/RotationDetection.
2021-11-15T00:00:00
no_new_dataset
false
0.708629
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06685
Kunal Dahiya
Kunal Dahiya, Deepak Saini, Anshul Mittal, Ankush Shaw, Kushal Dave, Akshay Soni, Himanshu Jain, Sumeet Agarwal, Manik Varma
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
null
Web Search and Data Mining 2021
10.1145/3437963.3441810
null
cs.LG cs.IR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels from an extremely large label set. This paper develops the DeepXML framework that addresses these challenges by decomposing the deep extreme multi-label task into four simpler sub-tasks each of which can be trained accurately and efficiently. Choosing different components for the four sub-tasks allows DeepXML to generate a family of algorithms with varying trade-offs between accuracy and scalability. In particular, DeepXML yields the Astec algorithm that could be 2-12% more accurate and 5-30x faster to train than leading deep extreme classifiers on publically available short text datasets. Astec could also efficiently train on Bing short text datasets containing up to 62 million labels while making predictions for billions of users and data points per day on commodity hardware. This allowed Astec to be deployed on the Bing search engine for a number of short text applications ranging from matching user queries to advertiser bid phrases to showing personalized ads where it yielded significant gains in click-through-rates, coverage, revenue and other online metrics over state-of-the-art techniques currently in production. DeepXML's code is available at https://github.com/Extreme-classification/deepxml
2021-11-15T00:00:00
no_new_dataset
false
0.710245
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06686
Yuhong Song
Yuhong Song, Edwin Hsing-Mean Sha, Qingfeng Zhuge, Rui Xu, Yongzhuo Zhang, Bingzhe Li, Lei Yang
BSC: Block-based Stochastic Computing to Enable Accurate and Efficient TinyML
Accept by ASP-DAC 2022
null
null
null
cs.LG cs.AR
http://creativecommons.org/licenses/by/4.0/
Along with the progress of AI democratization, machine learning (ML) has been successfully applied to edge applications, such as smart phones and automated driving. Nowadays, more applications require ML on tiny devices with extremely limited resources, like implantable cardioverter defibrillator (ICD), which is known as TinyML. Unlike ML on the edge, TinyML with a limited energy supply has higher demands on low-power execution. Stochastic computing (SC) using bitstreams for data representation is promising for TinyML since it can perform the fundamental ML operations using simple logical gates, instead of the complicated binary adder and multiplier. However, SC commonly suffers from low accuracy for ML tasks due to low data precision and inaccuracy of arithmetic units. Increasing the length of the bitstream in the existing works can mitigate the precision issue but incur higher latency. In this work, we propose a novel SC architecture, namely Block-based Stochastic Computing (BSC). BSC divides inputs into blocks, such that the latency can be reduced by exploiting high data parallelism. Moreover, optimized arithmetic units and output revision (OUR) scheme are proposed to improve accuracy. On top of it, a global optimization approach is devised to determine the number of blocks, which can make a better latency-power trade-off. Experimental results show that BSC can outperform the existing designs in achieving over 10% higher accuracy on ML tasks and over 6 times power reduction.
2021-11-15T00:00:00
no_new_dataset
false
0.709573
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06689
Lin Chen
Lin Chen, Fengli Xu, Zhenyu Han, Kun Tang, Pan Hui, James Evans, Yong Li
Strategic COVID-19 vaccine distribution can simultaneously elevate social utility and equity
25 pages, 4 figures
null
null
null
cs.CY
http://creativecommons.org/licenses/by-nc-nd/4.0/
Balancing social utility and equity in distributing limited vaccines represents a critical policy concern for protecting against the prolonged COVID-19 pandemic. What is the nature of the trade-off between maximizing collective welfare and minimizing disparities between more and less privileged communities? To evaluate vaccination strategies, we propose a novel epidemic model that explicitly accounts for both demographic and mobility differences among communities and their association with heterogeneous COVID-19 risks, then calibrate it with large-scale data. Using this model, we find that social utility and equity can be simultaneously improved when vaccine access is prioritized for the most disadvantaged communities, which holds even when such communities manifest considerable vaccine reluctance. Nevertheless, equity among distinct demographic features are in tension due to their complex correlation in society. We design two behavior-and-demography-aware indices, community risk and societal harm, which capture the risks communities face and those they impose on society from not being vaccinated, to inform the design of comprehensive vaccine distribution strategies. Our study provides a framework for uniting utility and equity-based considerations in vaccine distribution, and sheds light on how to balance multiple ethical values in complex settings for epidemic control.
2021-11-15T00:00:00
no_new_dataset
false
0.705943
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06692
Asaf Levin
G. Jaykrishnan and Asaf Levin
EPTAS for parallel identical machine scheduling with time restrictions
null
null
null
null
cs.DS cs.DM math.CO math.OC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We consider the non-preemptive scheduling problem on identical machines where there is a parameter B and each machine in every unit length time interval can process up to B different jobs. The goal function we consider is the makespan minimization and we develop an EPTAS for this problem. Prior to our work a PTAS was known only for the case of one machine and constant values of B, and even the case of non-constant values of B on one machine was not known to admit a PTAS.
2021-11-15T00:00:00
no_new_dataset
false
0.708036
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06693
Matthias Griebel
Matthias Griebel, Dennis Segebarth, Nikolai Stein, Nina Schukraft, Philip Tovote, Robert Blum, Christoph M. Flath
Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2
null
null
null
null
q-bio.QM cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present deepflash2, a deep learning solution that facilitates the objective and reliable segmentation of ambiguous bioimages through multi-expert annotations and integrated quality assurance. Thereby, deepflash2 addresses typical challenges that arise during training, evaluation, and application of deep learning models in bioimaging. The tool is embedded in an easy-to-use graphical user interface and offers best-in-class predictive performance for semantic and instance segmentation under economical usage of computational resources.
2021-11-15T00:00:00
no_new_dataset
false
0.710879
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06706
Alexandre Cauquoin
Alexandre Cauquoin, Philippe Jean-Baptiste, Camille Risi, \'Elise Fourr\'e, Barbara Stenni and Amaelle Landais
The global distribution of natural tritium in precipitation simulated with an Atmospheric General Circulation Model and comparison with observations
Accepted paper version. See published version in EPSL Elsevier website
Earth and Planetary Science Letters, 427, October 2015, 160-170
10.1016/j.epsl.2015.06.043
null
physics.ao-ph
http://creativecommons.org/licenses/by-nc-nd/4.0/
The description of the hydrological cycle in Atmospheric General Circulation Models (GCMs) can be validated using water isotopes as tracers. Many GCMs now simulate the movement of the stable isotopes of water, but here we present the first GCM simulations modelling the content of natural tritium in water. These simulations were obtained using a version of the LMDZ General Circulation Model enhanced by water isotopes diagnostics, LMDZ-iso. To avoid tritium generated by nuclear bomb testing, the simulations have been evaluated against a compilation of published tritium datasets dating from before 1950, or measured recently. LMDZ-iso correctly captures the observed tritium enrichment in precipitation as oceanic air moves inland (the so-called continental effect) and the observed north-south variations due to the latitudinal dependency of the cosmogenic tritium production rate. The seasonal variability, linked to the stratospheric intrusions of air masses with higher tritium content into the troposphere, is correctly reproduced for Antarctica with a maximum in winter. LMDZ-iso reproduces the spring maximum of tritium over Europe, but underestimates it and produces a peak in winter that is not apparent in the data. This implementation of tritium in a GCM promises to provide a better constraint on: (1) the intrusions and transport of air masses from the stratosphere and (2) the dynamics of the modelled water cycle. The method complements the existing approach of using stable water isotopes.
2021-11-15T00:00:00
no_new_dataset
false
0.708572
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06707
Ming Lu
Ming Lu, Peiyao Guo, Huiqing Shi, Chuntong Cao, and Zhan Ma
Transformer-based Image Compression
null
null
null
null
eess.IV cs.CV
http://creativecommons.org/licenses/by-nc-sa/4.0/
A Transformer-based Image Compression (TIC) approach is developed which reuses the canonical variational autoencoder (VAE) architecture with paired main and hyper encoder-decoders. Both main and hyper encoders are comprised of a sequence of neural transformation units (NTUs) to analyse and aggregate important information for more compact representation of input image, while the decoders mirror the encoder-side operations to generate pixel-domain image reconstruction from the compressed bitstream. Each NTU is consist of a Swin Transformer Block (STB) and a convolutional layer (Conv) to best embed both long-range and short-range information; In the meantime, a casual attention module (CAM) is devised for adaptive context modeling of latent features to utilize both hyper and autoregressive priors. The TIC rivals with state-of-the-art approaches including deep convolutional neural networks (CNNs) based learnt image coding (LIC) methods and handcrafted rules-based intra profile of recently-approved Versatile Video Coding (VVC) standard, and requires much less model parameters, e.g., up to 45% reduction to leading-performance LIC.
2021-11-15T00:00:00
no_new_dataset
false
0.708994
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06714
Rune Jacobsen H
Liping Shi, N\'estor J. Hern\'andez Marcano, and Rune Hylsberg Jacobsen
A Review on Communication Protocols for Autonomous Unmanned Aerial Vehicles for Inspection Application
28 pages
null
10.1016/j.micpro.2021.104340
null
cs.NI cs.RO
http://creativecommons.org/licenses/by/4.0/
The communication system is a critical part of the system design for the autonomous UAV. It has to address different considerations, including efficiency, reliability and mobility of the UAV. In addition, a multi-UAV system requires a communication system to assist information sharing, task allocation and collaboration in a team of UAVs. In this paper, we review communication solutions for supporting a team of UAVs while considering an application in the power line inspection industry. We provide a review of candidate wireless communication technologies {for supporting communication in UAV applications. Performance measurements and UAV-related channel modeling of those candidate technologies are reviewed. A discussion of current technologies for building UAV mesh networks is presented. We then analyze the structure, interface and performance of robotic communication middleware, ROS and ROS2. Based on our review, the features and dependencies of candidate solutions in each layer of the communication system are presented.
2021-11-15T00:00:00
no_new_dataset
false
0.709057
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06718
Denys Rybalka
D. O. Rybalka
SimpleTensor -- a user-friendly Mathematica package for elementary tensor and differential-geometric calculations
13 pages
null
null
null
nucl-th cs.MS cs.SC hep-th physics.comp-ph
http://creativecommons.org/licenses/by/4.0/
In this paper we present a short overview of the new Wolfram Mathematica package intended for elementary "in-basis" tensor and differential-geometric calculations. In contrast to alternatives our package is designed to be easy-to-use, short, all-purpose, and hackable. It supports tensor contractions using Einstein notation, transformations between different bases, tensor derivative operator, expansion in basis vectors and forms, exterior derivative, and interior product.
2021-11-15T00:00:00
no_new_dataset
false
0.711224
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06720
Alexandre Cauquoin
Alexandre Cauquoin, Camille Risi and \'Etienne Vignon
Importance of the advection scheme for the simulation of water isotopes over Antarctica by atmospheric general circulation models: A case study for present-day and Last Glacial Maximum with LMDZ-iso
Accepted paper version. See published version in EPSL Elsevier website
Earth and Planetary Science Letters, 524, October 2019, 115731
10.1016/j.epsl.2019.115731
null
physics.ao-ph
http://creativecommons.org/licenses/by-nc-nd/4.0/
Atmospheric general circulation models (AGCMs) are known to have a warm and isotopically enriched bias over Antarctica. We test here the hypothesis that these biases are partly consequences of a too diffusive advection. Exploiting the LMDZ-iso model, we show that a less diffusive representation of the advection, especially on the horizontal, is very important to reduce the bias in the isotopic contents of precipitation above this area. The choice of an appropriate representation of the advection is thus essential when using GCMs for paleoclimate applications based on polar water isotopes. Too much diffusive mixing along the poleward transport leads to overestimated isotopic contents in water vapor because dehydration by mixing follows a more enriched path than dehydration by Rayleigh distillation. The near-air surface temperature is also influenced, to a lesser extent, by the diffusive properties of the advection scheme directly via the advection of the air and indirectly via the radiative effects of changes in high cloud fraction and water vapor. A too diffusive horizontal advection increases the temperature and so also contributes to enrich the isotopic contents of water vapor over Antarctica through a reduction of the distillation. The temporal relationship, from Last Glacial Maximum (LGM) to present-day conditions, between the mean annual near-air surface temperature and the water isotopic contents of precipitation for a specific location can also be impacted, with significant consequences on the paleo-temperature reconstruction from observed changes in water isotopes.
2021-11-15T00:00:00
no_new_dataset
false
0.70844
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06723
Donglin Wang
Donglin Wang, Qiuheng Zhou, Sanket Partani, Anjie Qiu and Hans D. Schotten
Mobility prediction Based on Machine Learning Algorithms
5 pages, 7 figures, MKT'21 osnabruck
null
null
ITG-Fachbericht 299
cs.LG cs.NI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Nowadays mobile communication is growing fast in the 5G communication industry. With the increasing capacity requirements and requirements for quality of experience, mobility prediction has been widely applied to mobile communication and has becoming one of the key enablers that utilizes historical traffic information to predict future locations of traffic users, Since accurate mobility prediction can help enable efficient radio resource management, assist route planning, guide vehicle dispatching, or mitigate traffic congestion. However, mobility prediction is a challenging problem due to the complicated traffic network. In the past few years, plenty of researches have been done in this area, including Non-Machine-Learning (Non-ML)- based and Machine-Learning (ML)-based mobility prediction. In this paper, firstly we introduce the state of the art technologies for mobility prediction. Then, we selected Support Vector Machine (SVM) algorithm, the ML algorithm for practical traffic date training. Lastly, we analyse the simulation results for mobility prediction and introduce a future work plan where mobility prediction will be applied for improving mobile communication.
2021-11-15T00:00:00
no_new_dataset
false
0.710597
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06725
Ivan Tanasijevi\'c
Ivan Tanasijevi\'c and Eric Lauga
Hydrodynamic interactions between a point force and a slender filament
null
null
null
null
physics.flu-dyn cond-mat.soft
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The Green's function of the incompressible Stokes equations, the stokeslet, represents the singular flow due to a point force. Its classical value in an unbounded fluid has been extended near surfaces of various shapes, including flat walls and spheres, and in most cases the presence of a surface leads to an advection flow induced at the location of the point force. In this paper, motivated by the biological transport of cargo along polymeric filaments inside eukaryotic cells, we investigate the reaction flow at the location of the point force due to a rigid slender filament located at a separation distance intermediate between the filament radius and its length (i.e. we compute the advection of the point force induced by the presence of the filament). An asymptotic analysis of the problem reveals that the leading-order approximation for the force distribution along the axis of the filament takes a form analogous to resistive-force theory but with drag coefficients that depend logarithmically on the distance between the point force and the filament. A comparison of our theoretical prediction with boundary element computations show good agreement. We finally briefly extend the model to the case of curved filaments.
2021-11-15T00:00:00
no_new_dataset
false
0.712001
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06726
Dongda Li
Dongda Li, Zhaoquan Gu, Yuexuan Wang, Changwei Ren, Francis C.M. Lau
One model Packs Thousands of Items with Recurrent Conditional Query Learning
16 pages, 5 figures, 3 tables. Accepted to Knowledge-Based Systems, 2022
Knowledge-Based Systems, Volume 235, 2022, 107683, ISSN 0950-7051
10.1016/j.knosys.2021.107683
null
cs.AI cs.LG cs.NE
http://creativecommons.org/licenses/by/4.0/
Recent studies have revealed that neural combinatorial optimization (NCO) has advantages over conventional algorithms in many combinatorial optimization problems such as routing, but it is less efficient for more complicated optimization tasks such as packing which involves mutually conditioned action spaces. In this paper, we propose a Recurrent Conditional Query Learning (RCQL) method to solve both 2D and 3D packing problems. We first embed states by a recurrent encoder, and then adopt attention with conditional queries from previous actions. The conditional query mechanism fills the information gap between learning steps, which shapes the problem as a Markov decision process. Benefiting from the recurrence, a single RCQL model is capable of handling different sizes of packing problems. Experiment results show that RCQL can effectively learn strong heuristics for offline and online strip packing problems (SPPs), outperforming a wide range of baselines in space utilization ratio. RCQL reduces the average bin gap ratio by 1.83% in offline 2D 40-box cases and 7.84% in 3D cases compared with state-of-the-art methods. Meanwhile, our method also achieves 5.64% higher space utilization ratio for SPPs with 1000 items than the state of the art.
2021-11-15T00:00:00
no_new_dataset
false
0.709598
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06735
Surajit Chakrabarti
Sanjoy Kumar Pal, Soumen sarkar, and Surajit Chakrabarti
Determination of the refractive index of water and glass using smartphone cameras by estimating the apparent depth of an object
12 pages, 1 figure
null
null
null
physics.ed-ph
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
A smartphone camera can be used for measuring the width and distance of an object by taking its photograph. The focal length of the camera lens can be determined very accurately by finding the image width of an object on the camera sensor to micron level accuracy. The level of accuracy achieved with the help of camera sensors, allows us to determine the refractive index of water upto four significant digits by finding the apparent depth of an object immersed in it. We have also measured the refractive index of glass by the same method, using three glass slides of different thicknesses, the smallest being 1.2 mm.
2021-11-15T00:00:00
no_new_dataset
false
0.698265
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06736
Burcu Sayin G\"unel
Burcu Sayin, Jie Yang, Andrea Passerini, Fabio Casati
The Science of Rejection: A Research Area for Human Computation
To appear in the Proceedings of The 9th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2021)
null
null
null
cs.LG
http://creativecommons.org/licenses/by-nc-nd/4.0/
We motivate why the science of learning to reject model predictions is central to ML, and why human computation has a lead role in this effort.
2021-11-15T00:00:00
no_new_dataset
false
0.706007
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06737
Marcello Calvanese Strinati
Marcello Calvanese Strinati, Davide Pierangeli, Claudio Conti
All-optical scalable spatial coherent Ising machine
7 pages, 3 figures
Phys. Rev. Applied 16, 054022 (2021)
10.1103/PhysRevApplied.16.054022
null
cs.ET physics.comp-ph physics.optics
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Networks of optical oscillators simulating coupled Ising spins have been recently proposed as a heuristic platform to solve hard optimization problems. These networks, called coherent Ising machines (CIMs), exploit the fact that the collective nonlinear dynamics of coupled oscillators can drive the system close to the global minimum of the classical Ising Hamiltonian, encoded in the coupling matrix of the network. To date, realizations of large-scale CIMs have been demonstrated using hybrid optical-electronic setups, where optical oscillators simulating different spins are subject to electronic feedback mechanisms emulating their mutual interaction. While the optical evolution ensures an ultrafast computation, the electronic coupling represents a bottleneck that causes the computational time to severely depend on the system size. Here, we propose an all-optical scalable CIM with fully-programmable coupling. Our setup consists of an optical parametric amplifier with a spatial light modulator (SLM) within the parametric cavity. The spin variables are encoded in the binary phases of the optical wavefront of the signal beam at different spatial points, defined by the pixels of the SLM. We first discuss how different coupling topologies can be achieved by different configurations of the SLM, and then benchmark our setup with a numerical simulation that mimics the dynamics of the proposed machine. In our proposal, both the spin dynamics and the coupling are fully performed in parallel, paving the way towards the realization of size-independent ultrafast optical hardware for large-scale computation purposes.
2021-11-15T00:00:00
no_new_dataset
false
0.711994
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06738
Baoguang Shi
Baoguang Shi, Wenfeng Cheng, Yijuan Lu, Cha Zhang, Dinei Florencio
Improving Structured Text Recognition with Regular Expression Biasing
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
We study the problem of recognizing structured text, i.e. text that follows certain formats, and propose to improve the recognition accuracy of structured text by specifying regular expressions (regexes) for biasing. A biased recognizer recognizes text that matches the specified regexes with significantly improved accuracy, at the cost of a generally small degradation on other text. The biasing is realized by modeling regexes as a Weighted Finite-State Transducer (WFST) and injecting it into the decoder via dynamic replacement. A single hyperparameter controls the biasing strength. The method is useful for recognizing text lines with known formats or containing words from a domain vocabulary. Examples include driver license numbers, drug names in prescriptions, etc. We demonstrate the efficacy of regex biasing on datasets of printed and handwritten structured text and measures its side effects.
2021-11-15T00:00:00
no_new_dataset
false
0.712076
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06742
Sriram Siva
Sriram Siva, Maggie Wigness, John G. Rogers, Long Quang, and Hao Zhang
Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation
13 pages, 7 figures, IJRR21
null
null
null
cs.RO cs.LG
http://creativecommons.org/licenses/by/4.0/
Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot's actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the challenge, we propose a novel method of self-reflective terrain-aware adaptation for ground robots to generate consistent controls to navigate over unstructured off-road terrains, which enables robots to more accurately execute the expected behaviors through robot self-reflection while adapting to varying unstructured terrains. To evaluate our method's performance, we conduct extensive experiments using real ground robots with various functionality changes over diverse unstructured off-road terrains. The comprehensive experimental results have shown that our self-reflective terrain-aware adaptation method enables ground robots to generate consistent navigational behaviors and outperforms the compared previous and baseline techniques.
2021-11-15T00:00:00
no_new_dataset
false
0.712426
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06743
Dian Echevarr\'ia P\'erez
Dian Echevarr\'ia P\'erez, Onel L. Alcaraz L\'opez, Hirley Alves, Matti Latva-aho
Self-energy recycling for low-power reliable networks: Half-duplex or Full-duplex?
The paper is not published yet but it was accepted to be published in IEEE Systems Journal
null
null
null
cs.NI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Self-energy recycling (sER), which allows transmit energy re-utilization, has emerged as a viable option for improving the energy efficiency (EE) in low-power Internet of Things networks. In this work, we investigate its benefits also in terms of reliability improvements and compare the performance of full-duplex (FD) and half-duplex (HD) schemes when using multi-antenna techniques in a communication system. We analyze the trade-offs when considering not only the energy spent on transmission but also the circuitry power consumption, thus making the analysis of much more practical interest. In addition to the well known spectral efficiency improvements, results show that FD also outperforms HD in terms of reliability. We show that sER introduces not only benefits in EE matters but also some modifications on how to achieve maximum reliability fairness between uplink and downlink transmissions, which is the main goal in this work. In order to achieve this objective, we propose the use of a dynamic FD scheme where the small base station (SBS) determines the optimal allocation of antennas for transmission and reception. We show the significant improvement gains of this strategy for the system outage probability when compared to the simple HD and FD schemes.
2021-11-15T00:00:00
no_new_dataset
false
0.711224
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06745
Scott Hertel
Ricochet Collaboration: G. Beaulieu, V. Belov, L. Berge, J. Billard, G. Bres, J-.L. Bret, A. Broniatowski, M. Calvo, A. Cazes, D. Chaize, M. Chapellier, L. Chaplinsky, G. Chemin, R. Chen, J. Colas, M. De Jesus, P. de Marcillac, L. Dumoulin, O. Exshaw, S. Ferriol, E. Figueroa-Feliciano, J. B. Filippini, J. A. Formaggio, S. Fuard, J. Gascon, A. Giuliani, J. Goupy, C. Goy, C. Guerin, C. F. Hirjibehedin, P. Harrington, S. T. Heine, S. A. Hertel, M. Heusch, C. Hoarau, Z. Hong, J.-C. Ianigro, Y. Jin, J. P. Johnston, A. Juillard, S. Kazarcev, J. Lamblin, H. Lattaud, A. Lubashevskiy, D. W. Mayer, S. Marnieros, J. Minet, D. Misiak, A. Monfardini, F. Mounier, E. Olivieri, C. Oriol, P. K. Patel, E. Perbet, H. D. Pinckney, D. Ponomarev, D. Poda, F. Rarbi, J.-S. Real, J.-S. Ricol, T. Redon, A. Robert, S. Rozov, I. Rozova, T. Salagnac, V. Sanglard, B. Schmidt, Ye. Shevchik, V. Sibille, T. Soldner, J. Stachurska, A. Stutz, L. Vagneron, W. Van De Ponteseele, F. Vezzu, S. Weber, L. Winslow, E. Yakushev, D. Zinatulina
Ricochet Progress and Status
Proceedings for the 19th International Workshop on Low Temperature Detectors (LTD19)
null
null
null
physics.ins-det nucl-ex
http://creativecommons.org/licenses/by/4.0/
We present an overview of recent progress towards the Ricochet coherent elastic neutrino nucleus scattering CE$\nu$NS experiment. The ILL research reactor in Grenoble, France has been selected as the experiment site, after in situ studies of vibration and particle backgrounds. We present background rate estimates specific to that site, along with descriptions of the planned CryoCube and Q-Array detector payloads.
2021-11-15T00:00:00
no_new_dataset
false
0.711875
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06746
Pengfei Nan
Pengfei Nan, Zhiyao Liang, Yue Zhang, Yangrui Liu, Dongsheng Song, Binghui Ge
Fast determination of thickness through scanning moir\'e fringe in scanning transmission electron microscopy
11 pages, 4 figures
null
null
null
cond-mat.mtrl-sci physics.ins-det
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sample thickness is an important parameter in transmission electron microscopy (TEM) imaging, for interpreting image contrast and understanding the relationship between properties and microstructure. In this study, we introduce a method for determining thickness in scanning transmission electron microscopy (STEM) mode based on scanning moir\'e fringe (SMF). Sample thickness can be determined in situ in the medium magnification using focal-series SMF imaging, with beam damage and contamination avoided to a large extent. This method provides a fast and convenient way for determining thickness in TEM imaging, which is particularly useful for beam-sensitive materials such as Metal-Organic Frameworks.
2021-11-15T00:00:00
no_new_dataset
false
0.712876
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06748
Sunil Kumar Maurya
Sunil Kumar Maurya, Xin Liu and Tsuyoshi Murata
Simplifying approach to Node Classification in Graph Neural Networks
arXiv admin note: substantial text overlap with arXiv:2105.07634
null
null
null
stat.ML cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Graph Neural Networks have become one of the indispensable tools to learn from graph-structured data, and their usefulness has been shown in wide variety of tasks. In recent years, there have been tremendous improvements in architecture design, resulting in better performance on various prediction tasks. In general, these neural architectures combine node feature aggregation and feature transformation using learnable weight matrix in the same layer. This makes it challenging to analyze the importance of node features aggregated from various hops and the expressiveness of the neural network layers. As different graph datasets show varying levels of homophily and heterophily in features and class label distribution, it becomes essential to understand which features are important for the prediction tasks without any prior information. In this work, we decouple the node feature aggregation step and depth of graph neural network, and empirically analyze how different aggregated features play a role in prediction performance. We show that not all features generated via aggregation steps are useful, and often using these less informative features can be detrimental to the performance of the GNN model. Through our experiments, we show that learning certain subsets of these features can lead to better performance on wide variety of datasets. We propose to use softmax as a regularizer and "soft-selector" of features aggregated from neighbors at different hop distances; and L2-Normalization over GNN layers. Combining these techniques, we present a simple and shallow model, Feature Selection Graph Neural Network (FSGNN), and show empirically that the proposed model achieves comparable or even higher accuracy than state-of-the-art GNN models in nine benchmark datasets for the node classification task, with remarkable improvements up to 51.1%.
2021-11-15T00:00:00
no_new_dataset
false
0.710176
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06754
Andreanne Lemay
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Didem Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer
Monte Carlo dropout increases model repeatability
Machine Learning for Health (ML4H) at NeurIPS 2021 - Extended Abstract
null
null
null
cs.CV cs.LG eess.IV
http://creativecommons.org/licenses/by/4.0/
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Among the main features of robustness is repeatability. Much attention is given to classification performance without assessing the model repeatability, leading to the development of models that turn out to be unusable in practice. In this work, we evaluate the repeatability of four model types on images from the same patient that were acquired during the same visit. We study the performance of binary, multi-class, ordinal, and regression models on three medical image analysis tasks: cervical cancer screening, breast density estimation, and retinopathy of prematurity classification. Moreover, we assess the impact of sampling Monte Carlo dropout predictions at test time on classification performance and repeatability. Leveraging Monte Carlo predictions significantly increased repeatability for all tasks on the binary, multi-class, and ordinal models leading to an average reduction of the 95% limits of agreement by 17% points.
2021-11-15T00:00:00
no_new_dataset
false
0.71262
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06757
J.-M. Chauvet
J.-M. Chauvet
Multiway Storage Modification Machines
15 pages, 6 figures
null
null
null
cs.AI cs.CC
http://creativecommons.org/licenses/by/4.0/
We present a parallel version of Sch\"onhage's Storage Modification Machine, the Multiway Storage Modification Machine (MWSMM). Like the alternative Association Storage Modification Machine of Tromp and van Emde Boas, MWSMMs recognize in polynomial time what Turing Machines recognize in polynomial space. Falling thus into the Second Machine Class, the MWSMM is a parallel machine model conforming to the Parallel Computation Thesis. We illustrate MWSMMs by a simple implementation of Wolfram's String Substitution System.
2021-11-15T00:00:00
no_new_dataset
false
0.709799
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06760
Jan Kierfeld
Tobias A. Kampmann, Thevashangar Sathiyanesan, Jan Kierfeld
Kinetic Event-Chain Algorithm for Active Matter
5 pages + supplemental material
null
null
null
cond-mat.soft physics.comp-ph
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present a cluster kinetic Monte-Carlo algorithm for active matter systems of self-propelled hard particles. The kinetic event-chain algorithm is based on the event-chain Monte-Carlo method and is applied to active hard disks in two dimensions. The algorithm assigns Monte-Carlo moves of active disks a mean time based on their mean move length in force direction. This time is used to perform diffusional rotation of their propulsion force. We show that the algorithm reproduces the motility induced phase separated region in the phase diagram of hard disks correctly and efficiently. We extend the algorithm to mixtures of active and passive particles and uncover the microscopic mechanism behind the enhanced diffusion of a completely symmetric passive tracer disk in a bath of active hard disks.
2021-11-15T00:00:00
no_new_dataset
false
0.711055
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset
2111.06762
Chao Zhang
Chunzhi Gu, Shuofeng Zhao, Chao Zhang
Diversity-Promoting Human Motion Interpolation via Conditional Variational Auto-Encoder
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we present a deep generative model based method to generate diverse human motion interpolation results. We resort to the Conditional Variational Auto-Encoder (CVAE) to learn human motion conditioned on a pair of given start and end motions, by leveraging the Recurrent Neural Network (RNN) structure for both the encoder and the decoder. Additionally, we introduce a regularization loss to further promote sample diversity. Once trained, our method is able to generate multiple plausible coherent motions by repetitively sampling from the learned latent space. Experiments on the publicly available dataset demonstrate the effectiveness of our method, in terms of sample plausibility and diversity.
2021-11-15T00:00:00
no_new_dataset
false
0.709982
2025-08-19T16:18:03.910982
davanstrien/ModernBERT-base-is-new-arxiv-dataset