pwc-archive/papers-with-abstracts huggingface dataset:
Early Diagnostic Prediction of Covid-19 using Gradient-Boosting Machine Model
Authors: Satvik Tripathi
Date: 2021-10-12 00:00:00
Tasks: Diagnostic

With the huge spike in the COVID-19 cases across the globe and reverse transcriptase-polymerase chain reaction (RT-PCR) test remains a key component for rapid and accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In recent months there has been an acute shortage of medical supplies in developing countries, especially a lack of RT-PCR testing resulting in delayed patient care and high infection rates. We present a gradient-boosting machine model that predicts the diagnostics result of SARS-CoV- 2 in an RT-PCR test by utilizing eight binary features. We used the publicly available nationwide dataset released by the Israeli Ministry of Health.
Mixed Reality using Illumination-aware Gradient Mixing in Surgical Telepresence: Enhanced Multi-layer Visualization
Authors: Nirakar Puri, Abeer Alsadoon, P. W. C. Prasad, Nada Alsalami, Tarik A. Rashid
Date: 2021-08-21 00:00:00
Tasks: Mixed Reality

Background and aim: Surgical telepresence using augmented perception has been applied, but mixed reality is still being researched and is only theoretical. The aim of this work is to propose a solution to improve the visualization in the final merged video by producing globally consistent videos when the intensity of illumination in the input source and target video varies. Methodology: The proposed system uses an enhanced multi-layer visualization with illumination-aware gradient mixing using Illumination Aware Video Composition algorithm. Particle Swarm Optimization Algorithm is used to find the best sample pair from foreground and background region and image pixel correlation to estimate the alpha matte. Particle Swarm Optimization algorithm helps to get the original colour and depth of the unknown pixel in the unknown region. Result: Our results showed improved accuracy caused by reducing the Mean squared Error for selecting the best sample pair for unknown region in 10 each sample for bowel, jaw and breast. The amount of this reduction is 16.48% from the state of art system. As a result, the visibility accuracy is improved from 89.4 to 97.7% which helped to clear the hand vision even in the difference of light. Conclusion: Illumination effect and alpha pixel correlation improves the visualization accuracy and produces a globally consistent composition results and maintains the temporal coherency when compositing two videos with high and inverse illumination effect. In addition, this paper provides a solution for selecting the best sampling pair for the unknown region to obtain the original colour and depth.
MuJoCo: A physics engine for model-based control
Authors: Emanuel Todorov, Tom Erez, Yuval Tassa
Date: 2012-10-07 00:00:00
Proceeding: IEEE/RSJ IROS 2012 10
Tasks: model, MuJoCo

We describe a new physics engine tailored to model-based control. Multi-joint dynamics are represented in generalized coordinates and computed via recursive algorithms. Contact responses are computed via efficient new algorithms we have developed, based on the modern velocity-stepping approach which avoids the difficulties with spring-dampers. Models are specified using either a high-level C++ API or an intuitive XML file format. A built-in compiler transforms the user model into an optimized data structure used for runtime computation. The engine can compute both forward and inverse dynamics. The latter are well-defined even in the presence of contacts and equality constraints. The model can include tendon wrapping as well as actuator activation states (e.g. pneumatic cylinders or muscles). To facilitate optimal control applications and in particular sampling and finite differencing, the dynamics can be evaluated for different states and controls in parallel. Around 400,000 dynamics evaluations per second are possible on a 12-core machine, for a 3D homanoid with 18 dofs and 6 active contacts. We have already used the engine in a number of control applications. It will soon be made publicly available.
Videoconferencing Software Options for Telemedicine: A Review for Movement Disorder Neurologists
Authors: Esther Cubo, Adrián Arnaiz-Rodríguez, Álvar Arnaiz-González, José Francisco Díez-Pastor, Meredith Spindler, Adriana Cardozo, Alvaro Garcia-Bustillo, Zoltan Mari and Bastiaan R. Bloem
Date: 2021-10-11 00:00:00
Proceeding: Frontiers in Neurology 2021 10
Tasks: Articles

Background: The use of telemedicine has increased to address the ongoing healthcare needs of patients with movement disorders.

Objective: We aimed to describe the technical and basic security features of the most popular telemedicine videoconferencing software.

Methods: We conducted a systematic review of articles/websites about “Telemedicine,” “Cybersecurity,” and “Videoconferencing software.” Technical capabilities and basic security features were determined for each videoconferencing software.

Results: Twenty-six videoconferencing software programs were reviewed, 13 (50.0%) were specifically designed for general healthcare, and 6/26 (23.0%) were compliant with European and US regulations. Overall technical and security information were found in 5/26 software (19.2%), including Microsoft Teams, Google Hangout, Coviu, Doxy.me, and Thera platforms.

Conclusions: Detailed information about technical capabilities and data security of videoconferencing tools is not easily and openly retrievable. Our data serves as a guide for practitioners seeking to understand what features should be examined when choosing software and what options are available.
Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy
Authors: Jay J. Jiang, David W. Conrath
Date: 1997-09-20 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.
CORAD: Correlation-Aware Compression of Massive Time Series using Sparse Dictionary Coding
Authors: Abdelouahab Khelifati, Mourad Khayati, Philippe Cudré-Mauroux
Date: 2019-12-12 00:00:00
Proceeding: Big Data 2019 12
Tasks: Autonomous Vehicles, Data Compression, Dictionary Learning, Lossy-Compression Artifact Reduction, Sequential Correlation Estimation, Time Series, Time Series Analysis, Time Series Streams

Time series streams are ubiquitous in many application domains, e.g., transportation, network monitoring, autonomous vehicles, or the Internet of Things (IoT). Transmitting and storing large amounts of such fine-grained data is however expensive, which makes compression schemes necessary in practice. Time series streams that are transmitted together often share properties or evolve together, making them significantly correlated. Despite the rich literature on compression methods, the state-of-the-art approaches do not typically avail correlation information when compressing times series. In this work, we demonstrate how one can leverage the correlation across several related time series streams to both drastically improve the compression efficiency and reduce the accuracy loss.We present a novel compression algorithm for time series streams called CORAD (CORelation-Aware compression of time series streams based on sparse Dictionary coding). Based on sparse dictionary learning, CORAD has the unique ability to exploit the correlation across multiple related time series to eliminate redundancy and perform a more efficient compression. To ensure the accuracy of the compressed time series, we further introduce a method to threshold the information loss of the compression. Extensive validation on real-world datasets shows that CORAD drastically outperforms state-of-the-art approaches achieving up to 40:1 compression ratios while minimizing the information loss.
Adaptive Linear Programming Decoding of Polar Codes
Authors: V eeresh Taranalli, Paul H. Siegel
Date: 2014-08-14 00:00:00
Proceeding: IEEE International Symposium on Information Theory 2014 8
Tasks: Decoder

Polar codes are high density parity check codes
and hence the sparse factor graph, instead of the parity check
matrix, has been used to practically represent an LP polytope
for LP decoding. Although LP decoding on this polytope has
the ML-certificate property, it performs poorly over a BAWGN
channel. In this paper, we propose modifications to adaptive cut
generation based LP decoding techniques and apply the modified-
adaptive LP decoder to short blocklength polar codes over a
BAWGN channel. The proposed decoder provides significant
FER performance gain compared to the previously proposed LP
decoder and its performance approaches that of ML decoding
at high SNRs. We also present an algorithm to obtain a smaller
factor graph from the original sparse factor graph of a polar
code. This reduced factor graph preserves the small check node
degrees needed to represent the LP polytope in practice. We show
that the fundamental polytope of the reduced factor graph can
be obtained from the projection of the polytope represented by
the original sparse factor graph and the frozen bit information.
Thus, the LP decoding time complexity is decreased without
changing the FER performance by using the reduced factor graph
representation.
Combining Learning and Model Based Control via Discrete-Time Chen-Fliess Series
Authors: W. Steven Gray, G. S. Venkatesh, Luis A. Duffaut Espinosa
Date: 2019-06-26 00:00:00

A learning control system is presented suitable for control affine nonlinear plants based on discrete-time Chen-Fliess series and capable of incorporating knowledge of a given physical model. The underlying noncommutative algebraic and combinatorial structures needed to realize the multivariable case are also described. The method is demonstrated using a two-input, two-output Lotka-Volterra system.
Learning Not to Reconstruct Anomalies
Authors: Marcella Astrid, Muhammad Zaigham Zaheer, Jae-Yeong Lee, Seung-Ik Lee
Date: 2021-10-19 00:00:00
Tasks: Anomaly Detection, One-Class Classification, Video Anomaly Detection

Video anomaly detection is often seen as one-class classification (OCC) problem due to the limited availability of anomaly examples. Typically, to tackle this problem, an autoencoder (AE) is trained to reconstruct the input with training set consisting only of normal data. At test time, the AE is then expected to well reconstruct the normal data while poorly reconstructing the anomalous data. However, several studies have shown that, even with only normal data training, AEs can often start reconstructing anomalies as well which depletes the anomaly detection performance. To mitigate this problem, we propose a novel methodology to train AEs with the objective of reconstructing only normal data, regardless of the input (i.e., normal or abnormal). Since no real anomalies are available in the OCC settings, the training is assisted by pseudo anomalies that are generated by manipulating normal data to simulate the out-of-normal-data distribution. We additionally propose two ways to generate pseudo anomalies: patch and skip frame based. Extensive experiments on three challenging video anomaly datasets demonstrate the effectiveness of our method in improving conventional AEs, achieving state-of-the-art performance.
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection
Authors: Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
Date: 2021-10-19 00:00:00
Tasks: Anomaly Detection, One-Class Classification, Video Anomaly Detection

Due to the limited availability of anomaly examples, video anomaly detection is often seen as one-class classification (OCC) problem. A popular way to tackle this problem is by utilizing an autoencoder (AE) trained only on normal data. At test time, the AE is then expected to reconstruct the normal input well while reconstructing the anomalies poorly. However, several studies show that, even with normal data only training, AEs can often start reconstructing anomalies as well which depletes their anomaly detection performance. To mitigate this, we propose a temporal pseudo anomaly synthesizer that generates fake-anomalies using only normal data. An AE is then trained to maximize the reconstruction loss on pseudo anomalies while minimizing this loss on normal data. This way, the AE is encouraged to produce distinguishable reconstructions for normal and anomalous frames. Extensive experiments and analysis on three challenging video anomaly datasets demonstrate the effectiveness of our approach to improve the basic AEs in achieving superiority against several existing state-of-the-art models.
CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis
Authors: Peng Zhou, Lingxi Xie, Bingbing Ni, Qi Tian
Date: 2021-10-19 00:00:00
Tasks: 3D-Aware Image Synthesis, Image Generation, NeRF, Transfer Learning

The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating high-quality images, but it lacks explicit and precise control over camera poses. The recently proposed NeRF-based GANs made great progress towards 3D-aware generators, but they are unable to generate high-quality images yet. This paper presents CIPS-3D, a style-based, 3D-aware generator that is composed of a shallow NeRF network and a deep implicit neural representation (INR) network. The generator synthesizes each pixel value independently without any spatial convolution or upsampling operation. In addition, we diagnose the problem of mirror symmetry that implies a suboptimal solution and solve it by introducing an auxiliary discriminator. Trained on raw, single-view images, CIPS-3D sets new records for 3D-aware image synthesis with an impressive FID of 6.97 for images at the $256\times256$ resolution on FFHQ. We also demonstrate several interesting directions for CIPS-3D such as transfer learning and 3D-aware face stylization. The synthesis results are best viewed as videos, so we recommend the readers to check our github project at https://github.com/PeterouZh/CIPS-3D
Toward Understanding Convolutional Neural Networks from Volterra Convolution Perspective
Authors: Tenghui Li, Guoxu Zhou, Yuning Qiu, Qibin Zhao
Date: 2021-10-19 00:00:00
Tasks: Form

We make an attempt to understanding convolutional neural network by exploring the relationship between (deep) convolutional neural networks and Volterra convolutions. We propose a novel approach to explain and study the overall characteristics of neural networks without being disturbed by the horribly complex architectures. Specifically, we attempt to convert the basic structures of a convolutional neural network (CNN) and their combinations to the form of Volterra convolutions. The results show that most of convolutional neural networks can be approximated in the form of Volterra convolution, where the approximated proxy kernels preserve the characteristics of the original network. Analyzing these proxy kernels may give valuable insight about the original network. Base on this setup, we presented methods to approximating the order-zero and order-one proxy kernels, and verified the correctness and effectiveness of our results.
Learning Representations that Support Robust Transfer of Predictors
Authors: Yilun Xu, Tommi Jaakkola
Date: 2021-10-19 00:00:00
Tasks: Domain Generalization, Out-of-Distribution Generalization

Ensuring generalization to unseen environments remains a challenge. Domain shift can lead to substantially degraded performance unless shifts are well-exercised within the available training environments. We introduce a simple robust estimation criterion -- transfer risk -- that is specifically geared towards optimizing transfer to new environments. Effectively, the criterion amounts to finding a representation that minimizes the risk of applying any optimal predictor trained on one environment to another. The transfer risk essentially decomposes into two terms, a direct transfer term and a weighted gradient-matching term arising from the optimality of per-environment predictors. Although inspired by IRM, we show that transfer risk serves as a better out-of-distribution generalization criterion, both theoretically and empirically. We further demonstrate the impact of optimizing such transfer risk on two controlled settings, each representing a different pattern of environment shift, as well as on two real-world datasets. Experimentally, the approach outperforms baselines across various out-of-distribution generalization tasks. Code is available at \url{https://github.com/Newbeeer/TRM}.
Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures
Authors: Kazuo J. Ezawa, Til Schuermann
Date: 2013-02-20 00:00:00
Tasks: regression

The fraud/uncollectible debt problem in the telecommunications industry presents two technical challenges: the detection and the treatment of the account given the detection. In this paper, we focus on the first problem of detection using Bayesian network models, and we briefly discuss the application of a normative expert system for the treatment at the end. We apply Bayesian network models to the problem of fraud/uncollectible debt detection for telecommunication services. In addition to being quite successful at predicting rare event outcomes, it is able to handle a mixture of categorical and continuous data. We present a performance comparison using linear and non-linear discriminant analysis, classification and regression trees, and Bayesian network models
System Norm Regularization Methods for Koopman Operator Approximation
Authors: Steven Dahdah, James Richard Forbes
Date: 2021-10-18 00:00:00
Tasks: regression

Approximating the Koopman operator from data is numerically challenging when many lifting functions are considered. Even low-dimensional systems can yield unstable or ill-conditioned results in a high-dimensional lifted space. In this paper, Extended Dynamic Mode Decomposition (DMD) and DMD with control, two methods for approximating the Koopman operator, are reformulated as convex optimization problems with linear matrix inequality constraints. Asymptotic stability constraints and system norm regularizers are then incorporated as methods to improve the numerical conditioning of the Koopman operator. Specifically, the H-infinity norm is used to penalize the input-output gain of the Koopman system. Weighting functions are then applied to penalize the system gain at specific frequencies. These constraints and regularizers introduce bilinear matrix inequality constraints to the regression problem, which are handled by solving a sequence of convex optimization problems. Experimental results using data from an aircraft fatigue structural test rig and a soft robot arm highlight the advantages of the proposed regression methods.
Continuous Control with Action Quantization from Demonstrations
Authors: Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin
Date: 2021-10-19 00:00:00
Tasks: continuous-control, Continuous Control, Imitation Learning, Quantization, Reinforcement Learning (RL)

In this paper, we propose a novel Reinforcement Learning (RL) framework for problems with continuous action spaces: Action Quantization from Demonstrations (AQuaDem). The proposed approach consists in learning a discretization of continuous action spaces from human demonstrations. This discretization returns a set of plausible actions (in light of the demonstrations) for each input state, thus capturing the priors of the demonstrator and their multimodal behavior. By discretizing the action space, any discrete action deep RL technique can be readily applied to the continuous control problem. Experiments show that the proposed approach outperforms state-of-the-art methods such as SAC in the RL setup, and GAIL in the Imitation Learning setup. We provide a website with interactive videos: https://google-research.github.io/aquadem/ and make the code available: https://github.com/google-research/google-research/tree/master/aquadem.
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents
Authors: Sam Powers, Eliot Xing, Eric Kolve, Roozbeh Mottaghi, Abhinav Gupta
Date: 2021-10-19 00:00:00
Tasks: NetHack, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Progress in continual reinforcement learning has been limited due to several barriers to entry: missing code, high compute requirements, and a lack of suitable benchmarks. In this work, we present CORA, a platform for Continual Reinforcement Learning Agents that provides benchmarks, baselines, and metrics in a single code package. The benchmarks we provide are designed to evaluate different aspects of the continual RL challenge, such as catastrophic forgetting, plasticity, ability to generalize, and sample-efficient learning. Three of the benchmarks utilize video game environments (Atari, Procgen, NetHack). The fourth benchmark, CHORES, consists of four different task sequences in a visually realistic home simulator, drawn from a diverse set of task and scene parameters. To compare continual RL methods on these benchmarks, we prepare three metrics in CORA: Continual Evaluation, Isolated Forgetting, and Zero-Shot Forward Transfer. Finally, CORA includes a set of performant, open-source baselines of existing algorithms for researchers to use and expand on. We release CORA and hope that the continual RL community can benefit from our contributions, to accelerate the development of new continual RL algorithms.
Risks of AI Foundation Models in Education
Authors: Su Lin Blodgett, Michael Madaio
Date: 2021-10-19 00:00:00

If the authors of a recent Stanford report (Bommasani et al., 2021) on the opportunities and risks of "foundation models" are to be believed, these models represent a paradigm shift for AI and for the domains in which they will supposedly be used, including education. Although the name is new (and contested (Field, 2021)), the term describes existing types of algorithmic models that are "trained on broad data at scale" and "fine-tuned" (i.e., adapted) for particular downstream tasks, and is intended to encompass large language models such as BERT or GPT-3 and computer vision models such as CLIP. Such technologies have the potential for harm broadly speaking (e.g., Bender et al., 2021), but their use in the educational domain is particularly fraught, despite the potential benefits for learners claimed by the authors. In section 3.3 of the Stanford report, Malik et al. argue that achieving the goal of providing education for all learners requires more efficient computational approaches that can rapidly scale across educational domains and across educational contexts, for which they argue foundation models are uniquely well-suited. However, evidence suggests that not only are foundation models not likely to achieve the stated benefits for learners, but their use may also introduce new risks for harm.