pwc-archive/papers-with-abstracts huggingface dataset:
Accelerating Simulation-based Inference with Emerging AI Hardware
Authors: Sourabh Kulkarni, Alexander Tsyplikhin, Mario Michael Krell, Csaba Andras Moritz
Date: 2020-12-12 00:00:00
Tasks: Epidemiology, GPU

Developing models of natural phenomena by capturing their underlying complex interactions is a core tenet of various scientific disciplines. These models are useful as simulators and can help in understanding the natural processes being studied. One key challenge in this pursuit has been to enable statistical inference over these models, which would allow these simulation-based models to learn from real-world observations. Recent efforts, such as Approximate Bayesian Computation (ABC), show promise in performing a new kind of inference to leverage these models. While the scope of applicability of these inference algorithms is limited by the capabilities of contemporary computational hardware, they show potential of being greatly parallelized. In this work, we explore hardware accelerated simulation-based inference over probabilistic models, by combining massively parallelized ABC inference algorithm with the cutting-edge AI chip solutions that are uniquely suited for this purpose. As a proof-of-concept, we demonstrate inference over a probabilistic epidemiology model used to predict the spread of COVID-19. Two hardware acceleration platforms are compared - the Tesla V100 GPU and the Graphcore Mark1 IPU. Our results show that while both of these platforms outperform multi-core CPUs, the Mk1 IPUs are 7.5x faster than the Tesla V100 GPUs for this workload.
Extraction of Heart Rate from PPG Signal: A Machine Learning Approach using Decision Tree Regression Algorithm
Authors: Shikder Shafiul Bashar, Md. Sazal Miah, A.H.M. Zadidul Karim, Md. Abdullah Al Mahmud
Date: 2019-12-22 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, Photoplethysmography (PPG), regression

This research article shows a novel technique which used to measure the heart rate (HR) from wearable devices such as fingertip device, wrist type device. For HR monitoring Photoplethysmography (PPG) signal is severely used. HR measurement precision is affected by noise and motion artifacts (MA) at the moment of physical movement. There are many conventional algorithms to decrease the effect of MA and measure the HR variations. In this work, a novel method called multi-model machine learning approach (MMMLA) is used to predict HR. The technique is shown in this work, which primarily trains and tests the algorithm for
various features and various data sets. K-means cluster is employed for splitting noisy and non noisy data. This process helps the machine to learn from noisy and non noisy data. After separation, Decision Tree Regression method is employed to fit data and measure HR from test data. In this research feature engineering is completed in different words, a special set of the feature is chosen and check their behavior with the projected model and therefore the error rate for each set of the feature was computed. In this work, model feature is also reduced and check the behavior in estimating HR. For each case root mean square (RMS) error and average absolute error of HR was computed. The minimum average absolute error was traced in his work was 1.18 beats per minute (BPM). The outcome of this work demonstrates that the algorithm has a significant possibility to be used for PPG-based HR monitoring.
Control Barriers in Bayesian Learning of System Dynamics
Authors: Vikas Dhiman, Mohammad Javad Khojasteh, Massimo Franceschetti, Nikolay Atanasov
Date: 2020-12-29 00:00:00

This paper focuses on learning a model of system dynamics online while satisfying safety constraints. Our objective is to avoid offline system identification or hand-specified models and allow a system to safely and autonomously estimate and adapt its own model during operation. Given streaming observations of the system state, we use Bayesian learning to obtain a distribution over the system dynamics. Specifically, we propose a new matrix variate Gaussian process (MVGP) regression approach with an efficient covariance factorization to learn the drift and input gain terms of a nonlinear control-affine system. The MVGP distribution is then used to optimize the system behavior and ensure safety with high probability, by specifying control Lyapunov function (CLF) and control barrier function (CBF) chance constraints. We show that a safe control policy can be synthesized for systems with arbitrary relative degree and probabilistic CLF-CBF constraints by solving a second order cone program (SOCP). Finally, we extend our design to a self-triggering formulation, adaptively determining the time at which a new control input needs to be applied in order to guarantee safety.
Quasi-Deterministic Channel Model for mmWaves: Mathematical Formalization and Validation
Authors: Mattia Lecci, Michele Polese, Chiehping Lai, Jian Wang, Camillo Gentile, Nada Golmie, Michele Zorzi
Date: 2020-06-01 00:00:00

5G and beyond networks will use, for the first time ever, the millimeter wave (mmWave) spectrum for mobile communications. Accurate performance evaluation is fundamental to the design of reliable mmWave networks, with accuracy rooted in the fidelity of the channel models. At mmWaves, the model must account for the spatial characteristics of propagation since networks will employ highly directional antennas to counter the much greater pathloss. In this regard, Quasi-Deterministic (QD) models are highly accurate channel models, which characterize the propagation in terms of clusters of multipath components, given by a reflected ray and multiple diffuse components of any given Computer Aided Design (CAD) scenario. This paper introduces a detailed mathematical formulation for QD models at mmWaves, that can be used as a reference for their implementation and development. Moreover, it compares channel instances obtained with an open source NIST QD model implementation against real measurements at 60 GHz, substantiating the accuracy of the model. Results show that, when comparing the proposed model and deterministic rays alone with a measurement campaign, the Kolmogorov-Smirnov (KS) test of the QD model improves by up to 0.537.
One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features
Authors: Aram Ter-Sarkisov
Date: 2021-01-04 00:00:00
Tasks: COVID-19 Diagnosis, Instance Segmentation, Semi-Supervised Image Classification, Sensitivity

We introduce a model that segments lesions and predicts COVID-19 from chest CT scans through the derivation
of an affinity matrix between lesion masks. The novelty of the methodology is based on the computation of the
affinity between the lesion masks’ features extracted from the image. First, a batch of vectorized lesion masks is
constructed. Then, the model learns the parameters of the affinity matrix that captures the relationship between features
in each vector. Finally, the affinity is expressed as a single vector of pre-defined length. Without any complicated data
manipulation, class balancing tricks, and using only a fraction of the training data, we achieve a 91.74% COVID-19
sensitivity, 85.35% common pneumonia sensitivity, 97.26% true negative rate and 91.94% F1-score. Ablation studies show
that the method can quickly generalize to new datasets. All source code, models and results are publicly available on
https://github.com/AlexTS1980/COVID-Affinity-Model.
Solving path dependent PDEs with LSTM networks and path signatures
Date: 2020-11-20 00:00:00

Using a combination of recurrent neural networks and signature methods from the rough paths theory we design efficient algorithms for solving parametric families of path dependent partial differential equations (PPDEs) that arise in pricing and hedging of path-dependent derivatives or from use of non-Markovian model, such as rough volatility models in Jacquier and Oumgari, 2019. The solutions of PPDEs are functions of time, a continuous path (the asset price history) and model parameters. As the domain of the solution is infinite dimensional many recently developed deep learning techniques for solving PDEs do not apply. Similarly as in Vidales et al. 2018, we identify the objective function used to learn the PPDE by using martingale representation theorem. As a result we can de-bias and provide confidence intervals for then neural network-based algorithm. We validate our algorithm using classical models for pricing lookback and auto-callable options and report errors for approximating both prices and hedging strategies.
InpaintFusion: Incremental RGB-D Inpainting for 3D Scenes
Authors: Shohei Mori, Okan Erat, Wolfgang Broll, Hideo Saito, Dieter Schmalstieg, Denis Kalkofen
Date: 2020-10-01 00:00:00
Tasks: global-optimization, Image Inpainting, Simultaneous Localization and Mapping, Video Inpainting

State-of-the-art methods for diminished reality propagate pixel information from a keyframe to subsequent frames for real-time inpainting. However, these approaches produce artifacts, if the scene geometry is not sufficiently planar. In this paper, we present InpaintFusion, a new real-time method that extends inpainting to non-planar scenes by considering both color and depth information in the inpainting process. We use an RGB-D sensor for simultaneous localization and mapping, in order to both track the camera and obtain a surfel map in addition to RGB images. We use the RGB-D information in a cost function for both the color and the geometric appearance to derive a global optimization for simultaneous inpainting of color and depth. The inpainted depth is merged in a global map by depth fusion. For the final rendering, we project the map model into image space, where we can use it for effects such as relighting and stereo rendering of otherwise hidden structures. We demonstrate the capabilities of our method by comparing it to inpainting results with methods using planar geometric proxies.
A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement
Authors: Zhuqing Jiang, Haotian Li, Liangjie Liu, Aidong Men, Haiying Wang
Date: 2021-01-03 00:00:00
Tasks: Image Enhancement, Low-Light Image Enhancement

Self-regularized low-light image enhancement does not require any normal-light image in training, thereby freeing from the chains on paired or unpaired low-/normal-images. However, existing methods suffer color deviation and fail to generalize to various lighting conditions. This paper presents a novel self-regularized method based on Retinex, which, inspired by HSV, preserves all colors (Hue, Saturation) and only integrates Retinex theory into brightness (Value). We build a reflectance estimation network by restricting the consistency of reflectances embedded in both the original and a novel random disturbed form of the brightness of the same scene. The generated reflectance, which is assumed to be irrelevant of illumination by Retinex, is treated as enhanced brightness. Our method is efficient as a low-light image is decoupled into two subspaces, color and brightness, for better preservation and enhancement. Extensive experiments demonstrate that our method outperforms multiple state-of-the-art algorithms qualitatively and quantitatively and adapts to more lighting conditions.
Generative Deep Learning for Virtuosic Classical Music: Generative Adversarial Networks as Renowned Composers
Authors: Daniel Szelogowski
Date: 2021-01-01 00:00:00

Current AI-generated music lacks fundamental principles of good compositional techniques. By narrowing down implementation issues both programmatically and musically, we can create a better understanding of what parameters are necessary for a generated composition nearly indistinguishable from that of a master composer.
A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition
Authors: Houjin Yu, Xian-Ling Mao, Zewen Chi, Wei Wei, Heyan Huang
Date: 2021-01-02 00:00:00
Tasks: Low Resource Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Recently, it has attracted much attention to build reliable named entity recognition (NER) systems using limited annotated data. Nearly all existing works heavily rely on domain-specific resources, such as external lexicons and knowledge bases. However, such domain-specific resources are often not available, meanwhile it's difficult and expensive to construct the resources, which has become a key obstacle to wider adoption. To tackle the problem, in this work, we propose a novel robust and domain-adaptive approach RDANER for low-resource NER, which only uses cheap and easily obtainable resources. Extensive experiments on three benchmark datasets demonstrate that our approach achieves the best performance when only using cheap and easily obtainable resources, and delivers competitive results against state-of-the-art methods which use difficultly obtainable domainspecific resources. All our code and corpora can be found on https://github.com/houking-can/RDANER.
An Elo-like System for Massive Multiplayer Competitions
Authors: Aram Ebtekar, Paul Liu
Date: 2021-01-02 00:00:00

Rating systems play an important role in competitive sports and games. They provide a measure of player skill, which incentivizes competitive performances and enables balanced match-ups. In this paper, we present a novel Bayesian rating system for contests with many participants. It is widely applicable to competition formats with discrete ranked matches, such as online programming competitions, obstacle courses races, and some video games. The simplicity of our system allows us to prove theoretical bounds on robustness and runtime. In addition, we show that the system aligns incentives: that is, a player who seeks to maximize their rating will never want to underperform. Experimentally, the rating system rivals or surpasses existing systems in prediction accuracy, and computes faster than existing systems by up to an order of magnitude.
Brain Tumor Detection and Classification based on Hybrid Ensemble Classifier
Authors: Ginni Garg, Ritu Garg
Date: 2021-01-01 00:00:00
Tasks: Classification, General Classification

To improve patient survival and treatment outcomes, early diagnosis of brain tumors is an essential task. It is a difficult task to evaluate the magnetic resonance imaging (MRI) images manually. Thus, there is a need for digital methods for tumor diagnosis with better accuracy. However, it is still a very challenging task in assessing their shape, volume, boundaries, tumor detection, size, segmentation, and classification. In this proposed work, we propose a hybrid ensemble method using Random Forest (RF), K-Nearest Neighbour, and Decision Tree (DT) (KNN-RF-DT) based on Majority Voting Method. It aims to calculate the area of the tumor region and classify brain tumors as benign and malignant. In the beginning, segmentation is done by using Otsu's Threshold method. Feature Extraction is done by using Stationary Wavelet Transform (SWT), Principle Component Analysis (PCA), and Gray Level Co-occurrence Matrix (GLCM), which gives thirteen features for classification. The classification is done by hybrid ensemble classifier (KNN-RF-DT) based on the Majority Voting method. Overall it aimed at improving the performance by traditional classifiers instead of going to deep learning. Traditional classifiers have an advantage over deep learning algorithms because they require small datasets for training and have low computational time complexity, low cost to the users, and can be easily adopted by less skilled people. Overall, our proposed method is tested upon dataset of 2556 images, which are used in 85:15 for training and testing respectively and gives good accuracy of 97.305%.
DSXplore: Optimizing Convolutional Neural Networks via Sliding-Channel Convolutions
Authors: yuke wang, Boyuan Feng, Yufei Ding
Date: 2021-01-04 00:00:00
Tasks: GPU

As the key advancement of the convolutional neural networks (CNNs), depthwise separable convolutions (DSCs) are becoming one of the most popular techniques to reduce the computations and parameters size of CNNs meanwhile maintaining the model accuracy. It also brings profound impact to improve the applicability of the compute- and memory-intensive CNNs to a broad range of applications, such as mobile devices, which are generally short of computation power and memory. However, previous research in DSCs are largely focusing on compositing the limited existing DSC designs, thus, missing the opportunities to explore more potential designs that can achieve better accuracy and higher computation/parameter reduction. Besides, the off-the-shelf convolution implementations offer limited computing schemes, therefore, lacking support for DSCs with different convolution patterns. To this end, we introduce, DSXplore, the first optimized design for exploring DSCs on CNNs. Specifically, at the algorithm level, DSXplore incorporates a novel factorized kernel -- sliding-channel convolution (SCC), featured with input-channel overlapping to balance the accuracy performance and the reduction of computation and memory cost. SCC also offers enormous space for design exploration by introducing adjustable kernel parameters. Further, at the implementation level, we carry out an optimized GPU-implementation tailored for SCC by leveraging several key techniques, such as the input-centric backward design and the channel-cyclic optimization. Intensive experiments on different datasets across mainstream CNNs show the advantages of DSXplore in balancing accuracy and computation/parameter reduction over the standard convolution and the existing DSCs.
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Authors: Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, Connor Leahy
Date: 2020-12-31 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling

Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language models. With this in mind, we present \textit{the Pile}: an 825 GiB English text corpus targeted at training large-scale language models. The Pile is constructed from 22 diverse high-quality subsets -- both existing and newly constructed -- many of which derive from academic or professional sources. Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing. Conversely, models trained on the Pile improve significantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations. Through an in-depth exploratory analysis, we document potentially concerning aspects of the data for prospective users. We make publicly available the code used in its construction.
Measure of Strength of Evidence for Visually Observed Differences between Subpopulations
Authors: Xi Yang, Jan Hannig, Katherine A. Hoadley, Iain Carmichael, J. S. Marron
Date: 2021-01-02 00:00:00

For measuring the strength of visually-observed subpopulation differences, the Population Difference Criterion is proposed to assess the statistical significance of visually observed subpopulation differences. It addresses the following challenges: in high-dimensional contexts, distributional models can be dubious; in high-signal contexts, conventional permutation tests give poor pairwise comparisons. We also make two other contributions: Based on a careful analysis we find that a balanced permutation approach is more powerful in high-signal contexts than conventional permutations. Another contribution is the quantification of uncertainty due to permutation variation via a bootstrap confidence interval. The practical usefulness of these ideas is illustrated in the comparison of subpopulations of modern cancer data.
A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization
Authors: Ashraful Islam, Chengjiang Long, Richard Radke
Date: 2021-01-03 00:00:00
Tasks: Action Localization, Hard Attention, Multiple Instance Learning, Temporal Action Localization, Weakly-supervised Temporal Action Localization

Weakly supervised temporal action localization is a challenging vision task due to the absence of ground-truth temporal locations of actions in the training videos. With only video-level supervision during training, most existing methods rely on a Multiple Instance Learning (MIL) framework to predict the start and end frame of each action category in a video. However, the existing MIL-based approach has a major limitation of only capturing the most discriminative frames of an action, ignoring the full extent of an activity. Moreover, these methods cannot model background activity effectively, which plays an important role in localizing foreground activities. In this paper, we present a novel framework named HAM-Net with a hybrid attention mechanism which includes temporal soft, semi-soft and hard attentions to address these issues. Our temporal soft attention module, guided by an auxiliary background class in the classification module, models the background activity by introducing an "action-ness" score for each video snippet. Moreover, our temporal semi-soft and hard attention modules, calculating two attention scores for each video snippet, help to focus on the less discriminative frames of an action to capture the full action boundary. Our proposed approach outperforms recent state-of-the-art methods by at least 2.2% mAP at IoU threshold 0.5 on the THUMOS14 dataset, and by at least 1.3% mAP at IoU threshold 0.75 on the ActivityNet1.2 dataset. Code can be found at: https://github.com/asrafulashiq/hamnet.
Transformers in Vision: A Survey
Authors: Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, Mubarak Shah
Date: 2021-01-04 00:00:00
Tasks: Action Recognition, Activity Recognition, Colorization, image-classification, Image Classification, Image Enhancement, Image Super-Resolution, object-detection, Object Detection, Point Cloud Classification, Question Answering, Super-Resolution, Survey, Video Forecasting, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA), Visual Reasoning

Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers i.e., self-attention, large-scale pre-training, and bidirectional encoding. We then cover extensive applications of transformers in vision including popular recognition tasks (e.g., image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks (e.g., visual-question answering, visual reasoning, and visual grounding), video processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution, image enhancement, and colorization) and 3D analysis (e.g., point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value. Finally, we provide an analysis on open research directions and possible future works.