pwc-archive/papers-with-abstracts huggingface dataset:
BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments
Authors: Divya Kothandaraman, Rohan Chandra, Dinesh Manocha
Date: 2020-09-22 00:00:00
Tasks: Domain Adaptation, Scene Understanding, Semantic Segmentation, Unsupervised Domain Adaptation

We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments. Our method is designed for unstructured real-world scenarios with dense and heterogeneous traffic consisting of cars, trucks, two-and three-wheelers, and pedestrians. We describe a new semantic segmentation technique based on unsupervised domain adaptation (DA), that can identify the class or category of each region in RGB images or videos. We also present a novel self-training algorithm (Alt-Inc) for multi-source DA that improves the accuracy. Our overall approach is a deep learning-based technique and consists of an unsupervised neural network that achieves 87.18% accuracy on the challenging India Driving Dataset. Our method works well on roads that may not be well-marked or may include dirt, unidentifiable debris, potholes, etc. A key aspect of our approach is that it can also identify objects that are encountered by the model for the fist time during the testing phase. We compare our method against the state-of-the-art methods and show an improvement of 5.17% - 42.9%. Furthermore, we also conduct user studies that qualitatively validate the improvements in visual scene understanding of unstructured driving environments.
Predictive Capability Maturity Quantification using Bayesian Network
Authors: Linyu Lin, Nam Dinh
Date: 2020-08-31 00:00:00
Tasks: Decision Making

In nuclear engineering, modeling and simulations (M&Ss) are widely applied to support risk-informed safety analysis. Since nuclear safety analysis has important implications, a convincing validation process is needed to assess simulation adequacy, i.e., the degree to which M&S tools can adequately represent the system quantities of interest. However, due to data gaps, validation becomes a decision-making process under uncertainties. Expert knowledge and judgments are required to collect, choose, characterize, and integrate evidence toward the final adequacy decision. However, in validation frameworks CSAU: Code Scaling, Applicability, and Uncertainty (NUREG/CR-5249) and EMDAP: Evaluation Model Development and Assessment Process (RG 1.203), such a decision-making process is largely implicit and obscure. When scenarios are complex, knowledge biases and unreliable judgments can be overlooked, which could increase uncertainty in the simulation adequacy result and the corresponding risks. Therefore, a framework is required to formalize the decision-making process for simulation adequacy in a practical, transparent, and consistent manner. This paper suggests a framework "Predictive Capability Maturity Quantification using Bayesian network (PCMQBN)" as a quantified framework for assessing simulation adequacy based on information collected from validation activities. A case study is prepared for evaluating the adequacy of a Smoothed Particle Hydrodynamic simulation in predicting the hydrodynamic forces onto static structures during an external flooding scenario. Comparing to the qualitative and implicit adequacy assessment, PCMQBN is able to improve confidence in the simulation adequacy result and to reduce expected loss in the risk-informed safety analysis.
Keyphrase Extraction from Bengali Document using LSTM Recurrent Neural Network
Authors: Nishat Tasnim Ahmed Meem1
Date: 2018-09-01 00:00:00
Tasks: Keyphrase Extraction

Abstract—Keyphrases are single or multiple word phrases
of a document which portrays the principal points of that
document. These keyphrases help readers to get an overview
of the document. In this paper, we proposed a system that uses
Long Short-Term Memory (LSTM) Recurrent Neural Network
(RNN) to automatically detect keyphrases from a document.
We also implemented Multilayer Perceptron (MLP) network to
compare the performance of our proposed LSTM approach. We
applied several pre-processing steps on a document to generate
the candidate keyphrases. Finally, we found better performance
from our proposed approach with compared to the MLP network.
Keywords—Keyphrase, Keyphrase extraction, Keywords,
LSTM, RNN, MLP, Multi Layer Perceptron, Neural Network
System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
Authors: Zhenlin Wang, Xiaoxuan Zhang, Gregory Teichert, Mariana Carrasco-Teja, Krishna Garikipati
Date: 2020-07-02 00:00:00

We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment protocols, while diffusivity incorporates a mobile population. This model has been applied to data on the evolution of the COVID-19 pandemic in the US state of Michigan. For system inference, we use recent advances; specifically our framework for Variational System Identification (Wang et al., Comp. Meth. App. Mech. Eng., 356, 44-74, 2019; arXiv:2001.04816 [cs.CE]) as well as Bayesian machine learning methods.
3D faces in motion: Fully automatic registration and statistical analysis
Authors: Timo Bolkart, Stefanie Wuhrer
Date: 2014-06-24 00:00:00
Tasks: 3D Face Animation, 3D Face Modelling, Face Alignment, Face Model

This paper presents a representation of 3D facial motion sequences that allows performing statistical analysis of 3D face shapes in motion. The resulting statistical analysis is applied to automatically generate realistic facial animations and to recognize dynamic facial expressions. To perform statistical analysis of 3D facial shapes in motion over different subjects and different motion sequences, a large database of motion sequences needs to be brought in full correspondence. Existing algorithms that compute correspondences between 3D facial motion sequences either require manual input or suffer from instabilities caused by drift. For large databases, algorithms that require manual interaction are not practical. We propose an approach to robustly compute correspondences between a large set of facial motion sequences in a fully automatic way using a multilinear model as statistical prior. In order to register the motion sequences, a good initialization is needed. We obtain this initialization by introducing a landmark prediction method for 3D motion sequences based on Markov Random Fields. Using this motion sequence registration, we find a compact representation of each motion sequence consisting of one vector of coefficients for identity and a high dimensional curve for expression. Based on this representation, we synthesize new motion sequences and perform expression recognition. We show experimentally that the obtained registration is of high quality, where 56% of all vertices are at distance at most 1 mm from the input data, and that our synthesized motion sequences look realistic.
Review of Statistical Shape Spaces for 3D Data with ComparativeAnalysis for Human Faces
Authors: Alan Brunton, Augusto Salazar, Timo Bolkart, Stefanie Wuhrer
Date: 2014-04-04 00:00:00
Tasks: 3D Face Modelling, Face Model, Face Reconstruction

With systems for acquiring 3D surface data being evermore commonplace, it has become important to reliably extract specific shapes from the acquired data.  In the presence of noise and occlusions, this can be done through the use of statistical shape models, which are learned from databases of clean examples of the shape in question.  In this paper, we review,analyze and compare different statistical models:  from those that analyze the variation in geometry globally to those that analyze the variation in geometry locally. We first review how different types of models have been used in the literature, then proceed to define the models and analyze them theoretically,in  terms  of both  their  statistical  and  computational aspects.We then perform extensive experimental comparison on the task of model fitting, and give intuition about which type of model is better for a few applications. Due to the wide avail-ability of databases of high-quality data,  we use the human face as the specific shape we wish to extract from corrupted data.
A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images
Authors: Mohammad Tariqul Islam, Dipayan Saha, S. M. Mahbubur Rahman, M. Omair Ahmad, M. N. S. Swamy
Date: 2018-11-01 00:00:00
Tasks: Denoising

Reduction of mixed noise is an ill posed problem for the occurrence of contrasting distributions of noise in the image. The mixed noise that is usually encountered is the simultaneous presence of additive white Gaussian noise (AWGN) and impulse noise (IN). A standard approach to denoise an image with such corruption is to apply a rank order filter (ROF) followed by an efficient linear filter to remove the residual noise. However, ROF cannot completely remove the heavy tail of the noise distribution originating from the IN and thus the denoising performance can be suboptimal. In this paper, we present a variational step to remove the heavy tail of the noise distribution. Through experiments, it is shown that this approach can significantly improve the denoising performance of mixed AWGN-IN using well-established methods.
Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends
Authors: Jian Wang, Yongxin Liu, Houbing Song
Date: 2020-08-28 00:00:00

Unmanned aircraft systems (UAS), or unmanned aerial vehicles (UAVs), often referred to as drones, have been experiencing healthy growth in the United States and around the world. The positive uses of UAS have the potential to save lives, increase safety and efficiency, and enable more effective science and engineering research. However, UAS are subject to threats stemming from increasing reliance on computer and communication technologies, which place public safety, national security, and individual privacy at risk. To promote safe, secure and privacy-respecting UAS operations, there is an urgent need for innovative technologies for detecting, tracking, identifying and mitigating UAS. A Counter-UAS (C-UAS) system is defined as a system or device capable of lawfully and safely disabling, disrupting, or seizing control of an unmanned aircraft or unmanned aircraft system. Over the past 5 years, significant research efforts have been made to detect, and mitigate UAS: detection technologies are based on acoustic, vision, passive radio frequency, radar, and data fusion; and mitigation technologies include physical capture or jamming. In this paper, we provide a comprehensive survey of existing literature in the area of C-UAS, identify the challenges in countering unauthorized or unsafe UAS, and evaluate the trends of detection and mitigation for protecting against UAS-based threats. The objective of this survey paper is to present a systematic introduction of C-UAS technologies, thus fostering a research community committed to the safe integration of UAS into the airspace system.
RNA-seq data science: From raw data to effective interpretation
Authors: Dhrithi Deshpande, Karishma Chhugani, Yutong Chang, Aaron Karlsberg, Caitlin Loeffler, Jinyang Zhang, Agata Muszynska, Jeremy Rotman, Laura Tao, Lana S. Martin, Brunilda Balliu, Elizabeth Tseng, Fangqing Zhao, Pejman Mohammadi, Pawel P Labaj, Serghei Mangul
Date: 2020-10-05 00:00:00

RNA-sequencing (RNA-seq) has become an exemplar technology in modern biology and clinical applications over the past decade. It has gained immense popularity in the recent years driven by continuous efforts of the bioinformatics community to develop accurate and scalable computational tools. RNA-seq is a method of analyzing the RNA content of a sample using the modern sequencing platforms. It generates enormous amounts of transcriptomic data in the form of nucleotide sequences, known as reads. RNA-seq analysis enables the probing of genes and corresponding transcripts which is essential for answering important biological questions, such as detecting novel exons, transcripts, gene expressions, and studying alternative splicing structure. However, obtaining meaningful biological signals from raw data using computational methods is challenging due to the limitations of modern sequencing technologies. The need to leverage these technological challenges have pushed the rapid development of many novel computational tools which have evolved and diversified in accordance with technological advancements, leading to the current myriad population of RNA-seq tools. Our review provides a systemic overview of RNA-seq technology and 235 available RNA-seq tools across various domains published from 2008 to 2020, discussing the interdisciplinary nature of bioinformatics involved in RNA sequencing, analysis, and software development.
Primordial Sex Facilitates the Emergence of Evolution
Authors: Sam Sinai, Jason Olejarz, Iulia A. Neagu, Martin A. Nowak
Date: 2016-12-02 00:00:00

Compartments are ubiquitous throughout biology, yet their importance stretches back to the origin of cells. In the context of origin of life, we assume that a protocell, a compartment enclosing functional components, requires $N$ components to be evolvable. We take interest in the timescale in which a minimal evolvable protocell is produced. We show that when protocells fuse and share information, the time to produce an evolvable protocell scales algebraically in $N$, in contrast to an exponential scaling in the absence of fusion. We discuss the implications of this result for origins of life, as well as other biological processes.
Adaptive Momentum Coefficient for Neural Network Optimization
Authors: Zana Rashidi, Kasra Ahmadi K. A., Aijun An, Xiaogang Wang
Date: 2020-06-04 00:00:00

We propose a novel and efficient momentum-based first-order algorithm for optimizing neural networks which uses an adaptive coefficient for the momentum term. Our algorithm, called Adaptive Momentum Coefficient (AMoC), utilizes the inner product of the gradient and the previous update to the parameters, to effectively control the amount of weight put on the momentum term based on the change of direction in the optimization path. The algorithm is easy to implement and its computational overhead over momentum methods is negligible. Extensive empirical results on both convex and neural network objectives show that AMoC performs well in practise and compares favourably with other first and second-order optimization algorithms. We also provide a convergence analysis and a convergence rate for AMoC, showing theoretical guarantees similar to those provided by other efficient first-order methods.
Integrity-Based Path Planning Strategy for Urban Autonomous Vehicular Navigation Using GPS and Cellular Signals
Authors: Halim Lee, Jiwon Seo, Zaher M. Kassas
Date: 2020-10-08 00:00:00
Tasks: Navigate

An integrity-based path planning strategy for autonomous ground vehicle (AGV) navigation in urban environments is developed. The vehicle is assumed to navigate by utilizing cellular long-term evolution (LTE) signals in addition to Global Positioning System (GPS) signals. Given a desired destination, an optimal path is calculated, which minimizes a cost function that considers both the horizontal protection level (HPL) and travel distance. The constraints are that (i) the ratio of nodes with faulty signals to the total nodes be lower than a maximum allowable ratio and (ii) the HPLs along each candidate path be lower than the horizontal alert limit (HAL). To predict the faults and HPL before the vehicle is driven, GPS and LTE pseudoranges along the candidate paths are generated utilizing a commercial ray-tracing software and three-dimensional (3D) terrain and building maps. Simulated pseudoranges inform the path planning algorithm about potential biases due to reflections from buildings in urban environments. Simulation results are presented showing that the optimal path produced by the proposed path planning strategy has the minimum average HPL among the candidate paths.
Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning
Authors: Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Date: 2020-10-08 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This reward function enables IoT sensor devices to learn to spend available energy on measurements at otherwise unpredictable moments, while conserving energy at times when measurements would provide little new information. This is a highly general approach, which allows for a wide range of use cases without significant human design effort or hyper-parameter tuning. We illustrate the approach in a scenario of workplace noise monitoring, where results show that the learned behavior outperforms a uniform sampling strategy and comes close to a near-optimal oracle solution.
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
Authors: Thang Doan, Mehdi Bennani, Bogdan Mazoure, Guillaume Rabusseau, Pierre Alquier
Date: 2020-10-07 00:00:00
Tasks: Continual Learning

Continual learning (CL) is a setting in which an agent has to learn from an incoming stream of data during its entire lifetime. Although major advances have been made in the field, one recurring problem which remains unsolved is that of Catastrophic Forgetting (CF). While the issue has been extensively studied empirically, little attention has been paid from a theoretical angle. In this paper, we show that the impact of CF increases as two tasks increasingly align. We introduce a measure of task similarity called the NTK overlap matrix which is at the core of CF. We analyze common projected gradient algorithms and demonstrate how they mitigate forgetting. Then, we propose a variant of Orthogonal Gradient Descent (OGD) which leverages structure of the data through Principal Component Analysis (PCA). Experiments support our theoretical findings and show how our method can help reduce CF on classical CL datasets.
Person Retrieval in Surveillance Video using Height, Color and Gender
Authors: Hiren Galiyawala, Kenil Shah, Vandit Gajjar, Mehul S. Raval
Date: 2018-09-24 00:00:00
Proceeding: 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) 2019 2
Tasks: Person Retrieval, Retrieval

A person is commonly described by attributes like height, build, cloth color, cloth type, and gender. Such attributes are known as soft biometrics. They bridge the semantic gap between human description and person retrieval in surveillance video. The paper proposes a deep learning-based linear filtering approach for person retrieval using height, cloth color, and gender. The proposed approach uses Mask R-CNN for pixel-wise person segmentation. It removes background clutter and provides precise boundary around the person. Color and gender models are fine-tuned using AlexNet and the algorithm is tested on SoftBioSearch dataset. It achieves good accuracy for person retrieval using the semantic query in challenging conditions.
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates
Authors: Alex Renda, Yishen Chen, Charith Mendis, Michael Carbin
Date: 2020-10-08 00:00:00
Tasks: CPU, Scheduling

CPU simulators are useful tools for modeling CPU execution behavior. However, they suffer from inaccuracies due to the cost and complexity of setting their fine-grained parameters, such as the latencies of individual instructions. This complexity arises from the expertise required to design benchmarks and measurement frameworks that can precisely measure the values of parameters at such fine granularity. In some cases, these parameters do not necessarily have a physical realization and are therefore fundamentally approximate, or even unmeasurable. In this paper we present DiffTune, a system for learning the parameters of x86 basic block CPU simulators from coarse-grained end-to-end measurements. Given a simulator, DiffTune learns its parameters by first replacing the original simulator with a differentiable surrogate, another function that approximates the original function; by making the surrogate differentiable, DiffTune is then able to apply gradient-based optimization techniques even when the original function is non-differentiable, such as is the case with CPU simulators. With this differentiable surrogate, DiffTune then applies gradient-based optimization to produce values of the simulator's parameters that minimize the simulator's error on a dataset of ground truth end-to-end performance measurements. Finally, the learned parameters are plugged back into the original simulator. DiffTune is able to automatically learn the entire set of microarchitecture-specific parameters within the Intel x86 simulation model of llvm-mca, a basic block CPU simulator based on LLVM's instruction scheduling model. DiffTune's learned parameters lead llvm-mca to an average error that not only matches but lowers that of its original, expert-provided parameter values.
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors
Authors: Ardavan Afshar, Kejing Yin, Sherry Yan, Cheng Qian, Joyce C. Ho, Haesun Park, Jimeng Sun
Date: 2020-10-08 00:00:00
Tasks: Computational Efficiency

Existing tensor factorization methods assume that the input tensor follows some specific distribution (i.e. Poisson, Bernoulli, and Gaussian), and solve the factorization by minimizing some empirical loss functions defined based on the corresponding distribution. However, it suffers from several drawbacks: 1) In reality, the underlying distributions are complicated and unknown, making it infeasible to be approximated by a simple distribution. 2) The correlation across dimensions of the input tensor is not well utilized, leading to sub-optimal performance. Although heuristics were proposed to incorporate such correlation as side information under Gaussian distribution, they can not easily be generalized to other distributions. Thus, a more principled way of utilizing the correlation in tensor factorization models is still an open challenge. Without assuming any explicit distribution, we formulate the tensor factorization as an optimal transport problem with Wasserstein distance, which can handle non-negative inputs. We introduce SWIFT, which minimizes the Wasserstein distance that measures the distance between the input tensor and that of the reconstruction. In particular, we define the N-th order tensor Wasserstein loss for the widely used tensor CP factorization and derive the optimization algorithm that minimizes it. By leveraging sparsity structure and different equivalent formulations for optimizing computational efficiency, SWIFT is as scalable as other well-known CP algorithms. Using the factor matrices as features, SWIFT achieves up to 9.65% and 11.31% relative improvement over baselines for downstream prediction tasks. Under the noisy conditions, SWIFT achieves up to 15% and 17% relative improvements over the best competitors for the prediction tasks.