pwc-archive/papers-with-abstracts huggingface dataset:
A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing
Authors: B. Ahmmed, M. K. Mudunuru, S. Karra, S. C. James, V. V. Vesselinov
Date: 2020-02-24 00:00:00
Tasks: BIG-bench Machine Learning, Ensemble Learning, Uncertainty Quantification

Accurate predictions of reactive mixing are critical for many Earth and environmental science problems. To investigate mixing dynamics over time under different scenarios, a high-fidelity, finite-element-based numerical model is built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations are performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast, and molecular diffusion. Outputs comprise concentration profiles of the reactants and products. The inputs and outputs of these simulations are concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) emulators to approximate system behavior. The 20 ML emulators based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptron (MLP), are compared to assess these models. The ML emulators are specifically trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay, and degree of mixing. Linear classifiers and regressors fail to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP accurately classify the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predict the QoIs. At run time, trained ML emulators are $\approx10^5$ times faster than the high-fidelity numerical simulations. Speed and accuracy of the ensemble and MLP models facilitate uncertainty quantification, which usually requires 1,000s of model run, to estimate the uncertainty bounds on the QoIs.
Multi-frequency calibration for DOA estimation with distributed sensors
Authors: Martin Brossard, Virginie Ollier, Mohammed Nabil El Korso, Rémy Boyer, Pascal Larzabal
Date: 2020-02-24 00:00:00
Tasks: Computational Efficiency, Distributed Optimization

In this work, we investigate direction finding in the presence of sensor gain uncertainties and directional perturbations for sensor array processing in a multi-frequency scenario. Specifically, we adopt a distributed optimization scheme in which coherence models are incorporated and local agents exchange information only between connected nodes in the network, i.e., without a fusion center. Numerical simulations highlight the advantages of the proposed parallel iterative technique in terms of statistical and computational efficiency.
Video Monitoring Queries
Authors: Nick Koudas, Raymond Li, Ioannis Xarchakos
Date: 2020-02-24 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection

Recent advances in video processing utilizing deep learning primitives achieved breakthroughs in fundamental problems in video analysis such as frame classification and object detection enabling an array of new applications. In this paper we study the problem of interactive declarative query processing on video streams. In particular we introduce a set of approximate filters to speed up queries that involve objects of specific type (e.g., cars, trucks, etc.) on video frames with associated spatial relationships among them (e.g., car left of truck). The resulting filters are able to assess quickly if the query predicates are true to proceed with further analysis of the frame or otherwise not consider the frame further avoiding costly object detection operations. We propose two classes of filters $IC$ and $OD$, that adapt principles from deep image classification and object detection. The filters utilize extensible deep neural architectures and are easy to deploy and utilize. In addition, we propose statistical query processing techniques to process aggregate queries involving objects with spatial constraints on video streams and demonstrate experimentally the resulting increased accuracy on the resulting aggregate estimation. Combined these techniques constitute a robust set of video monitoring query processing techniques. We demonstrate that the application of the techniques proposed in conjunction with declarative queries on video streams can dramatically increase the frame processing rate and speed up query processing by at least two orders of magnitude. We present the results of a thorough experimental study utilizing benchmark video data sets at scale demonstrating the performance benefits and the practical relevance of our proposals.
Using Distributional Thesaurus Embedding for Co-hyponymy Detection
Authors: Abhik Jana, Nikhil Reddy Varimalla, Pawan Goyal
Date: 2020-02-24 00:00:00
Proceeding: LREC 2020 5
Tasks: Binary Classification, General Classification, Network Embedding

Discriminating lexical relations among distributionally similar words has always been a challenge for natural language processing (NLP) community. In this paper, we investigate whether the network embedding of distributional thesaurus can be effectively utilized to detect co-hyponymy relations. By extensive experiments over three benchmark datasets, we show that the vector representation obtained by applying node2vec on distributional thesaurus outperforms the state-of-the-art models for binary classification of co-hyponymy vs. hypernymy, as well as co-hyponymy vs. meronymy, by huge margins.
An Information Diffusion Approach to Rumor Propagation and Identification on Twitter
Authors: Abiola Osho, Caden Waters, George Amariucai
Date: 2020-02-24 00:00:00
Tasks: feature selection, Misinformation, Prediction

With the increasing use of online social networks as a source of news and information, the propensity for a rumor to disseminate widely and quickly poses a great concern, especially in disaster situations where users do not have enough time to fact-check posts before making the informed decision to react to a post that appears to be credible. In this study, we explore the propagation pattern of rumors on Twitter by exploring the dynamics of microscopic-level misinformation spread, based on the latent message and user interaction attributes. We perform supervised learning for feature selection and prediction. Experimental results with real-world data sets give the models' prediction accuracy at about 90\% for the diffusion of both True and False topics. Our findings confirm that rumor cascades run deeper and that rumor masked as news, and messages that incite fear, will diffuse faster than other messages. We show that the models for True and False message propagation differ significantly, both in the prediction parameters and in the message features that govern the diffusion. Finally, we show that the diffusion pattern is an important metric in identifying the credibility of a tweet.
Behavior Cloning in OpenAI using Case Based Reasoning
Authors: Chad Peters, Babak Esfandiari, Mohamad Zalat, Robert West
Date: 2020-02-23 00:00:00
Tasks: OpenAI Gym

Learning from Observation (LfO), also known as Behavioral Cloning, is an approach for building software agents by recording the behavior of an expert (human or artificial) and using the recorded data to generate the required behavior. jLOAF is a platform that uses Case-Based Reasoning to achieve LfO. In this paper we interface jLOAF with the popular OpenAI Gym environment. Our experimental results show how our approach can be used to provide a baseline for comparison in this domain, as well as identify the strengths and weaknesses when dealing with environmental complexity.
A machine-learning software-systems approach to capture social, regulatory, governance, and climate problems
Authors: Christopher A. Tucker
Date: 2020-02-23 00:00:00
Tasks: BIG-bench Machine Learning

This paper will discuss the role of an artificially-intelligent computer system as critique-based, implicit-organizational, and an inherently necessary device, deployed in synchrony with parallel governmental policy, as a genuine means of capturing nation-population complexity in quantitative form, public contentment in societal-cooperative economic groups, regulatory proposition, and governance-effectiveness domains. It will discuss a solution involving a well-known algorithm and proffer an improved mechanism for knowledge-representation, thereby increasing range of utility, scope of influence (in terms of differentiating class sectors) and operational efficiency. It will finish with a discussion of these and other historical implications.
A Simulation Model Demonstrating the Impact of Social Aspects on Social Internet of Things
Authors: Kashif Zia
Date: 2020-02-23 00:00:00

In addition to seamless connectivity and smartness, the objects in the Internet of Things (IoT) are expected to have the social capabilities -- these objects are termed as ``social objects''. In this paper, an intuitive paradigm of social interactions between these objects are argued and modeled. The impact of social behavior on the interaction pattern of social objects is studied taking Peer-to-Peer (P2P) resource sharing as an example application. The model proposed in this paper studies the implications of competitive vs. cooperative social paradigm, while peers attempt to attain the shared resources / services. The simulation results divulge that the social capabilities of the peers impart a significant increase in the quality of interactions between social objects. Through an agent-based simulation study, it is proved that cooperative strategy is more efficient than competitive strategy. Moreover, cooperation with an underpinning on real-life networking structure and mobility does not negatively impact the efficiency of the system at all; rather it helps.
A binarized-domains arc-consistency algorithm for TCSPs: its computational analysis and its use as a filtering procedure in solution search algorithms
Authors: Amar Isli
Date: 2020-02-22 00:00:00
Tasks: ARC

TCSPs (Temporal Constraint Satisfaction Problems), as defined in [Dechter et al., 1991], get rid of unary constraints by binarizing them after having added an "origin of the world" variable. In this work, we look at the constraints between the "origin of the world" variable and the other variables, as the (binarized) domains of these other variables. With this in mind, we define a notion of arc-consistency for TCSPs, which we will refer to as binarized-domains Arc-Consistency, or bdArc-Consistency for short. We provide an algorithm achieving bdArc-Consistency for a TCSP, which we will refer to as bdAC-3, for it is an adaptation of Mackworth's [1977] well-known arc-consistency algorithm AC-3. We show that if a convex TCSP, referred to in [Dechter et al., 1991] as an STP (Simple Temporal Problem), is bdArc-Consistent, and its "origin of the world" variable disconnected from none of the other variables, its binarized domains are minimal. We provide two polynomial backtrack-free procedures: one for the task of getting, from a bdArc-Consistent STP, either that it is inconsistent or, in case of consistency, a bdArc-Consistent STP refinement whose "origin of the world" variable is disconnected from none of the other variables; the other for the task of getting a solution from a bdArc-Consistent STP whose "origin of the world" variable is disconnected from none of the other variables. We then show how to use our results both in a general TCSP solver and in a TCSP-based job shop scheduler. From our work can be extracted a one-to-all all-to-one shortest paths algorithm of an IR-labelled directed graph. Finally, we show that an existing adaptation to TCSPs of Mackworth's [1977] path-consistency algorithm PC-2 is not guaranteed to always terminate, and correct it.
Quantum Cognitive Triad. Semantic geometry of context representation
Authors: Ilya A. Surov
Date: 2020-02-22 00:00:00

The paper describes an algorithm for semantic representation of behavioral contexts relative to a dichotomic decision alternative. The contexts are represented as quantum qubit states in two-dimensional Hilbert space visualized as points on the Bloch sphere. The azimuthal coordinate of this sphere functions as a one-dimensional semantic space in which the contexts are accommodated according to their subjective relevance to the considered uncertainty. The contexts are processed in triples defined by knowledge of a subject about a binary situational factor. The obtained triads of context representations function as stable cognitive structure at the same time allowing a subject to model probabilistically-variative behavior. The developed algorithm illustrates an approach for quantitative subjectively-semantic modeling of behavior based on conceptual and mathematical apparatus of quantum theory.
Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks
Authors: Hao Liu, Chang Liu, Jason T. L. Wang, Haimin Wang
Date: 2020-02-22 00:00:00
Tasks: Time Series Analysis

We present two recurrent neural networks (RNNs), one based on gated recurrent units and the other based on long short-term memory, for predicting whether an active region (AR) that produces an M- or X-class flare will also produce a coronal mass ejection (CME). We model data samples in an AR as time series and use the RNNs to capture temporal information of the data samples. Each data sample has 18 physical parameters, or features, derived from photospheric vector magnetic field data taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). We survey M- and X-class flares that occurred from 2010 May to 2019 May using the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and select those flares with identified ARs in the NCEI catalogs. In addition, we extract the associations of flares and CMEs from the Space Weather Database Of Notifications, Knowledge, Information (DONKI). We use the information gathered above to build the labels (positive versus negative) of the data samples at hand. Experimental results demonstrate the superiority of our RNNs over closely related machine learning methods in predicting the labels of the data samples. We also discuss an extension of our approach to predict a probabilistic estimate of how likely an M- or X-class flare will initiate a CME, with good performance results. To our knowledge this is the first time that RNNs have been used for CME prediction.
Back to the Future: Joint Aware Temporal Deep Learning 3D Human Pose Estimation
Authors: Vikas Gupta
Date: 2020-02-22 00:00:00
Tasks: 3D Human Pose Estimation, Action Detection, Pose Estimation, Position

We propose a new deep learning network that introduces a deeper CNN channel filter and constraints as losses to reduce joint position and motion errors for 3D video human body pose estimation. Our model outperforms the previous best result from the literature based on mean per-joint position error, velocity error, and acceleration errors on the Human 3.6M benchmark corresponding to a new state-of-the-art mean error reduction in all protocols and motion metrics. Mean per joint error is reduced by 1%, velocity error by 7% and acceleration by 13% compared to the best results from the literature. Our contribution increasing positional accuracy and motion smoothness in video can be integrated with future end to end networks without increasing network complexity. Our model and code are available at https://vnmr.github.io/ Keywords: 3D, human, image, pose, action, detection, object, video, visual, supervised, joint, kinematic
Online Learning in Contextual Bandits using Gated Linear Networks
Authors: Eren Sezener, Marcus Hutter, David Budden, Jianan Wang, Joel Veness
Date: 2020-02-21 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Multi-Armed Bandits

We introduce a new and completely online contextual bandit algorithm called Gated Linear Contextual Bandits (GLCB). This algorithm is based on Gated Linear Networks (GLNs), a recently introduced deep learning architecture with properties well-suited to the online setting. Leveraging data-dependent gating properties of the GLN we are able to estimate prediction uncertainty with effectively zero algorithmic overhead. We empirically evaluate GLCB compared to 9 state-of-the-art algorithms that leverage deep neural networks, on a standard benchmark suite of discrete and continuous contextual bandit problems. GLCB obtains median first-place despite being the only online method, and we further support these results with a theoretical study of its convergence properties.
Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization
Authors: Manuel Kaspar, Juan David Munoz Osorio, Jürgen Bock
Date: 2020-02-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work we show how to use the Operational Space Control framework (OSC) under joint and cartesian constraints for reinforcement learning in cartesian space. Our method is therefore able to learn fast and with adjustable degrees of freedom, while we are able to transfer policies without additional dynamics randomizations on a KUKA LBR iiwa peg in-hole task. Before learning in simulation starts, we perform a system identification for aligning the simulation environment as far as possible with the dynamics of a real robot. Adding constraints to the OSC controller allows us to learn in a safe way on the real robot or to learn a flexible, goal conditioned policy that can be easily transferred from simulation to the real robot.
RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition
Authors: Peiyan Dong, Siyue Wang, Wei Niu, Chengming Zhang, Sheng Lin, Zhengang Li, Yifan Gong, Bin Ren, Xue Lin, Yanzhi Wang, Dingwen Tao
Date: 2020-02-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), GPU, speech-recognition, Speech Recognition

Recurrent neural networks (RNNs) based automatic speech recognition has nowadays become prevalent on mobile devices such as smart phones. However, previous RNN compression techniques either suffer from hardware performance overhead due to irregularity or significant accuracy loss due to the preserved regularity for hardware friendliness. In this work, we propose RTMobile that leverages both a novel block-based pruning approach and compiler optimizations to accelerate RNN inference on mobile devices. Our proposed RTMobile is the first work that can achieve real-time RNN inference on mobile platforms. Experimental results demonstrate that RTMobile can significantly outperform existing RNN hardware acceleration methods in terms of inference accuracy and time. Compared with prior work on FPGA, RTMobile using Adreno 640 embedded GPU on GRU can improve the energy-efficiency by about 40$\times$ while maintaining the same inference time.
Learning a Directional Soft Lane Affordance Model for Road Scenes Using Self-Supervision
Authors: Robin Karlsson, Erik Sjoberg
Date: 2020-02-17 00:00:00
Tasks: Autonomous Vehicles, Navigate

Humans navigate complex environments in an organized yet flexible manner, adapting to the context and implicit social rules. Understanding these naturally learned patterns of behavior is essential for applications such as autonomous vehicles. However, algorithmically defining these implicit rules of human behavior remains difficult. This work proposes a novel self-supervised method for training a probabilistic network model to estimate the regions humans are most likely to drive in as well as a multimodal representation of the inferred direction of travel at each point. The model is trained on individual human trajectories conditioned on a representation of the driving environment. The model is shown to successfully generalize to new road scenes, demonstrating potential for real-world application as a prior for socially acceptable driving behavior in challenging or ambiguous scenarios which are poorly handled by explicit traffic rules.
ARMA Nets: Expanding Receptive Field for Dense Prediction
Authors: Jiahao Su, Shiqi Wang, Furong Huang
Date: 2020-02-15 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: image-classification, Image Classification, Prediction, Semantic Segmentation, Video Prediction

Global information is essential for dense prediction problems, whose goal is to compute a discrete or continuous label for each pixel in the images. Traditional convolutional layers in neural networks, initially designed for image classification, are restrictive in these problems since the filter size limits their receptive fields. In this work, we propose to replace any traditional convolutional layer with an autoregressive moving-average (ARMA) layer, a novel module with an adjustable receptive field controlled by the learnable autoregressive coefficients. Compared with traditional convolutional layers, our ARMA layer enables explicit interconnections of the output neurons and learns its receptive field by adapting the autoregressive coefficients of the interconnections. ARMA layer is adjustable to different types of tasks: for tasks where global information is crucial, it is capable of learning relatively large autoregressive coefficients to allow for an output neuron's receptive field covering the entire input; for tasks where only local information is required, it can learn small or near zero autoregressive coefficients and automatically reduces to a traditional convolutional layer. We show both theoretically and empirically that the effective receptive field of networks with ARMA layers (named as ARMA networks) expands with larger autoregressive coefficients. We also provably solve the instability problem of learning and prediction in the ARMA layer through a re-parameterization mechanism. Additionally, we demonstrate that ARMA networks substantially improve their baselines on challenging dense prediction tasks including video prediction and semantic segmentation.
End-to-End Models for the Analysis of System 1 and System 2 Interactions based on Eye-Tracking Data
Authors: Alessandro Rossi, Sara Ermini, Dario Bernabini, Dario Zanca, Marino Todisco, Alessandro Genovese, Antonio Rizzo
Date: 2020-02-03 00:00:00

While theories postulating a dual cognitive system take hold, quantitative confirmations are still needed to understand and identify interactions between the two systems or conflict events. Eye movements are among the most direct markers of the individual attentive load and may serve as an important proxy of information. In this work we propose a computational method, within a modified visual version of the well-known Stroop test, for the identification of different tasks and potential conflicts events between the two systems through the collection and processing of data related to eye movements. A statistical analysis shows that the selected variables can characterize the variation of attentive load within different scenarios. Moreover, we show that Machine Learning techniques allow to distinguish between different tasks with a good classification accuracy and to investigate more in depth the gaze dynamics.
The Differentially Private Lottery Ticket Mechanism
Authors: Lovedeep Gondara, Ke Wang, Ricardo Silva Carvalho
Date: 2020-02-16 00:00:00

We propose the differentially private lottery ticket mechanism (DPLTM). An end-to-end differentially private training paradigm based on the lottery ticket hypothesis. Using "high-quality winners", selected via our custom score function, DPLTM significantly improves the privacy-utility trade-off over the state-of-the-art. We show that DPLTM converges faster, allowing for early stopping with reduced privacy budget consumption. We further show that the tickets from DPLTM are transferable across datasets, domains, and architectures. Our extensive evaluation on several public datasets provides evidence to our claims.