pwc-archive/papers-with-abstracts huggingface dataset:
DeepRacing: Parameterized Trajectories for Autonomous Racing
Authors: Trent Weiss, Madhur Behl
Date: 2020-05-06 00:00:00
Tasks: Autonomous Racing, Trajectory Prediction

We consider the challenging problem of high speed autonomous racing in a realistic Formula One environment. DeepRacing is a novel end-to-end framework, and a virtual testbed for training and evaluating algorithms for autonomous racing. The virtual testbed is implemented using the realistic F1 series of video games, developed by Codemasters, which many Formula One drivers use for training. This virtual testbed is released under an open-source license both as a standalone C++ API and as a binding to the popular Robot Operating System 2 (ROS2) framework. This open-source API allows anyone to use the high fidelity physics and photo-realistic capabilities of the F1 game as a simulator, and without hacking any game engine code. We use this framework to evaluate several neural network methodologies for autonomous racing. Specifically, we consider several fully end-to-end models that directly predict steering and acceleration commands for an autonomous race car as well as a model that predicts a list of waypoints to follow in the car's local coordinate system, with the task of selecting a steering/throttle angle left to a classical control algorithm. We also present a novel method of autonomous racing by training a deep neural network to predict a parameterized representation of a trajectory rather than a list of waypoints. We evaluate these models performance in our open-source simulator and show that trajectory prediction far outperforms end-to-end driving. Additionally, we show that open-loop performance for an end-to-end model, i.e. root-mean-square error for a model's predicted control values, does not necessarily correlate with increased driving performance in the closed-loop sense, i.e. actual ability to race around a track. Finally, we show that our proposed model of parameterized trajectory prediction outperforms both end-to-end control and waypoint prediction.
Do we know the operating principles of our computers better than those of our brain?
Authors: János Végh, Ádám J. Berki
Date: 2020-05-06 00:00:00

The increasing interest in understanding the behavior of the biological neural networks, and the increasing utilization of artificial neural networks in different fields and scales, both require a thorough understanding of how neuromorphic computing works. On the one side, the need to program those artificial neuron-like elements, and, on the other side, the necessity for a large number of such elements to cooperate, communicate and compute during tasks, need to be scrutinized to determine how efficiently conventional computing can assist in implementing such systems. Some electronic components bear a surprising resemblance to some biological structures. However, combining them with components that work using different principles can result in systems with very poor efficacy. The paper discusses how the conventional principles, components and thinking about computing limit mimicking the biological systems. We describe what changes will be necessary in the computing paradigms to get closer to the marvelously efficient operation of biological neural networks.
Functional Decision Theory in an Evolutionary Environment
Authors: Noah Topper
Date: 2020-05-06 00:00:00

Functional decision theory (FDT) is a fairly new mode of decision theory and a normative viewpoint on how an agent should maximize expected utility. The current standard in decision theory and computer science is causal decision theory (CDT), largely seen as superior to the main alternative evidential decision theory (EDT). These theories prescribe three distinct methods for maximizing utility. We explore how FDT differs from CDT and EDT, and what implications it has on the behavior of FDT agents and humans. It has been shown in previous research how FDT can outperform CDT and EDT. We additionally show FDT performing well on more classical game theory problems and argue for its extension to human problems to show that its potential for superiority is robust. We also make FDT more concrete by displaying it in an evolutionary environment, competing directly against other theories.
Low-Rank Nonlinear Decoding of $μ$-ECoG from the Primary Auditory Cortex
Authors: Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Alyson K. Fletcher, Sundeep Rangan, Michael Trumpis, Brinnae Bent, Chia-Han Chiang, Jonathan Viventi
Date: 2020-05-06 00:00:00
Tasks: Decoder, Dimensionality Reduction

This paper considers the problem of neural decoding from parallel neural measurements systems such as micro-electrocorticography ($\mu$-ECoG). In systems with large numbers of array elements at very high sampling rates, the dimension of the raw measurement data may be large. Learning neural decoders for this high-dimensional data can be challenging, particularly when the number of training samples is limited. To address this challenge, this work presents a novel neural network decoder with a low-rank structure in the first hidden layer. The low-rank constraints dramatically reduce the number of parameters in the decoder while still enabling a rich class of nonlinear decoder maps. The low-rank decoder is illustrated on $\mu$-ECoG data from the primary auditory cortex (A1) of awake rats. This decoding problem is particularly challenging due to the complexity of neural responses in the auditory cortex and the presence of confounding signals in awake animals. It is shown that the proposed low-rank decoder significantly outperforms models using standard dimensionality reduction techniques such as principal component analysis (PCA).
Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection
Authors: Anna Guerra, Francesco Guidi, Davide Dardari, Petar M. Djuric
Date: 2020-05-05 00:00:00
Tasks: Autonomous Navigation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment. The goal is to optimize its trajectory with the purpose of maximizing the mapping accuracy and, at the same time, to avoid areas where measurements might not be sufficiently informative from the perspective of a target detection. This problem is formulated as a Markov decision process (MDP) where the UAV is an agent that runs either a state estimator for target detection and for environment mapping, and a reinforcement learning (RL) algorithm to infer its own policy of navigation (i.e., the control law). Numerical results show the feasibility of the proposed idea, highlighting the UAV's capability of autonomously exploring areas with high probability of target detection while reconstructing the surrounding environment.
Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations
Authors: Christopher J Arthurs, Andrew P. King
Date: 2020-05-02 00:00:00
Tasks: Active Learning, Data Compression, Medical Diagnosis

The goal of this work is to train a neural network which approximates solutions to the Navier-Stokes equations across a region of parameter space, in which the parameters define physical properties such as domain shape and boundary conditions. The contributions of this work are threefold: 1) To demonstrate that neural networks can be efficient aggregators of whole families of parameteric solutions to physical problems, trained using data created with traditional, trusted numerical methods such as finite elements. Advantages include extremely fast evaluation of pressure and velocity at any point in physical and parameter space (asymptotically, ~3 $\mu s$ / query), and data compression (the network requires 99\% less storage space compared to its own training data). 2) To demonstrate that the neural networks can accurately interpolate between finite element solutions in parameter space, allowing them to be instantly queried for pressure and velocity field solutions to problems for which traditional simulations have never been performed. 3) To introduce an active learning algorithm, so that during training, a finite element solver can automatically be queried to obtain additional training data in locations where the neural network's predictions are in most need of improvement, thus autonomously acquiring and efficiently distributing training data throughout parameter space. In addition to the obvious utility of Item 2, above, we demonstrate an application of the network in rapid parameter sweeping, very precisely predicting the degree of narrowing in a tube which would result in a 50\% increase in end-to-end pressure difference at a given flow rate. This capability could have applications in both medical diagnosis of arterial disease, and in computer-aided design.
Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
Authors: Prachi Jain, Sushant Rathi, Mausam, Soumen Chakrabarti
Date: 2020-05-02 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Knowledge Base Completion, Knowledge Graph Completion, Knowledge Graphs, Link Prediction, Prediction, Temporal Information Extraction, Temporal Knowledge Graph Completion, Time-interval Prediction, valid

Temporal knowledge bases associate relational (s,r,o) triples with a set of times (or a single time instant) when the relation is valid. While time-agnostic KB completion (KBC) has witnessed significant research, temporal KB completion (TKBC) is in its early days. In this paper, we consider predicting missing entities (link prediction) and missing time intervals (time prediction) as joint TKBC tasks where entities, relations, and time are all embedded in a uniform, compatible space. We present TIMEPLEX, a novel time-aware KBC method, that also automatically exploits the recurrent nature of some relations and temporal interactions between pairs of relations. TIMEPLEX achieves state-of-the-art performance on both prediction tasks. We also find that existing TKBC models heavily overestimate link prediction performance due to imperfect evaluation mechanisms. In response, we propose improved TKBC evaluation protocols for both link and time prediction tasks, dealing with subtle issues that arise from the partial overlap of time intervals in gold instances and system predictions.
RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors
Authors: Liyao Gao, Yifan Du, Hongshan Li, Guang Lin
Date: 2020-04-28 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation

In the recent application of scientific modeling, machine learning models are largely applied to facilitate computational simulations of fluid systems. Rotation symmetry is a general property for most symmetric fluid systems. However, in general, current machine learning methods have no theoretical way to guarantee rotational symmetry. By observing an important property of contraction and rotation operation on high-order symmetric tensors, we prove that the rotation operation is preserved via tensor contraction. Based on this theoretical justification, in this paper, we introduce Rotation-Equivariant Network (RotEqNet) to guarantee the property of rotation-equivariance for high-order tensors in fluid systems. We implement RotEqNet and evaluate our claims through four case studies on various fluid systems. The property of error reduction and rotation-equivariance is verified in these case studies. Results from the comparative study show that our method outperforms conventional methods, which rely on data augmentation.
Computable Phenotypes of Patient Acuity in the Intensive Care Unit
Authors: Yuanfang Ren, Jeremy Balch, Kenneth L. Abbott, Tyler J. Loftus, Benjamin Shickel, Parisa Rashidi, Azra Bihorac, Tezcan Ozrazgat-Baslanti
Date: 2020-04-27 00:00:00
Tasks: Decision Making, ICU Admission

Continuous monitoring and patient acuity assessments are key aspects of Intensive Care Unit (ICU) practice, but both are limited by time constraints imposed on healthcare providers. Moreover, anticipating clinical trajectories remains imprecise. The objectives of this study are to (1) develop an electronic phenotype of acuity using automated variable retrieval within the electronic health records and (2) describe transitions between acuity states that illustrate the clinical trajectories of ICU patients. We gathered two single-center, longitudinal electronic health record datasets for 51,372 adult ICU patients admitted to the University of Florida Health (UFH) Gainesville (GNV) and Jacksonville (JAX). We developed algorithms to quantify acuity status at four-hour intervals for each ICU admission and identify acuity phenotypes using continuous acuity status and k-means clustering approach. 51,073 admissions for 38,749 patients in the UFH GNV dataset and 22,219 admissions for 12,623 patients in the UFH JAX dataset had at least one ICU stay lasting more than four hours. There were three phenotypes: persistently stable, persistently unstable, and transitioning from unstable to stable. For stable patients, approximately 0.7%-1.7% would transition to unstable, 0.02%-0.1% would expire, 1.2%-3.4% would be discharged, and the remaining 96%-97% would remain stable in the ICU every four hours. For unstable patients, approximately 6%-10% would transition to stable, 0.4%-0.5% would expire, and the remaining 89%-93% would remain unstable in the ICU in the next four hours. We developed phenotyping algorithms for patient acuity status every four hours while admitted to the ICU. This approach may be useful in developing prognostic and clinical decision-support tools to aid patients, caregivers, and providers in shared decision-making processes regarding escalation of care and patient values.
Facial Electromyography-based Adaptive Virtual Reality Gaming for Cognitive Training
Authors: Lorcan Reidy, Dennis Chan, Charles Nduka, Hatice Gunes
Date: 2020-04-27 00:00:00
Tasks: Electromyography (EMG), Emotion Recognition, Information Retrieval, Reading Comprehension, Retrieval, valid

Cognitive training has shown promising results for delivering improvements in human cognition related to attention, problem solving, reading comprehension and information retrieval. However, two frequently cited problems in cognitive training literature are a lack of user engagement with the training programme, and a failure of developed skills to generalise to daily life. This paper introduces a new cognitive training (CT) paradigm designed to address these two limitations by combining the benefits of gamification, virtual reality (VR), and affective adaptation in the development of an engaging, ecologically valid, CT task. Additionally, it incorporates facial electromyography (EMG) as a means of determining user affect while engaged in the CT task. This information is then utilised to dynamically adjust the game's difficulty in real-time as users play, with the aim of leading them into a state of flow. Affect recognition rates of 64.1% and 76.2%, for valence and arousal respectively, were achieved by classifying a DWT-Haar approximation of the input signal using kNN. The affect-aware VR cognitive training intervention was then evaluated with a control group of older adults. The results obtained substantiate the notion that adaptation techniques can lead to greater feelings of competence and a more appropriate challenge of the user's skills.
Prescribing Deep Attentive Score Prediction Attracts Improved Student Engagement
Authors: Youngnam Lee, Byung-soo Kim, Dongmin Shin, JungHoon Kim, Jineon Baek, Jinhwan Lee, Youngduck Choi
Date: 2020-04-27 00:00:00
Tasks: Collaborative Filtering, Diagnostic, Prediction

Intelligent Tutoring Systems (ITSs) have been developed to provide students with personalized learning experiences by adaptively generating learning paths optimized for each individual. Within the vast scope of ITS, score prediction stands out as an area of study that enables students to construct individually realistic goals based on their current position. Via the expected score provided by the ITS, a student can instantaneously compare one's expected score to one's actual score, which directly corresponds to the reliability that the ITS can instill. In other words, refining the precision of predicted scores strictly correlates to the level of confidence that a student may have with an ITS, which will evidently ensue improved student engagement. However, previous studies have solely concentrated on improving the performance of a prediction model, largely lacking focus on the benefits generated by its practical application. In this paper, we demonstrate that the accuracy of the score prediction model deployed in a real-world setting significantly impacts user engagement by providing empirical evidence. To that end, we apply a state-of-the-art deep attentive neural network-based score prediction model to Santa, a multi-platform English ITS with approximately 780K users in South Korea that exclusively focuses on the TOEIC (Test of English for International Communications) standardized examinations. We run a controlled A/B test on the ITS with two models, respectively based on collaborative filtering and deep attentive neural networks, to verify whether the more accurate model engenders any student engagement. The results conclude that the attentive model not only induces high student morale (e.g. higher diagnostic test completion ratio, number of questions answered, etc.) but also encourages active engagement (e.g. higher purchase rate, improved total profit, etc.) on Santa.
Random thoughts about Complexity, Data and Models
Authors: Hykel Hosni, Angelo Vulpiani
Date: 2020-04-16 00:00:00
Tasks: Time Series Analysis

Data Science and Machine learning have been growing strong for the past decade. We argue that to make the most of this exciting field we should resist the temptation of assuming that forecasting can be reduced to brute-force data analytics. This owes to the fact that modelling, as we illustrate below, requires mastering the art of selecting relevant variables. More specifically, we investigate the subtle relation between "data and models" by focussing on the role played by algorithmic complexity, which contributed to making mathematically rigorous the long-standing idea that to understand empirical phenomena is to describe the rules which generate the data in terms which are "simpler" than the data itself. A key issue for the appraisal of the relation between algorithmic complexity and algorithmic learning is to do with a much needed clarification on the related but distinct concepts of compressibility, determinism and predictability. To this end we will illustrate that the evolution law of a chaotic system is compressibile, but a generic initial condition for it is not, making the time series generated by chaotic systems incompressible in general. Hence knowledge of the rules which govern an empirical phenomenon are not sufficient for predicting its outcomes. In turn this implies that there is more to understanding phenomena than learning -- even from data alone -- such rules. This can be achieved only in those cases when we are capable of "good modelling". Clearly, the very idea of algorithmic complexity rests on Turing's seminal analysis of computation. This motivates our remarks on this extremely telling example of analogy-based abstract modelling which is nonetheless heavily informed by empirical facts.
PointTriNet: Learned Triangulation of 3D Point Sets
Authors: Nicholas Sharp, Maks Ovsjanikov
Date: 2020-04-19 00:00:00
Proceeding: ECCV 2020 8

This work considers a new task in geometric deep learning: generating a triangulation among a set of points in 3D space. We present PointTriNet, a differentiable and scalable approach enabling point set triangulation as a layer in 3D learning pipelines. The method iteratively applies two neural networks: a classification network predicts whether a candidate triangle should appear in the triangulation, while a proposal network suggests additional candidates. Both networks are structured as PointNets over nearby points and triangles, using a novel triangle-relative input encoding. Since these learning problems operate on local geometric data, our method is efficient and scalable, and generalizes to unseen shape categories. Our networks are trained in an unsupervised manner from a collection of shapes represented as point clouds. We demonstrate the effectiveness of this approach for classical meshing tasks, robustness to outliers, and as a component in end-to-end learning systems.
BIOSSES: A Semantic Sentence Similarity Estimation System for the Biomedical Domain
Authors: Gizem Sogancioglu, Hakime Öztürk, Arzucan Özgür
Date: 2017-07-15 00:00:00
Proceeding: Bioinformatics 2017 7
Tasks: Retrieval, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Embeddings For Biomedical Texts, Sentence Similarity, Text Retrieval

Motivation: The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many NLP tasks including text retrieval and summarization. A number of approaches have been proposed for semantic sentence similarity estimation for generic English. However, our experiments showed that such approaches do not effectively cover biomedical knowledge and produce poor results for biomedical text.

Methods: We propose several approaches for sentence-level semantic similarity computation in the biomedical domain, including string similarity measures and measures based on the distributed vector representations of sentences learned in an unsupervised manner from a large biomedical corpus. In addition, ontology-based approaches are presented that utilize general and domain-specific ontologies. Finally, a supervised regression based model is developed that effectively combines the different similarity computation metrics. A benchmark data set consisting of 100 sentence pairs from the biomedical literature is manually annotated by five human experts and used for evaluating the proposed methods.

Results: The experiments showed that the supervised semantic sentence similarity computation approach obtained the best performance (0.836 correlation with gold standard human annotations) and improved over the state-of-the-art domain-independent systems up to 42.6% in terms of the Pearson correlation metric.