October 19, 2019

3301 words 16 mins read

Paper Group ANR 139

Teaching Autonomous Driving Using a Modular and Integrated Approach. Towards Coinductive Theory Exploration in Horn Clause Logic: Position Paper. Neural Lander: Stable Drone Landing Control using Learned Dynamics. Denoising Weak Lensing Mass Maps with Deep Learning. Learning to Drive from Simulation without Real World Labels. A framework for Cultur …

Teaching Autonomous Driving Using a Modular and Integrated Approach


Title	Teaching Autonomous Driving Using a Modular and Integrated Approach
Authors	Jie Tang, Shaoshan Liu, Songwen Pei, Stephane Zuckerman, Chen Liu, Weisong Shi, Jean-Luc Gaudiot
Abstract	Autonomous driving is not one single technology but rather a complex system integrating many technologies, which means that teaching autonomous driving is a challenging task. Indeed, most existing autonomous driving classes focus on one of the technologies involved. This not only fails to provide a comprehensive coverage, but also sets a high entry barrier for students with different technology backgrounds. In this paper, we present a modular, integrated approach to teaching autonomous driving. Specifically, we organize the technologies used in autonomous driving into modules. This is described in the textbook we have developed as well as a series of multimedia online lectures designed to provide technical overview for each module. Then, once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other. To verify this teaching approach, we present three case studies: an introductory class on autonomous driving for students with only a basic technology background; a new session in an existing embedded systems class to demonstrate how embedded system technologies can be applied to autonomous driving; and an industry professional training session to quickly bring up experienced engineers to work in autonomous driving. The results show that students can maintain a high interest level and make great progress by starting with familiar concepts before moving onto other modules.
Tasks	Autonomous Driving
Published	2018-02-22
URL	http://arxiv.org/abs/1802.09355v2
PDF	http://arxiv.org/pdf/1802.09355v2.pdf
PWC	https://paperswithcode.com/paper/teaching-autonomous-driving-using-a-modular
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Towards Coinductive Theory Exploration in Horn Clause Logic: Position Paper


Title	Towards Coinductive Theory Exploration in Horn Clause Logic: Position Paper
Authors	Ekaterina Komendantskaya Dr, Yue Li
Abstract	Coinduction occurs in two guises in Horn clause logic: in proofs of self-referencing properties and relations, and in proofs involving construction of (possibly irregular) infinite data. Both instances of coinductive reasoning appeared in the literature before, but a systematic analysis of these two kinds of proofs and of their relation was lacking. We propose a general proof-theoretic framework for handling both kinds of coinduction arising in Horn clause logic. To this aim, we propose a coinductive extension of Miller et al’s framework of uniform proofs and prove its soundness relative to coinductive models of Horn clause logic.
Tasks
Published	2018-09-13
URL	http://arxiv.org/abs/1809.04771v1
PDF	http://arxiv.org/pdf/1809.04771v1.pdf
PWC	https://paperswithcode.com/paper/towards-coinductive-theory-exploration-in
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Neural Lander: Stable Drone Landing Control using Learned Dynamics


Title	Neural Lander: Stable Drone Landing Control using Learned Dynamics
Authors	Guanya Shi, Xichen Shi, Michael O’Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung
Abstract	Precise near-ground trajectory control is difficult for multi-rotor drones, due to the complex aerodynamic effects caused by interactions between multi-rotor airflow and the environment. Conventional control methods often fail to properly account for these complex effects and fall short in accomplishing smooth landing. In this paper, we present a novel deep-learning-based robust nonlinear controller (Neural Lander) that improves control performance of a quadrotor during landing. Our approach combines a nominal dynamics model with a Deep Neural Network (DNN) that learns high-order interactions. We apply spectral normalization (SN) to constrain the Lipschitz constant of the DNN. Leveraging this Lipschitz property, we design a nonlinear feedback linearization controller using the learned model and prove system stability with disturbance rejection. To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets. Experimental results demonstrate that the proposed controller significantly outperforms a Baseline Nonlinear Tracking Controller in both landing and cross-table trajectory tracking cases. We also empirically show that the DNN generalizes well to unseen data outside the training domain.
Tasks
Published	2018-11-19
URL	http://arxiv.org/abs/1811.08027v2
PDF	http://arxiv.org/pdf/1811.08027v2.pdf
PWC	https://paperswithcode.com/paper/neural-lander-stable-drone-landing-control
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Denoising Weak Lensing Mass Maps with Deep Learning


Title	Denoising Weak Lensing Mass Maps with Deep Learning
Authors	Masato Shirasaki, Naoki Yoshida, Shiro Ikeda
Abstract	Weak gravitational lensing is a powerful probe of the large-scale cosmic matter distribution. Wide-field galaxy surveys allow us to generate the so-called weak lensing maps, but actual observations suffer from noise due to imperfect measurement of galaxy shape distortions and to the limited number density of the source galaxies. In this paper, we explore a deep-learning approach to reduce the noise. We develop an image-to-image translation method with conditional adversarial networks (CANs), which learn efficient mapping from an input noisy weak lensing map to the underlying noise field. We train the CANs using $30000$ image pairs obtained from $1000$ ray-tracing simulations of weak gravitational lensing. We show that the trained CANs reproduce the true one-point probability distribution function (PDF) of the noiseless lensing map with a bias less than $1\sigma$ on average, where $\sigma$ is the statistical error. We perform a Fisher analysis to make forecast for cosmological parameter inference with the one-point lensing PDF. By our denoising method using CANs, the first derivative of the PDF with respect to the cosmic mean matter density and the amplitude of the primordial curvature perturbations becomes larger by $\sim50%$. This allows us to improve the cosmological constraints by $\sim30-40%$ with using observational data from ongoing and upcoming galaxy imaging surveys.
Tasks	Denoising, Image-to-Image Translation
Published	2018-12-14
URL	https://arxiv.org/abs/1812.05781v2
PDF	https://arxiv.org/pdf/1812.05781v2.pdf
PWC	https://paperswithcode.com/paper/denoising-weak-lensing-mass-maps-with-deep
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Learning to Drive from Simulation without Real World Labels


Title	Learning to Drive from Simulation without Real World Labels
Authors	Alex Bewley, Jessica Rigley, Yuxuan Liu, Jeffrey Hawke, Richard Shen, Vinh-Dieu Lam, Alex Kendall
Abstract	Simulation can be a powerful tool for understanding machine learning systems and designing methods to solve real-world problems. Training and evaluating methods purely in simulation is often “doomed to succeed” at the desired task in a simulated environment, but the resulting models are incapable of operation in the real world. Here we present and evaluate a method for transferring a vision-based lane following driving policy from simulation to operation on a rural road without any real-world labels. Our approach leverages recent advances in image-to-image translation to achieve domain transfer while jointly learning a single-camera control policy from simulation control labels. We assess the driving performance of this method using both open-loop regression metrics, and closed-loop performance operating an autonomous vehicle on rural and urban roads.
Tasks	Image-to-Image Translation
Published	2018-12-10
URL	http://arxiv.org/abs/1812.03823v2
PDF	http://arxiv.org/pdf/1812.03823v2.pdf
PWC	https://paperswithcode.com/paper/learning-to-drive-from-simulation-without
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A framework for Culture-aware Robots based on Fuzzy Logic


Title	A framework for Culture-aware Robots based on Fuzzy Logic
Authors	Barbara Bruno, Fulvio Mastrogiovanni, Federico Pecora, Antonio Sgorbissa, Alessandro Saffiotti
Abstract	Cultural adaptation, i.e., the matching of a robot’s behaviours to the cultural norms and preferences of its user, is a well known key requirement for the success of any assistive application. However, culture-dependent robot behaviours are often implicitly set by designers, thus not allowing for an easy and automatic adaptation to different cultures. This paper presents a method for the design of culture-aware robots, that can automatically adapt their behaviour to conform to a given culture. We propose a mapping from cultural factors to related parameters of robot behaviours which relies on linguistic variables to encode heterogeneous cultural factors in a uniform formalism, and on fuzzy rules to encode qualitative relations among multiple variables. We illustrate the approach in two practical case studies.
Tasks
Published	2018-03-22
URL	http://arxiv.org/abs/1803.08343v1
PDF	http://arxiv.org/pdf/1803.08343v1.pdf
PWC	https://paperswithcode.com/paper/a-framework-for-culture-aware-robots-based-on
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SUSAN: Segment Unannotated image Structure using Adversarial Network


Title	SUSAN: Segment Unannotated image Structure using Adversarial Network
Authors	Fang Liu
Abstract	Segmentation of magnetic resonance (MR) images is a fundamental step in many medical imaging-based applications. The recent implementation of deep convolutional neural networks (CNNs) in image processing has been shown to have significant impacts on medical image segmentation. Network training of segmentation CNNs typically requires images and paired annotation data representing pixel-wise tissue labels referred to as masks. However, the supervised training of highly efficient CNNs with deeper structure and more network parameters requires a large number of training images and paired tissue masks. Thus, there is great need to develop a generalized CNN-based segmentation method which would be applicable for a wide variety of MR image datasets with different tissue contrasts. The purpose of this study was to develop and evaluate a generalized CNN-based method for fully-automated segmentation of different MR image datasets using a single set of annotated training data. A technique called cycle-consistent generative adversarial network (CycleGAN) is applied as the core of the proposed method to perform image-to-image translation between MR image datasets with different tissue contrasts. A joint segmentation network is incorporated into the adversarial network to obtain additional segmentation functionality. The proposed method was evaluated for segmenting bone and cartilage on two clinical knee MR image datasets acquired at our institution using only a single set of annotated data from a publicly available knee MR image dataset. The new technique may further improve the applicability and efficiency of CNN-based segmentation of medical images while eliminating the need for large amounts of annotated training data.
Tasks	Image-to-Image Translation, Medical Image Segmentation, Semantic Segmentation
Published	2018-12-03
URL	http://arxiv.org/abs/1812.00555v1
PDF	http://arxiv.org/pdf/1812.00555v1.pdf
PWC	https://paperswithcode.com/paper/susan-segment-unannotated-image-structure
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Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals


Title	Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals
Authors	Adam Hartwell, Visakan Kadirkamanathan, Sean R Anderson
Abstract	Machine learning classifiers using surface electromyography are important for human-machine interfacing and device control. Conventional classifiers such as support vector machines (SVMs) use manually extracted features based on e.g. wavelets. These features tend to be fixed and non-person specific, which is a key limitation due to high person-to-person variability of myography signals. Deep neural networks, by contrast, can automatically extract person specific features - an important advantage. However, deep neural networks typically have the drawback of large numbers of parameters, requiring large training data sets and powerful hardware not suited to embedded systems. This paper solves these problems by introducing a compact deep neural network architecture that is much smaller than existing counterparts. The performance of the compact deep net is benchmarked against an SVM and compared to other contemporary architectures across 10 human subjects, comparing Myo and Delsys Trigno electrode sets. The accuracy of the compact deep net was found to be 84.2 +/- 6% versus 70.5 +/- 7% for the SVM on the Myo, and 80.3+/- 7% versus 67.8 +/- 9% for the Delsys system, demonstrating the superior effectiveness of the proposed compact network, which had just 5,889 parameters - orders of magnitude less than some contemporary alternatives in this domain while maintaining better performance.
Tasks
Published	2018-06-22
URL	http://arxiv.org/abs/1806.08641v3
PDF	http://arxiv.org/pdf/1806.08641v3.pdf
PWC	https://paperswithcode.com/paper/compact-deep-neural-networks-for
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Over-representation of Extreme Events in Decision-Making: A Rational Metacognitive Account


Title	Over-representation of Extreme Events in Decision-Making: A Rational Metacognitive Account
Authors	Ardavan S. Nobandegani, Kevin da Silva Castanheira, A. Ross Otto, Thomas R. Shultz
Abstract	The Availability bias, manifested in the over-representation of extreme eventualities in decision-making, is a well-known cognitive bias, and is generally taken as evidence of human irrationality. In this work, we present the first rational, metacognitive account of the Availability bias, formally articulated at Marr’s algorithmic level of analysis. Concretely, we present a normative, metacognitive model of how a cognitive system should over-represent extreme eventualities, depending on the amount of time available at its disposal for decision-making. Our model also accounts for two well-known framing effects in human decision-making under risk—the fourfold pattern of risk preferences in outcome probability (Tversky & Kahneman, 1992) and in outcome magnitude (Markovitz, 1952)—thereby providing the first metacognitively-rational basis for those effects. Empirical evidence, furthermore, confirms an important prediction of our model. Surprisingly, our model is unimaginably robust with respect to its focal parameter. We discuss the implications of our work for studies on human decision-making, and conclude by presenting a counterintuitive prediction of our model, which, if confirmed, would have intriguing implications for human decision-making under risk. To our knowledge, our model is the first metacognitive, resource-rational process model of cognitive biases in decision-making.
Tasks	Decision Making
Published	2018-01-30
URL	http://arxiv.org/abs/1801.09848v1
PDF	http://arxiv.org/pdf/1801.09848v1.pdf
PWC	https://paperswithcode.com/paper/over-representation-of-extreme-events-in
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D{é}tection de locuteurs dans les s{é}ries TV


Title	D{é}tection de locuteurs dans les s{é}ries TV
Authors	Xavier Bost, Georges Linares
Abstract	Speaker diarization of audio streams turns out to be particularly challenging when applied to fictional films, where many characters talk in various acoustic conditions (background music, sound effects, variations in intonation…). Despite this acoustic variability, such movies exhibit specific visual patterns, particularly within dialogue scenes. In this paper, we introduce a two-step method to achieve speaker diarization in TV series: speaker diarization is first performed locally within scenes visually identified as dialogues; then, the hypothesized local speakers are compared to each other during a second clustering process in order to detect recurring speakers: this second stage of clustering is subject to the constraint that the different speakers involved in the same dialogue have to be assigned to different clusters. The performances of our approach are compared to those obtained by standard speaker diarization tools applied to the same data.
Tasks	Speaker Diarization
Published	2018-12-18
URL	http://arxiv.org/abs/1812.07200v1
PDF	http://arxiv.org/pdf/1812.07200v1.pdf
PWC	https://paperswithcode.com/paper/detection-de-locuteurs-dans-les-series-tv
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Model-Based Reinforcement Learning for Sepsis Treatment


Title	Model-Based Reinforcement Learning for Sepsis Treatment
Authors	Aniruddh Raghu, Matthieu Komorowski, Sumeetpal Singh
Abstract	Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we explore the use of continuous state-space model-based reinforcement learning (RL) to discover high-quality treatment policies for sepsis patients. Our quantitative evaluation reveals that by blending the treatment strategy discovered with RL with what clinicians follow, we can obtain improved policies, potentially allowing for better medical treatment for sepsis.
Tasks
Published	2018-11-23
URL	http://arxiv.org/abs/1811.09602v1
PDF	http://arxiv.org/pdf/1811.09602v1.pdf
PWC	https://paperswithcode.com/paper/model-based-reinforcement-learning-for-sepsis
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Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis


Title	Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis
Authors	Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol
Abstract	Sepsis is a life-threatening condition affecting one million people per year in the US in which dysregulation of the body’s own immune system causes damage to its tissues, resulting in a 28 - 50% mortality rate. Clinical trials for sepsis treatment over the last 20 years have failed to produce a single currently FDA approved drug treatment. In this study, we attempt to discover an effective cytokine mediation treatment strategy for sepsis using a previously developed agent-based model that simulates the innate immune response to infection: the Innate Immune Response agent-based model (IIRABM). Previous attempts at reducing mortality with multi-cytokine mediation using the IIRABM have failed to reduce mortality across all patient parameterizations and motivated us to investigate whether adaptive, personalized multi-cytokine mediation can control the trajectory of sepsis and lower patient mortality. We used the IIRABM to compute a treatment policy in which systemic patient measurements are used in a feedback loop to inform future treatment. Using deep reinforcement learning, we identified a policy that achieves 0% mortality on the patient parameterization on which it was trained. More importantly, this policy also achieves 0.8% mortality over 500 randomly selected patient parameterizations with baseline mortalities ranging from 1 - 99% (with an average of 49%) spanning the entire clinically plausible parameter space of the IIRABM. These results suggest that adaptive, personalized multi-cytokine mediation therapy could be a promising approach for treating sepsis. We hope that this work motivates researchers to consider such an approach as part of future clinical trials. To the best of our knowledge, this work is the first to consider adaptive, personalized multi-cytokine mediation therapy for sepsis, and is the first to exploit deep reinforcement learning on a biological simulation.
Tasks
Published	2018-02-08
URL	http://arxiv.org/abs/1802.10440v1
PDF	http://arxiv.org/pdf/1802.10440v1.pdf
PWC	https://paperswithcode.com/paper/precision-medicine-as-a-control-problem-using
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Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning


Title	Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning
Authors	Qingkai Liang, Fanyu Que, Eytan Modiano
Abstract	Constrained Markov Decision Process (CMDP) is a natural framework for reinforcement learning tasks with safety constraints, where agents learn a policy that maximizes the long-term reward while satisfying the constraints on the long-term cost. A canonical approach for solving CMDPs is the primal-dual method which updates parameters in primal and dual spaces in turn. Existing methods for CMDPs only use on-policy data for dual updates, which results in sample inefficiency and slow convergence. In this paper, we propose a policy search method for CMDPs called Accelerated Primal-Dual Optimization (APDO), which incorporates an off-policy trained dual variable in the dual update procedure while updating the policy in primal space with on-policy likelihood ratio gradient. Experimental results on a simulated robot locomotion task show that APDO achieves better sample efficiency and faster convergence than state-of-the-art approaches for CMDPs.
Tasks
Published	2018-02-19
URL	http://arxiv.org/abs/1802.06480v1
PDF	http://arxiv.org/pdf/1802.06480v1.pdf
PWC	https://paperswithcode.com/paper/accelerated-primal-dual-policy-optimization
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Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System


Title	Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
Authors	Yuqi Chen, Christopher M. Poskitt, Jun Sun
Abstract	Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults (“mutants”). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85% of them from the data logs generated at runtime.
Tasks
Published	2018-01-03
URL	http://arxiv.org/abs/1801.00903v2
PDF	http://arxiv.org/pdf/1801.00903v2.pdf
PWC	https://paperswithcode.com/paper/learning-from-mutants-using-code-mutation-to
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Adversarial Recommendation: Attack of the Learned Fake Users


Title	Adversarial Recommendation: Attack of the Learned Fake Users
Authors	Konstantina Christakopoulou, Arindam Banerjee
Abstract	Can machine learning models for recommendation be easily fooled? While the question has been answered for hand-engineered fake user profiles, it has not been explored for machine learned adversarial attacks. This paper attempts to close this gap. We propose a framework for generating fake user profiles which, when incorporated in the training of a recommendation system, can achieve an adversarial intent, while remaining indistinguishable from real user profiles. We formulate this procedure as a repeated general-sum game between two players: an oblivious recommendation system $R$ and an adversarial fake user generator $A$ with two goals: (G1) the rating distribution of the fake users needs to be close to the real users, and (G2) some objective $f_A$ encoding the attack intent, such as targeting the top-K recommendation quality of $R$ for a subset of users, needs to be optimized. We propose a learning framework to achieve both goals, and offer extensive experiments considering multiple types of attacks highlighting the vulnerability of recommendation systems.
Tasks	Recommendation Systems
Published	2018-09-21
URL	http://arxiv.org/abs/1809.08336v1
PDF	http://arxiv.org/pdf/1809.08336v1.pdf
PWC	https://paperswithcode.com/paper/adversarial-recommendation-attack-of-the
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