April 1, 2020

3375 words 16 mins read

Paper Group ANR 509

Social Science Guided Feature Engineering: A Novel Approach to Signed Link Analysis. Evolutionary Neural Architecture Search for Retinal Vessel Segmentation. The side effect profile of Clozapine in real world data of three large mental hospitals. G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning. A Computational Approach …


Title	Social Science Guided Feature Engineering: A Novel Approach to Signed Link Analysis
Authors	Ghazaleh Beigi, Jiliang Tang, Huan Liu
Abstract	Many real-world relations can be represented by signed networks with positive links (e.g., friendships and trust) and negative links (e.g., foes and distrust). Link prediction helps advance tasks in social network analysis such as recommendation systems. Most existing work on link analysis focuses on unsigned social networks. The existence of negative links piques research interests in investigating whether properties and principles of signed networks differ from those of unsigned networks, and mandates dedicated efforts on link analysis for signed social networks. Recent findings suggest that properties of signed networks substantially differ from those of unsigned networks and negative links can be of significant help in signed link analysis in complementary ways. In this article, we center our discussion on a challenging problem of signed link analysis. Signed link analysis faces the problem of data sparsity, i.e. only a small percentage of signed links are given. This problem can even get worse when negative links are much sparser than positive ones as users are inclined more towards positive disposition rather than negative. We investigate how we can take advantage of other sources of information for signed link analysis. This research is mainly guided by three social science theories, Emotional Information, Diffusion of Innovations, and Individual Personality. Guided by these, we extract three categories of related features and leverage them for signed link analysis. Experiments show the significance of the features gleaned from social theories for signed link prediction and addressing the data sparsity challenge.
Tasks	Feature Engineering, Link Prediction, Recommendation Systems
Published	2020-01-04
URL	https://arxiv.org/abs/2001.01015v1
PDF	https://arxiv.org/pdf/2001.01015v1.pdf
PWC	https://paperswithcode.com/paper/social-science-guided-feature-engineering-a
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Evolutionary Neural Architecture Search for Retinal Vessel Segmentation


Title	Evolutionary Neural Architecture Search for Retinal Vessel Segmentation
Authors	Zhun Fan, Jiahong Wei, Guijie Zhu, Jiajie Mo, Wenji Li
Abstract	The accurate retinal vessel segmentation (RVS) is of great significance to assist doctors in the diagnosis of ophthalmology diseases and other systemic diseases. Manually designing a valid neural network architecture for retinal vessel segmentation requires high expertise and a large workload. In order to improve the performance of vessel segmentation and reduce the workload of manually designing neural network, we propose novel approach which applies neural architecture search (NAS) to optimize an encoder-decoder architecture for retinal vessel segmentation. A modified evolutionary algorithm is used to evolve the architectures of encoder-decoder framework with limited computing resources. The evolved model obtained by the proposed approach achieves top performance among all compared methods on the three datasets, namely DRIVE, STARE and CHASE_DB1, but with much fewer parameters. Moreover, the results of cross-training show that the evolved model is with considerable scalability, which indicates a great potential for clinical disease diagnosis.
Tasks	Neural Architecture Search, Retinal Vessel Segmentation
Published	2020-01-18
URL	https://arxiv.org/abs/2001.06678v3
PDF	https://arxiv.org/pdf/2001.06678v3.pdf
PWC	https://paperswithcode.com/paper/enas-u-net-evolutionary-neural-architecture
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The side effect profile of Clozapine in real world data of three large mental hospitals


Title	The side effect profile of Clozapine in real world data of three large mental hospitals
Authors	Ehtesham Iqbal, Risha Govind, Alvin Romero, Olubanke Dzahini, Matthew Broadbent, Robert Stewart, Tanya Smith, Chi-Hun Kim, Nomi Werbeloff, Richard Dobson, Zina Ibrahim
Abstract	Objective: Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing what we know from Randomised control trials (RCTs). We Propose a text mining approach to detect adverse events and medication episodes from the clinical text to enhance our understanding of adverse effects related to Clozapine, the most effective antipsychotic drug for the management of treatment-resistant schizophrenia, but underutilised due to concerns over its side effects. Material and Methods: We used data from de-identified EHRs of three mental health trusts in the UK (>50 million documents, over 500,000 patients, 2835 of which were prescribed Clozapine). We explored the prevalence of 33 adverse effects by age, gender, ethnicity, smoking status and admission type three months before and after the patients started Clozapine treatment. We compared the prevalence of adverse effects with those reported in the Side Effects Resource (SIDER) where possible. Results: Sedation, fatigue, agitation, dizziness, hypersalivation, weight gain, tachycardia, headache, constipation and confusion were amongst the highest recorded Clozapine adverse effect in the three months following the start of treatment. Higher percentages of all adverse effects were found in the first month of Clozapine therapy. Using a significance level of (p< 0.05) out chi-square tests show a significant association between most of the ADRs in smoking status and hospital admissions and some in gender and age groups. Further, the data was combined from three trusts, and chi-square tests were applied to estimate the average effect of ADRs in each monthly interval. Conclusion: A better understanding of how the drug works in the real world can complement clinical trials and precision medicine.
Tasks
Published	2020-01-27
URL	https://arxiv.org/abs/2001.09698v1
PDF	https://arxiv.org/pdf/2001.09698v1.pdf
PWC	https://paperswithcode.com/paper/the-side-effect-profile-of-clozapine-in-real
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G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning


Title	G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning
Authors	Matthew Dixon, Igor Halperin
Abstract	We present a reinforcement learning approach to goal based wealth management problems such as optimization of retirement plans or target dated funds. In such problems, an investor seeks to achieve a financial goal by making periodic investments in the portfolio while being employed, and periodically draws from the account when in retirement, in addition to the ability to re-balance the portfolio by selling and buying different assets (e.g. stocks). Instead of relying on a utility of consumption, we present G-Learner: a reinforcement learning algorithm that operates with explicitly defined one-step rewards, does not assume a data generation process, and is suitable for noisy data. Our approach is based on G-learning - a probabilistic extension of the Q-learning method of reinforcement learning. In this paper, we demonstrate how G-learning, when applied to a quadratic reward and Gaussian reference policy, gives an entropy-regulated Linear Quadratic Regulator (LQR). This critical insight provides a novel and computationally tractable tool for wealth management tasks which scales to high dimensional portfolios. In addition to the solution of the direct problem of G-learning, we also present a new algorithm, GIRL, that extends our goal-based G-learning approach to the setting of Inverse Reinforcement Learning (IRL) where rewards collected by the agent are not observed, and should instead be inferred. We demonstrate that GIRL can successfully learn the reward parameters of a G-Learner agent and thus imitate its behavior. Finally, we discuss potential applications of the G-Learner and GIRL algorithms for wealth management and robo-advising.
Tasks	Q-Learning
Published	2020-02-25
URL	https://arxiv.org/abs/2002.10990v1
PDF	https://arxiv.org/pdf/2002.10990v1.pdf
PWC	https://paperswithcode.com/paper/g-learner-and-girl-goal-based-wealth
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A Computational Approach to Packet Classification


Title	A Computational Approach to Packet Classification
Authors	Alon Rashelbach, Ori Rottenstreich, Mark Silberstein
Abstract	Multi-field packet classification is a crucial component in modern software-defined data center networks. To achieve high throughput and low latency, state-of-the-art algorithms strive to fit the rule lookup data structures into on-die caches; however, they do not scale well with the number of rules. We present a novel approach, NuevoMatch, which improves the memory scaling of existing methods. A new data structure, Range Query Recursive Model Index (RQ-RMI), is the key component that enables NuevoMatch to replace most of the accesses to main memory with model inference computations. We describe an efficient training algorithm which guarantees the correctness of the RQ-RMI-based classification. The use of RQ-RMI allows the packet rules to be compressed into model weights that fit into the hardware cache and takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector processing engines, achieving a rate of tens of nanoseconds per lookup. Our evaluation using 500K multi-field rules from the standard ClassBench benchmark shows a geomean compression factor of 4.9X, 8X, and 82X, and an average performance improvement of 2.7X, 4.4X and 2.6X in latency and 1.3X, 2.2X, and 1.2X in throughput compared to CutSplit, NeuroCuts, and TupleMerge, all state-of-the-art algorithms.
Tasks
Published	2020-02-10
URL	https://arxiv.org/abs/2002.07584v1
PDF	https://arxiv.org/pdf/2002.07584v1.pdf
PWC	https://paperswithcode.com/paper/a-computational-approach-to-packet
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A One-to-One Correspondence between Natural Numbers and Binary Trees


Title	A One-to-One Correspondence between Natural Numbers and Binary Trees
Authors	Osvaldo Skliar, Sherry Gapper, Ricardo E. Monge
Abstract	A characterization is provided for each natural number except one (1) by means of an ordered pair of elements. The first element is a natural number called the type of the natural number characterized, and the second is a natural number called the order of the number characterized within those of its type. A one-to-one correspondence is specified between the set of binary trees such that a) a given node has no child nodes (that is, it is a terminal node), or b) it has exactly two child nodes. Thus, binary trees such that one of their parent nodes has only one child node are excluded from the set considered here.
Tasks
Published	2020-02-07
URL	https://arxiv.org/abs/2002.04477v2
PDF	https://arxiv.org/pdf/2002.04477v2.pdf
PWC	https://paperswithcode.com/paper/a-one-to-one-correspondence-between-natural
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Fast Cross-domain Data Augmentation through Neural Sentence Editing


Title	Fast Cross-domain Data Augmentation through Neural Sentence Editing
Authors	Guillaume Raille, Sandra Djambazovska, Claudiu Musat
Abstract	Data augmentation promises to alleviate data scarcity. This is most important in cases where the initial data is in short supply. This is, for existing methods, also where augmenting is the most difficult, as learning the full data distribution is impossible. For natural language, sentence editing offers a solution - relying on small but meaningful changes to the original ones. Learning which changes are meaningful also requires large amounts of training data. We thus aim to learn this in a source domain where data is abundant and apply it in a different, target domain, where data is scarce - cross-domain augmentation. We create the Edit-transformer, a Transformer-based sentence editor that is significantly faster than the state of the art and also works cross-domain. We argue that, due to its structure, the Edit-transformer is better suited for cross-domain environments than its edit-based predecessors. We show this performance gap on the Yelp-Wikipedia domain pairs. Finally, we show that due to this cross-domain performance advantage, the Edit-transformer leads to meaningful performance gains in several downstream tasks.
Tasks	Data Augmentation
Published	2020-03-23
URL	https://arxiv.org/abs/2003.10254v1
PDF	https://arxiv.org/pdf/2003.10254v1.pdf
PWC	https://paperswithcode.com/paper/fast-cross-domain-data-augmentation-through
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MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas


Title	MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas
Authors	Hongkai Chen, Nicola Paoletti, Scott A. Smolka, Shan Lin
Abstract	Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, which are infeasible for resource-constrained medical devices. MPC also typically relies on state estimation, an error-prone process. In this paper, we introduce a novel approach to AP control that uses Imitation Learning to synthesize neural-network insulin policies from MPC-computed demonstrations. Such policies are computationally efficient and, by instrumenting MPC at training time with full state information, they can directly map measurements into optimal therapy decisions, thus bypassing state estimation. We apply Bayesian inference via Monte Carlo Dropout to learn policies, which allows us to quantify prediction uncertainty and thereby derive safer therapy decisions. We show that our control policies trained under a specific patient model readily generalize (in terms of model parameters and disturbance distributions) to patient cohorts, consistently outperforming traditional MPC with state estimation.
Tasks	Bayesian Inference, Imitation Learning
Published	2020-03-03
URL	https://arxiv.org/abs/2003.01283v1
PDF	https://arxiv.org/pdf/2003.01283v1.pdf
PWC	https://paperswithcode.com/paper/mpc-guided-imitation-learning-of-neural
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Causal Transfer for Imitation Learning and Decision Making under Sensor-shift


Title	Causal Transfer for Imitation Learning and Decision Making under Sensor-shift
Authors	Jalal Etesami, Philipp Geiger
Abstract	Learning from demonstrations (LfD) is an efficient paradigm to train AI agents. But major issues arise when there are differences between (a) the demonstrator’s own sensory input, (b) our sensors that observe the demonstrator and (c) the sensory input of the agent we train. In this paper, we propose a causal model-based framework for transfer learning under such “sensor-shifts”, for two common LfD tasks: (1) inferring the effect of the demonstrator’s actions and (2) imitation learning. First we rigorously analyze, on the population-level, to what extent the relevant underlying mechanisms (the action effects and the demonstrator policy) can be identified and transferred from the available observations together with prior knowledge of sensor characteristics. And we device an algorithm to infer these mechanisms. Then we introduce several proxy methods which are easier to calculate, estimate from finite data and interpret than the exact solutions, alongside theoretical bounds on their closeness to the exact ones. We validate our two main methods on simulated and semi-real world data.
Tasks	Decision Making, Imitation Learning, Transfer Learning
Published	2020-03-02
URL	https://arxiv.org/abs/2003.00806v1
PDF	https://arxiv.org/pdf/2003.00806v1.pdf
PWC	https://paperswithcode.com/paper/causal-transfer-for-imitation-learning-and
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Framework

Media Forensics and DeepFakes: an overview


Title	Media Forensics and DeepFakes: an overview
Authors	Luisa Verdoliva
Abstract	With the rapid progress of recent years, techniques that generate and manipulate multimedia content can now guarantee a very advanced level of realism. The boundary between real and synthetic media has become very thin. On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, video games. On the other hand, it poses enormous security threats. Software packages freely available on the web allow any individual, without special skills, to create very realistic fake images and videos. So-called deepfakes can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Potential abuses are limited only by human imagination. Therefore, there is an urgent need for automated tools capable of detecting false multimedia content and avoiding the spread of dangerous false information. This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos. Special emphasis will be placed on the emerging phenomenon of deepfakes and, from the point of view of the forensic analyst, on modern data-driven forensic methods. The analysis will help to highlight the limits of current forensic tools, the most relevant issues, the upcoming challenges, and suggest future directions for research.
Tasks
Published	2020-01-18
URL	https://arxiv.org/abs/2001.06564v1
PDF	https://arxiv.org/pdf/2001.06564v1.pdf
PWC	https://paperswithcode.com/paper/media-forensics-and-deepfakes-an-overview
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Deep Learning in Mining Biological Data


Title	Deep Learning in Mining Biological Data
Authors	Mufti Mahmud, M Shamim Kaiser, Amir Hussain
Abstract	Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Broadly categorized in three types (i.e., sequences, images, and signals), these data are huge in amount and complex in nature. Mining such an enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities and lately their deep architectures - known as deep learning (DL) - have been successfully applied to solve many complex pattern recognition problems. Highlighting the role of DL in recognizing patterns in biological data, this article provides - applications of DL to biological sequences, images, and signals data; overview of open access sources of these data; description of open source DL tools applicable on these data; and comparison of these tools from qualitative and quantitative perspectives. At the end, it outlines some open research challenges in mining biological data and puts forward a number of possible future perspectives.
Tasks
Published	2020-02-28
URL	https://arxiv.org/abs/2003.00108v1
PDF	https://arxiv.org/pdf/2003.00108v1.pdf
PWC	https://paperswithcode.com/paper/deep-learning-in-mining-biological-data
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Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks


Title	Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks
Authors	Farnaz Behnia, Ali Mirzaeian, Mohammad Sabokrou, Sai Manoj, Tinoosh Mohsenin, Khaled N. Khasawneh, Liang Zhao, Houman Homayoun, Avesta Sasan
Abstract	In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models’ computational complexity. More specifically, we propose a stacked encoder-convolutional model, in which the input image is first encoded by the encoder module of a denoising auto-encoder, and then the resulting latent representation (without being decoded) is fed to a reduced complexity CNN for image classification. We illustrate that this network not only is more robust to adversarial examples but also has a significantly lower computational complexity when compared to the prior art defenses.
Tasks	Denoising, Image Classification
Published	2020-01-16
URL	https://arxiv.org/abs/2001.06099v1
PDF	https://arxiv.org/pdf/2001.06099v1.pdf
PWC	https://paperswithcode.com/paper/code-bridged-classifier-cbc-a-low-or-negative
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Boosting Occluded Image Classification via Subspace Decomposition Based Estimation of Deep Features


Title	Boosting Occluded Image Classification via Subspace Decomposition Based Estimation of Deep Features
Authors	Feng Cen, Guanghui Wang
Abstract	Classification of partially occluded images is a highly challenging computer vision problem even for the cutting edge deep learning technologies. To achieve a robust image classification for occluded images, this paper proposes a novel scheme using subspace decomposition based estimation (SDBE). The proposed SDBE-based classification scheme first employs a base convolutional neural network to extract the deep feature vector (DFV) and then utilizes the SDBE to compute the DFV of the original occlusion-free image for classification. The SDBE is performed by projecting the DFV of the occluded image onto the linear span of a class dictionary (CD) along the linear span of an occlusion error dictionary (OED). The CD and OED are constructed respectively by concatenating the DFVs of a training set and the occlusion error vectors of an extra set of image pairs. Two implementations of the SDBE are studied in this paper: the $l_1$-norm and the squared $l_2$-norm regularized least-squares estimates. By employing the ResNet-152, pre-trained on the ILSVRC2012 training set, as the base network, the proposed SBDE-based classification scheme is extensively evaluated on the Caltech-101 and ILSVRC2012 datasets. Extensive experimental results demonstrate that the proposed SDBE-based scheme dramatically boosts the classification accuracy for occluded images, and achieves around $22.25%$ increase in classification accuracy under $20%$ occlusion on the ILSVRC2012 dataset.
Tasks	Image Classification
Published	2020-01-13
URL	https://arxiv.org/abs/2001.04066v1
PDF	https://arxiv.org/pdf/2001.04066v1.pdf
PWC	https://paperswithcode.com/paper/boosting-occluded-image-classification-via
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Topological Mapping for Manhattan-like Repetitive Environments


Title	Topological Mapping for Manhattan-like Repetitive Environments
Authors	Sai Shubodh Puligilla, Satyajit Tourani, Tushar Vaidya, Udit Singh Parihar, Ravi Kiran Sarvadevabhatla, K. Madhava Krishna
Abstract	We showcase a topological mapping framework for a challenging indoor warehouse setting. At the most abstract level, the warehouse is represented as a Topological Graph where the nodes of the graph represent a particular warehouse topological construct (e.g. rackspace, corridor) and the edges denote the existence of a path between two neighbouring nodes or topologies. At the intermediate level, the map is represented as a Manhattan Graph where the nodes and edges are characterized by Manhattan properties and as a Pose Graph at the lower-most level of detail. The topological constructs are learned via a Deep Convolutional Network while the relational properties between topological instances are learnt via a Siamese-style Neural Network. In the paper, we show that maintaining abstractions such as Topological Graph and Manhattan Graph help in recovering an accurate Pose Graph starting from a highly erroneous and unoptimized Pose Graph. We show how this is achieved by embedding topological and Manhattan relations as well as Manhattan Graph aided loop closure relations as constraints in the backend Pose Graph optimization framework. The recovery of near ground-truth Pose Graph on real-world indoor warehouse scenes vindicate the efficacy of the proposed framework.
Tasks
Published	2020-02-16
URL	https://arxiv.org/abs/2002.06575v3
PDF	https://arxiv.org/pdf/2002.06575v3.pdf
PWC	https://paperswithcode.com/paper/topological-mapping-for-manhattan-like
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Framework

Provably Efficient Third-Person Imitation from Offline Observation


Title	Provably Efficient Third-Person Imitation from Offline Observation
Authors	Aaron Zweig, Joan Bruna
Abstract	Domain adaptation in imitation learning represents an essential step towards improving generalizability. However, even in the restricted setting of third-person imitation where transfer is between isomorphic Markov Decision Processes, there are no strong guarantees on the performance of transferred policies. We present problem-dependent, statistical learning guarantees for third-person imitation from observation in an offline setting, and a lower bound on performance in the online setting.
Tasks	Domain Adaptation, Imitation Learning
Published	2020-02-27
URL	https://arxiv.org/abs/2002.12446v1
PDF	https://arxiv.org/pdf/2002.12446v1.pdf
PWC	https://paperswithcode.com/paper/provably-efficient-third-person-imitation
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