pwc-archive/papers-with-abstracts huggingface dataset:
Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants
Authors: Craig Knuth, Glen Chou, Necmiye Ozay, Dmitry Berenson
Date: 2020-10-18 00:00:00
Tasks: Motion Planning, valid

We present a method for feedback motion planning of systems with unknown dynamics which provides probabilistic guarantees on safety, reachability, and goal stability. To find a domain in which a learned control-affine approximation of the true dynamics can be trusted, we estimate the Lipschitz constant of the difference between the true and learned dynamics, and ensure the estimate is valid with a given probability. Provided the system has at least as many controls as states, we also derive existence conditions for a one-step feedback law which can keep the real system within a small bound of a nominal trajectory planned with the learned dynamics. Our method imposes the feedback law existence as a constraint in a sampling-based planner, which returns a feedback policy around a nominal plan ensuring that, if the Lipschitz constant estimate is valid, the true system is safe during plan execution, reaches the goal, and is ultimately invariant in a small set about the goal. We demonstrate our approach by planning using learned models of a 6D quadrotor and a 7DOF Kuka arm. We show that a baseline which plans using the same learned dynamics without considering the error bound or the existence of the feedback law can fail to stabilize around the plan and become unsafe.
TweetBERT: A Pretrained Language Representation Model for Twitter Text Analysis
Authors: Mohiuddin Md Abdul Qudar, Vijay Mago
Date: 2020-10-17 00:00:00

Twitter is a well-known microblogging social site where users express their views and opinions in real-time. As a result, tweets tend to contain valuable information. With the advancements of deep learning in the domain of natural language processing, extracting meaningful information from tweets has become a growing interest among natural language researchers. Applying existing language representation models to extract information from Twitter does not often produce good results. Moreover, there is no existing language representation models for text analysis specific to the social media domain. Hence, in this article, we introduce two TweetBERT models, which are domain specific language presentation models, pre-trained on millions of tweets. We show that the TweetBERT models significantly outperform the traditional BERT models in Twitter text mining tasks by more than 7% on each Twitter dataset. We also provide an extensive analysis by evaluating seven BERT models on 31 different datasets. Our results validate our hypothesis that continuously training language models on twitter corpus help performance with Twitter.
Lexicon generation for detecting fake news
Authors: Uğur Mertoğlu Burkay Genç
Date: 2020-10-16 00:00:00
Tasks: Fake News Detection

With the digitization of media, an immense amount of news data has been generated by online sources, including mainstream media outlets as well as social networks. However, the ease of production and distribution resulted in circulation of fake news as well as credible, authentic news. The pervasive dissemination of fake news has extreme negative impacts on individuals and society. Therefore, fake news detection has recently become an emerging topic as an interdisciplinary research field that is attracting significant attention from many research disciplines, including social sciences and linguistics. In this study, we propose a method primarily based on lexicons including a scoring system to facilitate the detection of the fake news in Turkish. We contribute to the literature by collecting a novel, large scale, and credible dataset of Turkish news, and by constructing the first fake news detection lexicon for Turkish.
A primer on model-guided exploration of fitness landscapes for biological sequence design
Authors: Sam Sinai, Eric D Kelsic
Date: 2020-10-04 00:00:00
Tasks: BIG-bench Machine Learning, Experimental Design

Machine learning methods are increasingly employed to address challenges faced by biologists. One area that will greatly benefit from this cross-pollination is the problem of biological sequence design, which has massive potential for therapeutic applications. However, significant inefficiencies remain in communication between these fields which result in biologists finding the progress in machine learning inaccessible, and hinder machine learning scientists from contributing to impactful problems in bioengineering. Sequence design can be seen as a search process on a discrete, high-dimensional space, where each sequence is associated with a function. This sequence-to-function map is known as a "Fitness Landscape". Designing a sequence with a particular function is hence a matter of "discovering" such a (often rare) sequence within this space. Today we can build predictive models with good interpolation ability due to impressive progress in the synthesis and testing of biological sequences in large numbers, which enables model training and validation. However, it often remains a challenge to find useful sequences with the properties that we like using these models. In particular, in this primer we highlight that algorithms for experimental design, what we call "exploration strategies", are a related, yet distinct problem from building good models of sequence-to-function maps. We review advances and insights from current literature -- by no means a complete treatment -- while highlighting desirable features of optimal model-guided exploration, and cover potential pitfalls drawn from our own experience. This primer can serve as a starting point for researchers from different domains that are interested in the problem of searching a sequence space with a model, but are perhaps unaware of approaches that originate outside their field.
Representing Point Clouds with Generative Conditional Invertible Flow Networks
Authors: Michał Stypułkowski, Kacper Kania, Maciej Zamorski, Maciej Zięba, Tomasz Trzciński, Jan Chorowski
Date: 2020-10-07 00:00:00
Tasks: Point Cloud Registration

In this paper, we propose a simple yet effective method to represent point clouds as sets of samples drawn from a cloud-specific probability distribution. This interpretation matches intrinsic characteristics of point clouds: the number of points and their ordering within a cloud is not important as all points are drawn from the proximity of the object boundary. We postulate to represent each cloud as a parameterized probability distribution defined by a generative neural network. Once trained, such a model provides a natural framework for point cloud manipulation operations, such as aligning a new cloud into a default spatial orientation. To exploit similarities between same-class objects and to improve model performance, we turn to weight sharing: networks that model densities of points belonging to objects in the same family share all parameters with the exception of a small, object-specific embedding vector. We show that these embedding vectors capture semantic relationships between objects. Our method leverages generative invertible flow networks to learn embeddings as well as to generate point clouds. Thanks to this formulation and contrary to similar approaches, we are able to train our model in an end-to-end fashion. As a result, our model offers competitive or superior quantitative results on benchmark datasets, while enabling unprecedented capabilities to perform cloud manipulation tasks, such as point cloud registration and regeneration, by a generative network.
EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising
Authors: Haoming Zhang, Mingqi Zhao, Chen Wei, Dante Mantini, Zherui Li, Quanying Liu
Date: 2020-09-24 00:00:00
Tasks: Deep Learning, Denoising, EEG, EEG Denoising, Electroencephalogram (EEG)

Deep learning networks are increasingly attracting attention in various fields, including electroencephalography (EEG) signal processing. These models provided comparable performance with that of traditional techniques. At present, however, lacks of well-structured and standardized datasets with specific benchmark limit the development of deep learning solutions for EEG denoising. Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing deep learning-based denoising models, as well as for performance comparisons across models. EEGdenoiseNet contains 4514 clean EEG segments, 3400 ocular artifact segments and 5598 muscular artifact segments, allowing users to synthesize contaminated EEG segments with the ground-truth clean EEG. We used EEGdenoiseNet to evaluate denoising performance of four classical networks (a fully-connected network, a simple and a complex convolution network, and a recurrent neural network). Our analysis suggested that deep learning methods have great potential for EEG denoising even under high noise contamination. Through EEGdenoiseNet, we hope to accelerate the development of the emerging field of deep learning-based EEG denoising.
Polar Deconvolution of Mixed Signals
Authors: Zhenan Fan, Halyun Jeong, Babhru Joshi, Michael P. Friedlander
Date: 2020-10-14 00:00:00

The signal demixing problem seeks to separate a superposition of multiple signals into its constituent components. This paper studies a two-stage approach that first decompresses and subsequently deconvolves the noisy and undersampled observations of the superposition using two convex programs. Probabilistic error bounds are given on the accuracy with which this process approximates the individual signals. The theory of polar convolution of convex sets and gauge functions plays a central role in the analysis and solution process. If the measurements are random and the noise is bounded, this approach stably recovers low-complexity and mutually incoherent signals, with high probability and with near-optimal sample complexity. We develop an efficient algorithm, based on level-set and conditional-gradient methods, that solves the convex optimization problems with sublinear iteration complexity and linear space requirements. Numerical experiments on both real and synthetic data confirm the theory and the efficiency of the approach.
Progressive Defense Against Adversarial Attacks for Deep Learning as a Service in Internet of Things
Authors: Ling Wang, Cheng Zhang, Zejian Luo, ChenGuang Liu, Jie Liu, Xi Zheng, Athanasios Vasilakos
Date: 2020-10-15 00:00:00

Nowadays, Deep Learning as a service can be deployed in Internet of Things (IoT) to provide smart services and sensor data processing. However, recent research has revealed that some Deep Neural Networks (DNN) can be easily misled by adding relatively small but adversarial perturbations to the input (e.g., pixel mutation in input images). One challenge in defending DNN against these attacks is to efficiently identifying and filtering out the adversarial pixels. The state-of-the-art defense strategies with good robustness often require additional model training for specific attacks. To reduce the computational cost without loss of generality, we present a defense strategy called a progressive defense against adversarial attacks (PDAAA) for efficiently and effectively filtering out the adversarial pixel mutations, which could mislead the neural network towards erroneous outputs, without a-priori knowledge about the attack type. We evaluated our progressive defense strategy against various attack methods on two well-known datasets. The result shows it outperforms the state-of-the-art while reducing the cost of model training by 50% on average.
A simulation framework for particle magnetization dynamics of large ensembles of single domain particles: Numerical treatment of Brown/Néel dynamics and parameter identification problems in magnetic particle imaging
Authors: Hannes Albers, Tobias Kluth, Tobias Knopp
Date: 2020-10-13 00:00:00

Magnetic nanoparticles and their magnetization dynamics play an important role in many applications. We focus on magnetization dynamics in large ensembles of single domain nanoparticles being characterized by either Brownian or N\'{e}el rotation mechanisms. Simulations of the respective behavior are obtained by solving advection-diffusion equations on the sphere, for which a unified computational framework is developed and investigated. This builds the basis for solving two parameter identification problems, which are formulated in the context of the chosen application, magnetic particle imaging. The functionality of the computational framework is illustrated by numerical results in the parameter identification problems either compared quantitatively or qualitatively to measured data.
Look-Ahead SCOPF (LASCOPF) for Tracking Demand Variation via Auxiliary Proximal Message Passing (APMP) Algorithm
Authors: Sambuddha Chakrabarti, Ross Baldick
Date: 2019-09-18 00:00:00
Tasks: Model Predictive Control

In this paper, we will consider the Look-Ahead Security Constrained Optimal Power Flow (LASCOPF) problem looking forward multiple dispatch intervals, in which the load demand varies over dispatch intervals according to some forecast. We will consider the base-case and several contingency scenarios in the upcoming as well as in the subsequent dispatch intervals. We will formulate and solve the problem in a Model Predictive Control (MPC) paradigm. We will present the Auxiliary Proximal Message Passing (APMP) algorithm to solve this problem, which is a bi-layered decomposition-coordination type distributed algorithm, consisting of an outer Auxiliary Problem Principle (APP) layer and an inner Proximal Message Passing (PMP) layer. The APP part of the algorithm distributes the computation across several dispatch intervals and the PMP part performs the distributed computation within each of the dispatch interval across different devices (i.e. generators, transmission lines, loads) and nodes or nets. We will demonstrate the effectiveness of our method with a series of numerical simulations.
The spread of COVID-19 conspiracy theories on social media and the effect of content moderation
Authors: Orestis Papakyriakopoulos, Juan Carlos Medina Serrano, Simon Hegelich
Date: 2020-08-01 00:00:00
Tasks: Misinformation, Opinion Mining

We investigate the diffusion of conspiracy theories related to the origin of COVID-19 on social media. By analyzing third-party content on four social media platforms, we show that:(a) In contrast to conventional wisdom, mainstream sources contribute overall more to conspiracy theories diffusion than alternative and other sources; and (b) Platforms’ content moderation practices are able to mitigate the spread of conspiracy theories. Nevertheless, we locate issues regarding the timeliness and magnitude of content moderation, as well as that platforms filter significantly fewer conspiracy theories coming from mainstream sources. Given this, we discuss policy steps that can contribute to the containment of conspiracy theories by media sources, platform owners, and users.
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
Authors: Wenxuan Zhou, Kevin Huang, Tengyu Ma, Jing Huang
Date: 2020-10-21 00:00:00
Tasks: Document-level Relation Extraction, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Relation, Relation Extraction, Sentence

Document-level relation extraction (RE) poses new challenges compared to its sentence-level counterpart. One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multiple possible relations. In this paper, we propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems. The adaptive thresholding replaces the global threshold for multi-label classification in the prior work with a learnable entities-dependent threshold. The localized context pooling directly transfers attention from pre-trained language models to locate relevant context that is useful to decide the relation. We experiment on three document-level RE benchmark datasets: DocRED, a recently released large-scale RE dataset, and two datasets CDRand GDA in the biomedical domain. Our ATLOP (Adaptive Thresholding and Localized cOntext Pooling) model achieves an F1 score of 63.4, and also significantly outperforms existing models on both CDR and GDA.
Convex Polytope Trees
Authors: Mohammadreza Armandpour, Mingyuan Zhou
Date: 2020-10-21 00:00:00

A decision tree is commonly restricted to use a single hyperplane to split the covariate space at each of its internal nodes. It often requires a large number of nodes to achieve high accuracy, hurting its interpretability. In this paper, we propose convex polytope trees (CPT) to expand the family of decision trees by an interpretable generalization of their decision boundary. The splitting function at each node of CPT is based on the logical disjunction of a community of differently weighted probabilistic linear decision-makers, which also geometrically corresponds to a convex polytope in the covariate space. We use a nonparametric Bayesian prior at each node to infer the community's size, encouraging simpler decision boundaries by shrinking the number of polytope facets. We develop a greedy method to efficiently construct CPT and scalable end-to-end training algorithms for the tree parameters when the tree structure is given. We empirically demonstrate the efficiency of CPT over existing state-of-the-art decision trees in several real-world classification and regression tasks from diverse domains.
Predicting human decision making in psychological tasks with recurrent neural networks
Authors: Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi
Date: 2020-10-22 00:00:00
Tasks: Decision Making, Time Series, Time Series Analysis

Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions, and theory of mind, i.e., what others are thinking. This makes predicting human decision-making challenging to be treated agnostically to the underlying psychological mechanisms. We propose here to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by human subjects engaged in gaming activity, the first application of such methods in this research domain. In this study, we collate the human data from 8 published literature of the Iterated Prisoner's Dilemma comprising 168,386 individual decisions and post-process them into 8,257 behavioral trajectories of 9 actions each for both players. Similarly, we collate 617 trajectories of 95 actions from 10 different published studies of Iowa Gambling Task experiments with healthy human subjects. We train our prediction networks on the behavioral data and demonstrate a clear advantage over the state-of-the-art methods in predicting human decision-making trajectories in both the single-agent scenario of the Iowa Gambling Task and the multi-agent scenario of the Iterated Prisoner's Dilemma. Moreover, we observe that the weights of the LSTM networks modeling the top performers tend to have a wider distribution compared to poor performers, as well as a larger bias, which suggest possible interpretations for the distribution of strategies adopted by each group.
Conditional independence by typing
Authors: Maria I. Gorinova, Andrew D. Gordon, Charles Sutton, Matthijs Vákár
Date: 2020-10-22 00:00:00
Tasks: Probabilistic Programming

A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference. However, this goal is hard to achieve in practice. Users are often forced to re-write their models in order to improve efficiency of inference or meet restrictions imposed by the PPL. Conditional independence (CI) relationships among parameters are a crucial aspect of probabilistic models that capture a qualitative summary of the specified model and can facilitate more efficient inference. We present an information flow type system for probabilistic programming that captures conditional independence (CI) relationships, and show that, for a well-typed program in our system, the distribution it implements is guaranteed to have certain CI-relationships. Further, by using type inference, we can statically deduce which CI-properties are present in a specified model. As a practical application, we consider the problem of how to perform inference on models with mixed discrete and continuous parameters. Inference on such models is challenging in many existing PPLs, but can be improved through a workaround, where the discrete parameters are used implicitly, at the expense of manual model re-writing. We present a source-to-source semantics-preserving transformation, which uses our CI-type system to automate this workaround by eliminating the discrete parameters from a probabilistic program. The resulting program can be seen as a hybrid inference algorithm on the original program, where continuous parameters can be drawn using efficient gradient-based inference methods, while the discrete parameters are inferred using variable elimination. We implement our CI-type system and its example application in SlicStan: a compositional variant of Stan.
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning Approach
Authors: Fenghe Hu, Yansha Deng, A. Hamid Aghvami
Date: 2020-10-21 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL), Scheduling

With the stringent requirement of receiving video from unmanned aerial vehicle (UAV) from anywhere in the stadium of sports events and the significant-high per-cell throughput for video transmission to virtual reality (VR) users, a promising solution is a cell-free multi-group broadcast (CF-MB) network with cooperative reception and broadcast access points (AP). To explore the benefit of broadcasting user-correlated decode-dependent video resources to spatially correlated VR users, the network should dynamically schedule the video and cluster APs into virtual cells for a different group of VR users with overlapped video requests. By decomposition the problem into scheduling and association sub-problems, we first introduce the conventional non-learning-based scheduling and association algorithms, and a centralized deep reinforcement learning (DRL) association approach based on the rainbow agent with a convolutional neural network (CNN) to generate decisions from observation. To reduce its complexity, we then decompose the association problem into multiple sub-problems, resulting in a networked-distributed Partially Observable Markov decision process (ND-POMDP). To solve it, we propose a multi-agent deep DRL algorithm. To jointly solve the coupled association and scheduling problems, we further develop a hierarchical federated DRL algorithm with scheduler as meta-controller, and association as the controller. Our simulation results shown that our CF-MB network can effectively handle real-time video transmission from UAVs to VR users. Our proposed learning architectures is effective and scalable for a high-dimensional cooperative association problem with increasing APs and VR users. Also, our proposed algorithms outperform non-learning based methods with significant performance improvement.