pwc-archive/papers-with-abstracts huggingface dataset:
Spacecraft Collision Avoidance Challenge: design and results of a machine learning competition
Authors: Thomas Uriot, Dario Izzo, Luís F. Sim{õ}es, Rasit Abay, Nils Einecke, Sven Rebhan, Jose Martinez-Heras, Francesca Letizia, Jan Siminski, Klaus Merz
Date: 2020-08-07 00:00:00
Tasks: BIG-bench Machine Learning, Collision Avoidance

Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform the various operators who can then plan risk mitigation measures. Such measures could be aided by the development of suitable machine learning models predicting, for example, the evolution of the collision risk in time. In an attempt to study this opportunity, the European Space Agency released, in October 2019, a large curated dataset containing information about close approach events, in the form of Conjunction Data Messages (CDMs), collected from 2015 to 2019. This dataset was used in the Spacecraft Collision Avoidance Challenge, a machine learning competition where participants had to build models to predict the final collision risk between orbiting objects. This paper describes the design and results of the competition and discusses the challenges and lessons learned when applying machine learning methods to this problem domain.
Evaluating Efficient Performance Estimators of Neural Architectures
Authors: Xuefei Ning, Changcheng Tang, Wenshuo Li, Zixuan Zhou, Shuang Liang, Huazhong Yang, Yu Wang
Date: 2020-08-07 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Neural Architecture Search

Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS). To reduce the architecture training costs in NAS, one-shot estimators (OSEs) amortize the architecture training costs by sharing the parameters of one "supernet" between all architectures. Recently, zero-shot estimators (ZSEs) that involve no training are proposed to further reduce the architecture evaluation cost. Despite the high efficiency of these estimators, the quality of such estimations has not been thoroughly studied. In this paper, we conduct an extensive and organized assessment of OSEs and ZSEs on five NAS benchmarks: NAS-Bench-101/201/301, and NDS ResNet/ResNeXt-A. Specifically, we employ a set of NAS-oriented criteria to study the behavior of OSEs and ZSEs and reveal that they have certain biases and variances. After analyzing how and why the OSE estimations are unsatisfying, we explore how to mitigate the correlation gap of OSEs from several perspectives. Through our analysis, we give out suggestions for future application and development of efficient architecture performance estimators. Furthermore, the analysis framework proposed in our work could be utilized in future research to give a more comprehensive understanding of newly designed architecture performance estimators. All codes are available at https://github.com/walkerning/aw_nas.
A General Framework for Stable Roommates Problems using Answer Set Programming
Authors: Esra Erdem, Muge Fidan, David Manlove, Patrick Prosser
Date: 2020-08-07 00:00:00

The Stable Roommates problem (SR) is characterized by the preferences of agents over other agents as roommates: each agent ranks all others in strict order of preference. A solution to SR is then a partition of the agents into pairs so that each pair shares a room, and there is no pair of agents that would block this matching (i.e., who prefers the other to their roommate in the matching). There are interesting variations of SR that are motivated by applications (e.g., the preference lists may be incomplete (SRI) and involve ties (SRTI)), and that try to find a more fair solution (e.g., Egalitarian SR). Unlike the Stable Marriage problem, every SR instance is not guaranteed to have a solution. For that reason, there are also variations of SR that try to find a good-enough solution (e.g., Almost SR). Most of these variations are NP-hard. We introduce a formal framework, called SRTI-ASP, utilizing the logic programming paradigm Answer Set Programming, that is provable and general enough to solve many of such variations of SR. Our empirical analysis shows that SRTI-ASP is also promising for applications. This paper is under consideration for acceptance in TPLP.
Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems
Authors: Kailai Zhou, Linsen Chen, Xun Cao
Date: 2020-08-07 00:00:00
Proceeding: ECCV 2020 8
Tasks: Computational Efficiency, Multispectral Object Detection, Pedestrian Detection

Multispectral pedestrian detection is capable of adapting to insufficient illumination conditions by leveraging color-thermal modalities. On the other hand, it is still lacking of in-depth insights on how to fuse the two modalities effectively. Compared with traditional pedestrian detection, we find multispectral pedestrian detection suffers from modality imbalance problems which will hinder the optimization process of dual-modality network and depress the performance of detector. Inspired by this observation, we propose Modality Balance Network (MBNet) which facilitates the optimization process in a much more flexible and balanced manner. Firstly, we design a novel Differential Modality Aware Fusion (DMAF) module to make the two modalities complement each other. Secondly, an illumination aware feature alignment module selects complementary features according to the illumination conditions and aligns the two modality features adaptively. Extensive experimental results demonstrate MBNet outperforms the state-of-the-arts on both the challenging KAIST and CVC-14 multispectral pedestrian datasets in terms of the accuracy and the computational efficiency. Code is available at https://github.com/CalayZhou/MBNet.
A boosted outlier detection method based on the spectrum of the Laplacian matrix of a graph
Authors: Nicolas Cofre
Date: 2020-08-07 00:00:00
Tasks: Clustering, Outlier Detection

This paper explores a new outlier detection algorithm based on the spectrum of the Laplacian matrix of a graph. Taking advantage of boosting together with sparse-data based learners. The sparcity of the Laplacian matrix significantly decreases the computational burden, enabling a spectrum based outlier detection method to be applied to larger datasets compared to spectral clustering. The method is competitive on synthetic datasets with commonly used outlier detection algorithms like Isolation Forest and Local Outlier Factor.
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos
Authors: Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang
Date: 2020-08-07 00:00:00
Proceeding: ICML 2020 1
Tasks: parameter estimation, Stochastic Block Model

We propose a novel stochastic network model, called Fractal Gaussian Network (FGN), that embodies well-defined and analytically tractable fractal structures. Such fractal structures have been empirically observed in diverse applications. FGNs interpolate continuously between the popular purely random geometric graphs (a.k.a. the Poisson Boolean network), and random graphs with increasingly fractal behavior. In fact, they form a parametric family of sparse random geometric graphs that are parametrized by a fractality parameter which governs the strength of the fractal structure. FGNs are driven by the latent spatial geometry of Gaussian Multiplicative Chaos (GMC), a canonical model of fractality in its own right. We asymptotically characterize the expected number of edges, triangles, cliques and hub-and-spoke motifs in FGNs, unveiling a distinct pattern in their scaling with the size parameter of the network. We then examine the natural question of detecting the presence of fractality and the problem of parameter estimation based on observed network data, in addition to fundamental properties of the FGN as a random graph model. We also explore fractality in community structures by unveiling a natural stochastic block model in the setting of FGNs. Finally, we substantiate our results with phenomenological analysis of the FGN in the context of available scientific literature for fractality in networks, including applications to real-world massive network data.
Evaluating probabilistic classifiers: Reliability diagrams and score decompositions revisited
Authors: Timo Dimitriadis, Tilmann Gneiting, Alexander I. Jordan
Date: 2020-08-07 00:00:00
Tasks: scoring rule, Uncertainty Quantification

A probability forecast or probabilistic classifier is reliable or calibrated if the predicted probabilities are matched by ex post observed frequencies, as examined visually in reliability diagrams. The classical binning and counting approach to plotting reliability diagrams has been hampered by a lack of stability under unavoidable, ad hoc implementation decisions. Here we introduce the CORP approach, which generates provably statistically Consistent, Optimally binned, and Reproducible reliability diagrams in an automated way. CORP is based on non-parametric isotonic regression and implemented via the Pool-adjacent-violators (PAV) algorithm - essentially, the CORP reliability diagram shows the graph of the PAV- (re)calibrated forecast probabilities. The CORP approach allows for uncertainty quantification via either resampling techniques or asymptotic theory, furnishes a new numerical measure of miscalibration, and provides a CORP based Brier score decomposition that generalizes to any proper scoring rule. We anticipate that judicious uses of the PAV algorithm yield improved tools for diagnostics and inference for a very wide range of statistical and machine learning methods.
Deep Robust Clustering by Contrastive Learning
Authors: Huasong Zhong, Chong Chen, Zhongming Jin, Xian-Sheng Hua
Date: 2020-08-07 00:00:00
Tasks: Clustering, Contrastive Learning, Data Augmentation, Deep Clustering, Online Clustering

Recently, many unsupervised deep learning methods have been proposed to learn clustering with unlabelled data. By introducing data augmentation, most of the latest methods look into deep clustering from the perspective that the original image and its transformation should share similar semantic clustering assignment. However, the representation features could be quite different even they are assigned to the same cluster since softmax function is only sensitive to the maximum value. This may result in high intra-class diversities in the representation feature space, which will lead to unstable local optimal and thus harm the clustering performance. To address this drawback, we proposed Deep Robust Clustering (DRC). Different from existing methods, DRC looks into deep clustering from two perspectives of both semantic clustering assignment and representation feature, which can increase inter-class diversities and decrease intra-class diversities simultaneously. Furthermore, we summarized a general framework that can turn any maximizing mutual information into minimizing contrastive loss by investigating the internal relationship between mutual information and contrastive learning. And we successfully applied it in DRC to learn invariant features and robust clusters. Extensive experiments on six widely-adopted deep clustering benchmarks demonstrate the superiority of DRC in both stability and accuracy. e.g., attaining 71.6% mean accuracy on CIFAR-10, which is 7.1% higher than state-of-the-art results.
Peking Opera Synthesis via Duration Informed Attention Network
Authors: Yusong Wu, Shengchen Li, Chengzhu Yu, Heng Lu, Chao Weng, Liqiang Zhang, Dong Yu
Date: 2020-08-07 00:00:00
Tasks: Rhythm, Singing Voice Synthesis

Peking Opera has been the most dominant form of Chinese performing art since around 200 years ago. A Peking Opera singer usually exhibits a very strong personal style via introducing improvisation and expressiveness on stage which leads the actual rhythm and pitch contour to deviate significantly from the original music score. This inconsistency poses a great challenge in Peking Opera singing voice synthesis from a music score. In this work, we propose to deal with this issue and synthesize expressive Peking Opera singing from the music score based on the Duration Informed Attention Network (DurIAN) framework. To tackle the rhythm mismatch, Lagrange multiplier is used to find the optimal output phoneme duration sequence with the constraint of the given note duration from music score. As for the pitch contour mismatch, instead of directly inferring from music score, we adopt a pseudo music score generated from the real singing and feed it as input during training. The experiments demonstrate that with the proposed system we can synthesize Peking Opera singing voice with high-quality timbre, pitch and expressiveness.
A Context-based Disambiguation Model for Sentiment Concepts Using a Bag-of-concepts Approach
Authors: Zeinab Rajabi, MohammadReza Valavi, Maryam Hourali
Date: 2020-08-07 00:00:00
Tasks: Sentiment Analysis

With the widespread dissemination of user-generated content on different social networks, and online consumer systems such as Amazon, the quantity of opinionated information available on the Internet has been increased. One of the main tasks of the sentiment analysis is to detect polarity within a text. The existing polarity detection methods mainly focus on keywords and their naive frequency counts; however, they less regard the meanings and implicit dimensions of the natural concepts. Although background knowledge plays a critical role in determining the polarity of concepts, it has been disregarded in polarity detection methods. This study presents a context-based model to solve ambiguous polarity concepts using commonsense knowledge. First, a model is presented to generate a source of ambiguous sentiment concepts based on SenticNet by computing the probability distribution. Then the model uses a bag-of-concepts approach to remove ambiguities and semantic augmentation with the ConceptNet handling to overcome lost knowledge. ConceptNet is a large-scale semantic network with a large number of commonsense concepts. In this paper, the point mutual information (PMI) measure is used to select the contextual concepts having strong relationships with ambiguous concepts. The polarity of the ambiguous concepts is precisely detected using positive/negative contextual concepts and the relationship of the concepts in the semantic knowledge base. The text representation scheme is semantically enriched using Numberbatch, which is a word embedding model based on the concepts from the ConceptNet semantic network. The proposed model is evaluated by applying a corpus of product reviews, called Semeval. The experimental results revealed an accuracy rate of 82.07%, representing the effectiveness of the proposed model.
A Multi-Task Learning Approach for Human Activity Segmentation and Ergonomics Risk Assessment
Authors: Behnoosh Parsa, Ashis G. Banerjee
Date: 2020-08-07 00:00:00
Tasks: Action Detection, Decoder, Multi-Task Learning

We propose a new approach to Human Activity Evaluation (HAE) in long videos using graph-based multi-task modeling. Previous works in activity evaluation either directly compute a metric using a detected skeleton or use the scene information to regress the activity score. These approaches are insufficient for accurate activity assessment since they only compute an average score over a clip, and do not consider the correlation between the joints and body dynamics. Moreover, they are highly scene-dependent which makes the generalizability of these methods questionable. We propose a novel multi-task framework for HAE that utilizes a Graph Convolutional Network backbone to embed the interconnections between human joints in the features. In this framework, we solve the Human Activity Segmentation (HAS) problem as an auxiliary task to improve activity assessment. The HAS head is powered by an Encoder-Decoder Temporal Convolutional Network to semantically segment long videos into distinct activity classes, whereas, HAE uses a Long-Short-Term-Memory-based architecture. We evaluate our method on the UW-IOM and TUM Kitchen datasets and discuss the success and failure cases in these two datasets.
An Aggregate Method for Thorax Diseases Classification
Authors: Bayu A. Nugroho
Date: 2020-08-07 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Medical Image Classification

A common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural network, a training pattern is treated as a positive pattern in one output node and negative in all the remaining output nodes. In this paper, the weights of a training pattern in the loss function are designed based not only on the number of the training patterns in the class but also on the different nodes where one of them treats this training pattern as positive and the others treat it as negative. We propose a combined approach of weights calculation algorithm for deep network training and the training optimization from the state-of-the-art deep network architecture for thorax diseases classification problem. Experimental results on the Chest X-Ray image dataset demonstrate that this new weighting scheme improves classification performances, also the training optimization from the EfficientNet improves the performance furthermore. We compare the aggregate method with several performances from the previous study of thorax diseases classifications to provide the fair comparisons against the proposed method.
Modelling Multi-Agent Epistemic Planning in ASP. Theory and Practice of Logic Programming
Authors: Alessandro Burigana, Francesco Fabiano, Agostino Dovier, Enrico Pontelli
Date: 2020-08-07 00:00:00
Tasks: Epistemic Reasoning

Designing agents that reason and act upon the world has always been one of the main objectives of the Artificial Intelligence community. While for planning in "simple" domains the agents can solely rely on facts about the world, in several contexts, e.g., economy, security, justice and politics, the mere knowledge of the world could be insufficient to reach a desired goal. In these scenarios, epistemic reasoning, i.e., reasoning about agents' beliefs about themselves and about other agents' beliefs, is essential to design winning strategies. This paper addresses the problem of reasoning in multi-agent epistemic settings exploiting declarative programming techniques. In particular, the paper presents an actual implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings, called PLATO (ePistemic muLti-agent Answer seT programming sOlver). The ASP paradigm enables a concise and elegant design of the planner, w.r.t. other imperative implementations, facilitating the development of formal verification of correctness. The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature. It is under consideration for acceptance in TPLP.
Polynomial-time algorithms for Multimarginal Optimal Transport problems with structure
Authors: Jason M. Altschuler, Enric Boix-Adsera
Date: 2020-08-07 00:00:00
Tasks: BIG-bench Machine Learning

Multimarginal Optimal Transport (MOT) has attracted significant interest due to applications in machine learning, statistics, and the sciences. However, in most applications, the success of MOT is severely limited by a lack of efficient algorithms. Indeed, MOT in general requires exponential time in the number of marginals k and their support sizes n. This paper develops a general theory about what "structure" makes MOT solvable in poly(n,k) time. We develop a unified algorithmic framework for solving MOT in poly(n,k) time by characterizing the "structure" that different algorithms require in terms of simple variants of the dual feasibility oracle. This framework has several benefits. First, it enables us to show that the Sinkhorn algorithm, which is currently the most popular MOT algorithm, requires strictly more structure than other algorithms do to solve MOT in poly(n,k) time. Second, our framework makes it much simpler to develop poly(n,k) time algorithms for a given MOT problem. In particular, it is necessary and sufficient to (approximately) solve the dual feasibility oracle -- which is much more amenable to standard algorithmic techniques. We illustrate this ease-of-use by developing poly(n,k) time algorithms for three general classes of MOT cost structures: (1) graphical structure; (2) set-optimization structure; and (3) low-rank plus sparse structure. For structure (1), we recover the known result that Sinkhorn has poly(n,k) runtime; moreover, we provide the first poly(n,k) time algorithms for computing solutions that are exact and sparse. For structures (2)-(3), we give the first poly(n,k) time algorithms, even for approximate computation. Together, these three structures encompass many -- if not most -- current applications of MOT.
Single-stage intake gesture detection using CTC loss and extended prefix beam search
Authors: Philipp V. Rouast, Marc T. P. Adam
Date: 2020-08-07 00:00:00
Tasks: Temporal Action Localization

Accurate detection of individual intake gestures is a key step towards automatic dietary monitoring. Both inertial sensor data of wrist movements and video data depicting the upper body have been used for this purpose. The most advanced approaches to date use a two-stage approach, in which (i) frame-level intake probabilities are learned from the sensor data using a deep neural network, and then (ii) sparse intake events are detected by finding the maxima of the frame-level probabilities. In this study, we propose a single-stage approach which directly decodes the probabilities learned from sensor data into sparse intake detections. This is achieved by weakly supervised training using Connectionist Temporal Classification (CTC) loss, and decoding using a novel extended prefix beam search decoding algorithm. Benefits of this approach include (i) end-to-end training for detections, (ii) simplified timing requirements for intake gesture labels, and (iii) improved detection performance compared to existing approaches. Across two separate datasets, we achieve relative $F_1$ score improvements between 1.9% and 6.2% over the two-stage approach for intake detection and eating/drinking detection tasks, for both video and inertial sensors.
A Review on Modern Computational Optimal Transport Methods with Applications in Biomedical Research
Authors: Jingyi Zhang, Wenxuan Zhong, Ping Ma
Date: 2020-08-07 00:00:00

Optimal transport has been one of the most exciting subjects in mathematics, starting from the 18th century. As a powerful tool to transport between two probability measures, optimal transport methods have been reinvigorated nowadays in a remarkable proliferation of modern data science applications. To meet the big data challenges, various computational tools have been developed in the recent decade to accelerate the computation for optimal transport methods. In this review, we present some cutting-edge computational optimal transport methods with a focus on the regularization-based methods and the projection-based methods. We discuss their real-world applications in biomedical research.