pwc-archive/papers-with-abstracts huggingface dataset:
Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model
Authors: Qiyou Duan, Taejoon Kim, Hadi Ghauch
Date: 2020-07-27 00:00:00
Tasks: Two-sample testing

We present an enhancement to the problem of beam alignment in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, based on a modification of the machine learning-based criterion, called Kolmogorov model (KM), previously applied to the beam alignment problem. Unlike the previous KM, whose computational complexity is not scalable with the size of the problem, a new approach, centered on discrete monotonic optimization (DMO), is proposed, leading to significantly reduced complexity. We also present a Kolmogorov-Smirnov (KS) criterion for the advanced hypothesis testing, which does not require any subjective threshold setting compared to the frequency estimation (FE) method developed for the conventional KM. Simulation results that demonstrate the efficacy of the proposed KM learning for mmWave beam alignment are presented.
RANDOM MASK: Towards Robust Convolutional Neural Networks
Authors: Tiange Luo, Tianle Cai, Mengxiao Zhang, Siyu Chen, Li-Wei Wang
Date: 2020-07-27 00:00:00
Proceeding: ICLR 2019 5

Robustness of neural networks has recently been highlighted by the adversarial examples, i.e., inputs added with well-designed perturbations which are imperceptible to humans but can cause the network to give incorrect outputs. In this paper, we design a new CNN architecture that by itself has good robustness. We introduce a simple but powerful technique, Random Mask, to modify existing CNN structures. We show that CNN with Random Mask achieves state-of-the-art performance against black-box adversarial attacks without applying any adversarial training. We next investigate the adversarial examples which 'fool' a CNN with Random Mask. Surprisingly, we find that these adversarial examples often 'fool' humans as well. This raises fundamental questions on how to define adversarial examples and robustness properly.
Defining Digital Quadruplets in the Cyber-Physical-Social Space for Parallel Driving
Authors: Teng Liu, Yang Xing, Long Chen, Dongpu Cao, Fei-Yue Wang
Date: 2020-07-26 00:00:00
Tasks: Descriptive

Parallel driving is a novel framework to synthesize vehicle intelligence and transport automation. This article aims to define digital quadruplets in parallel driving. In the cyber-physical-social systems (CPSS), based on the ACP method, the names of the digital quadruplets are first given, which are descriptive, predictive, prescriptive and real vehicles. The objectives of the three virtual digital vehicles are interacting, guiding, simulating and improving with the real vehicles. Then, the three virtual components of the digital quadruplets are introduced in detail and their applications are also illustrated. Finally, the real vehicles in the parallel driving system and the research process of the digital quadruplets are depicted. The presented digital quadruplets in parallel driving are expected to make the future connected automated driving safety, efficiently and synergistically.
Langevin Monte Carlo: random coordinate descent and variance reduction
Authors: Zhiyan Ding, Qin Li
Date: 2020-07-26 00:00:00
Tasks: Computational Efficiency

Langevin Monte Carlo (LMC) is a popular Bayesian sampling method. For the log-concave distribution function, the method converges exponentially fast, up to a controllable discretization error. However, the method requires the evaluation of a full gradient in each iteration, and for a problem on $\mathbb{R}^d$, this amounts to $d$ times partial derivative evaluations per iteration. The cost is high when $d\gg1$. In this paper, we investigate how to enhance computational efficiency through the application of RCD (random coordinate descent) on LMC. There are two sides of the theory: 1 By blindly applying RCD to LMC, one surrogates the full gradient by a randomly selected directional derivative per iteration. Although the cost is reduced per iteration, the total number of iteration is increased to achieve a preset error tolerance. Ultimately there is no computational gain; 2 We then incorporate variance reduction techniques, such as SAGA (stochastic average gradient) and SVRG (stochastic variance reduced gradient), into RCD-LMC. It will be proved that the cost is reduced compared with the classical LMC, and in the underdamped case, convergence is achieved with the same number of iterations, while each iteration requires merely one-directional derivative. This means we obtain the best possible computational cost in the underdamped-LMC framework.
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics
Authors: Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia
Date: 2020-07-25 00:00:00
Tasks: CPU, GPU

While deep neural networks (DNNs) are an increasingly popular way to query large corpora of data, their significant runtime remains an active area of research. As a result, researchers have proposed systems and optimizations to reduce these costs by allowing users to trade off accuracy and speed. In this work, we examine end-to-end DNN execution in visual analytics systems on modern accelerators. Through a novel measurement study, we show that the preprocessing of data (e.g., decoding, resizing) can be the bottleneck in many visual analytics systems on modern hardware. To address the bottleneck of preprocessing, we introduce two optimizations for end-to-end visual analytics systems. First, we introduce novel methods of achieving accuracy and throughput trade-offs by using natively present, low-resolution visual data. Second, we develop a runtime engine for efficient visual DNN inference. This runtime engine a) efficiently pipelines preprocessing and DNN execution for inference, b) places preprocessing operations on the CPU or GPU in a hardware- and input-aware manner, and c) efficiently manages memory and threading for high throughput execution. We implement these optimizations in a novel system, Smol, and evaluate Smol on eight visual datasets. We show that its optimizations can achieve up to 5.9x end-to-end throughput improvements at a fixed accuracy over recent work in visual analytics.
Automated Database Indexing using Model-free Reinforcement Learning
Authors: Gabriel Paludo Licks, Felipe Meneguzzi
Date: 2020-07-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Configuring databases for efficient querying is a complex task, often carried out by a database administrator. Solving the problem of building indexes that truly optimize database access requires a substantial amount of database and domain knowledge, the lack of which often results in wasted space and memory for irrelevant indexes, possibly jeopardizing database performance for querying and certainly degrading performance for updating. We develop an architecture to solve the problem of automatically indexing a database by using reinforcement learning to optimize queries by indexing data throughout the lifetime of a database. In our experimental evaluation, our architecture shows superior performance compared to related work on reinforcement learning and genetic algorithms, maintaining near-optimal index configurations and efficiently scaling to large databases.
Simulation Based Algorithms for Markov Decision Processes and Multi-Action Restless Bandits
Authors: Rahul Meshram, Kesav Kaza
Date: 2020-07-25 00:00:00

We consider multi-dimensional Markov decision processes and formulate a long term discounted reward optimization problem. Two simulation based algorithms---Monte Carlo rollout policy and parallel rollout policy are studied, and various properties for these policies are discussed. We next consider a restless multi-armed bandit (RMAB) with multi-dimensional state space and multi-actions bandit model. A standard RMAB consists of two actions for each arms whereas in multi-actions RMAB, there are more that two actions for each arms. A popular approach for RMAB is Whittle index based heuristic policy. Indexability is an important requirement to use index based policy. Based on this, an RMAB is classified into indexable or non-indexable bandits. Our interest is in the study of Monte-Carlo rollout policy for both indexable and non-indexable restless bandits. We first analyze a standard indexable RMAB (two-action model) and discuss an index based policy approach. We present approximate index computation algorithm using Monte-Carlo rollout policy. This algorithm's convergence is shown using two-timescale stochastic approximation scheme. Later, we analyze multi-actions indexable RMAB, and discuss the index based policy approach. We also study non-indexable RMAB for both standard and multi-actions bandits using Monte-Carlo rollout policy.
Non-parallel Emotion Conversion using a Deep-Generative Hybrid Network and an Adversarial Pair Discriminator
Authors: Ravi Shankar, Jacob Sager, Archana Venkataraman
Date: 2020-07-25 00:00:00

We introduce a novel method for emotion conversion in speech that does not require parallel training data. Our approach loosely relies on a cycle-GAN schema to minimize the reconstruction error from converting back and forth between emotion pairs. However, unlike the conventional cycle-GAN, our discriminator classifies whether a pair of input real and generated samples corresponds to the desired emotion conversion (e.g., A to B) or to its inverse (B to A). We will show that this setup, which we refer to as a variational cycle-GAN (VC-GAN), is equivalent to minimizing the empirical KL divergence between the source features and their cyclic counterpart. In addition, our generator combines a trainable deep network with a fixed generative block to implement a smooth and invertible transformation on the input features, in our case, the fundamental frequency (F0) contour. This hybrid architecture regularizes our adversarial training procedure. We use crowd sourcing to evaluate both the emotional saliency and the quality of synthesized speech. Finally, we show that our model generalizes to new speakers by modifying speech produced by Wavenet.
Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection
Authors: Farzaneh Khoshnevisan, Zhewen Fan, Vitor R. Carvalho
Date: 2020-07-25 00:00:00
Tasks: Anomaly Detection, Generative Adversarial Network, Time Series, Time Series Analysis, Time Series Anomaly Detection

Robust Anomaly Detection (AD) on time series data is a key component for monitoring many complex modern systems. These systems typically generate high-dimensional time series that can be highly noisy, seasonal, and inter-correlated. This paper explores some of the challenges in such data, and proposes a new approach that makes inroads towards increased robustness on seasonal and contaminated data, while providing a better root cause identification of anomalies. In particular, we propose the use of Robust Seasonal Multivariate Generative Adversarial Network (RSM-GAN) that extends recent advancements in GAN with the adoption of convolutional-LSTM layers and attention mechanisms to produce excellent performance on various settings. We conduct extensive experiments in which not only do this model displays more robust behavior on complex seasonality patterns, but also shows increased resistance to training data contamination. We compare it with existing classical and deep-learning AD models, and show that this architecture is associated with the lowest false positive rate and improves precision by 30% and 16% in real-world and synthetic data, respectively.
A Competitive Deep Neural Network Approach for the ImageCLEFmed Caption 2020 Task
Authors: Marimuthu Kalimuthu, Fabrizio Nunnari, Daniel Sonntag
Date: 2020-07-11 00:00:00

The aim of ImageCLEFmed Caption task is to develop a system that automatically labels radiology images with relevant medical concepts. We describe our Deep Neural Network (DNN) based approach for tackling this problem. On the challenge test set of 3,534 radiology images, our system achieves an F1 score of 0.375 and ranks high, 12th among all systems that were successfully submitted to the challenge, whereby we only rely on the provided data sources and do not use any external medical knowledge or ontologies, or pretrained models from other medical image repositories or application domains.
Bayesian Multi Scale Neural Network for Crowd Counting
Authors: Abhinav Sagar
Date: 2020-07-11 00:00:00
Tasks: Crowd Counting

Crowd Counting is a difficult but important problem in computer vision. Convolutional Neural Networks based on estimating the density map over the image has been highly successful in this domain. However dense crowd counting remains an open problem because of severe occlusion and perspective view in which people can be present at various sizes. In this work, we propose a new network which uses a ResNet based feature extractor, downsampling block which uses dilated convolutions and upsampling block using transposed convolutions. We present a novel aggregation module which makes our network robust to the perspective view problem. We present the optimization details, loss functions and the algorithm used in our work. On evaluating on ShanghaiTech, UCF-CC-50 and UCF-QNRF datasets using MSE and MAE as evaluation metrics, our network outperforms previous state of the art approaches while giving uncertainty estimates in a principled bayesian manner.
Structured Weight Priors for Convolutional Neural Networks
Authors: Tim Pearce, Andrew Y. K. Foong, Alexandra Brintrup
Date: 2020-07-12 00:00:00

Selection of an architectural prior well suited to a task (e.g. convolutions for image data) is crucial to the success of deep neural networks (NNs). Conversely, the weight priors within these architectures are typically left vague, e.g.~independent Gaussian distributions, which has led to debate over the utility of Bayesian deep learning. This paper explores the benefits of adding structure to weight priors. It initially considers first-layer filters of a convolutional NN, designing a prior based on random Gabor filters. Second, it considers adding structure to the prior of final-layer weights by estimating how each hidden feature relates to each class. Empirical results suggest that these structured weight priors lead to more meaningful functional priors for image data. This contributes to the ongoing discussion on the importance of weight priors.
Dive Deeper Into Box for Object Detection
Authors: Ran Chen, Yong liu, Mengdan Zhang, Shu Liu, Bei Yu, Yu-Wing Tai
Date: 2020-07-15 00:00:00
Proceeding: ECCV 2020 8
Tasks: Object, object-detection, Object Detection

Anchor free methods have defined the new frontier in state-of-the-art object detection researches where accurate bounding box estimation is the key to the success of these methods. However, even the bounding box has the highest confidence score, it is still far from perfect at localization. To this end, we propose a box reorganization method(DDBNet), which can dive deeper into the box for more accurate localization. At the first step, drifted boxes are filtered out because the contents in these boxes are inconsistent with target semantics. Next, the selected boxes are broken into boundaries, and the well-aligned boundaries are searched and grouped into a sort of optimal boxes toward tightening instances more precisely. Experimental results show that our method is effective which leads to state-of-the-art performance for object detection.
Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection
Authors: Zhou Huang, Huai-Xin Chen, Tao Zhou, Yun-Zhi Yang, Bi-Yuan Liu
Date: 2020-07-10 00:00:00
Tasks: object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Salient Object Detection

Depth cues with affluent spatial information have been proven beneficial in boosting salient object detection (SOD), while the depth quality directly affects the subsequent SOD performance. However, it is inevitable to obtain some low-quality depth cues due to limitations of its acquisition devices, which can inhibit the SOD performance. Besides, existing methods tend to combine RGB images and depth cues in a direct fusion or a simple fusion module, which makes they can not effectively exploit the complex correlations between the two sources. Moreover, few methods design an appropriate module to fully fuse multi-level features, resulting in cross-level feature interaction insufficient. To address these issues, we propose a novel Multi-level Cross-modal Interaction Network (MCINet) for RGB-D based SOD. Our MCI-Net includes two key components: 1) a cross-modal feature learning network, which is used to learn the high-level features for the RGB images and depth cues, effectively enabling the correlations between the two sources to be exploited; and 2) a multi-level interactive integration network, which integrates multi-level cross-modal features to boost the SOD performance. Extensive experiments on six benchmark datasets demonstrate the superiority of our MCI-Net over 14 state-of-the-art methods, and validate the effectiveness of different components in our MCI-Net. More important, our MCI-Net significantly improves the SOD performance as well as has a higher FPS.
Using local dynamics to explain analog forecasting of chaotic systems
Authors: P Platzer, P. Yiou, P. Naveau, P Tandeo, Y Zhen, P Ailliot, J-F Filipot
Date: 2020-07-22 00:00:00

Analogs are nearest neighbors of the state of a system. By using analogs and their successors in time, one is able to produce empirical forecasts. Several analog forecasting methods have been used in atmospheric applications and tested on well-known dynamical systems. Although efficient in practice, theoretical connections between analog methods and dynamical systems have been overlooked. Analog forecasting can be related to the real dynamical equations of the system of interest. This study investigates the properties of different analog forecasting strategies by taking local approximations of the system's dynamics. We find that analog forecasting performances are highly linked to the local Jacobian matrix of the flow map, and that analog forecasting combined with linear regression allows to capture projections of this Jacobian matrix. The proposed methodology allows to estimate analog forecasting errors, and to compare different analog methods. These results are derived analytically and tested numerically on two simple chaotic dynamical systems.
\'Etude de la lisibilit\'e des documents de sant\'e avec des m\'ethodes d'oculom\'etrie (Study of readability of health documents with eye-tracking methods)
Authors: Natalia Grabar, Emmanuel Farce, Laurent Sparrow
Date: 2018-05-01 00:00:00
Proceeding: JEPTALNRECITAL 2018 5

Le domaine m{\'e}dical fait partie de la vie quotidienne pour des raisons de sant{\'e}, mais la disponibilit{\'e} des informations m{\'e}dicales ne garantit pas leur compr{\'e}hension correcte par les patients. Plusieurs {\'e}tudes ont d{\'e}montr{\'e} qu{'}il existe une difficult{\'e} r{\'e}elle dans la compr{\'e}hension de contenus m{\'e}dicaux par les patients. Nous proposons d{'}exploiter les m{\'e}thodes d{'}oculom{\'e}trie pour {\'e}tudier ces questions et pour d{\'e}tecter quelles unit{\'e}s linguistiques posent des difficult{\'e}s de compr{\'e}hension. Pour cela, des textes m{\'e}dicaux en version originale et simplifi{\'e}e sont exploit{\'e}s. L{'}oculom{\'e}trie permet de suivre le regard des participants de l{'}{\'e}tude et de r{\'e}v{\'e}ler les indicateurs de lecture, comme la dur{\'e}e des fixations, les r{\'e}gressions et les saccades. Les r{\'e}sultats indiquent qu{'}il existe une diff{\'e}rence statistiquement significative lors de la lecture des versions originales et simplifi{\'e}es des documents de sant{\'e} test{\'e}s.
Alignement de termes de longueur variable en corpus comparables sp\'ecialis\'es (Alignment of variable length terms in specialized comparable corpora)
Authors: Jingshu Liu, Emmanuel Morin, Sebasti{\'a}n Pe{\~n}a Saldarriaga
Date: 2018-05-01 00:00:00
Proceeding: JEPTALNRECITAL 2018 5

Nous proposons dans cet article une adaptation de l{'}approche compositionnelle {\'e}tendue capable d{'}aligner des termes de longueurs variables {\`a} partir de corpus comparables, en modifiant la repr{\'e}sentation des termes complexes. Nous proposons {\'e}galement de nouveaux modes de pond{\'e}ration pour l{'}approche standard qui am{\'e}liorent les r{\'e}sultats des approches {\'e}tat de l{'}art pour les termes simples et complexes en domaine de sp{\'e}cialit{\'e}.
Etude de la reproductibilit\'e des word embeddings : rep\'erage des zones stables et instables dans le lexique (Reproducibility of word embeddings : identifying stable and unstable zones in the semantic space)
Authors: B{\'e}n{\'e}dicte Pierrejean, Ludovic Tanguy
Date: 2018-05-01 00:00:00
Proceeding: JEPTALNRECITAL 2018 5
Tasks: Word Embeddings

Les mod{\`e}les vectoriels de s{\'e}mantique distributionnelle (ou word embeddings), notamment ceux produits par les m{\'e}thodes neuronales, posent des questions de reproductibilit{\'e} et donnent des repr{\'e}sentations diff{\'e}rentes {\`a} chaque utilisation, m{\^e}me sans modifier leurs param{\`e}tres. Nous pr{\'e}sentons ici un ensemble d{'}exp{\'e}rimentations permettant de mesurer cette instabilit{\'e}, {\`a} la fois globalement et localement. Globalement, nous avons mesur{\'e} le taux de variation du voisinage des mots sur trois corpus diff{\'e}rents, qui est estim{\'e} autour de 17{\%} pour les 25 plus proches voisins d{'}un mot. Localement, nous avons identifi{\'e} et caract{\'e}ris{\'e} certaines zones de l{'}espace s{\'e}mantique qui montrent une relative stabilit{\'e}, ainsi que des cas de grande instabilit{\'e}.
Modeling infant segmentation of two morphologically diverse languages
Authors: Georgia-Rengina Loukatou, Sabine Stoll, Damian Blasi, Alej Cristia, rina
Date: 2018-05-01 00:00:00
Proceeding: JEPTALNRECITAL 2018 5
Tasks: Language Modeling, Language Modelling, Segmentation, Vocal Bursts Valence Prediction

A rich literature explores unsupervised segmentation algorithms infants could use to parse their input, mainly focusing on English, an analytic language where word, morpheme, and syllable boundaries often coincide. Synthetic languages, where words are multi-morphemic, may present unique difficulties for segmentation. Our study tests corpora of two languages selected to differ in the extent of complexity of their morphological structure, Chintang and Japanese. We use three conceptually diverse word segmentation algorithms and we evaluate them on both word- and morpheme-level representations. As predicted, results for the simpler Japanese are better than those for the more complex Chintang. However, the difference is small compared to the effect of the algorithm (with the lexical algorithm outperforming sub-lexical ones) and the level (scores were lower when evaluating on words versus morphemes). There are also important interactions between language, model, and evaluation level, which ought to be considered in future work.
\'Evaluation morphologique pour la traduction automatique : adaptation au fran\ccais (Morphological Evaluation for Machine Translation : Adaptation to French)
Authors: Franck Burlot, Fran{\c{c}}ois Yvon
Date: 2018-05-01 00:00:00
Proceeding: JEPTALNRECITAL 2018 5
Tasks: Machine Translation, Translation

Le nouvel {\'e}tat de l{'}art en traduction automatique (TA) s{'}appuie sur des m{\'e}thodes neuronales, qui diff{\'e}rent profond{\'e}ment des m{\'e}thodes utilis{\'e}es ant{\'e}rieurement. Les m{\'e}triques automatiques classiques sont mal adapt{\'e}es pour rendre compte de la nature du saut qualitatif observ{\'e}. Cet article propose un protocole d{'}{\'e}valuation pour la traduction de l{'}anglais vers le fran{\c{c}}ais sp{\'e}cifiquement focalis{\'e} sur la comp{\'e}tence morphologique des syst{\`e}mes de TA, en {\'e}tudiant leurs performances sur diff{\'e}rents ph{\'e}nom{\`e}nes grammaticaux.