pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End JPEG Decoding and Artifacts Suppression Using Heterogeneous Residual Convolutional Neural Network
Authors: Jun Niu
Date: 2020-07-01 00:00:00
Tasks: Computational Efficiency, CPU, GPU

Existing deep learning models separate JPEG artifacts suppression from the decoding protocol as independent task. In this work, we take one step forward to design a true end-to-end heterogeneous residual convolutional neural network (HR-CNN) with spectrum decomposition and heterogeneous reconstruction mechanism. Benefitting from the full CNN architecture and GPU acceleration, the proposed model considerably improves the reconstruction efficiency. Numerical experiments show that the overall reconstruction speed reaches to the same magnitude of the standard CPU JPEG decoding protocol, while both decoding and artifacts suppression are completed together. We formulate the JPEG artifacts suppression task as an interactive process of decoding and image detail reconstructions. A heterogeneous, fully convolutional, mechanism is proposed to particularly address the uncorrelated nature of different spectral channels. Directly starting from the JPEG code in k-space, the network first extracts the spectral samples channel by channel, and restores the spectral snapshots with expanded throughput. These intermediate snapshots are then heterogeneously decoded and merged into the pixel space image. A cascaded residual learning segment is designed to further enhance the image details. Experiments verify that the model achieves outstanding performance in JPEG artifacts suppression, while its full convolutional operations and elegant network structure offers higher computational efficiency for practical online usage compared with other deep learning models on this topic.
Using Affective Features from Media Content Metadata for Better Movie Recommendations
Authors: John Kalung Leung, Igor Griva, William G. Kennedy
Date: 2020-07-01 00:00:00
Tasks: Decision Making

This paper investigates the causality in the decision making of movie recommendations through the users' affective profiles. We advocate a method of assigning emotional tags to a movie by the auto-detection of the affective features in the movie's overview. We apply a text-based Emotion Detection and Recognition model, which trained by tweets short messages and transfers the learned model to detect movie overviews' implicit affective features. We vectorized the affective movie tags to represent the mood embeddings of the movie. We obtain the user's emotional features by taking the average of all the movies' affective vectors the user has watched. We apply five-distance metrics to rank the Top-N movie recommendations against the user's emotion profile. We found Cosine Similarity distance metrics performed better than other distance metrics measures. We conclude that by replacing the top-N recommendations generated by the Recommender with the reranked recommendations list made by the Cosine Similarity distance metrics, the user will effectively get affective aware top-N recommendations while making the Recommender feels like an Emotion Aware Recommender.
Causal Discovery in Physical Systems from Videos
Authors: Yunzhu Li, Antonio Torralba, Animashree Anandkumar, Dieter Fox, Animesh Garg
Date: 2020-07-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Causal Discovery, counterfactual

Causal discovery is at the core of human cognition. It enables us to reason about the environment and make counterfactual predictions about unseen scenarios that can vastly differ from our previous experiences. We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure. In particular, our goal is to discover the structural dependencies among environmental and object variables: inferring the type and strength of interactions that have a causal effect on the behavior of the dynamical system. Our model consists of (a) a perception module that extracts a semantically meaningful and temporally consistent keypoint representation from images, (b) an inference module for determining the graph distribution induced by the detected keypoints, and (c) a dynamics module that can predict the future by conditioning on the inferred graph. We assume access to different configurations and environmental conditions, i.e., data from unknown interventions on the underlying system; thus, we can hope to discover the correct underlying causal graph without explicit interventions. We evaluate our method in a planar multi-body interaction environment and scenarios involving fabrics of different shapes like shirts and pants. Experiments demonstrate that our model can correctly identify the interactions from a short sequence of images and make long-term future predictions. The causal structure assumed by the model also allows it to make counterfactual predictions and extrapolate to systems of unseen interaction graphs or graphs of various sizes.
Deriving Bounds and Inequality Constraints Using LogicalRelations Among Counterfactuals
Authors: Noam Finkelstein, Ilya Shpitser
Date: 2020-07-01 00:00:00
Tasks: counterfactual

Causal parameters may not be point identified in the presence of unobserved confounding. However, information about non-identified parameters, in the form of bounds, may still be recovered from the observed data in some cases. We develop a new general method for obtaining bounds on causal parameters using rules of probability and restrictions on counterfactuals implied by causal graphical models. We additionally provide inequality constraints on functionals of the observed data law implied by such causal models. Our approach is motivated by the observation that logical relations between identified and non-identified counterfactual events often yield information about non-identified events. We show that this approach is powerful enough to recover known sharp bounds and tight inequality constraints, and to derive novel bounds and constraints.
A Multi-spectral Dataset for Evaluating Motion Estimation Systems
Authors: Weichen Dai, Yu Zhang, Shenzhou Chen, Donglei Sun, Da Kong
Date: 2020-07-01 00:00:00
Tasks: Motion Estimation, Stereo Matching

Visible images have been widely used for motion estimation. Thermal images, in contrast, are more challenging to be used in motion estimation since they typically have lower resolution, less texture, and more noise. In this paper, a novel dataset for evaluating the performance of multi-spectral motion estimation systems is presented. All the sequences are recorded from a handheld multi-spectral device. It consists of a standard visible-light camera, a long-wave infrared camera, an RGB-D camera, and an inertial measurement unit (IMU). The multi-spectral images, including both color and thermal images in full sensor resolution (640 x 480), are obtained from a standard and a long-wave infrared camera at 32Hz with hardware-synchronization. The depth images are captured by a Microsoft Kinect2 and can have benefits for learning cross-modalities stereo matching. For trajectory evaluation, accurate ground-truth camera poses obtained from a motion capture system are provided. In addition to the sequences with bright illumination, the dataset also contains dim, varying, and complex illumination scenes. The full dataset, including raw data and calibration data with detailed data format specifications, is publicly available.
Gradient Temporal-Difference Learning with Regularized Corrections
Authors: Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White
Date: 2020-07-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Q-Learning

It is still common to use Q-learning and temporal difference (TD) learning-even though they have divergence issues and sound Gradient TD alternatives exist-because divergence seems rare and they typically perform well. However, recent work with large neural network learning systems reveals that instability is more common than previously thought. Practitioners face a difficult dilemma: choose an easy to use and performant TD method, or a more complex algorithm that is more sound but harder to tune and all but unexplored with non-linear function approximation or control. In this paper, we introduce a new method called TD with Regularized Corrections (TDRC), that attempts to balance ease of use, soundness, and performance. It behaves as well as TD, when TD performs well, but is sound in cases where TD diverges. We empirically investigate TDRC across a range of problems, for both prediction and control, and for both linear and non-linear function approximation, and show, potentially for the first time, that gradient TD methods could be a better alternative to TD and Q-learning.
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction
Authors: Christina Runkel, Stefan Dorenkamp, Hartmut Bauermeister, Michael Moeller
Date: 2020-07-01 00:00:00
Tasks: Gesture Recognition, Sign Language Recognition, Transfer Learning

Machine learning techniques have excelled in the automatic semantic analysis of images, reaching human-level performances on challenging benchmarks. Yet, the semantic analysis of videos remains challenging due to the significantly higher dimensionality of the input data, respectively, the significantly higher need for annotated training examples. By studying the automatic recognition of German sign language videos, we demonstrate that on the relatively scarce training data of 2.800 videos, modern deep learning architectures for video analysis (such as ResNeXt) along with transfer learning on large gesture recognition tasks, can achieve about 75% character accuracy. Considering that this leaves us with a probability of under 25% that a 5 letter word is spelled correctly, spell-correction systems are crucial for producing readable outputs. The contribution of this paper is to propose a convolutional neural network for spell-correction that expects the softmax outputs of the character recognition network (instead of a misspelled word) as an input. We demonstrate that purely learning on softmax inputs in combination with scarce training data yields overfitting as the network learns the inputs by heart. In contrast, training the network on several variants of the logits of the classification output i.e. scaling by a constant factor, adding of random noise, mixing of softmax and hardmax inputs or purely training on hardmax inputs, leads to better generalization while benefitting from the significant information hidden in these outputs (that have 98% top-5 accuracy), yielding a readable text despite the comparably low character accuracy.
A New Basis for Sparse Principal Component Analysis
Authors: Fan Chen, Karl Rohe
Date: 2020-07-01 00:00:00
Tasks: Clustering, Dimensionality Reduction

Previous versions of sparse principal component analysis (PCA) have presumed that the eigen-basis (a $p \times k$ matrix) is approximately sparse. We propose a method that presumes the $p \times k$ matrix becomes approximately sparse after a $k \times k$ rotation. The simplest version of the algorithm initializes with the leading $k$ principal components. Then, the principal components are rotated with an $k \times k$ orthogonal rotation to make them approximately sparse. Finally, soft-thresholding is applied to the rotated principal components. This approach differs from prior approaches because it uses an orthogonal rotation to approximate a sparse basis. One consequence is that a sparse component need not to be a leading eigenvector, but rather a mixture of them. In this way, we propose a new (rotated) basis for sparse PCA. In addition, our approach avoids "deflation" and multiple tuning parameters required for that. Our sparse PCA framework is versatile; for example, it extends naturally to a two-way analysis of a data matrix for simultaneous dimensionality reduction of rows and columns. We provide evidence showing that for the same level of sparsity, the proposed sparse PCA method is more stable and can explain more variance compared to alternative methods. Through three applications -- sparse coding of images, analysis of transcriptome sequencing data, and large-scale clustering of social networks, we demonstrate the modern usefulness of sparse PCA in exploring multivariate data.
HydroNets: Leveraging River Structure for Hydrologic Modeling
Authors: Zach Moshe, Asher Metzger, Gal Elidan, Frederik Kratzert, Sella Nevo, Ran El-Yaniv
Date: 2020-07-01 00:00:00
Tasks: Management

Accurate and scalable hydrologic models are essential building blocks of several important applications, from water resource management to timely flood warnings. However, as the climate changes, precipitation and rainfall-runoff pattern variations become more extreme, and accurate training data that can account for the resulting distributional shifts become more scarce. In this work we present a novel family of hydrologic models, called HydroNets, which leverages river network structure. HydroNets are deep neural network models designed to exploit both basin specific rainfall-runoff signals, and upstream network dynamics, which can lead to improved predictions at longer horizons. The injection of the river structure prior knowledge reduces sample complexity and allows for scalable and more accurate hydrologic modeling even with only a few years of data. We present an empirical study over two large basins in India that convincingly support the proposed model and its advantages.
Navigating the Dynamics of Financial Embeddings over Time
Authors: Antonia Gogoglou, Brian Nguyen, Alan Salimov, Jonathan Rider, C. Bayan Bruss
Date: 2020-07-01 00:00:00
Tasks: Graph Representation Learning, Representation Learning

Financial transactions constitute connections between entities and through these connections a large scale heterogeneous weighted graph is formulated. In this labyrinth of interactions that are continuously updated, there exists a variety of similarity-based patterns that can provide insights into the dynamics of the financial system. With the current work, we propose the application of Graph Representation Learning in a scalable dynamic setting as a means of capturing these patterns in a meaningful and robust way. We proceed to perform a rigorous qualitative analysis of the latent trajectories to extract real world insights from the proposed representations and their evolution over time that is to our knowledge the first of its kind in the financial sector. Shifts in the latent space are associated with known economic events and in particular the impact of the recent Covid-19 pandemic to consumer patterns. Capturing such patterns indicates the value added to financial modeling through the incorporation of latent graph representations.
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo
Authors: Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, Lingjiong Zhu
Date: 2020-07-01 00:00:00
Tasks: Bayesian Inference, regression

Stochastic gradient Langevin dynamics (SGLD) and stochastic gradient Hamiltonian Monte Carlo (SGHMC) are two popular Markov Chain Monte Carlo (MCMC) algorithms for Bayesian inference that can scale to large datasets, allowing to sample from the posterior distribution of the parameters of a statistical model given the input data and the prior distribution over the model parameters. However, these algorithms do not apply to the decentralized learning setting, when a network of agents are working collaboratively to learn the parameters of a statistical model without sharing their individual data due to privacy reasons or communication constraints. We study two algorithms: Decentralized SGLD (DE-SGLD) and Decentralized SGHMC (DE-SGHMC) which are adaptations of SGLD and SGHMC methods that allow scaleable Bayesian inference in the decentralized setting for large datasets. We show that when the posterior distribution is strongly log-concave and smooth, the iterates of these algorithms converge linearly to a neighborhood of the target distribution in the 2-Wasserstein distance if their parameters are selected appropriately. We illustrate the efficiency of our algorithms on decentralized Bayesian linear regression and Bayesian logistic regression problems.
Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series
Authors: Vivien Sainte Fare Garnot, Loic Landrieu
Date: 2020-07-01 00:00:00
Tasks: Earth Observation, Time Series, Time Series Analysis, Time Series Classification

The increasing accessibility and precision of Earth observation satellite data offers considerable opportunities for industrial and state actors alike. This calls however for efficient methods able to process time-series on a global scale. Building on recent work employing multi-headed self-attention mechanisms to classify remote sensing time sequences, we propose a modification of the Temporal Attention Encoder. In our network, the channels of the temporal inputs are distributed among several compact attention heads operating in parallel. Each head extracts highly-specialized temporal features which are in turn concatenated into a single representation. Our approach outperforms other state-of-the-art time series classification algorithms on an open-access satellite image dataset, while using significantly fewer parameters and with a reduced computational complexity.
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Authors: Pushpak Pati, Guillaume Jaume, Lauren Alisha Fernandes, Antonio Foncubierta, Florinda Feroce, Anna Maria Anniciello, Giosue Scognamiglio, Nadia Brancati, Daniel Riccio, Maurizio Do Bonito, Giuseppe De Pietro, Gerardo Botti, Orcun Goksel, Jean-Philippe Thiran, Maria Frucci, Maria Gabrani
Date: 2020-07-01 00:00:00
Tasks: Diagnostic, General Classification, Graph Neural Network, Histopathological Image Classification, image-classification, Image Classification, Prognosis

Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue. Recent approaches acknowledging the structure-function relationship, have linked the structural and spatial patterns of cell organization in tissue via cell-graphs to tumor grades. Though cell organization is imperative, it is insufficient to entirely represent the histopathological structure. We propose a novel hierarchical cell-to-tissue-graph (HACT) representation to improve the structural depiction of the tissue. It consists of a low-level cell-graph, capturing cell morphology and interactions, a high-level tissue-graph, capturing morphology and spatial distribution of tissue parts, and cells-to-tissue hierarchies, encoding the relative spatial distribution of the cells with respect to the tissue distribution. Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently map the HACT representations to histopathological breast cancer subtypes. We assess the methodology on a large set of annotated tissue regions of interest from H\&E stained breast carcinoma whole-slides. Upon evaluation, the proposed method outperformed recent convolutional neural network and graph neural network approaches for breast cancer multi-class subtyping. The proposed entity-based topological analysis is more inline with the pathological diagnostic procedure of the tissue. It provides more command over the tissue modelling, therefore encourages the further inclusion of pathological priors into task-specific tissue representation.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Authors: Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi R. Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed Elsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych
Date: 2020-07-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: graph construction, Knowledge Graphs, Question Answering

To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities and their visual chemical structures, relations, and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence.
Reasoning with Contextual Knowledge and Influence Diagrams
Authors: Erman Acar, Rafael Peñaloza
Date: 2020-07-01 00:00:00

Influence diagrams (IDs) are well-known formalisms extending Bayesian networks to model decision situations under uncertainty. Although they are convenient as a decision theoretic tool, their knowledge representation ability is limited in capturing other crucial notions such as logical consistency. We complement IDs with the light-weight description logic (DL) EL to overcome such limitations. We consider a setup where DL axioms hold in some contexts, yet the actual context is uncertain. The framework benefits from the convenience of using DL as a domain knowledge representation language and the modelling strength of IDs to deal with decisions over contexts in the presence of contextual uncertainty. We define related reasoning problems and study their computational complexity.
FVV Live: A real-time free-viewpoint video system with consumer electronics hardware
Authors: Pablo Carballeira, Carlos Carmona, César Díaz, Daniel Berjón, Daniel Corregidor, Julián Cabrera, Francisco Morán, Carmen Doblado, Sergio Arnaldo, María del Mar Martín, Narciso García
Date: 2020-07-01 00:00:00

FVV Live is a novel end-to-end free-viewpoint video system, designed for low cost and real-time operation, based on off-the-shelf components. The system has been designed to yield high-quality free-viewpoint video using consumer-grade cameras and hardware, which enables low deployment costs and easy installation for immersive event-broadcasting or videoconferencing. The paper describes the architecture of the system, including acquisition and encoding of multiview plus depth data in several capture servers and virtual view synthesis on an edge server. All the blocks of the system have been designed to overcome the limitations imposed by hardware and network, which impact directly on the accuracy of depth data and thus on the quality of virtual view synthesis. The design of FVV Live allows for an arbitrary number of cameras and capture servers, and the results presented in this paper correspond to an implementation with nine stereo-based depth cameras. FVV Live presents low motion-to-photon and end-to-end delays, which enables seamless free-viewpoint navigation and bilateral immersive communications. Moreover, the visual quality of FVV Live has been assessed through subjective assessment with satisfactory results, and additional comparative tests show that it is preferred over state-of-the-art DIBR alternatives.