pwc-archive/papers-with-abstracts huggingface dataset:
Unbox the Blackbox: Predict and Interpret YouTube Viewership Using Deep Learning
Authors: Jiaheng Xie, Xiao Liu
Date: 2020-12-21 00:00:00
Tasks: Misinformation, Prediction, Video Description

Predicting video viewership is a top priority for content creators and video-sharing sites. Content creators live on such predictions to maximize influences and minimize budgets. Video-sharing sites rely on this prediction to promote credible videos and curb violative videos. Although deep learning champions viewership prediction, it lacks interpretability, which is fundamental to increasing the adoption of predictive models and prescribing measurements to improve viewership. Following the design-science paradigm, we propose a novel interpretable IT system, Precise Wide and Deep Learning (PrecWD), to precisely interpret viewership prediction. Improving upon state-of-the-art frameworks, PrecWD offers precise feature effects and designs an unstructured component. PrecWD outperforms benchmarks in two contexts: health video viewership prediction and misinformation viewership prediction. A user study confirms the superior interpretability of PrecWD. This study contributes to IS design theory with generalizable design principles and an interpretable predictive framework. Our findings provide implications to improve video viewership and credibility.
Using attention to model long-term dependencies in occupancy behavior
Authors: Max Kleinebrahm, Jacopo Torriti, Russell McKenna, Armin Ardone, Wolf Fichtner
Date: 2021-01-04 00:00:00
Tasks: Imputation

Models simulating household energy demand based on different occupant and household types and their behavioral patterns have received increasing attention over the last years due the need to better understand fundamental characteristics that shape the demand side. Most of the models described in the literature are based on Time Use Survey data and Markov chains. Due to the nature of the underlying data and the Markov property, it is not sufficiently possible to consider day to day dependencies in occupant behavior. An accurate mapping of day to day dependencies is of increasing importance for accurately reproducing mobility patterns and therefore for assessing the charging flexibility of electric vehicles. This study bridges the gap between energy related activity modelling and novel machine learning approaches with the objective to better incorporate findings from the field of social practice theory in the simulation of occupancy behavior. Weekly mobility data are merged with daily time use survey data by using attention based models. In a first step an autoregressive model is presented, which generates synthetic weekly mobility schedules of individual occupants and thereby captures day to day dependencies in mobility behavior. In a second step, an imputation model is presented, which enriches the weekly mobility schedules with detailed information about energy relevant at home activities. The weekly activity profiles build the basis for modelling consistent electricity, heat and mobility demand profiles of households. Furthermore, the approach presented forms the basis for providing data on socio-demographically differentiated occupant behavior to the general public.
"Big Data" and its Origins
Authors: Francis X. Diebold
Date: 2020-08-13 00:00:00
Tasks: BIG-bench Machine Learning

Against the background of explosive growth in data volume, velocity, and variety, I investigate the origins of the term "Big Data". Its origins are a bit murky and hence intriguing, involving both academics and industry, statistics and computer science, ultimately winding back to lunch-table conversations at Silicon Graphics Inc. (SGI) in the mid 1990s. The Big Data phenomenon continues unabated, and the ongoing development of statistical machine learning tools continues to help us confront it.
Data-driven analysis and controller design for discrete-time systems under aperiodic sampling
Authors: Stefan Wildhagen, Julian Berberich, Michael Hertneck, Frank Allgöwer
Date: 2021-01-04 00:00:00

This article is concerned with data-driven analysis of discrete-time systems under aperiodic sampling, and in particular with a data-driven estimation of the maximum sampling interval (MSI). The MSI is relevant for analysis of and controller design for cyber-physical, embedded and networked systems, since it gives a limit on the time span between sampling instants such that stability is guaranteed. We propose tools to compute the MSI for a given controller and to design a controller with a preferably large MSI, both directly from a finite-length, noise-corrupted state-input trajectory of the system. We follow two distinct approaches for stability analysis, one taking a robust control perspective and the other a switched systems perspective on the aperiodically sampled system. In a numerical example and a subsequent discussion, we demonstrate the efficacy of our developed tools and compare the two approaches.
A Mixed Integer Least-Squares Formulation of the GNSS Snapshot Positioning Problem
Authors: Eyal Waserman, Sivan Toledo
Date: 2021-01-04 00:00:00
Tasks: Position

This paper presents a formulation of Snapshot Positioning as a mixed-integer least-squares problem. In snapshot positioning one estimates a position from code-phase and possibly Doppler observations of a Global Navigation Satellite Systems (GNSS) without knowing the time of departure (timestamp) of the codes. Solving the problem allows a receiver to determine a fix from short radio-frequency snapshots missing the time-stamp information embedded in the GNSS data stream. This is used to reduced the time to first fix in some receivers, and it is used in certain wildlife trackers. This paper presents two new formulations of the problem and an algorithm that solves the resulting mixed-integer least-squares problems. We also show that the new formulations can produce fixes even with huge initial errors, much larger than permitted in Van Diggelen's widely-cited coarse-time navigation method.
The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies
Authors: Anna Baiardi, Andrea A. Naghi
Date: 2021-01-04 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference

A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods and identify several advantages of using these techniques. We show that these advantages and their implications are empirically relevant and that the use of these methods can improve the credibility of causal analysis.
Symbolic Control for Stochastic Systems via Finite Parity Games
Authors: Rupak Majumdar, Kaushik Mallik, Anne-Kathrin Schmuck, Sadegh Soudjani
Date: 2021-01-04 00:00:00

We consider the problem of computing the maximal probability of satisfying an omega-regular specification for stochastic nonlinear systems evolving in discrete time. The problem reduces, after automata-theoretic constructions, to finding the maximal probability of satisfying a parity condition on a (possibly hybrid) state space. While characterizing the exact satisfaction probability is open, we show that a lower bound on this probability can be obtained by (I) computing an under-approximation of the qualitative winning region, i.e., states from which the parity condition can be enforced almost surely, and (II) computing the maximal probability of reaching this qualitative winning region. The heart of our approach is a technique to symbolically compute the under-approximation of the qualitative winning region in step (I) via a finite-state abstraction of the original system as a 2.5-player parity game. Our abstraction procedure uses only the support of the probabilistic evolution; it does not use precise numerical transition probabilities. We prove that the winning set in the abstract 2.5-player game induces an under-approximation of the qualitative winning region in the original synthesis problem, along with a policy to solve it. By combining these contributions with (a) a symbolic fixpoint algorithm to solve 2.5-player games and (b) existing techniques for reachability policy synthesis in stochastic nonlinear systems, we get an abstraction-based algorithm for finding a lower bound on the maximal satisfaction probability. We have implemented the abstraction-based algorithm in Mascot-SDS (Majumdar et al., 2020), where we combined the outlined abstraction step with our recent tool FairSyn. We evaluated our implementation on the nonlinear model of a perturbed bistable switch from the literature. We outperform a recently proposed tool for solving this problem by a large margin.
Adversarially trained LSTMs on reduced order models of urban air pollution simulations
Authors: César Quilodrán-Casas, Rossella Arcucci, Christopher Pain, Yike Guo
Date: 2021-01-05 00:00:00

This paper presents an approach to improve computational fluid dynamics simulations forecasts of air pollution using deep learning. Our method, which integrates Principal Components Analysis (PCA) and adversarial training, is a way to improve the forecast skill of reduced order models obtained from the original model solution. Once the reduced-order model (ROM) is obtained via PCA, a Long Short-Term Memory network (LSTM) is adversarially trained on the ROM to make forecasts. Once trained, the adversarially trained LSTM outperforms a LSTM trained in a classical way. The study area is in London, including velocities and a concentration tracer that replicates a busy traffic junction. This adversarially trained LSTM-based approach is used on the ROM in order to produce faster forecasts of the air pollution tracer.
Recurrent Neural Networks for Stochastic Control Problems with Delay
Authors: Jiequn Han, Ruimeng Hu
Date: 2021-01-05 00:00:00
Tasks: Portfolio Optimization

Stochastic control problems with delay are challenging due to the path-dependent feature of the system and thus its intrinsic high dimensions. In this paper, we propose and systematically study deep neural networks-based algorithms to solve stochastic control problems with delay features. Specifically, we employ neural networks for sequence modeling (\emph{e.g.}, recurrent neural networks such as long short-term memory) to parameterize the policy and optimize the objective function. The proposed algorithms are tested on three benchmark examples: a linear-quadratic problem, optimal consumption with fixed finite delay, and portfolio optimization with complete memory. Particularly, we notice that the architecture of recurrent neural networks naturally captures the path-dependent feature with much flexibility and yields better performance with more efficient and stable training of the network compared to feedforward networks. The superiority is even evident in the case of portfolio optimization with complete memory, which features infinite delay.
Analysis of the satellite navigational data in the Baseband signal processing of Galileo E5 AltBOC signal
Authors: Subhan Khan, Yiqun Zhu, Muhammad Jawad, Muhammad Umair Safder, Mujtaba Hussain Jaffery, Salman Javid
Date: 2021-01-05 00:00:00

The advancement in the world of global satellite systems has enhanced the accuracy and reliability between various constellations. However, these enhancements have made numerous challenges for the receivers designer. For instance, comparing the acquisition and tracking of the Galileo signals turn into relatively complex processes after the utilization of Alternate Binary Offset Carrier AltBOC modulation scheme. This paper presents an efficient and unique method for comparing baseband signal processing of the complex receiver structure of the Galileo E5 AltBOC signal. More specifically, the data demodulation has attained after comparing the noisy satellite data sets with the clean data sets. Moreover, the paper presents the implementation of signal acquisition, code tracking, multipath noise characteristics, and carrier tracking for various datasets of the satellite after the eradication of noise. The results obtained in the paper are promising and provide the through treatment to the problem.
Phase-locking of resting-state brain networks with the gastric basal electrical rhythm
Authors: Ann S Choe, Boao Tang, Kimberly R Smith, Hamed Honari, Martin A Lindquist, Brian S Caffo, James J Pekar
Date: 2021-01-05 00:00:00
Tasks: Rhythm

A network of myenteric interstitial cells of Cajal in the corpus of the stomach serves as its “pacemaker”, continuously generating a ca 0.05 Hz electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. A recent study combining resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG), with cutaneous electrodes placed on the epigastrium, found 12 brain regions with activity that was significantly phase-locked with this gastric basal electrical rhythm. Therefore, we asked whether fluctuations in brain resting state networks (RSNs), estimated using a spatial independent component analysis (ICA) approach, might be synchronized with the stomach. In the present study, in order to determine whether any RSNs are phase-locked with the gastric rhythm, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data. The rsfMRI data were analyzed using group ICA; RSN time courses were estimated; for each run, the phase-locking value (PLV) was computed between each RSN and the gastric signal. To assess statistical significance, PLVs from all pairs of “mismatched” data (EGG and rsfMRI data acquired on different days) were used as surrogate data to generate a null distribution for each RSN. Of a total of 18 RSNs, three were found to be significantly phase-locked with the basal gastric rhythm, namely, a cerebellar network, a dorsal somatosensory-motor network, and a default mode network. Disruptions to the gut-brain axis, which sustains interoceptive feedback between the central nervous system and the viscera, are thought to be involved in various disorders; manifestation of the infra-slow rhythm of the stomach in brain rsfMRI data could be useful for studies in clinical populations.
Investigating the impact of autocorrelation on time-varying connectivity
Authors: Hamed Honari, Ann S. Choe, James J. Pekar, Martin A. Lindquist
Date: 2019-04-22 00:00:00
Tasks: Functional Connectivity, State Estimation, Time Series, Time Series Analysis

In recent years, a number of studies have reported on the existence of time-varying functional connectivity (TVC) in resting-state functional magnetic resonance imaging (rs-fMRI) data. The sliding-window technique is currently one of the most commonly used methods to estimate TVC. Although previous studies have shown that autocorrelation can negatively impact estimates of static functional connectivity, its impact on TVC estimates is not well known at this time. In this paper, we show both theoretically and empirically that the existence of autocorrelation within a time series can inflate the sampling variability of TVC estimated using the sliding-window technique. This can in turn increase the risk of misinterpreting noise as true TVC and negatively impact subsequent estimation of whole-brain time-varying FC profiles, or “brain states”. The latter holds as more variable input measures lead to more variable output measures in the state estimation procedure. Finally, we demonstrate that prewhitening the data prior to analysis can lower the variance of the estimated TVC and improve brain state estimation. These results suggest that careful consideration is required when making inferences on TVC.
Density Filtering for a Flame-Embedding Approach Based on LargeEddy Simulation and the One-Dimensional Turbulence Model
Authors: Hamed Honari
Date: 2013-03-23 00:00:00

The complex nature of turbulent combustion flows requires simulation and modeling of diverse scales. An approach to capturing the inherent multi-scale physics of the combustion phenomena is introduced by the present flame-embedding approach. The approach represents a multi-scale Large-Eddy Simulation (LES) framework used for turbulent combustion. The large-scale grid and LES model account for the low-resolution physics of the flow whereas the fine-scale phenomena, including chemistry, subgrid scale transport, are captured by the One-Dimensional Turbulence (ODT) model. In Lagrangian LES-ODT, ODT elements are attached to the flame surface and transported along with the flow. Due to the coupling between the two solutions, variables such as velocity and mixture fraction are passed from LES to ODT; while, the ODT density is passed from ODT grids to LES grids. Density fields filtered from ODT solutions are calculated. An approach is proposed to spatially-filter the ODT density to the LES cell centers where there is a flame. The spatially filtered LES density is blended with density solution where only
pure mixing is present to obtain a density for LES cells throughout the computational domain.
PointCutMix: Regularization Strategy for Point Cloud Classification
Authors: Jinlai Zhang, Lyujie Chen, Bo Ouyang, Binbin Liu, Jihong Zhu, Yujing Chen, Yanmei Meng, Danfeng Wu
Date: 2021-01-05 00:00:00
Tasks: 3D Point Cloud Classification, Classification, General Classification, Point Cloud Classification

As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent. In this paper, we propose a simple and effective augmentation method for the point cloud data, named PointCutMix, to alleviate those problems. It finds the optimal assignment between two point clouds and generates new training data by replacing the points in one sample with their optimal assigned pairs. Two replacement strategies are proposed to adapt to the accuracy or robustness requirement for different tasks, one of which is to randomly select all replacing points while the other one is to select k nearest neighbors of a single random point. Both strategies consistently and significantly improve the performance of various models on point cloud classification problems. By introducing the saliency maps to guide the selection of replacing points, the performance further improves. Moreover, PointCutMix is validated to enhance the model robustness against the point attack. It is worth noting that when using as a defense method, our method outperforms the state-of-the-art defense algorithms. The code is available at:https://github.com/cuge1995/PointCutMix
Local Memory Attention for Fast Video Semantic Segmentation
Authors: Matthieu Paul, Martin Danelljan, Luc van Gool, Radu Timofte
Date: 2021-01-05 00:00:00
Tasks: Decoder, Segmentation, Semantic Segmentation, Video Semantic Segmentation

We propose a novel neural network module that transforms an existing single-frame semantic segmentation model into a video semantic segmentation pipeline. In contrast to prior works, we strive towards a simple, fast, and general module that can be integrated into virtually any single-frame architecture. Our approach aggregates a rich representation of the semantic information in past frames into a memory module. Information stored in the memory is then accessed through an attention mechanism. In contrast to previous memory-based approaches, we propose a fast local attention layer, providing temporal appearance cues in the local region of prior frames. We further fuse these cues with an encoding of the current frame through a second attention-based module. The segmentation decoder processes the fused representation to predict the final semantic segmentation. We integrate our approach into two popular semantic segmentation networks: ERFNet and PSPNet. We observe an improvement in segmentation performance on Cityscapes by 1.7% and 2.1% in mIoU respectively, while increasing inference time of ERFNet by only 1.5ms.
Learning from Synthetic Shadows for Shadow Detection and Removal
Authors: Naoto Inoue, Toshihiko Yamasaki
Date: 2021-01-05 00:00:00
Tasks: Diversity, Shadow Detection, Shadow Detection And Removal, Shadow Removal

Shadow removal is an essential task in computer vision and computer graphics. Recent shadow removal approaches all train convolutional neural networks (CNN) on real paired shadow/shadow-free or shadow/shadow-free/mask image datasets. However, obtaining a large-scale, diverse, and accurate dataset has been a big challenge, and it limits the performance of the learned models on shadow images with unseen shapes/intensities. To overcome this challenge, we present SynShadow, a novel large-scale synthetic shadow/shadow-free/matte image triplets dataset and a pipeline to synthesize it. We extend a physically-grounded shadow illumination model and synthesize a shadow image given an arbitrary combination of a shadow-free image, a matte image, and shadow attenuation parameters. Owing to the diversity, quantity, and quality of SynShadow, we demonstrate that shadow removal models trained on SynShadow perform well in removing shadows with diverse shapes and intensities on some challenging benchmarks. Furthermore, we show that merely fine-tuning from a SynShadow-pre-trained model improves existing shadow detection and removal models. Codes are publicly available at https://github.com/naoto0804/SynShadow.
Learning Accurate Dense Correspondences and When to Trust Them
Authors: Prune Truong, Martin Danelljan, Luc van Gool, Radu Timofte
Date: 2021-01-05 00:00:00
Proceeding: CVPR 2021 1
Tasks: 3D Reconstruction, Dense Pixel Correspondence Estimation, Geometric Matching, Image Manipulation, Optical Flow Estimation, Pose Estimation, Prediction

Establishing dense correspondences between a pair of images is an important and general problem. However, dense flow estimation is often inaccurate in the case of large displacements or homogeneous regions. For most applications and down-stream tasks, such as pose estimation, image manipulation, or 3D reconstruction, it is crucial to know when and where to trust the estimated matches. In this work, we aim to estimate a dense flow field relating two images, coupled with a robust pixel-wise confidence map indicating the reliability and accuracy of the prediction. We develop a flexible probabilistic approach that jointly learns the flow prediction and its uncertainty. In particular, we parametrize the predictive distribution as a constrained mixture model, ensuring better modelling of both accurate flow predictions and outliers. Moreover, we develop an architecture and training strategy tailored for robust and generalizable uncertainty prediction in the context of self-supervised training. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets. We further validate the usefulness of our probabilistic confidence estimation for the task of pose estimation. Code and models are available at https://github.com/PruneTruong/PDCNet.