pwc-archive/papers-with-abstracts huggingface dataset:
Automated Seismic Source Characterisation Using Deep Graph Neural Networks
Authors: Martijn van den Ende, Jean-Paul Ampuero
Date: 2020-09-16 00:00:00
Tasks: BIG-bench Machine Learning, Graph Neural Network, Seismic source localization

Most seismological analysis methods require knowledge of the geographic location of the stations comprising a seismic network. However, common machine learning tools used in seismology do not account for this spatial information, and so there is an underutilised potential for improving the performance of machine learning models. In this work, we propose a Graph Neural Network (GNN) approach that explicitly incorporates and leverages spatial information for the task of seismic source characterisation (specifically, location and magnitude estimation), based on multi-station waveform recordings. Even using a modestly-sized GNN, we achieve model prediction accuracy that outperforms methods that are agnostic to station locations. Moreover, the proposed method is flexible to the number of seismic stations included in the analysis, and is invariant to the order in which the stations are arranged, which opens up new applications in the automation of seismological tasks and in earthquake early warning systems.
Field Trial of a Flexible Real-time Software-defined GPU-based Optical Receiver
Authors: Sjoerd van der Heide, Ruben S. Luis, Benjamin J. Puttnam, Georg Rademacher, Ton Koonen, Satoshi Shinada, Yohinari Awaji, Hideaki Furukawa, Chigo Okonkwo
Date: 2020-11-27 00:00:00
Tasks: GPU

We introduce a flexible, software-defined real-time multi-modulation format receiver implemented on an off-the-shelf general-purpose graphics processing unit (GPU). The flexible receiver is able to process 2 GBaud 2-, 4-, 8-, and 16-ary pulse-amplitude modulation (PAM) signals as well as 1 GBaud 4-, 16- and 64-ary quadrature amplitude modulation (QAM) signals, with the latter detected using a Kramers-Kronig (KK) coherent receiver. Experimental performance evaluation is shown for back-to-back. In addition, by using the JGN high speed R&D network testbed, performance is evaluated after transmission over 91 km field-deployed optical fiber and reconfigurable optical add-drop multiplexers (ROADMs).
(Stochastic) Model Predictive Control -- a Simulation Example
Authors: Tim Brüdigam
Date: 2021-01-28 00:00:00
Tasks: model, Model Predictive Control

This brief introduction to Model Predictive Control specifically addresses stochastic Model Predictive Control, where probabilistic constraints are considered. A simple linear system subject to uncertainty serves as an example. The Matlab code for this stochastic Model Predictive Control example is available online.
Finite Horizon Robust Synthesis Using Integral Quadratic Constraints
Authors: Jyot Buch, Peter Seiler
Date: 2020-06-02 00:00:00

We present a robust synthesis algorithm for uncertain linear time-varying (LTV) systems on finite horizons. The uncertain system is described as an interconnection of a known LTV system and a perturbation. The input-output behavior of the perturbation is specified by time-domain Integral Quadratic Constraints (IQCs). The objective is to synthesize a controller to minimize the worst-case performance. This leads to a non-convex optimization. The proposed approach alternates between an LTV synthesis step and an IQC analysis step. Both induced $\mathcal{L}_2$ and terminal Euclidean norm penalties on output are considered for finite horizon performance. The proposed algorithm ensures that the robust performance is non-increasing at each iteration step. The effectiveness of this method is demonstrated using numerical examples.
Multi-Antenna Joint Radar and Communications: Precoder Optimization and Weighted Sum-Rate vs Probing Power Tradeoff
Authors: Chengcheng Xu, Bruno Clerckx, Jianyun Zhang
Date: 2021-01-28 00:00:00

In order to further exploit the potential of joint multi-antenna radar-communication (RadCom) system, we propose two transmission techniques respectively based on separated and shared antenna deployments. Both techniques are designed to maximize the weighted sum rate (WSR) and the probing power at target's location under average power constraints at the antennas such that the system can simultaneously communicate with downlink users and detect the target within the same frequency band. Based on a Weighted Minimized Mean Square Errors (WMMSE) method, the separated deployment transmission is designed via semidefinite programming (SDP) while the shared deployment problem is solved by majorization-minimization (MM) algorithm. Numerical results show that the shared deployment outperforms the separated deployment in radar beamforming. The tradeoffs between WSR and probing power at target are compared among both proposed transmissions and two practically simpler dual-function implementations i.e., time division and frequency division. Results show that although the separated deployment enables spectrum sharing, it experiences a performance loss compared with frequency division, while the shared deployment outperforms both and surpasses time division in certain conditions.
Identifying the Effect of Persuasion
Authors: Sung Jae Jun, Sokbae Lee
Date: 2018-12-06 00:00:00
Tasks: Causal Inference

This paper examines a commonly used measure of persuasion whose precise interpretation has been obscure in the literature. By using the potential outcome framework, we define the causal persuasion rate by a proper conditional probability of taking the action of interest with a persuasive message conditional on not taking the action without the message. We then formally study identification under empirically relevant data scenarios and show that the commonly adopted measure generally does not estimate, but often overstates, the causal rate of persuasion. We discuss several new parameters of interest and provide practical methods for causal inference.
Respiratory diseases recognition through respiratory sound with the help of deep neural network
Authors: Victor Basu, Srinibas Rana
Date: 2020-04-30 00:00:00
Tasks: Lung Disease Classification, Lung Sound Classification

Prediction of respiratory diseases such as COPD(Chronic obstructive pulmonary disease), URTI(upper respiratory tract infection), Bronchiectasis, Pneumonia, Bronchiolitis with the help of deep neural networks or deep learning. We have constructed a deep neural network model that takes in respiratory sound as input and classifies the condition of its respiratory system. It not only classifies among the above-mentioned disease but also classifies if a person’s respiratory system is healthy or not with higher accuracy and precision.
End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++
Authors: Peng, Daifeng; Zhang, Yongjun; Guan, Haiyan.
Date: 2019-06-10 00:00:00
Tasks: Change Detection, Change detection for remote sensing images, Decoder, Earth Observation, Semantic Segmentation

Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets. Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial accuracy. Then, the fusion strategy of multiple side outputs is adopted to combine change maps from different semantic levels, thereby generating a final change map with high accuracy. The effectiveness and reliability of our proposed CD method are verified on very-high-resolution (VHR) satellite image datasets. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art CD methods.
Moral Panic and Social Justice: A Guide for Analyzing Social Problems
Authors: Michael H. Eversman, Jason D. P. Bird
Date: 2016-12-23 00:00:00

Professional social work has long been concerned with social justice, social policy, and the
relationship between social treatment and social control. However, at times, potential
threats to social cohesion become exaggerated in the service of supporting suppressive policies. British sociologist Stanley Cohen referred to such periods as moral panics, which assign
unwarranted blame and stigma to sociopolitically weaker, unpopular groups. By constructing those associated with a given social problem as deviant and downplaying underlying
structural causes, moral panics foster the enactment of social policies that entrench social disparity and injustice. Understanding how moral panics influence perceptions of social problems and resultant policies will enable social workers to identify whether particular societal
groups are unjustly targeted. By synthesizing theoretical and empirical literature on moral
panics in U.S. policy arenas relevant to social workers (such as illicit drugs, sexuality, and
immigration), this article offers guidance for practitioners, policy advocates, and researchers
on assessing their presence.
Siamese NestedUNet Networks for Change Detection of High Resolution Satellite Image
Authors: Kaiyu Li, Zhe Li, Sheng Fang
Date: 2020-10-27 00:00:00
Tasks: Change Detection, Change detection for remote sensing images, Semantic Segmentation, Vocal Bursts Intensity Prediction

Change detection is an important task in remote sensing (RS) image analysis. With the development of deep learning and the increase of RS data, there are more and more change detection methods based on supervised learning. In this paper, we improve the semantic segmentation network UNet++ and propose a fully convolutional siamese network (Siam-NestedUNet) for change detection. We combine three types of siamese structures with UNet++ respectively to explore the impact of siamese structures on the change detection task under the condition of a backbone network with strong feature extraction capabilities. In addition, for the characteristics of multiple outputs in Siam-NestedUNet, we design a set of experiments to explore the importance level of the output at different semantic levels. According to the experimental results, our method improves greatly on a number of indicators, including precision, recall, F1-Score and overall accuracy, and has better performance than other SOTA change detection methods. Our implementation will be released at https://github.com/likyoo/Siam-NestedUNet.
CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS
Authors: Lebedev, M. A.; Vizilter, Yu. V.; Vygolov, O. V.; Knyaz, V. A.; Rubis, A. Yu.
Date: 2018-06-01 00:00:00
Tasks: Change Detection

We present a method for change detection in images using Conditional Adversarial Network approach. The original network architecture based on pix2pix is proposed and evaluated for difference map creation. The paper address three types of experiments: change detection in synthetic images without objects relative shift, change detection in synthetic images with small relative shift of objects, and change detection in real season-varying remote sensing images.
A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images
Authors: Zhang, Chenxiao; Yue, Peng; Tapete, Deodato; Jiang, Liangcun; Shangguan, Boyi; Huang, Li; Liu, Guangchao
Date: 2020-06-01 00:00:00
Tasks: Change Detection, Semantic Segmentation

Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are not suitable for the task considering the challenges brought by the fine image details and complex texture features conveyed in high resolution images, a number of deep learning-based change detection methods have been proposed to improve the change detection performance. Although the state-of-the-art deep feature based methods outperform all the other deep learning-based change detection methods, networks in the existing deep feature based methods are mostly modified from architectures that are originally proposed for single-image semantic segmentation. Transferring these networks for change detection task still poses some key issues. In this paper, we propose a deeply supervised image fusion network (IFN) for change detection in high resolution bi-temporal remote sensing images. Specifically, highly representative deep features of bi-temporal images are firstly extracted through a fully convolutional two-stream architecture. Then, the extracted deep features are fed into a deeply supervised difference discrimination network (DDN) for change detection. To improve boundary completeness and internal compactness of objects in the output change maps, multi-level deep features of raw images are fused with image difference features by means of attention modules for change map reconstruction. DDN is further enhanced by directly introducing change map losses to intermediate layers in the network, and the whole network is trained in an end-to-end manner. IFN is applied to a publicly available dataset, as well as a challenging dataset consisting of multi-source bi-temporal images from Google Earth covering different cities in China. Both visual interpretation and quantitative assessment confirm that IFN outperforms four benchmark methods derived from the literature, by returning changed areas with complete boundaries and high internal compactness compared to the state-of-the-art methods.
Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease
Authors: Chengyang Zhou, Thao Vy Dinh, Heyi Kong, Jonathan Yap, Khung Keong Yeo, Hwee Kuan Lee, Kaicheng Liang
Date: 2021-01-29 00:00:00

The evaluation of obstructions (stenosis) in coronary arteries is currently done by a physician's visual assessment of coronary angiography video sequences. It is laborious, and can be susceptible to interobserver variation. Prior studies have attempted to automate this process, but few have demonstrated an integrated suite of algorithms for the end-to-end analysis of angiograms. We report an automated analysis pipeline based on deep learning to rapidly and objectively assess coronary angiograms, highlight coronary vessels of interest, and quantify potential stenosis. We propose a 3-stage automated analysis method consisting of key frame extraction, vessel segmentation, and stenosis measurement. We combined powerful deep learning approaches such as ResNet and U-Net with traditional image processing and geometrical analysis. We trained and tested our algorithms on the Left Anterior Oblique (LAO) view of the right coronary artery (RCA) using anonymized angiograms obtained from a tertiary cardiac institution, then tested the generalizability of our technique to the Right Anterior Oblique (RAO) view. We demonstrated an overall improvement on previous work, with key frame extraction top-5 precision of 98.4%, vessel segmentation F1-Score of 0.891 and stenosis measurement 20.7% Type I Error rate.
Community Detection in the Stochastic Block Model by Mixed Integer Programming
Authors: Breno Serrano, Thibaut Vidal
Date: 2021-01-26 00:00:00
Tasks: Community Detection, Stochastic Block Model

The Degree-Corrected Stochastic Block Model (DCSBM) is a popular model to generate random graphs with community structure given an expected degree sequence. The standard approach of community detection based on the DCSBM is to search for the model parameters that are the most likely to have produced the observed network data through maximum likelihood estimation (MLE). Current techniques for the MLE problem are heuristics, and therefore do not guarantee convergence to the optimum. We present mathematical programming formulations and exact solution methods that can provably find the model parameters and community assignments of maximum likelihood given an observed graph. We compare these exact methods with classical heuristic algorithms based on expectation-maximization (EM). The solutions given by exact methods give us a principled way of measuring the experimental performance of classical heuristics and comparing different variations thereof.
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
Authors: Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S. Du
Date: 2021-01-29 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: LEMMA

This paper presents new \emph{variance-aware} confidence sets for linear bandits and linear mixture Markov Decision Processes (MDPs). With the new confidence sets, we obtain the follow regret bounds: For linear bandits, we obtain an $\tilde{O}(poly(d)\sqrt{1 + \sum_{k=1}^{K}\sigma_k^2})$ data-dependent regret bound, where $d$ is the feature dimension, $K$ is the number of rounds, and $\sigma_k^2$ is the \emph{unknown} variance of the reward at the $k$-th round. This is the first regret bound that only scales with the variance and the dimension but \emph{no explicit polynomial dependency on $K$}. When variances are small, this bound can be significantly smaller than the $\tilde{\Theta}\left(d\sqrt{K}\right)$ worst-case regret bound. For linear mixture MDPs, we obtain an $\tilde{O}(poly(d, \log H)\sqrt{K})$ regret bound, where $d$ is the number of base models, $K$ is the number of episodes, and $H$ is the planning horizon. This is the first regret bound that only scales \emph{logarithmically} with $H$ in the reinforcement learning with linear function approximation setting, thus \emph{exponentially improving} existing results, and resolving an open problem in \citep{zhou2020nearly}. We develop three technical ideas that may be of independent interest: 1) applications of the peeling technique to both the input norm and the variance magnitude, 2) a recursion-based estimator for the variance, and 3) a new convex potential lemma that generalizes the seminal elliptical potential lemma.
Post-OCR Paragraph Recognition by Graph Convolutional Networks
Authors: Renshen Wang, Yasuhisa Fujii, Ashok C. Popat
Date: 2021-01-29 00:00:00
Tasks: Clustering, Optical Character Recognition (OCR)

We propose a new approach for paragraph recognition in document images by spatial graph convolutional networks (GCN) applied on OCR text boxes. Two steps, namely line splitting and line clustering, are performed to extract paragraphs from the lines in OCR results. Each step uses a beta-skeleton graph constructed from bounding boxes, where the graph edges provide efficient support for graph convolution operations. With only pure layout input features, the GCN model size is 3~4 orders of magnitude smaller compared to R-CNN based models, while achieving comparable or better accuracies on PubLayNet and other datasets. Furthermore, the GCN models show good generalization from synthetic training data to real-world images, and good adaptivity for variable document styles.
N-grams Bayesian Differential Privacy
Authors: Osman Ramadan, James Withers, Douglas Orr
Date: 2021-01-29 00:00:00
Tasks: Language Modelling

Differential privacy has gained popularity in machine learning as a strong privacy guarantee, in contrast to privacy mitigation techniques such as k-anonymity. However, applying differential privacy to n-gram counts significantly degrades the utility of derived language models due to their large vocabularies. We propose a differential privacy mechanism that uses public data as a prior in a Bayesian setup to provide tighter bounds on the privacy loss metric epsilon, and thus better privacy-utility trade-offs. It first transforms the counts to log space, approximating the distribution of the public and private data as Gaussian. The posterior distribution is then evaluated and softmax is applied to produce a probability distribution. This technique achieves up to 85% reduction in KL divergence compared to previously known mechanisms at epsilon equals 0.1. We compare our mechanism to k-anonymity in a n-gram language modelling task and show that it offers competitive performance at large vocabulary sizes, while also providing superior privacy protection.
Speech Enhancement for Wake-Up-Word detection in Voice Assistants
Authors: David Bonet, Guillermo Cámbara, Fernando López, Pablo Gómez, Carlos Segura, Jordi Luque
Date: 2021-01-29 00:00:00
Tasks: Data Augmentation, Denoising, Keyword Spotting, Speech Enhancement

Keyword spotting and in particular Wake-Up-Word (WUW) detection is a very important task for voice assistants. A very common issue of voice assistants is that they get easily activated by background noise like music, TV or background speech that accidentally triggers the device. In this paper, we propose a Speech Enhancement (SE) model adapted to the task of WUW detection that aims at increasing the recognition rate and reducing the false alarms in the presence of these types of noises. The SE model is a fully-convolutional denoising auto-encoder at waveform level and is trained using a log-Mel Spectrogram and waveform reconstruction losses together with the BCE loss of a simple WUW classification network. A new database has been purposely prepared for the task of recognizing the WUW in challenging conditions containing negative samples that are very phonetically similar to the keyword. The database is extended with public databases and an exhaustive data augmentation to simulate different noises and environments. The results obtained by concatenating the SE with a simple and state-of-the-art WUW detectors show that the SE does not have a negative impact on the recognition rate in quiet environments while increasing the performance in the presence of noise, especially when the SE and WUW detector are trained jointly end-to-end.
BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge
Authors: Martin Kocour, Guillermo Cámbara, Jordi Luque, David Bonet, Mireia Farrús, Martin Karafiát, Karel Veselý, Jan ''Honza'' Ĉernocký
Date: 2021-01-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Speech-to-Text

This paper describes joint effort of BUT and Telef\'onica Research on development of Automatic Speech Recognition systems for Albayzin 2020 Challenge. We compare approaches based on either hybrid or end-to-end models. In hybrid modelling, we explore the impact of SpecAugment layer on performance. For end-to-end modelling, we used a convolutional neural network with gated linear units (GLUs). The performance of such model is also evaluated with an additional n-gram language model to improve word error rates. We further inspect source separation methods to extract speech from noisy environment (i.e. TV shows). More precisely, we assess the effect of using a neural-based music separator named Demucs. A fusion of our best systems achieved 23.33% WER in official Albayzin 2020 evaluations. Aside from techniques used in our final submitted systems, we also describe our efforts in retrieving high quality transcripts for training.