pwc-archive/papers-with-abstracts huggingface dataset:
Correlates of repeat abortions and their spacing: Evidence from registry data in Spain
Authors: Catia Nicodemo, Sonia Oreffice, Climent Quintana-Domeque
Date: 2022-08-10 00:00:00

Using administrative data on all induced abortions recorded in Spain in 2019, we analyze the characteristics of women undergoing repeat abortions and the spacing between these procedures. Our findings indicate that compared to women experiencing their first abortion, those who undergo repeat abortions are more likely to have lower education levels, have dependent children, live alone, or be foreign-born, with a non-monotonic relationship with age. We also report that being less educated, not employed, having dependent children, or being foreign-born are all strongly related to a higher number of repeat abortions. Lastly, we find that being less educated, foreign-born, or not employed is correlated with a shorter time interval between the last two abortions.
Pandora's Ballot Box: Electoral Politics of Direct Democracy
Authors: Peter Buisseret, Richard Van Weelden
Date: 2022-08-10 00:00:00

We study how office-seeking parties use direct democracy to shape elections. A party with a strong electoral base can benefit from using a binding referendum to resolve issues that divide its core supporters. When referendums do not bind, however, an electorally disadvantaged party may initiate a referendum to elevate new issues in order to divide the supporters of its stronger opponent. We identify conditions under which direct democracy improves congruence between policy outcomes and voter preferences, but also show that it can lead to greater misalignment both on issues subject to direct democracy and those that are not.
Chewing Detection from Commercial Smart-glasses
Authors: Vasileios Papapanagiotou, Anastasia Liapi, Anastasios Delopoulos
Date: 2022-08-11 00:00:00

Automatic dietary monitoring has progressed significantly during the last years, offering a variety of solutions, both in terms of sensors and algorithms as well as in terms of what aspect or parameters of eating behavior are measured and monitored. Automatic detection of eating based on chewing sounds has been studied extensively, however, it requires a microphone to be mounted on the subject's head for capturing the relevant sounds. In this work, we evaluate the feasibility of using an off-the-shelf commercial device, the Razer Anzu smart-glasses, for automatic chewing detection. The smart-glasses are equipped with stereo speakers and microphones that communicate with smart-phones via Bluetooth. The microphone placement is not optimal for capturing chewing sounds, however, we find that it does not significantly affect the detection effectiveness. We apply an algorithm from literature with some adjustments on a challenging dataset that we have collected in house. Leave-one-subject-out experiments yield promising results, with an F1-score of 0.96 for the best case of duration-based evaluation of eating time.
Robust Speaker Extraction Network Based on Iterative Refined Adaptation
Authors: Chengyun Deng, Shiqian Ma, Yi Zhang, Yongtao Sha, HUI ZHANG, Hui Song, Xiangang Li
Date: 2020-11-04 00:00:00

Speaker extraction aims to extract target speech signal from a multi-talker environment with interference speakers and surrounding noise, given the target speaker's reference information. Most speaker extraction systems achieve satisfactory performance on the premise that the test speakers have been encountered during training time. Such systems suffer from performance degradation given unseen target speakers and/or mismatched reference voiceprint information. In this paper we propose a novel strategy named Iterative Refined Adaptation (IRA) to improve the robustness and generalization capability of speaker extraction systems in the aforementioned scenarios. Given an initial speaker embedding encoded by an auxiliary network, the extraction network can obtain a latent representation of the target speaker, which is fed back to the auxiliary network to get a refined embedding to provide more accurate guidance for the extraction network. Experiments on WSJ0-2mix-extr and WHAM! dataset confirm the superior performance of the proposed method over the network without IRA in terms of SI-SDR and PESQ improvement.
Deep Multi-task Network for Delay Estimation and Echo Cancellation
Authors: Yi Zhang, Chengyun Deng, Shiqian Ma, Yongtao Sha, Hui Song
Date: 2020-11-04 00:00:00
Tasks: Acoustic echo cancellation, Data Augmentation

Echo path delay (or ref-delay) estimation is a big challenge in acoustic echo cancellation. Different devices may introduce various ref-delay in practice. Ref-delay inconsistency slows down the convergence of adaptive filters, and also degrades the performance of deep learning models due to 'unseen' ref-delays in the training set. In this paper, a multi-task network is proposed to address both ref-delay estimation and echo cancellation tasks. The proposed architecture consists of two convolutional recurrent networks (CRNNs) to estimate the echo and enhanced signals separately, as well as a fully-connected (FC) network to estimate the echo path delay. Echo signal is first predicted, and then is combined with reference signal together for delay estimation. At the end, delay compensated reference and microphone signals are used to predict the enhanced target signal. Experimental results suggest that the proposed method makes reliable delay estimation and outperforms the existing state-of-the-art solutions in inconsistent echo path delay scenarios, in terms of echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ). Furthermore, a data augmentation method is studied to evaluate the model performance on different portion of synthetical data with artificially introduced ref-delay.
AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing
Authors: Hajar Abedi, Ahmad Ansariyan, Plinio P Morita, Alexander Wong, Jennifer Boger, George Shaker
Date: 2022-08-11 00:00:00
Tasks: Activity Recognition, Cloud Computing

In this paper, leveraging AI, cloud computing and radar technology, we create intelligent sensing that enables smarter applications to improve people's daily lives.
Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet
Authors: Muhammad Karam Shehzad, Luca Rose, Muhammad Furqan Azam, Mohamad Assaad
Date: 2022-08-11 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

Channel state information (CSI) is of pivotal importance as it enables wireless systems to adapt transmission parameters more accurately, thus improving the system's overall performance. However, it becomes challenging to acquire accurate CSI in a highly dynamic environment, mainly due to multi-path fading. Inaccurate CSI can deteriorate the performance, particularly of a massive multiple-input multiple-output (mMIMO) system. This paper adapts machine learning (ML) for CSI prediction. Specifically, we exploit time-series models of deep learning (DL) such as recurrent neural network (RNN) and Bidirectional long-short term memory (BiLSTM). Further, we use NeuralProphet (NP), a recently introduced time-series model, composed of statistical components, e.g., auto-regression (AR) and Fourier terms, for CSI prediction. Inspired by statistical models, we also develop a novel hybrid framework comprising RNN and NP to achieve better prediction accuracy. The proposed channel predictors (CPs) performance is evaluated on a real-time dataset recorded at the Nokia Bell-Labs campus in Stuttgart, Germany. Numerical results show that DL brings performance gain when used with statistical models and showcases robustness.
Downlink Interference Analysis of UAV-based mmWave Fronthaul for Small Cell Networks
Authors: Mohammad Taghi Dabiri, Mazen Hasna, Walid Saad
Date: 2022-08-11 00:00:00

In this paper, an unmanned aerial vehicles (UAV)-based heterogeneous network is studied to solve the problem of transferring massive traffic of distributed small cells to the core network. First, a detailed three-dimensional (3D) model of the downlink channel is characterized by taking into account the real antenna pattern, UAVs' vibrations, random distribution of small cell base stations (SBSs), and the position of UAVs in 3D space. Then, a rigorous analysis of interference is performed for two types pf interference: intra-cell interference and inter-cell interference. The interference analysis results are then used to derive an upper bound of outage probability on the considered system. Using numerical results show that the analytical and simulation results match one another. The results show that, in the presence of UAV's fluctuations, optimizing radiation pattern shape requires balancing an inherent tradeoff between increasing pattern gain to reduce the interference as well as to compensate large path loss at mmWave frequencies and decreasing it to alleviate the adverse effect of a UAV's vibrations. The analytical derivations enable the derivation of the optimal antenna pattern for any condition in a short time instead of using time-consuming extensive simulations.
CB-DSL: Communication-efficient and Byzantine-robust Distributed Swarm Learning on Non-i.i.d. Data
Authors: Xin Fan, Yue Wang, Yan Huo, Zhi Tian
Date: 2022-08-10 00:00:00

The valuable data collected by IoT devices in edge networks together with the resurgence of ML stimulate the latest trend of edge AI. However, recent FL methods face major challenges including communication bottleneck, data heterogeneity and security concerns in edge IoT scenarios, especially when being adopted for distributed learning among massive IoT devices equipped with limited data and transmission resources. Meanwhile, the swarm nature of IoT systems is overlooked by most existing literature, which calls for new designs of distributed learning algorithms. Inspired by the success of biological intelligence (BI) of gregarious organisms, we propose a novel edge learning approach for swarm IoT, called communication-efficient and Byzantine-robust distributed swarm learning (CB-DSL), through a holistic integration of AI-enabled stochastic gradient descent and BI-enabled particle swarm optimization. To deal with non-i.i.d. data issues and Byzantine attacks, global data samples are introduced in CB-DSL and shared among IoT workers, which not only alleviates the local data heterogeneity effectively but also enables to fully utilize the exploration-exploitation mechanism of swarm intelligence. Further, we provide convergence analysis to theoretically demonstrate that the proposed CB-DSL is superior to the standard FL with better convergence behavior. In addition, to measure the effectiveness of the introduction of the globally shared dataset, we also evaluate the model divergence by deriving its upper bound, which is related to the distance between the data distribution at local IoT devices and the population distribution for the whole datasets. Numerical results verify that the proposed CB-DSL outperforms the existing benchmarks in terms of faster convergence speed, higher convergent accuracy, lower communication cost, and better robustness against non-i.i.d. data and Byzantine attacks.
Approximating quasi-stationary behaviour in network-based SIS dynamics
Authors: Christopher E. Overton, Robert R. Wilkinson, Adedapo Loyinmi, Joel C. Miller, Kieran J. Sharkey
Date: 2022-08-11 00:00:00

Deterministic approximations to stochastic Susceptible-Infectious-Susceptible models typically predict a stable endemic steady-state when above threshold. This can be hard to relate to the underlying stochastic dynamics, which has no endemic steady-state but can exhibit approximately stable behaviour. Here we relate the approximate models to the stochastic dynamics via the definition of the quasi-stationary distribution (QSD), which captures this approximately stable behaviour. We develop a system of ordinary differential equations that approximate the number of infected individuals in the QSD for arbitrary contact networks and parameter values. When the epidemic level is high, these QSD approximations coincide with the existing approximation methods. However, as we approach the epidemic threshold, the models deviate, with these models following the QSD and the existing methods approaching the all susceptible state. Through consistently approximating the QSD, the proposed methods provide a more robust link to the stochastic models.
Functional Connectivity via Total Correlation: Analytical results in Visual Areas
Authors: Qiang Li, Greg Ver Steeg, Jesus Malo
Date: 2022-08-11 00:00:00
Tasks: Functional Connectivity

Recent studies invoke the superiority of the multivariate Total Correlation concept over the conventional pairwise measures of functional connectivity in biological networks. Those seminal works certainly show that empirical measures of Total Correlation lead to connectivity patterns that differ from what is obtained using the most popular measure, linear correlation, or its higher order and nonlinear alternative Mutual Information. However, they do not provide analytical results that explain the differences beyond the obvious multivariate versus bivariate definitions. Moreover, the accuracy of the empirical estimators could not be addressed directly because no controlled scenario with known analytical result was provided either. This point is critical because empirical estimation of information theory measures is always challenging. As opposed to previous empirical approaches, in this work we present analytical results to prove the advantages of Total Correlation over Mutual Information to describe the functional connectivity. In particular, we do it in neural networks for early vision (retina-LGN-cortex) which are realistic but simple enough to get analytical results. The presented analytical setting is also useful to check empirical estimates of Total Correlation. Therefore, once certain estimate can be trusted, one can explore the behavior with natural signals where the analytical results (that assume Gaussian signals), may not be valid. In this regard, as applications (a) we explore the effect of connectivity and feedback in the analytical retina-LGN-cortex network with natural images, and (b) we assess the functional connectivity in visual areas V1-V2-V3-V4 from actual fMRI recordings.
PASS: De novo assembler for short peptide sequences
Authors: René L. Warren
Date: 2022-08-11 00:00:00

The ability to characterize proteins at sequence-level resolution is vital to biological research. Currently, the leading method for protein sequencing is by liquid chromatography mass spectrometry (LC-MS) whereas proteins are reduced to their constituent peptides by enzymatic digest and subsequently analyzed on an LC-MS instrument. The short peptide sequences that result from this analysis are used to characterize the original protein content of the sample. Here we present PASS, a de novo assembler for short peptide sequences that can be used to reconstruct large portions of protein targets, a step that can facilitate downstream sample characterization efforts. We show how, with adequate peptide sequence coverage and little-to-no additional sequence processing, PASS reconstructs protein sequences into relatively large (100 amino acid or longer) contigs having high (93.1 - 99.1%) sequence identity to reference antibody light and heavy chain proteins. Availability: PASS is released under the GNU General Public License Version 3 (GPLv3) and is publicly available from https://github.com/warrenlr/PASS
Family based HLA imputation and optimization of haplo-identical transplants
Authors: Zuriya Ansbacher-Feldman, Sapir Israeli, Martin Maiers, Loren Gragert, Dianne De Santis, Moshe Israeli, yoram louzoun
Date: 2022-08-11 00:00:00
Tasks: Imputation

Recently, haplo-identical transplantation with multiple HLA mismatches has become a viable option for system cell transplants. Haplotype sharing detection requires imputation of donor and recipient. We show that even in high-resolution typing when all alleles are known, there is a 15% error rate in haplotype phasing, and even more in low resolution typings. Similarly, in related donors, parents haplotypes should be imputed to determine what haplotype each child inherited. We propose GRAMM (GRaph bAsed FaMilly iMputation) to phase alleles in family pedigree HLA typing data, and in mother-cord blood unit pairs. We show that GRAMM has practically no phasing errors when pedigree data are available. We apply GRAMM to simulations with different typing resolutions as well as paired cord-mother typings, and show very high phasing accuracy, and improved alleles imputation accuracy. We use GRAMM to detect recombination events and show that the rate of falsely detected recombination events (False Positive Rate) in simulations is very low. We then apply recombination detection to typed families to estimate the recombination rate in Israeli and Australian population datasets. The estimated recombination rate has an upper bound of 10-20% per family (1-4% per individual). GRAMM is available at: https://gramm.math.biu.ac.il/.
Position Encoding Enhanced Feature Mapping for Image Anomaly Detection
Authors: Wan, Qian and Cao YunKang and Gao, Liang and Shen Weiming and Li, Xinyu
Date: 2022-06-10 00:00:00
Proceeding: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) 2022 2022 6
Tasks: Position

Image anomaly detection is an important stage for automatic visual inspection in intelligent manufacturing systems. The wide-ranging anomalies in images, such as various sizes, shapes, and colors, make automatic visual inspection challenging. Previous work on image anomaly detection has achieved significant advancements. However, there are still limitations in terms of detection performance and efficiency. In this paper, a novel Position Encoding enhanced Feature Mapping (PEFM) method is proposed to address the problem of image anomaly detection, detecting the anomalies by mapping a pair of pre-trained features embedded with position encodes. Experiment results show that the proposed PEFM achieves better performance and efficiency than the state-of-the-art methods on the MVTec AD dataset, an AUCROC of 98.30% and an AUCPRO of 95.52%, and achieves the AUCPRO of 94.0% on the MVTec 3D AD dataset.
Traffic state data imputation: An efficient generating method based on the graph aggregator
Authors: Dongwei Xu, Hang Peng, Chenchen Wei, Xuetian Shang, Haijian Li
Date: 2022-08-12 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2022 8
Tasks: Generative Adversarial Network, Imputation, State Estimation, Traffic Data Imputation

Road traffic state estimation is an essential component
of intelligent transportation systems (ITSs). However,
road traffic state data collected by traffic detectors are often
incomplete, which can cause problems across a variety of
transportation applications, such as traffic state prediction and
pattern recognition. We present GA-GAN (Graph Aggregate
Generative Adversarial Network), consisting of graph sample and
aggregate (GraphSAGE) and a generative adversarial network
(GAN), to impute missing road traffic state data. Instead of
using the original road network structure, which presents the
spatial information to process a graph operation, we reconstruct
the road network according to the correlation coefficients of
road historical data. We utilize GraphSAGE to aggregate the
temporal-spatial information from the neighbors of each road
in the reconstructed road network. GAN is used to generate
complete traffic state data from the extracted temporal-spatial
information to achieve traffic state data imputation. To illustrate
the efficient performance of the model, experiments are
conducted on traffic data collected from California and Seattle,
Washington, showing that the proposed model outperforms stateof-the-art
methods.
Stargazer: A transformer-based driver action detection system for intelligent transportation
Authors: Junwei Liang, He Zhu, Enwei Zhang, Jun Zhang
Date: 2022-06-01 00:00:00
Proceeding: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2022 6
Tasks: Action Detection, Action Recognition, Temporal Localization

Distracted driver actions can be dangerous and cause severe accidents. Thus, it is important to detect and eliminate distracted driving behaviors on the road to save lives. To this end, we study driver action detection using videos captured inside the vehicle. We propose Stargazer, an efficient, transformer-based system exploiting rich temporal features about the human behavioral information, with a simple yet effective action temporal localization framework. The core of our system contains an improved version of the multi-scale vision transformer network, which learns a hierarchy of robust representations. We then use a sliding-window classification strategy to facilitate temporal localization of actions-of-interest. The proposed system wins the second place in the Naturalistic Driving Action Recognition of AI City Challenge 2022 (Track 3). The code and models are released.
Improved Churn Causal Analysis Through Restrained High‑Dimensional Feature Space Efects in Financial Institutions
Authors: David Hason Rudd, Huan Huo, Guandong Xu
Date: 2022-07-27 00:00:00
Proceeding: Human-Centric Intelligent Systems 2022 7
Tasks: Causal Discovery, Management

Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific 
period. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with 
churn risk is wise. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future 
and identify effects and possible causes for churn. In general, this study presents a conceptual framework to discover the 
confounding features that correlate with independent variables and are causally related to those dependent variables that 
impact churn. We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address 
churn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions 
due to employing interval-based features used in Customer Relationship Management systems. The effects of the curse and 
blessing of dimensionality assessed by utilising the Recursive Feature Elimination method to overcome the high dimension 
feature space problem. Moreover, a causal discovery performed to find possible interpretation methods to describe cause 
probabilities that lead to customer churn. Evaluation metrics on validation data confirm the random forest and our ensemble 
ANN model, with %86 accuracy, outperformed other approaches. Causal analysis results confirm that some independent 
causal variables representing the level of super guarantee contribution, account growth, and account balance amount were 
identified as confounding variables that cause customer churn with a high degree of belief. This article provides a real-world 
customer churn analysis from current status inference to future directions in local superannuation funds.
Keywords Churn analysis · Bayesian networks · Deep neural networks · Data mining · Data sampling