pwc-archive/papers-with-abstracts huggingface dataset:
Difference-in-Differences for Policy Evaluation
Authors: Brantly Callaway
Date: 2022-03-29 00:00:00

Difference-in-differences is one of the most used identification strategies in empirical work in economics. This chapter reviews a number of important, recent developments related to difference-in-differences. First, this chapter reviews recent work pointing out limitations of two way fixed effects regressions (these are panel data regressions that have been the dominant approach to implementing difference-in-differences identification strategies) that arise in empirically relevant settings where there are more than two time periods, variation in treatment timing across units, and treatment effect heterogeneity. Second, this chapter reviews recently proposed alternative approaches that are able to circumvent these issues without being substantially more complicated to implement. Third, this chapter covers a number of extensions to these results, paying particular attention to (i) parallel trends assumptions that hold only after conditioning on observed covariates and (ii) strategies to partially identify causal effect parameters in difference-in-differences applications in cases where the parallel trends assumption may be violated.
Estimating Nonlinear Network Data Models with Fixed Effects
Authors: David W. Hughes
Date: 2022-03-29 00:00:00
Tasks: counterfactual

I introduce a new method for bias correction of dyadic models with agent-specific fixed effects, including the dyadic link formation model with homophily and degree heterogeneity. The proposed approach uses a jackknife procedure to deal with the incidental parameters problem. The method can be applied to both directed and undirected networks, allows for non-binary outcome variables, and can be used to bias correct estimates of average effects and counterfactual outcomes. I also show how the jackknife can be used to bias correct fixed-effect averages over functions that depend on multiple nodes, e.g. triads or tetrads in the network. As an example, I implement specification tests for dependence across dyads, such as reciprocity or transitivity. Finally, I demonstrate the usefulness of the estimator in an application to a gravity model for import/export relationships across countries.
Frequency-Directional Attention Model for Multilingual Automatic Speech Recognition
Authors: Akihiro Dobashi, Chee Siang Leow, Hiromitsu Nishizaki
Date: 2022-03-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper proposes a model for transforming speech features using the frequency-directional attention model for End-to-End (E2E) automatic speech recognition. The idea is based on the hypothesis that in the phoneme system of each language, the characteristics of the frequency bands of speech when uttering them are different. By transforming the input Mel filter bank features with an attention model that characterizes the frequency direction, a feature transformation suitable for ASR in each language can be expected. This paper introduces a Transformer-encoder as a frequency-directional attention model. We evaluated the proposed method on a multilingual E2E ASR system for six different languages and found that the proposed method could achieve, on average, 5.3 points higher accuracy than the ASR model for each language by introducing the frequency-directional attention mechanism. Furthermore, visualization of the attention weights based on the proposed method suggested that it is possible to transform acoustic features considering the frequency characteristics of each language.
Training Speaker Embedding Extractors Using Multi-Speaker Audio with Unknown Speaker Boundaries
Authors: Themos Stafylakis, Ladislav Mošner, Oldřich Plchot, Johan Rohdin, Anna Silnova, Lukáš Burget, Jan "Honza'' Černocký
Date: 2022-03-29 00:00:00
Tasks: speaker-diarization, Speaker Diarization

In this paper, we demonstrate a method for training speaker embedding extractors using weak annotation. More specifically, we are using the full VoxCeleb recordings and the name of the celebrities appearing on each video without knowledge of the time intervals the celebrities appear in the video. We show that by combining a baseline speaker diarization algorithm that requires no training or parameter tuning, a modified loss with aggregation over segments, and a two-stage training approach, we are able to train a competitive ResNet-based embedding extractor. Finally, we experiment with two different aggregation functions and analyze their behaviour in terms of their gradients.
Simultaneous Communication and Tracking in Arbitrary Trajectories via Beam-Space Processing
Authors: Fernando Pedraza, Saeid K. Dehkordi, Mari Kobayashi, Giuseppe Caire
Date: 2022-03-29 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we develop a beam tracking scheme for an orthogonal frequency division multiplexing (OFDM) Integrated Sensing and Communication (ISAC) system with a hybrid digital analog (HDA) architecture operating in the millimeter wave (mmWave) band. Our tracking method consists of an estimation step inspired by radar signal processing techniques, and a prediction step based on simple kinematic equations. The hybrid architecture exploits the predicted state information to focus only on the directions of interest, trading off beamforming gain, hardware complexity and multistream processing capabilities. Our extensive simulations in arbitrary trajectories show that the proposed method can outperform state of the art beam tracking methods in terms of prediction accuracy and consequently achievable communication rate, and is fully capable of dealing with highly non-linear dynamic motion patterns.
Federated Learning-Based Localization with Heterogeneous Fingerprint Database
Authors: Xin Cheng, Chuan Ma, Jun Li, Haiwei Song, Feng Shu, Jiangzhou Wang
Date: 2022-03-29 00:00:00
Tasks: Federated Learning

Fingerprint-based localization plays an important role in indoor location-based services, where the position information is usually collected in distributed clients and gathered in a centralized server. However, the overloaded transmission as well as the potential risk of divulging private information burdens the application.Owning the ability to address these challenges, federated learning (FL)-based fingerprinting localization comes into people's sights, which aims to train a global model while keeping raw data locally. However, in distributed machine learning (ML) scenarios, the unavoidable database heterogeneity usually degrades the performance of existing FL-based localization algorithm (FedLoc). In this paper, we first characterize the database heterogeneity with a computable metric, i.e., the area of convex hull, and verify it by experimental results. Then, a novel heterogeneous FL-based localization algorithm with the area of convex hull-based aggregation (FedLoc-AC) is proposed. Extensive experimental results, including real-word cases are conducted. We can conclude that the proposed FedLoc-AC can achieve an obvious prediction gain compared to FedLoc in heterogeneous scenarios and has almost the same prediction error with it in homogeneous scenarios. Moreover, the extension of FedLoc-AC in multi-floor cases is proposed and verified.
A Space-Time Neural Network for Analysis of Stress Evolution under DC Current Stressing
Authors: Tianshu Hou, Ngai Wong, Quan Chen, Zhigang Ji, Hai-Bao Chen
Date: 2022-03-29 00:00:00

The electromigration (EM)-induced reliability issues in very large scale integration (VLSI) circuits have attracted increased attention due to the continuous technology scaling. Traditional EM models often lead to overly pessimistic prediction incompatible with the shrinking design margin in future technology nodes. Motivated by the latest success of neural networks in solving differential equations in physical problems, we propose a novel mesh-free model to compute EM-induced stress evolution in VLSI circuits. The model utilizes a specifically crafted space-time physics-informed neural network (STPINN) as the solver for EM analysis. By coupling the physics-based EM analysis with dynamic temperature incorporating Joule heating and via effect, we can observe stress evolution along multi-segment interconnect trees under constant, time-dependent and space-time-dependent temperature during the void nucleation phase. The proposed STPINN method obviates the time discretization and meshing required in conventional numerical stress evolution analysis and offers significant computational savings. Numerical comparison with competing schemes demonstrates a 2x ~ 52x speedup with a satisfactory accuracy.
Reverse Link Analysis for Full-Duplex Cellular Networks with Low Resolution ADC/DAC
Authors: Elyes Balti, Brian L. Evans
Date: 2022-03-28 00:00:00
Tasks: Quantization

In this work, we consider a full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular network with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converter (DACs). Our first contribution is to provide a unified framework for reverse link (uplink) analysis where matched filters are applied at the FD base stations (BSs) under channel hardening. Second, we derive the expressions of the signal-to-quantization-plus-interference-plus-noise ratio (SQINR) for general and special cases. Finally, we quantify effects of quantization error, pilot contamination, and full duplexing for a hexagonal cell lattice on spectral efficiency and cumulative distribution function (CDF) to show that FD outperforms half duplex (HD) in a wide variety of scenarios.
A multimodal approach for Parkinson disease analysis
Authors: Marcos Faundez-Zanuy, Antonio Satue-Villar, Jiri Mekyska, Viridiana Arreola, Pilar Sanz, Carles Paul, Luis Guirao, Mateu Serra, Laia Rofes, Pere Clavé, Enric Sesa-Nogueras, Josep Roure
Date: 2022-03-10 00:00:00
Tasks: Diagnostic

Parkinson's disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1-1 %, and an annual incidence between 1.3-2.0/10000 inhabitants. The mean age at diagnosis of PD is 55 and most patients are between 50 and 80 years old. The most obvious symptoms are movement-related; these include tremor, rigidity, slowness of movement and walking difficulties. Frequently these are the symptoms that lead to the PD diagnoses. Later, thinking and behavioral problems may arise, and other symptoms include cognitive impairment and sensory, sleep and emotional problems. In this paper we will present an ongoing project that will evaluate if voice and handwriting analysis can be reliable predictors/indicators of swallowing and balance impairments in PD. An important advantage of voice and handwritten analysis is its low intrusiveness and easy implementation in clinical practice. Thus, if a significant correlation between these simple analyses and the gold standard video-fluoroscopic analysis will imply simpler and less stressing diagnostic test for the patients as well as the use of cheaper analysis systems.
Guns, Zombies, and Steelhead Axes: cost-effective recommendations for surviving human societies
Authors: Jacob Pacheco, Ahmani Roman, Kursad Tosun, Scott Greenhalgh
Date: 2022-03-28 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

In pop culture, there are many strategies to curtail zombie apocalypses. However, it remains unclear what routes we should take to eliminate zombies effectively and affordably. So, we created a mathematical model to examine interventions that armed adults with steelhead axes or provided enough ammunition and a 9mm handgun to kill zombies in either a "single-tap" or "double-tap". We investigate each case over two years under slow, moderate speed, and fast zombies scenarios. We quantify health burden by zombies averted, disability-adjusted life-years averted, and determine cost-effectiveness using the incremental cost-effectiveness ratio. Our predictions show the single=tap intervention is the best for stopping the zombie apocalypse, as it would avert hundreds of millions of zombies and deaths while also being the most cost-effective intervention. Altogether, this suggests conserving ammunition and supplying ranged weapons would be an effective use of limited resources in the event of a zombie uprising.
The vagus nerve regulates immunometabolic homeostasis in the ovine fetus near term: impact on terminal ileum
Authors: Mingju Cao, Shikha Kuthiala, Keven Jason Jean, Hai Lun Liu, Marc Courchesne, Karen Nygard, Patrick Burns, André Desrochers, Gilles Fecteau, Christophe Faure, Martin G. Frasch
Date: 2022-03-27 00:00:00

The contribution of the vagus nerve to inflammation and glucosensing in the fetus is not understood. We hypothesized that vagotomy (Vx) will trigger a rise in systemic glucose levels and this will be enhanced during systemic and organ-specific inflammation. Efferent vagus nerve stimulation (VNS) should reverse this phenotype. Near-term fetal sheep (n=57) were surgically prepared with vascular catheters and ECG electrodes as control and treatment groups (lipopolysaccharide (LPS), Vx+LPS, Vx+LPS+selective efferent VNS). Fetal arterial blood samples were drawn for 7 days to profile inflammation (IL-6), insulin, blood gas and metabolism (glucose). At 54 h, a necropsy was performed; terminal ileum macrophages; CD11c (M1 phenotype) immunofluorescence was quantified to detect inflammation. Across the treatment groups, blood gas and cardiovascular changes indicated mild septicemia. At 3 h, in the LPS group IL-6 peaked; that peak was decreased in Vx+LPS400 and doubled in Vx+LPS800 group; the efferent VNS sped up the reduction of the inflammatory response profile over 54 h. M1 macrophage activity was increased in the LPS and Vx+LPS800 groups only. Glucose and insulin levels in the Vx+LPS group were respectively 1.3-fold and 2.3-fold higher vs. control at 3 h, and the efferent VNS normalized glucose levels. Complete withdrawal of vagal innervation results in a 72h delayed onset of sustained increase in glucose levels for at least 54h and intermittent hyperinsulinemia. Under conditions of moderate fetal inflammation, this is related to higher levels of gut inflammation; the efferent VNS reduces the systemic inflammatory response as well as restores both the levels of glucose and terminal ileum inflammation, but not the insulin levels. Our findings reveal a novel regulatory, hormetic, role of the vagus nerve in the immunometabolic response to endotoxin in near-term fetuses.
Encryption and encoding of facial images into quick response and high capacity color 2d code for biometric passport security system
Authors: Ziaul Haque Choudhury
Date: 2022-03-17 00:00:00

In this thesis, a multimodal biometric, secure encrypted data and encrypted biometric encoded into the QR code-based biometric-passport authentication method is proposed for national security applications. Firstly, using the Extended Profile - Local Binary Patterns (EP-LBP), a Canny edge detector, and the Scale Invariant Feature Transform (SIFT) algorithm with Image File Information (IMFINFO) process, the facial mark size recognition is initially achieved. Secondly, by using the Active Shape Model (ASM) into Active Appearance Model (AAM) to follow the hand and infusion the hand geometry characteristics for verification and identification, hand geometry recognition is achieved. Thirdly, the encrypted biometric passport information that is publicly accessible is encoded into the QR code and inserted into the electronic passport to improve protection. Further, Personal information and biometric data are encrypted by applying the Advanced Encryption Standard (AES) and the Secure Hash Algorithm (SHA) 256 algorithm. It will enhance the biometric passport security system.
A network model for cellular aging
Authors: Hong Qin
Date: 2013-05-24 00:00:00
Tasks: model

What is aging? Mechanistic answers to this question remain elusive despite decades of research. Here, we propose a mathematical model of cellular aging based on a model gene interaction network. Our network model is made of only non-aging components - the biological functions of gene interactions decrease with a constant mortality rate. Death of a cell occurs in the model when an essential gene loses all of its interactions to other genes, equivalent to the deletion of an essential gene. Gene interactions are stochastic based on a binomial distribution. We show that the defining characteristic of biological aging, the exponential increase of mortality rate over time, can arise from this gene network model during the early stage of aging. Hence, we demonstrate that cellular aging is an emergent property of this model network. Our model predicts that the rate of aging, defined by the Gompertz coefficient, is approximately proportional to the average number of active interactions per gene and that the stochastic heterogeneity of gene interactions is an important factor in the dynamics of the aging process. This theoretic framework offers a mechanistic foundation for the pleiotropic nature of aging and can provide insights on cellular aging.
The forecast of COVID-19 spread risk at the county level
Authors: Murtadha D. Hssayeni, Arjuna Chala, Roger Dev, Lili Xu, Jesse Shaw, Borko Furht & Behnaz Ghoraani
Date: 2021-07-07 00:00:00
Proceeding: Journal of Big Data 2021 7
Tasks: Management

The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people’s lives and restart the economy quickly and safely. People’s social behavior, reflected in their mobility data, plays a major role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreaks in the United States. The daily data are fed to a deep learning model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p = 0.005)) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. A lower correlation was reported for the counties with total cases of <1000 during the test interval. The average mean absolute error (MAE) was 605.4 and decreased with a decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread. It showed that the average daily cases decreased with a decrease in the retiree percentage and increased with an increase in the young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also can help with early and effective management of possible future pandemics. The code used for this study was made publicly available on https://github.com/Murtadha44/covid-19-spread-risk.
Federated Adversarial Debiasing for Fair and Transferable Representations
Authors: Junyuan Hong, Zhuangdi Zhu, Shuyang Yu, Zhangyang Wang, Hiroko Dodge, Jiayu Zhou
Date: 2021-08-14 00:00:00
Proceeding: the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining 2021 8
Tasks: Domain Adaptation, Federated Learning, Unsupervised Domain Adaptation

Federated learning is a distributed learning framework that is communication efficient and provides protection over participating users' raw training data. One outstanding challenge of federate learning comes from the users' heterogeneity, and learning from such data may yield biased and unfair models for minority groups. While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework. In this work, we study these barriers and address them by proposing a novel approach Federated Adversarial DEbiasing (FADE). FADE does not require users' sensitive group information for debiasing and offers users the freedom to opt-out from the adversarial component when privacy or computational costs become a concern. We show that ideally, FADE can attain the same global optimality as the one by the centralized algorithm. We then analyze when its convergence may fail in practice and propose a simple yet effective method to address the problem. Finally, we demonstrate the effectiveness of the proposed framework through extensive empirical studies, including the problem settings of unsupervised domain adaptation and fair learning. Our codes and pretrained models are available at: https://github.com/illidanlab/FADE.
Neural Inertial Localization
Authors: Sachini Herath, David Caruso, Chen Liu, Yufan Chen, Yasutaka Furukawa
Date: 2022-03-29 00:00:00
Proceeding: CVPR 2022 1
Tasks: Indoor Localization, Privacy Preserving

This paper proposes the inertial localization problem, the task of estimating the absolute location from a sequence of inertial sensor measurements. This is an exciting and unexplored area of indoor localization research, where we present a rich dataset with 53 hours of inertial sensor data and the associated ground truth locations. We developed a solution, dubbed neural inertial localization (NILoc) which 1) uses a neural inertial navigation technique to turn inertial sensor history to a sequence of velocity vectors; then 2) employs a transformer-based neural architecture to find the device location from the sequence of velocities. We only use an IMU sensor, which is energy efficient and privacy preserving compared to WiFi, cameras, and other data sources. Our approach is significantly faster and achieves competitive results even compared with state-of-the-art methods that require a floorplan and run 20 to 30 times slower. We share our code, model and data at https://sachini.github.io/niloc.
LinkBERT: Pretraining Language Models with Document Links
Authors: Michihiro Yasunaga, Jure Leskovec, Percy Liang
Date: 2022-03-29 00:00:00
Proceeding: ACL 2022 5
Tasks: Document Classification, Language Modeling, Language Modelling, Masked Language Modeling, Medical Relation Extraction, Named Entity Recognition (NER), PICO, Question Answering, Relation Extraction, Relation Prediction, Semantic Similarity, Sentence Similarity, Text Classification, TriviaQA

Language model (LM) pretraining can learn various knowledge from text corpora, helping downstream tasks. However, existing methods such as BERT model a single document, and do not capture dependencies or knowledge that span across documents. In this work, we propose LinkBERT, an LM pretraining method that leverages links between documents, e.g., hyperlinks. Given a text corpus, we view it as a graph of documents and create LM inputs by placing linked documents in the same context. We then pretrain the LM with two joint self-supervised objectives: masked language modeling and our new proposal, document relation prediction. We show that LinkBERT outperforms BERT on various downstream tasks across two domains: the general domain (pretrained on Wikipedia with hyperlinks) and biomedical domain (pretrained on PubMed with citation links). LinkBERT is especially effective for multi-hop reasoning and few-shot QA (+5% absolute improvement on HotpotQA and TriviaQA), and our biomedical LinkBERT sets new states of the art on various BioNLP tasks (+7% on BioASQ and USMLE). We release our pretrained models, LinkBERT and BioLinkBERT, as well as code and data at https://github.com/michiyasunaga/LinkBERT.