pwc-archive/papers-with-abstracts huggingface dataset:
Improving Dual-Microphone Speech Enhancement by Learning Cross-Channel Features with Multi-Head Attention
Authors: Xinmeng Xu, Rongzhi Gu, Yuexian Zou
Date: 2022-05-03 00:00:00
Tasks: Decoder, Multi-Task Learning, Speech Enhancement

Hand-crafted spatial features, such as inter-channel intensity difference (IID) and inter-channel phase difference (IPD), play a fundamental role in recent deep learning based dual-microphone speech enhancement (DMSE) systems. However, learning the mutual relationship between artificially designed spatial and spectral features is hard in the end-to-end DMSE. In this work, a novel architecture for DMSE using a multi-head cross-attention based convolutional recurrent network (MHCA-CRN) is presented. The proposed MHCA-CRN model includes a channel-wise encoding structure for preserving intra-channel features and a multi-head cross-attention mechanism for fully exploiting cross-channel features. In addition, the proposed approach specifically formulates the decoder with an extra SNR estimator to estimate frame-level SNR under a multi-task learning framework, which is expected to avoid speech distortion led by end-to-end DMSE module. Finally, a spectral gain function is adopted to further suppress the unnatural residual noise. Experiment results demonstrated superior performance of the proposed model against several state-of-the-art models.
Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous Spread of Infection
Authors: Nirmal Kumar Sivaraman, Manas Gaur, Shivansh Baijal, Sakthi Balan Muthiah, Amit Sheth
Date: 2022-05-03 00:00:00

Epidemics like Covid-19 and Ebola have impacted people's lives significantly. The impact of mobility of people across the countries or states in the spread of epidemics has been significant. The spread of disease due to factors local to the population under consideration is termed the endogenous spread. The spread due to external factors like migration, mobility, etc. is called the exogenous spread. In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model. The novelty in our model is that it captures both the exogenous and endogenous spread of the virus. First, we present an analytical study. Second, we simulate the Exo-SIR model with and without assuming contact network for the population. Third, we implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. We found that endogenous infection is influenced by exogenous infection. Furthermore, we found that the Exo-SIR model predicts the peak time better than the SIR model. Hence, the Exo-SIR model would be helpful for governments to plan policy interventions at the time of a pandemic.
Optimal Thermal Management, Charging, and Eco-driving of Battery Electric Vehicles
Authors: Ahad Hamednia, Nikolce Murgovski, Jonas Fredriksson, Jimmy Forsman, Mitra Pourabdollah, Viktor Larsson
Date: 2022-05-03 00:00:00
Tasks: Computational Efficiency, Management

This paper addresses optimal battery thermal management (BTM), charging, and eco-driving of a battery electric vehicle (BEV) with the goal of improving its grid-to-meter energy efficiency. Thus, an optimisation problem is formulated, aiming at finding the optimal trade-off between trip time and charging cost. The formulated problem is then transformed into a hybrid dynamical system, where the dynamics in driving and charging modes are modeled with different functions and with different state and control vectors. Moreover, to improve computational efficiency, we propose modelling the driving dynamics in a spatial domain, where decisions are made along the traveled distance. Charging dynamics are modeled in a temporal domain, where decisions are made along a normalized charging time. The actual charging time is modeled as a scalar variable that is optimized simultaneously with the optimal state and control trajectories, for both charging and driving modes. The performance of the proposed algorithm is assessed over a road with a hilly terrain, where two charging possibilities are considered along the driving route. According to the results, trip time including driving and charging times, is reduced by 44 %, compared to a case without battery active heating/cooling.
A Unified Framework for Verification of Observational Properties for Partially-Observed Discrete-Event Systems
Authors: Jianing Zhao, Xiang Yin, ShaoYuan Li
Date: 2022-05-03 00:00:00

In this paper, we investigate property verification problems in partially-observed discrete-event systems (DES). Particularly, we are interested in verifying observational properties that are related to the information-flow of the system. Observational properties considered here include diagnosability, predictability, detectability and opacity, which have drawn considerable attentions in the literature. However, in contrast to existing results, where different verification procedures are developed for different properties case-by-case, in this work, we provide a unified framework for verifying all these properties by reducing each of them as an instance of HyperLTL model checking. Our approach is based on the construction of a Kripke structure that effectively captures the issue of unobservability as well as the finite string semantics in partially-observed DES so that HyperLTL model checking techniques can be suitably applied. Then for each observational property considered, we explicitly provide the HyperLTL formula to be checked over the Kripke structure for the purpose of verification. Our approach is uniform in the sense that all different properties can be verified with the same model checking engine. Furthermore, our unified framework also brings new insights for classifying observational properties for partially-observed DES in terms of their verification complexity.
A Mapping Approach to Convert MTPs into a Capability and Skill Ontology
Authors: Aljosha Köcher, Lasse Beers, Alexander Fay
Date: 2022-05-03 00:00:00

Being able to quickly integrate new equipment and functions into an existing plant is a major goal for both discrete and process manufacturing. But currently, these two industry domains use different approaches to achieve this goal. While the Module Type Package (MTP) is getting more and more adapted in practical applications of process manufacturing, so-called skill-based manufacturing approaches are favored in the context of discrete manufacturing. The two approaches are incompatible because their models feature different contents and they use different technologies. This contribution provides a comparison of the MTP with a skill-based approach as well as an automated mapping that can be used to transfer the contents of an MTP into a skill ontology. Through this mapping, an MTP can be semantically lifted in order to apply functions like querying or reasoning. Furthermore, machines that were previously described using two incompatible models can now be used in one production process.
A comparison study: the impact of age and gender distribution on age estimation
Authors: Chang Kong, Qiuming Luo, Guoliang Chen
Date: 2022-01-10 00:00:00
Proceeding: MMAsia 2022 1
Tasks: Age Estimation

Age estimation from a single facial image is a challenging and attractive research area in the computer vision community. Several facial datasets annotated with age and gender attributes became available in the literature. However, one major drawback is that these datasets do not consider the label distribution during data collection. Therefore, the models training on these datasets inevitably have bias for the age having least number of images. In this work, we analyze the age and gender distribution of previous datasets and publish an Uniform Age and Gender Dataset (UAGD) which has almost equal number of female and male images in each age. In addition, we investigate the impact of age and gender distribution on age estimation by comparing DEX CNN model trained on several different datasets. Our experiments show that UAGD dataset has good performance for age estimation task and also it is suitable for being an evaluation benchmark.
Fast algorithms for nonlinear and constrained phase retrieval in near-field X-ray holography based on Tikhonov regularization
Authors: Simon Huhn, Leon Merten Lohse, Jens Lucht, Tim Salditt
Date: 2022-05-02 00:00:00
Tasks: Retrieval

Based on phase retrieval, lensless coherent imaging and in particular holography offers quantitative phase and amplitude images. This is of particular importance for spectral ranges where suitable lenses are challenging, such as for hard x-rays. Here, we propose a phase retrieval approach for inline x-ray holography based on Tikhonov regularization applied to the full nonlinear forward model of image formation. The approach can be seen as a nonlinear generalization of the well-established contrast-transfer-function (CTF) reconstruction method. While similar methods have been proposed before, the current work achieves nonlinear, constrained phase retrieval at competitive computation times. We thus enable high-throughput imaging of optically strong objects beyond the scope of CTF. Using different examples of inline holograms obtained from illumination by a x-ray waveguide-source, we demonstrate superior image quality even for samples which do not obey the assumption of a weakly varying phase. Since the presented approach does not rely on linearization, we expect it to be well suited also for other probes such as visible light or electrons, which often exhibit strong phase interaction.
Tunable Quantum Neural Networks in the QPAC-Learning Framework
Authors: Viet Pham Ngoc, David Tuckey, Herbert Wiklicky
Date: 2022-05-03 00:00:00

In this paper, we investigate the performances of tunable quantum neural networks in the Quantum Probably Approximately Correct (QPAC) learning framework. Tunable neural networks are quantum circuits made of multi-controlled X gates. By tuning the set of controls these circuits are able to approximate any Boolean functions. This architecture is particularly suited to be used in the QPAC-learning framework as it can handle the superposition produced by the oracle. In order to tune the network so that it can approximate a target concept, we have devised and implemented an algorithm based on amplitude amplification. The numerical results show that this approach can efficiently learn concepts from a simple class.
Efficient Score Computation and Expectation-Maximization Algorithm in Regime-Switching Models
Authors: Chaojun Li, Shi Qiu
Date: 2022-05-03 00:00:00

This study proposes an efficient algorithm for score computation for regime-switching models, and derived from which, an efficient expectation-maximization (EM) algorithm. Different from existing algorithms, this algorithm does not rely on the forward-backward filtering for smoothed regime probabilities, and only involves forward computation. Moreover, the algorithm to compute score is readily extended to compute the Hessian matrix.
Heterogeneous Treatment Effects for Networks, Panels, and other Outcome Matrices
Authors: Eric Auerbach, Yong Cai
Date: 2022-05-02 00:00:00
Tasks: Experimental Design

We are interested in the distribution of treatment effects for an experiment where units are randomized to a treatment but outcomes are measured for pairs of units. For example, we might measure risk sharing links between households enrolled in a microfinance program, employment relationships between workers and firms exposed to a trade shock, or bids from bidders to items assigned to an auction format. Such a double randomized experimental design may be appropriate when there are social interactions, market externalities, or other spillovers across units assigned to the same treatment. Or it may describe a natural or quasi experiment given to the researcher. In this paper, we propose a new empirical strategy that compares the eigenvalues of the outcome matrices associated with each treatment. Our proposal is based on a new matrix analog of the Fr\'echet-Hoeffding bounds that play a key role in the standard theory. We first use this result to bound the distribution of treatment effects. We then propose a new matrix analog of quantile treatment effects that is given by a difference in the eigenvalues. We call this analog spectral treatment effects.
Potential gains of long-distance trade in electricity
Authors: Javier López Prol, Karl W. Steininger, Keith Williges, Wolf D. Grossmann, Iris Grossmann
Date: 2022-05-03 00:00:00

Electrification of all economic sectors and solar photovoltaics (PV) becoming the lowest-cost electricity generation technology in ever more regions give rise to new potential gains of trade. We develop a stylized analytical model to minimize unit energy cost in autarky, open it to different trade configurations, and evaluate it empirically. We identify large potential gains from interhemispheric and global electricity trade by combining complementary seasonal and diurnal cycles. The corresponding high willingness to pay for large-scale transmission suggests far-reaching political economy and regulatory implications.
Incorporating Financial Hardship in Measuring the Mental Health Impact of Housing Stress
Authors: Timothy Ludlow, Jonas Fooken, Christiern Rose, Kam Tang
Date: 2022-05-03 00:00:00

Housing expenditure tends to be sticky and costly to adjust, and makes up a large proportion of household expenditure. Additionally, the loss of housing can have catastrophic consequences. These specific features of housing expenditure imply that housing stress could cause negative mental health impacts. This research investigates the effects of housing stress on mental health, contributing to the literature by nesting housing stress within a measure of financial hardship, thus improving robustness to omitted variables and creating a natural comparison group for matching. Fixed effects (FE) regressions and a difference-in-differences (DID) methodology are estimated utilising data from the Household Income and Labour Dynamics in Australia (HILDA) Survey. The results show that renters who are in housing stress have a significant decline in self-reported mental health, with those in prior financial hardship being more severely affected. In contrast, there is little to no evidence of housing stress impacting on owners with a mortgage. The results also suggest that the mental health impact of housing stress is more important than some, but not all, aspects of financial hardship.
Estimating beneficiaries of the child tax credit: past, present, and future
Authors: Ashley Nunes, Chung Yi See, Lucas Woodley, Nicole A. Divers, Audrey L. Cui
Date: 2022-05-02 00:00:00

Government efforts to address child poverty commonly encompass economic assistance programs that bolster household income. The Child Tax Credit (CTC) is the most prominent example of this. Introduced by the United States Congress in 1997, the program endeavors to help working parents via income stabilization. Our work examines the extent to which the CTC has done so. Our study, which documents clear, consistent, and compelling evidence of gender inequity in benefits realization, yields four key findings. First, stringent requisite income thresholds disproportionally disadvantage single mothers, a reflection of the high concentration of this demographic in lower segments of the income distribution. Second, married parents and, to a lesser extent, single fathers, are the primary beneficiaries of the CTC program when benefits are structured as credits rather than refunds. Third, making program benefits more generous disproportionally reduces how many single mothers, relative to married parents and single fathers, can claim this benefit. Fourth and finally, increasing credit refundability can mitigate gender differences in relief eligibility, although doing so imposes externalities of its own. Our findings can inform public policy discourse surrounding the efficacy of programs like the CTC and the effectiveness of programs aimed at alleviating child poverty.
Integrating Structural and Reduced-Form Methods in Empirical Finance
Authors: Toni M. Whited
Date: 2022-05-02 00:00:00
Tasks: Form

I discuss various ways in which inference based on the estimation of the parameters of statistical models (reduced-form estimation) can be combined with inference based on the estimation of the parameters of economic models (structural estimation). I discuss five basic categories of integration: directly combining the two methods, using statistical models to simplify structural estimation, using structural estimation to extend the validity of reduced-form results, using reduced-form techniques to assess the external validity of structural estimations, and using structural estimation as a sample selection remedy. I illustrate each of these methods with examples from corporate finance, banking, and personal finance.
A Model of Financial Market Control
Authors: Yoshihiro Ohashi
Date: 2022-05-03 00:00:00
Tasks: model

This study investigates the prevention of market manipulation using a price-impact model of financial market trading as a linear system. First, I define a trading game between speculators such that they implement a manipulation trading strategy that exploits momentum traders. Second, I identify market intervention by a controller (e.g., a central bank) with a control of the system. The main result shows that there is a control strategy that prevents market manipulation as a subgame perfect equilibrium outcome of the trading game. On the equilibrium path, no intervention is realized. This study also characterizes the set of manipulation-proof linear pricing rules of the system. The set is very restrictive if there is no control, while the presence of control drastically expands the set.
Analysis of the Visually Detectable Wear Progress on Ball Screws
Authors: Tobias Schlagenhauf, Tim Scheurenbrand, Dennis Hofmann, Oleg Krasnikow
Date: 2022-05-02 00:00:00

The actual progression of pitting on ball screw drive spindles is not well known since previous studies have only relied on the investigation of indirect wear effects (e. g. temperature, motor current, structure-borne noise). Using images from a camera system for ball screw drives, this paper elaborates on the visual analysis of pitting itself. Due to its direct, condition-based assessment of the wear state, an image-based approach offers several advantages, such as: Good interpretability, low influence of environmental conditions, and high spatial resolution. The study presented in this paper is based on a dataset containing the entire wear progression from original condition to component failure of ten ball screw drive spindles. The dataset is being analyzed regarding the following parameters: Axial length, tangential length, and surface area of each pit, the total number of pits, and the time of initial visual appearance of each pit. The results provide evidence that wear development can be quantified based on visual wear characteristics. In addition, using the dedicated camera system, the actual course of the growth curve of individual pits can be captured during machine operation. Using the findings of the analysis, the authors propose a formula for standards-based wear quantification based on geometric wear characteristics.
GITz: Graphene-assisted IRS Design for THz Communication
Authors: Bhupendra Sharma, Anirudh Agarwal, Deepak Mishra, Soumitra Debnath
Date: 2022-05-03 00:00:00

Graphene-based intelligent reflecting surface (GIRS) has been proved to provide a promising propagation environment to enhance the quality of high frequency terahertz (THz) wireless communication. In this paper, we characterize GIRS for THz communication (GITz) using material specific parameters of graphene to tune the reflection of the incident wave at IRS. In particular, we propose a GITz design model considering the incident signal frequency material level parameters like conductivity, Fermi-level, patch width to control the reflection amplitude (RA) at the communication receiver. We have obtained the closed-form expression of RA for an accurate design and characterization of GIRS, which is incomplete in the existing research due to the inclusion of only phase-shift. The numerical simulation results demonstrate the effectiveness of the proposed characterization by providing key insights.
Sea Ice Concentration Estimation Techniques Using Machine Learning: An End-To-End Workflow for Estimating Concentration Maps from SAR Images
Authors: Stefan Dominicus, Amit Kumar Mishra
Date: 2022-05-03 00:00:00

Sea ice concentration is an important metric used to characterize polar sea ice behavior. Understanding this behavior and accurately representing it is of critical importance for climate science research, and also has important uses in the context of maritime navigation. An end-to-end workflow for generating learned concentration estimation models from synthetic aperture radar data, trained on existing passive microwave data, is presented here. A novel objective function was introduced to account for uncertainty in the passive microwave measurements, which can be extended to account for arbitrary sources of error in the training data, and a recent set of in-situ observations was used to evaluate the reliability of the chosen passive microwave concentration estimation model. Google Colaboratory was used as the development platform, and all notebooks, training data, and trained models are available on GitHub. This chapter is an overview of the most interesting aspects of this investigation, and a detailed report is also available on GitHub.
GeoTyper: Automated Pipeline from Raw scRNA-Seq Data to Cell Type Identification
Authors: Cecily Wolfe, Yayi Feng, David Chen, Edwin Purcell, Anne Talkington, Sepideh Dolatshahi, Heman Shakeri
Date: 2022-05-02 00:00:00

The cellular composition of the tumor microenvironment can directly impact cancer progression and the efficacy of therapeutics. Understanding immune cell activity, the body's natural defense mechanism, in the vicinity of cancerous cells is essential for developing beneficial treatments. Single cell RNA sequencing (scRNA-seq) enables the examination of gene expression on an individual cell basis, providing crucial information regarding both the disturbances in cell functioning caused by cancer and cell-cell communication in the tumor microenvironment. This novel technique generates large amounts of data, which require proper processing. Various tools exist to facilitate this processing but need to be organized to standardize the workflow from data wrangling to visualization, cell type identification, and analysis of changes in cellular activity, both from the standpoint of malignant cells and immune stromal cells that eliminate them. We aimed to develop a standardized pipeline (GeoTyper, https://github.com/celineyayifeng/GeoTyper) that integrates multiple scRNA-seq tools for processing raw sequence data extracted from NCBI GEO, visualization of results, statistical analysis, and cell type identification. This pipeline leverages existing tools, such as Cellranger from 10X Genomics, Alevin, and Seurat, to cluster cells and identify cell types based on gene expression profiles. We successfully tested and validated the pipeline on several publicly available scRNA-seq datasets, resulting in clusters corresponding to distinct cell types. By determining the cell types and their respective frequencies in the tumor microenvironment across multiple cancers, this workflow will help quantify changes in gene expression related to cell-cell communication and identify possible therapeutic targets.