pwc-archive/papers-with-abstracts huggingface dataset:
Testing the effectiveness of unconventional monetary policy in Japan and the United States
Authors: Daisuke Ikeda, Shangshang Li, Sophocles Mavroeidis, Francesco Zanetti
Date: 2020-12-30 00:00:00

Unconventional monetary policy (UMP) may make the effective lower bound (ELB) on the short-term interest rate irrelevant. We develop a theoretical model that underpins our empirical test of this `irrelevance hypothesis' based on the simple idea that under the hypothesis, the short rate can be excluded in any empirical model that accounts for alternative measures of monetary policy. We test the hypothesis for Japan and the United States using a structural vector autoregressive model with the ELB. We firmly reject the hypothesis but find that UMP has had strong delayed effects.
Leveraging latent persistency in United States patent and trademark applications to gain insight into the evolution of an innovation-driven economy
Authors: Iraj Daizadeh
Date: 2021-01-02 00:00:00
Tasks: Time Series Analysis

Objective: An understanding of when one or more external factors may influence the evolution of innovation tracking indices (such as US patent and trademark applications (PTA)) is an important aspect of examining economic progress/regress. Using exploratory statistics, the analysis uses a novel tool to leverage the long-range dependency (LRD) intrinsic to PTA to resolve when such factor(s) may have caused significant disruptions in the evolution of the indices, and thus give insight into substantive economic growth dynamics. Approach: This paper explores the use of the Chronological Hurst Exponent (CHE) to explore the LRD using overlapping time windows to quantify long-memory dynamics in the monthly PTA time-series spanning 1977 to 2016. Results/Discussion: The CHE is found to increase in a clear S-curve pattern, achieving persistence (H~1) from non-persistence (H~0.5). For patents, the inflection occurred over a span of 10 years (1980-1990), while it was much sharper (3 years) for trademarks (1977-1980). Conclusions/Originality/Value: This analysis suggests (in part) that the rapid augmentation in R&D expenditure and the introduction of the various patent directed policy acts (e.g., Bayh-Dole, Stevenson-Wydler) are the key impetuses behind persistency, latent in PTA. The post-1990s exogenic factors seem to be simply maintaining the high degree and consistency of the persistency metric. These findings suggest investigators should consider latent persistency when using these data and the CHE may be an important tool to investigate the impact of substantive exogenous variables on growth dynamics.
Estimates of the social cost of carbon have increased over time
Authors: Richard S. J. Tol
Date: 2021-05-08 00:00:00

A meta-analysis of published estimates shows that the social cost of carbon has increased as knowledge about climate change accumulates. Correcting for inflation and emission year and controlling for the discount rate, kernel density decomposition reveals a non-stationary distribution. In the last 10 years, estimates of the social cost of carbon have increased from $33/tC to $146/tC for a high discount rate and from $446/tC to $1925/tC for a low discount rate. Actual carbon prices are almost everywhere below its estimated value and should therefore go up.
Deeply decarbonizing residential and urban central districts through photovoltaics plus electric vehicle applications
Authors: Takuro Kobashi, Younghun Choi, Yujiro Hirano, Yoshiki Yamagata, Kelvin Say
Date: 2021-05-08 00:00:00

With the costs of renewable energy technologies declining, new forms of urban energy systems are emerging that can be established in a cost-effective way. The SolarEV City concept has been proposed that uses rooftop Photovoltaics (PV) to its maximum extent, combined with Electric Vehicle (EV) with bi-directional charging for energy storage. Urban environments consist of various areas, such as residential and commercial districts, with different energy consumption patterns, building structures, and car parks. The cost effectiveness and decarbonization potentials of PV + EV and PV (+ battery) systems vary across these different urban environments and change over time as cost structures gradually shift. To evaluate these characteristics, we performed techno-economic analyses of PV, battery, and EV technologies for a residential area in Shinchi, Fukushima and the central commercial district of Kyoto, Japan between 2020 and 2040. We found that PV + EV and PV only systems in 2020 are already cost competitive relative to existing energy systems (grid electricity and gasoline car). In particular, the PV + EV system rapidly increases its economic advantage over time, particularly in the residential district which has larger PV capacity and EV battery storage relative to the size of energy demand. Electricity exchanges between neighbors (e.g., peer-to-peer or microgrid) further enhanced the economic value (net present value) and decarbonization potential of PV + EV systems up to 23 percent and 7 percent in 2030, respectively. These outcomes have important strategic implications for urban decarbonization over the coming decades.
Investigating Socio-spatial Differences between Solo Ridehailing and Pooled Rides in Diverse Communities
Authors: Jason Soria, Amanda Stathopoulos
Date: 2021-05-07 00:00:00

Transformative mobility services present both considerable opportunities and challenges for urban mobility systems. Increasing attention is being paid to ridehailing platforms and connections between demand and continuous innovation in service features; one of these features is dynamic ride-pooling. To disentangle how ridehailing impacts existing transportation networks and its ability to support economic vitality and community livability it is essential to consider the distribution of demand across diverse communities. In this paper we expand the literature on ridehailing demand by exploring community variation and spatial dependence in ridehailing use. Specifically, we investigate the diffusion and role of solo requests versus ride-pooling to shed light on how different mobility services, with different environmental and accessibility implications, are used by diverse communities. This paper employs a Social Disadvantage Index, Transit Access Analysis, and a Spatial Durbin Model to investigate the influence of both local and spatial spillover effects on the demand for shared and solo ridehailing. The analysis of 127 million ridehailing rides, of which 15% are pooled, confirms the presence of spatial effects. Results indicate that density and vibrancy variables have analogue effects, both direct and indirect, on demand for solo vs pooled rides. Instead, our analysis reveals significant contrasting effects for socio-economic disadvantage, which is positively correlated with ride-pooling and negatively with solo rides. Additionally, we find that higher rail transit access is associated with higher demand for both solo and pooled ridehailing along with substantial spatial spillovers. We discuss implications for policy, operations and research related to the novel insight on how pooled ridesourcing relate to geography, living conditions, and transit interactions.
Dynamic Choices and Common Learning
Authors: Rahul Deb, Ludovic Renou
Date: 2021-05-08 00:00:00
Tasks: Decision Making

A researcher observes a finite sequence of choices made by multiple agents in a binary-state environment. Agents maximize expected utilities that depend on their chosen alternative and the unknown underlying state. Agents learn about the time-varying state from the same information and their actions change because of the evolving common belief. The researcher does not observe agents' preferences, the prior, the common information and the stochastic process for the state. We characterize the set of choices that are rationalized by this model and generalize the information environments to allow for private information. We discuss the implications of our results for uncovering discrimination and committee decision making.
Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition
Authors: Shoukang Hu, Xurong Xie, Shansong Liu, Jianwei Yu, Zi Ye, Mengzhe Geng, Xunying Liu, Helen Meng
Date: 2020-12-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Domain Adaptation, speech-recognition, Speech Recognition, Variational Inference

Discriminative training techniques define state-of-the-art performance for automatic speech recognition systems. However, they are inherently prone to overfitting, leading to poor generalization performance when using limited training data. In order to address this issue, this paper presents a full Bayesian framework to account for model uncertainty in sequence discriminative training of factored TDNN acoustic models. Several Bayesian learning based TDNN variant systems are proposed to model the uncertainty over weight parameters and choices of hidden activation functions, or the hidden layer outputs. Efficient variational inference approaches using a few as one single parameter sample ensure their computational cost in both training and evaluation time comparable to that of the baseline TDNN systems. Statistically significant word error rate (WER) reductions of 0.4%-1.8% absolute (5%-11% relative) were obtained over a state-of-the-art 900 hour speed perturbed Switchboard corpus trained baseline LF-MMI factored TDNN system using multiple regularization methods including F-smoothing, L2 norm penalty, natural gradient, model averaging and dropout, in addition to i-Vector plus learning hidden unit contribution (LHUC) based speaker adaptation and RNNLM rescoring. Consistent performance improvements were also obtained on a 450 hour HKUST conversational Mandarin telephone speech recognition task. On a third cross domain adaptation task requiring rapidly porting a 1000 hour LibriSpeech data trained system to a small DementiaBank elderly speech corpus, the proposed Bayesian TDNN LF-MMI systems outperformed the baseline system using direct weight fine-tuning by up to 2.5\% absolute WER reduction.
Detection of Clouds in Multiple Wind Velocity Fields using Ground-based Infrared Sky Images
Authors: Guillermo Terrén-Serrano, Manel Martínez-Ramón
Date: 2021-05-07 00:00:00
Tasks: Solar Irradiance Forecasting

Horizontal atmospheric wind shear causes wind velocity fields to have different directions and speeds. In images of clouds acquired using ground-based sky imagers, clouds may be moving in different wind layers. To increase the performance of an intra-hour global solar irradiance forecasting algorithm, it is important to detect multiple layers of clouds. The information provided by a solar forecasting algorithm is necessary to optimize and schedule the solar generation resources and storage devices in a smart grid. This investigation studies the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images. The images are acquired using an innovative infrared sky imager mounted on a solar tracker. Different mixture models are used to infer the distribution of the cloud features. The optimal decision criterion to find the number of clusters in the mixture models is analyzed and compared between different Bayesian metrics and a sequential hidden Markov model. The motion vectors are computed using a weighted implementation of the Lucas-Kanade algorithm. The correlations between the cloud velocity vectors and temperatures are analyzed to find the method that leads to the most accurate results. We have found that the sequential hidden Markov model outperformed the detection accuracy of the Bayesian metrics.
Practical Fingerprinting of RF Devices in the Wild
Authors: Silvija Kokalj-Filipovic, Luke Boegner, Robert D. Miller
Date: 2021-05-10 00:00:00

We present a new RF fingerprinting technique for wireless emitters that is based on a simple, easily and efficiently retrainable Ridge Regression (RR) classifier. The RR learns to identify devices using bursts of waveform samples, conveniently transformed and preprocessed by delay-loop reservoirs. Deep delay Loop Reservoir Computing (DLR) is our processing architecture that supports general machine learning algorithms on resource-constrained devices by leveraging delay-loop reservoir computing (RC) and innovative architectures of loop trees. In prior work, we trained and evaluated DLR using high SNR device emissions in clean channels. We here demonstrate how to use DLR for IoT authentication by performing RF-based Specific Emitter Identification (SEI), even in the presence of fading channels and heavy in-band jamming by leveraging a matched filter (MF) extension, dubbed MF-DLR. We show that the MF processing improves the SEI performance of RR without the RC transformation (MF-RR), but the MF-DLR is more robust and applicable for addressing signatures beyond waveform transients (e.g. turn-on).
Modelling of LIDAR sensor disturbances by solid airborne particles
Authors: Mokrane Hadj-Bachir, P de Souza, P Nordqvist, N Roy
Date: 2021-05-10 00:00:00
Tasks: object-detection, Object Detection, Object Recognition, Sand

This paper aims to introduce a method for simulating with a real time performance the automotive LIDAR disturbance by dust clouds caused by natural phenomena, mechanical or man-made processes like a traveling vehicle. In this study, we are interested to study the interaction of an automotive LIDAR sensor with a dust cloud composed of solid particles. The main objective of this study is to provide a simulation model to industry and research laboratories that help to study LIDAR performance in a dust-sand environment with the capability to reproduce the encountered problems in degraded conditions and the ability to parameterize the degradation model. Based on industrial projects with a passenger's vehicles and truck manufacturers, we present LIDAR sensor and functionalities to perceive objects in a scene (pedestrian, car, truck, ...) in clear or extreme weather conditions. Simulated and experimental data are compared and analyzed in this article. The features presented are evaluated according to their quality for object detection. This study can be applied to sensors post-processing algorithms (object recognition, tracking, data fusion...) and even to the design of cleaning systems.
Multi-Spectrally Constrained Transceiver Design against Signal-Dependent Interference
Authors: Jing Yang, Augusto Aubry, Antonio De Maio, Xianxiang Yu, Guolong Cui
Date: 2021-05-10 00:00:00

This paper focuses on the joint synthesis of constant envelope transmit signal and receive filter aimed at optimizing radar performance in signal-dependent interference and spectrally contested-congested environments. To ensure the desired Quality of Service (QoS) at each communication system, a precise control of the interference energy injected by the radar in each licensed/shared bandwidth is imposed. Besides, along with an upper bound to the maximum transmitted energy, constant envelope (with either arbitrary or discrete phases) and similarity constraints are forced to ensure compatibility with amplifiers operating in saturation regime and bestow relevant waveform features, respectively. To handle the resulting NP-hard design problems, new iterative procedures (with ensured convergence properties) are devised to account for continuous and discrete phase constraints, capitalizing on the Coordinate Descent (CD) framework. Two heuristic procedures are also proposed to perform valuable initializations. Numerical results are provided to assess the effectiveness of the conceived algorithms in comparison with the existing methods.
IRS-Assisted Active Device Detection
Authors: Friedemann Laue, Vahid Jamali, Robert Schober
Date: 2021-05-10 00:00:00

This paper studies intelligent reflecting surface (IRS) assisted active device detection. Since the locations of the devices are a priori unknown, optimal IRS beam alignment is not possible and a worst-case design for a given coverage area is developed. To this end, we propose a generalized likelihood ratio test (GLRT) detection scheme and an IRS phase-shift design that minimizes the worst-case probability of misdetection. In addition to the proposed optimization-based phase-shift design, we consider two alternative suboptimal designs based on closed-form expressions for the IRS phase shifts. Our performance analysis establishes the superiority of the optimization-based design, especially for large coverage areas. Furthermore, we investigate the impact of scatterers on the proposed line-of-sight based design using simulations.
ChainNet: Neural Network-Based Successive Spectral Analysis
Authors: Andreas Barthelme, Wolfgang Utschick
Date: 2021-05-08 00:00:00
Tasks: Classification, Direction of Arrival Estimation, General Classification, Position

We discuss a new neural network-based direction of arrival estimation scheme that tackles the estimation task as a multidimensional classification problem. The proposed estimator uses a classification chain with as many stages as the number of sources. Each stage is a multiclass classification network that estimates the position of one of the sources. This approach can be interpreted as the approximation of a successive evaluation of the maximum a posteriori estimator. By means of simulations for fully sampled antenna arrays and systems with subarray sampling, we show that it is able to outperform existing estimation techniques in terms of accuracy, while maintaining a very low computational complexity.
Structured Covariance Matrix Estimation with Missing-Data for Radar Applications via Expectation-Maximization
Authors: Augusto Aubry, Antonio De Maio, Stefano Marano, Massimo Rosamilia
Date: 2021-05-08 00:00:00

Structured covariance matrix estimation in the presence of missing data is addressed in this paper with emphasis on radar signal processing applications. After a motivation of the study, the array model is specified and the problem of computing the maximum likelihood estimate of a structured covariance matrix is formulated. A general procedure to optimize the observed-data likelihood function is developed resorting to the expectation-maximization algorithm. The corresponding convergence properties are thoroughly established and the rate of convergence is analyzed. The estimation technique is contextualized for two practically relevant radar problems: beamforming and detection of the number of sources. In the former case an adaptive beamformer leveraging the EM-based estimator is presented; in the latter, detection techniques generalizing the classic Akaike information criterion, minimum description length, and Hannan-Quinn information criterion, are introduced. Numerical results are finally presented to corroborate the theoretical study.
Dynamics of ramping bursts in a respiratory neuron model
Authors: Muhammad U. Abdulla, Ryan S. Phillips, Jonathan E. Rubin
Date: 2021-05-08 00:00:00

Intensive computational and theoretical work has led to the development of mutliple mathematical models for bursting in respiratory neurons in the pre-B\"otzinger Complex (pre-B\"otC) of the mammalian brainstem. Nonetheless, these previous models have not captured the pre-inspiratory ramping aspects of these neurons' activity patterns, in which relatively slow tonic spiking gradually progresses to faster spiking and a full blown burst, with a corresponding gradual development of an underlying plateau potential. In this work, we show that the incorporation of the dynamics of the extracellular potassium ion concentration into an existing model for pre-B\"otC neuron bursting, along with some parameter updates, suffices to induce this ramping behavior. Using fast-slow decomposition, we show that this activity can be considered as a form of parabolic bursting, but with burst termination at a homoclinic bifurcation rather than as a SNIC bifurcation. We also investigate the parameter-dependence of these solutions and show that the proposed model yields a greater dynamic range of burst frequencies, durations, and duty cycles than those produced by other models in the literature.
Computation of COVID-19 epidemiological data in Hungary using dynamic model inversion
Authors: Balazs Csutak, Peter Polcz, Gabor Szederkenyi
Date: 2021-05-07 00:00:00

In this paper, we estimate epidemiological data of the COVID-19 pandemic in Hungary using only the daily number of hospitalized patients, and applying well-known techniques from systems and control theory. We use a previously published and validated compartmental model for the description of epidemic spread. Exploiting the fact that an important subsystem of the model is linear, first we compute the number of latent infected persons in time. Then an estimate can be given for the number of people in other compartments. From these data, it is possible to track the time dependent reproduction numbers via a recursive least squares estimate. The credibility of the obtained results is discussed using available data from the literature.
Single Image Super-Resolution using Residual Channel Attention Network
Authors: Hritam Basak, Rohit Kundu, Anish Agarwal, Shreya Giri
Date: 2021-02-08 00:00:00
Tasks: Face Recognition, Image Super-Resolution, Super-Resolution

Single Image Super-resolution refers to the method of converting one low-resolution image to its high-resolution counterpart which is a very challenging task since a low-resolution image can yield several possible high-resolution images. Super-resolution has applications in several paradigms like biomedical engineering, face recognition, satellite imaging among others. Several techniques exist in literature with recent research focusing on using deep convolutional neural networks along with residual learning techniques for enhancing performance. In this paper, we propose a deep learning-based approach for the problem, wherein we use a fully convolutional attention network coupled with residual in the residual block (RIR), Residual Channel Attention Block (RCAB), and long and short skip connections. The RIR block allows us to bypass the redundant low-frequency features and focuses on the important high-frequency information. We have used the DIV2k dataset for training our network and then tested our model on five publicly available datasets for validating our results: Set5, Set14, B100, Manga109, and Urban 100. The proposed methodology achieves commendable results compared to other existing deep learning-based methodologies in this domain.
maplet: An extensible R toolbox for modular and reproducible omics pipelines
Authors: Kelsey Chetnik, Elisa Benedetti, Daniel P. Gomari, Annalise Schweickart, Richa Batra, Mustafa Buyukozkan, Zeyu Wang, Matthias Arnold, Jonas Zierer, Karsten Suhre, Jan Krumsiek
Date: 2021-05-06 00:00:00
Tasks: Data Visualization

This paper presents maplet, an open-source R package for the creation of highly customizable, fully reproducible statistical pipelines for omics data analysis, with a special focus on metabolomics-based methods. It builds on the SummarizedExperiment data structure to create a centralized pipeline framework for storing data, analysis steps, results, and visualizations. maplet's key design feature is its modularity, which offers several advantages, such as ensuring code quality through the individual maintenance of functions and promoting collaborative development by removing technical barriers to code contribution. With over 90 functions, the package includes a wide range of functionalities, covering many widely used statistical approaches and data visualization techniques.