pwc-archive/papers-with-abstracts huggingface dataset:
The Structure and Incentives of a COVID related Emergency Wage Subsidy
Authors: Jules Linden, Cathal O'Donoghue, Denisa M. Sologon
Date: 2021-08-09 00:00:00

During recent crisis, wage subsidies played a major role in sheltering firms and households from economic shocks. During COVID-19, most workers were affected and many liberal welfare states introduced new temporary wage subsidies to protected workers' earnings and employment (OECD, 2021). New wage subsidies marked a departure from the structure of traditional income support payments and required reform. This paper uses simulated datasets to assess the structure and incentives of the Irish COVID-19 wage subsidy scheme (CWS) under five designs. We use a nowcasting approach to update 2017 microdata, producing a near real time picture of the labour market at the peak of the crisis. Using microsimulation modelling, we assess the impact of different designs on income replacement, work incentives and income inequality. Our findings suggest that pro rata designs support middle earners more and flat rate designs support low earners more. We find evidence for strong work disincentives under all designs, though flat rate designs perform better. Disincentives are primarily driven by generous unemployment payments and work related costs. The impact of design on income inequality depends on the generosity of payments. Earnings related pro rata designs were associated to higher market earnings inequality. The difference in inequality levels falls once benefits, taxes and work related costs are considered. In our discussion, we turn to transaction costs, the rationale for reform and reintegration of CWS. We find some support for the claim that design changes were motivated by political considerations. We suggest that establishing permanent wage subsidies based on sectorial turnover rules could offer enhanced protection to middle-and high-earners and reduce uncertainty, the need for reform, and the risk of politically motivated designs.
Agricultural Growth Diagnostics: Identifying the Binding Constraints and Policy Remedies for Bihar, India
Authors: Elumalai Kannan, Sanjib Pohit
Date: 2021-08-09 00:00:00
Tasks: Marketing

Agriculture plays a significant role in economic development of the underdeveloped region. Multiple factors influence the performance of agricultural sector but a few of these have a strong bearing on its growth. We develop a growth diagnostics framework for agricultural sector in Bihar located in eastern India to identify the most binding constraints. Our results show that poor functioning of agricultural markets and low level of crop diversification are the important reasons for lower agricultural growth in Bihar. Rise in the level of instability in the prices of agricultural produces indicates a weak price transmission across the markets even after repealing the agricultural produce market committee act. Poor market linkages and non-functioning producer collectives at village level affect the farmers motivation for undertaking crop diversification. Our policy suggestions include state provision of basic market infrastructure to attract private investment in agricultural marketing, strengthening the farmer producer organisations, and a comprehensive policy on crop diversification.
Visualizing Income Distribution in the United States
Authors: Sang Truong, Humberto Barreto
Date: 2021-08-08 00:00:00

The distribution of household income is a central concern of modern economic policy due to its strong influence on life quality. Yet, non-expert audiences are unaware of the relationship between these two factors. To effectively communicate the effect of income inequality on the quality of life and among the strata, we have designed a novel technique for visualizing income distribution and inequality over time by using the U.S. household income microdata from the Current Population Survey. The result is a striking dynamic animation of income distribution over time, drawing public attention and further investigating economic inequality. Detailed implementation of this project is available at https://github.com/sangttruong/incomevis.
Knowledge for a warmer world: a patent analysis of climate change adaptation technologies
Authors: Kerstin Hötte, Su Jung Jee
Date: 2021-08-08 00:00:00

Technologies can help strengthen the resilience of our economy against existential climate-risks. We investigate climate change adaptation technologies (CCATs) in US patents to understand (1) historical patterns and drivers of innovation; (2) scientific and technological requirements to develop and use CCATs; and (3) CCATs' potential technological synergies with mitigation. First, in contrast to mitigation, innovation in CCATs only slowly takes off, indicating a relatively low awareness of investors for solutions to cope with climate risks. Historical trends in environmental regulation, energy prices, and public support can be associated with patenting in CCATs. Second, CCATs form two main clusters: science-intensive ones in agriculture, health, and monitoring technologies; and engineering-intensive ones in coastal, water, and infrastructure technologies. Analyses of technology-specific scientific and technological knowledge bases inform directions for how to facilitate advancement, transfer and use of CCATs. Lastly, CCATs show strong technological complementarities with mitigation as more than 25% of CCATs bear mitigation benefits. While not judging about the complementarity of mitigation and adaptation in general, our results suggest how policymakers can harness these technological synergies to achieve both goals simultaneously.
Grade Inflation and Stunted Effort in a Curved Economics Course
Authors: Alex Garivaltis
Date: 2021-08-08 00:00:00
Tasks: All

To protect his teaching evaluations, an economics professor uses the following exam curve: if the class average falls below a known target, $m$, then all students will receive an equal number of free points so as to bring the mean up to $m$. If the average is above $m$ then there is no curve; curved grades above $100\%$ will never be truncated to $100\%$ in the gradebook. The $n$ students in the course all have Cobb-Douglas preferences over the grade-leisure plane; effort corresponds exactly to earned (uncurved) grades in a $1:1$ fashion. The elasticity of each student's utility with respect to his grade is his ability parameter, or relative preference for a high score. I find, classify, and give complete formulas for all the pure Nash equilibria of my own game, which my students have been playing for some eight semesters. The game is supermodular, featuring strategic complementarities, negative spillovers, and nonsmooth payoffs that generate non-convexities in the reaction correspondence. The $n+2$ types of equilibria are totally ordered with respect to effort and Pareto preference, and the lowest $n+1$ of these types are totally ordered in grade-leisure space. In addition to the no-curve ("try-hard") and curved interior equilibria, we have the "$k$-don't care" equilibria, whereby the $k$ lowest-ability students are no-shows. As the class size becomes infinite in the curved interior equilibrium, all students increase their leisure time by a fixed percentage, i.e., $14\%$, in response to the disincentive, which amplifies any pre-existing ability differences. All students' grades inflate by this same (endogenous) factor, say, $1.14$ times what they would have been under the correct standard.
A characterization of lexicographic preferences
Authors: Mridu Prabal Goswami, Manipushpak Mitra, Debapriya Sen
Date: 2021-08-06 00:00:00
Tasks: Attribute

This paper characterizes lexicographic preferences over alternatives that are identified by a finite number of attributes. Our characterization is based on two key concepts: a weaker notion of continuity called 'mild continuity' (strict preference order between any two alternatives that are different with respect to every attribute is preserved around their small neighborhoods) and an 'unhappy set' (any alternative outside such a set is preferred to all alternatives inside). Three key aspects of our characterization are: (i) use of continuity arguments, (ii) the stepwise approach of looking at two attributes at a time and (iii) in contrast with the previous literature, we do not impose noncompensation on the preference and consider an alternative weaker condition.
Empirical Welfare Economics
Authors: Christopher P Chambers, Federico Echenique
Date: 2021-08-06 00:00:00
Tasks: counterfactual

Welfare economics relies on access to agents' utility functions: we revisit classical questions in welfare economics, assuming access to data on agents' past choices instead of their utilities. Our main result considers the existence of utilities that render a given allocation Pareto optimal. We show that a candidate allocation is efficient for some utilities consistent with the choice data if and only if it is efficient for an incomplete relation derived from the revealed preference relations and convexity. Similar ideas are used to make counterfactual choices for a single consumer, policy comparisons by the Kaldor criterion, and offer bounds on the degree of inefficiency in a Pareto suboptimal allocation.
End-to-End Speech Recognition With Joint Dereverberation Of Sub-Band Autoregressive Envelopes
Authors: Rohit Kumar, Anurenjan Purushothaman, Anirudh Sreeram, Sriram Ganapathy
Date: 2021-08-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The end-to-end (E2E) automatic speech recognition (ASR) systems are often required to operate in reverberant conditions, where the long-term sub-band envelopes of the speech are temporally smeared. In this paper, we develop a feature enhancement approach using a neural model operating on sub-band temporal envelopes. The temporal envelopes are modeled using the framework of frequency domain linear prediction (FDLP). The neural enhancement model proposed in this paper performs an envelope gain based enhancement of temporal envelopes. The model architecture consists of a combination of convolutional and long short term memory (LSTM) neural network layers. Further, the envelope dereverberation, feature extraction and acoustic modeling using transformer based E2E ASR can all be jointly optimized for the speech recognition task. The joint optimization ensures that the dereverberation model targets the ASR cost function. We perform E2E speech recognition experiments on the REVERB challenge dataset as well as on the VOiCES dataset. In these experiments, the proposed joint modeling approach yields significant improvements compared to the baseline E2E ASR system (average relative improvements of 21% on the REVERB challenge dataset and about 10% on the VOiCES dataset).
Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker
Authors: Maokui He, Desh Raj, Zili Huang, Jun Du, Zhuo Chen, Shinji Watanabe
Date: 2021-08-07 00:00:00
Tasks: Action Detection, Activity Detection, Region Proposal, speaker-diarization, Speaker Diarization

Target-speaker voice activity detection (TS-VAD) has recently shown promising results for speaker diarization on highly overlapped speech. However, the original model requires a fixed (and known) number of speakers, which limits its application to real conversations. In this paper, we extend TS-VAD to speaker diarization with unknown numbers of speakers. This is achieved by two steps: first, an initial diarization system is applied for speaker number estimation, followed by TS-VAD network output masking according to this estimate. We further investigate different diarization methods, including clustering-based and region proposal networks, for estimating the initial i-vectors. Since these systems have complementary strengths, we propose a fusion-based method to combine frame-level decisions from the systems for an improved initialization. We demonstrate through experiments on variants of the LibriCSS meeting corpus that our proposed approach can improve the DER by up to 50\% relative across varying numbers of speakers. This improvement also results in better downstream ASR performance approaching that using oracle segments.
Reproducibility and Evolution of Diffusion MRI Measurements within the Cervical Spinal Cord in Multiple Sclerosis
Authors: Haykel Snoussi, Emmanuel Caruyer, Benoit Combes, Olivier Commowick, Elise Bannier, Anne Kerbrat, Julien Cohen-Adad, Christian Barillot
Date: 2021-08-08 00:00:00
Tasks: Diffusion  MRI

In Multiple Sclerosis (MS), there is a large discrepancy between the clinical observations and how the pathology is exhibited on brain images, this is known as the clinical-radiological paradox (CRP). One of the hypotheses is that the clinical deficit may be more related to the spinal cord damage than the number or location of lesions in the brain. Therefore, investigating how the spinal cord is damaged becomes an acute challenge to better understand and overcome the CRP. Diffusion MRI is known to provide quantitative figures of neuronal degeneration and axonal loss, in the brain as well as in the spinal cord. In this paper, we propose to investigate how diffusion MRI metrics vary in the different cervical regions with the progression of the disease. We first study the reproducibility of diffusion MRI on healthy volunteers with a test-retest procedure using both standard diffusion tensor imaging (DTI) and multi-compartment Ball-and-Stick models. Then, based on the test re-test quantitative calibration, we provide quantitative figures of pathology evolution between M0 and M12 in the cervical spine on a set of 31 MS patients, exhibiting how the pathology damage spans in the cervical spinal cord.
Joint Active and Passive Beamforming for IRS-Assisted Radar
Authors: Fangzhou Wang, Hongbin Li, Jun Fang
Date: 2021-08-09 00:00:00

Intelligent reflecting surface (IRS) is a promising technology being considered for future wireless communications due to its ability to control signal propagation. This paper considers the joint active and passive beamforming problem for an IRS-assisted radar, where multiple IRSs are deployed to assist the surveillance of multiple targets in cluttered environments. Specifically, we aim to maximize the minimum target illumination power at multiple target locations by jointly optimizing the active beamformer at the radar transmitter and the passive phase-shift matrices at the IRSs, subject to an upperbound on the clutter power at each clutter scatterer. The resulting optimization problem is nonconvex and solved with a sequential optimization procedure along with semedefinite relaxation (SDR). Simulation results show that IRSs can help create effective line-of-sight (LOS) paths and thus substantially improve the radar robustness against target blockage.
PAPR Reduction of FBMC Signals Based on Uniform and Linear PDF Companding Schemes
Authors: Srinivas Ramavath, Umesh Chandra Samal
Date: 2021-08-08 00:00:00

In this paper, two new companders are designed to reduce the ratio of peak to average power (PAPR) experienced by filter bank multicarrier (FBMC) signals. Specifically, the compander basic model is generalized, which alter the distributed FBMC signal amplitude peak. The proposed companders design approach provides better performance in terms of reducing the PAPR, Bit Error Rate (BER) and phase error degradation over the previously existing compander schemes. Many PAPR reduction approaches, such as the Mu-law companding technique, are also available. It results in the formation of spectrum side lobes, although the proposed techniques result in a spectrum with fewer side lobes. The theoretical analysis of linear compander and expander transform for a few specific parameters are derived and analyzed. The suggested linear companding technique is analytically analysed using simulations to show that it efficiently decreases the high peaks in the FBMC system. Keywords- BER, CCDF, Compander, FBMC, OQAM, PAPR, PSD.
New strategies to improve minimap2 alignment accuracy
Authors: Heng Li
Date: 2021-08-07 00:00:00

Summary: We present several recent improvements to minimap2, a versatile pairwise aligner for nucleotide sequences. Now minimap2 v2.22 can more accurately map long reads to highly repetitive regions and align through insertions or deletions up to 100kb by default, addressing major weakness in minimap2 v2.18 or earlier. Availability and implementation: https://github.com/lh3/minimap2
Learning Orientations: a Discrete Geometry Model
Authors: Y. Dabaghian
Date: 2021-08-07 00:00:00
Tasks: model

In the mammalian brain, many neuronal ensembles are involved in representing spatial structure of the environment. In particular, there exist cells that encode the animal's location and cells that encode head direction. A number of studies have addressed properties of the spatial maps produced by these two populations of neurons, mainly by establishing correlations between their spiking parameters and geometric characteristics of the animal's environments. The question remains however, how the brain may intrinsically combine the direction and the location information into a unified spatial framework that enables animals' orientation. Below we propose a model of such a framework, using ideas and constructs from algebraic topology and synthetic affine geometry.
Spatial representability of neuronal activity
Authors: D. Akhtiamov, A. G. Cohn, Y. Dabaghian
Date: 2021-08-07 00:00:00

A common approach to interpreting spiking activity is based on identifying the firing fields---regions in physical or configuration spaces that elicit responses of neurons. Common examples include hippocampal place cells that fire at preferred locations in the navigated environment, head direction cells that fire at preferred orientations of the animal's head, view cells that respond to preferred spots in the visual field, etc. In all these cases, firing fields were discovered empirically, by trial and error. We argue that the existence and a number of properties of the firing fields can be established theoretically, through topological analyses of the neuronal spiking activity.
The Structure of Systematicity in the Brain
Authors: Randall C. O'Reilly, Charan Ranganath, Jacob L. Russin
Date: 2021-08-07 00:00:00

A hallmark of human intelligence is the ability to adapt to new situations, by applying learned rules to new content (systematicity) and thereby enabling an open-ended number of inferences and actions (generativity). Here, we propose that the human brain accomplishes these feats through pathways in the parietal cortex that encode the abstract structure of space, events, and tasks, and pathways in the temporal cortex that encode information about specific people, places, and things (content). Recent neural network models show how the separation of structure and content might emerge through a combination of architectural biases and learning, and these networks show dramatic improvements in the ability to capture systematic, generative behavior. We close by considering how the hippocampal formation may form integrative memories that enable rapid learning of new structure and content representations.
Reduced-form framework for multiple ordered default times under model uncertainty
Authors: Francesca Biagini, Andrea Mazzon, Katharina Oberpriller
Date: 2021-08-09 00:00:00
Tasks: Form

In this paper we introduce a sublinear conditional operator with respect to a family of possibly nondominated probability measures in presence of multiple ordered default times. In this way we generalize the results of [5], where a reduced-form framework under model uncertainty for a single default time is developed. Moreover, we use this operator for the valuation of credit portfolio derivatives under model uncertainty.
Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network
Authors: Sedat Golgiyaz, Muhammed Fatih Talu, Mahmut Daskin, Cem Onat
Date: 2021-07-23 00:00:00
Tasks: FLUE

It is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient ({\lambda}) has been modelled. The model has been obtained with a three-stage approach: 1) Data collection and synchronization: Obtaining the flame images by means of a CCD camera mounted on a 10 cm diameter observation port, {\lambda} data has been coordinately measured and recorded by the flue gas analyzer. 2) Feature extraction: Gridding the flame image, it is divided into small pieces. The uniformity of each piece to the optimal flame image has been calculated by means of modelling with single and multivariable Gaussian, calculating of color probabilities and Gauss mixture approach. 3) Matching and testing: A multilayer artificial neural network (ANN) has been used for the matching of feature-{\lambda}.
ABD-Net: Attention Based Decomposition Network for 3D Point Cloud Decomposition
Authors: Siddharth Katageri, Shashidhar V Kudari, Akshaykumar Gunari, Ramesh Ashok Tabib, Uma Mudenagudi
Date: 2021-07-09 00:00:00
Tasks: 3D Object Classification

In this paper, we propose Attention Based Decomposition Network (ABD-Net), for point cloud decomposition into basic geometric shapes namely, plane, sphere, cone and cylinder. We show improved performance of 3D object classification using attention features based on primitive shapes in point clouds. Point clouds, being the simple and compact representation of 3D objects have gained increasing popularity. They demand robust methods for feature extraction due to unorderness in point sets. In ABD-Net the proposed Local Proximity Encapsulator captures the local geometric variations along with spatial encoding around each point from the input point sets. The encapsulated local features are further passed to proposed Attention Feature Encoder to learn basic shapes in point cloud. Attention Feature Encoder models geometric relationship between the neighborhoods of all the points resulting in capturing global point cloud information. We demonstrate the results of our proposed ABD-Net on ANSI mechanical component and ModelNet40 datasets. We also demonstrate the effectiveness of ABD-Net over the acquired attention features by improving the performance of 3D object classification on ModelNet40 benchmark dataset and compare them with state-of-the-art techniques.
Merging Tasks for Video Panoptic Segmentation
Authors: Jake Rap, Panagiotis Meletis
Date: 2021-07-10 00:00:00
Tasks: Multi-Object Tracking, Object Tracking, Panoptic Segmentation, Segmentation, Semantic Segmentation, Video Panoptic Segmentation

In this paper, the task of video panoptic segmentation is studied and two different methods to solve the task will be proposed. Video panoptic segmentation (VPS) is a recently introduced computer vision task that requires classifying and tracking every pixel in a given video. The nature of this task makes the cost of annotating datasets for it prohibiting. To understand video panoptic segmentation, first, earlier introduced constituent tasks that focus on semantics and tracking separately will be researched. Thereafter, two data-driven approaches which do not require training on a tailored VPS dataset will be selected to solve it. The first approach will show how a model for video panoptic segmentation can be built by heuristically fusing the outputs of a pre-trained semantic segmentation model and a pre-trained multi-object tracking model. This can be desired if one wants to easily extend the capabilities of either model. The second approach will counter some of the shortcomings of the first approach by building on top of a shared neural network backbone with task-specific heads. This network is designed for panoptic segmentation and will be extended by a mask propagation module to link instance masks across time, yielding the video panoptic segmentation format.