pwc-archive/papers-with-abstracts huggingface dataset:
Voluntary Information Disclosure in Centralized Matching: Efficiency Gains and Strategic Properties
Authors: Andreas Bjerre-Nielsen, Emil Chrisander
Date: 2022-06-30 00:00:00
Tasks: Fairness

Information frictions can harm the welfare of participants in two-sided matching markets. Consider a centralized admission, where colleges cannot observe students' preparedness for success in a particular major or degree program. Colleges choose between using simple, cheap admission criteria, e.g., high school grades as a proxy for preparedness, or screening all applications, which is time-consuming for both students and colleges. To address issues of fairness and welfare, we introduce two novel mechanisms that allow students to disclose private information voluntarily and thus only require partial screening. The mechanisms are based on Deferred Acceptance and preserve its core strategic properties of credible preference revelation, particularly ordinal strategy-proofness. In addition, we demonstrate conditions for which cardinal welfare improves for market participants compared to not screening. Intuitively, students and colleges benefit from voluntary information disclosure if public information about students correlates weakly with students' private information and the cost of processing disclosed information is sufficiently low. Finally, we present empirical evidence from the Danish higher education system that supports critical features of our model. Our work has policy implications for the mechanism design of large two-sided markets where information frictions are inherent.
Can the Replication Rate Tell Us About Publication Bias?
Authors: Patrick Vu
Date: 2022-06-30 00:00:00

A leading explanation for widespread replication failures is publication bias. I show in a simple model of selective publication that, contrary to common perceptions, the replication rate is unaffected by the suppression of insignificant results in the publication process. I show further that the expected replication rate falls below intended power owing to issues with common power calculations. I empirically calibrate a model of selective publication and find that power issues alone can explain the entirety of the gap between the replication rate and intended power in experimental economics. In psychology, these issues explain two-thirds of the gap.
Gender gaps in frontier entrepreneurship? Evidence from 1901 Oklahoma land lottery winners
Authors: Jason Poulos
Date: 2022-06-29 00:00:00

The paper investigates gender differences in entrepreneurship by exploiting a large-scale land lottery in Oklahoma at the turn of the 20$^{\text{th}}$ century. Lottery winners claimed land in the order in which their names were drawn, so the draw number is an approximate rank ordering of lottery wealth. This mechanism allows for the estimation of a dose-response function, which relates each draw number to the expected outcome under each draw. I estimate dose-response functions on a linked dataset of lottery winners and land patent records, and find the probability of purchasing land from the government to be decreasing as a function of lottery wealth, which is evidence for the presence of liquidity constraints. I find female winners were more effective in leveraging lottery wealth to purchase additional land, as evidenced by significantly higher median dose-responses compared to those of male winners. For a sample of winners linked to the 1910 Census, I find that male winners have higher median dose-responses compared to female winners in terms of farm or home ownership. These results suggest that liquidity constraints may have been more binding for female entrepreneurs in the market economy.
Predicting Economic Welfare with Images on Wealth
Authors: Jeonggil Song
Date: 2022-06-29 00:00:00
Tasks: valid

Using images containing information on wealth, this research investigates that pictures are capable of reliably predicting the economic prosperity of households. Without surveys on wealth-related information and human-made standard of wealth quality that the traditional wealth-based approach relied on, this novel approach makes use of only images posted on Dollar Street as input data on household wealth across 66 countries and predicts the consumption or income level of each household using the Convolutional Neural Network (CNN) method. The best result predicts the log of consumption level with root mean squared error of 0.66 and R-squared of 0.80 in CNN regression problem. In addition, this simple model also performs well in classifying extreme poverty with an accuracy of 0.87 and F-beta score of 0.86. Since the model shows a higher performance in the extreme poverty classification when I applied the different threshold of poverty lines to countries by their income group, it is suggested that the decision of the World Bank to define poverty lines differently by income group was valid.
Challenges and Opportunities in Multi-device Speech Processing
Authors: Gregory Ciccarelli, Jarred Barber, Arun Nair, Israel Cohen, Tao Zhang
Date: 2022-06-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Keyword Spotting, Speech Enhancement, speech-recognition, Speech Recognition

We review current solutions and technical challenges for automatic speech recognition, keyword spotting, device arbitration, speech enhancement, and source localization in multidevice home environments to provide context for the INTERSPEECH 2022 special session, "Challenges and opportunities for signal processing and machine learning for multiple smart devices". We also identify the datasets needed to support these research areas. Based on the review and our research experience in the multi-device domain, we conclude with an outlook on the future evolution
Performance Analysis of Optimized Versatile Video Coding Software Decoders on Embedded Platforms
Authors: Anup Saha, Wassim Hamidouche, Miguel Chavarrías, Guillaume Gautier, Fernando Pescador, Ibrahim Farhat
Date: 2022-06-30 00:00:00

In recent years, the global demand for high-resolution videos and the emergence of new multimedia applications have created the need for a new video coding standard. Hence, in July 2020 the Versatile Video Coding (VVC) standard was released providing up to 50% bit-rate saving for the same video quality compared to its predecessor High Efficiency Video Coding (HEVC). However, this bit-rate saving comes at the cost of a high computational complexity, particularly for live applications and on resource-constraint embedded devices. This paper presents two optimized VVC software decoders, named OpenVVC and Versatile Video deCoder (VVdeC), designed for low resources platforms. They exploit optimization techniques such as data level parallelism using Single Instruction Multiple Data (SIMD) instructions and functional level parallelism using frame, tile and slice-based parallelisms. Furthermore, a comparison in terms of decoding run time, energy and memory consumption between the two decoders is presented while targeting two different resource-constraint embedded devices. The results showed that both decoders achieve real-time decoding of Full High definition (FHD) resolution over the first platform using 8 cores and High-definition (HD) real-time decoding for the second platform using only 4 cores with comparable results in terms of average consumed energy: around 26 J and 15 J for the 8 cores and 4 cores embedded platforms, respectively. Regarding the memory usage, OpenVVC showed better results with less average maximum memory consumed during run time compared to VVdeC.
Physics-Inspired Unsupervised Classification for Region of Interest in X-Ray Ptychography
Authors: Dergan Lin, Yi Jiang, Junjing Deng, Zichao Wendy Di
Date: 2022-06-29 00:00:00
Tasks: Object

X-ray ptychography allows for large fields to be imaged at high resolution at the cost of additional computational expense due to the large volume of data. Given limited information regarding the object, the acquired data often has an excessive amount of information that is outside the region of interest (RoI). In this work we propose a physics-inspired unsupervised learning algorithm to identify the RoI of an object using only diffraction patterns from a ptychography dataset before committing computational resources to reconstruction. Obtained diffraction patterns that are automatically identified as not within the RoI are filtered out, allowing efficient reconstruction by focusing only on important data within the RoI while preserving image quality.
Data-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks
Authors: Rui Ding, Hao Zhang, Fuhui Zhou, Qihui Wu, Zhu Han
Date: 2022-06-30 00:00:00
Tasks: Attribute, Automatic Modulation Recognition, Classification

Automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the classification accuracy is poor in the low signal-to-noise radio (SNR). In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations. The visual model is utilized to extract visual features. The attribute learning model is used to learn the attribute semantic representations. The transformation model is proposed to convert the attribute representation into the visual space. Extensive simulation results demonstrate that our proposed automatic modulation classification scheme can achieve better performance than the benchmark schemes in terms of the classification accuracy, especially in the low SNR. Moreover, the confusion among high-order modulations is reduced by using our proposed scheme compared with other traditional schemes.
A Dynamic Subarray Structure in Reconfigurable Intelligent Surfaces for TeraHertz Communication Systems
Authors: Yicong Liu, Weijie Li, Zihuai Lin
Date: 2022-06-30 00:00:00

Reconfigurable Intelligent Surface (RIS) has become a popular technology to improve the capability of a THz multiuser Multi-input multi-output (MIMO) communication system. THz wave characteristics, on the other hand, restrict THz beam coverage on RIS when using a uniform planar array (UPA) antenna. In this study, we propose a dynamic RIS subarray structure to improve the performance of a THz MIMO communication system. In more details, an RIS is divided into several RIS subarrays according to the number of users. Each RIS subarray is paired with a user and only reflects beams to the corresponding user. Based on the structure of RIS, we first propose a weighted minimum mean square error - RIS local search (WMMSE-LS) scheme, which requires that each RIS element has limited phase shifts. To improve the joint beamforming performance, we further develop an adaptive Block Coordinate Descent(BCD)-aided algorithm, an iterative optimization method. Numerical results demonstrate the effectiveness of the dynamic RIS subarray structure and the adaptive BCD-aided joint beamforming scheme and also show the merit of our proposed system.
Multi-band oscillations emerge from a simple spiking network
Authors: Tianyi Wu, Yuhang Cai, Ruilin Zhang, Zhongyi Wang, Louis Tao, Zhuo-Cheng Xiao
Date: 2022-06-29 00:00:00

In the brain, coherent neuronal activities often appear simultaneously in multiple frequency bands, e.g., as combinations of alpha (8-12 Hz), beta (12.5-30 Hz), gamma (30-120 Hz) oscillations, among others. These rhythms are believed to underlie information processing and cognitive functions and have been subjected to intense experimental and theoretical scrutiny. Computational modeling has provided a framework for the emergence of network-level oscillatory behavior from the interaction of spiking neurons. However, due to the strong nonlinear interactions between highly recurrent spiking populations, the interplay between cortical rhythms in multiple frequency bands has rarely been theoretically investigated. Many studies invoke multiple physiological timescales or oscillatory inputs to produce rhythms in multi-bands. Here we demonstrate the emergence of multi-band oscillations in a simple network consisting of one excitatory and one inhibitory neuronal population driven by constant input. First, we construct a data-driven, Poincar\'e section theory for robust numerical observations of single-frequency oscillations bifurcating into multiple bands. Then we develop model reductions of the stochastic, nonlinear, high-dimensional neuronal network to capture the appearance of multi-band dynamics and the underlying bifurcations theoretically. Furthermore, when viewed within the reduced state space, our analysis reveals conserved geometrical features of the bifurcations on low-dimensional dynamical manifolds. These results suggest a simple geometric mechanism behind the emergence of multi-band oscillations without appealing to oscillatory inputs or multiple synaptic or neuronal timescales. Thus our work points to unexplored regimes of stochastic competition between excitation and inhibition behind the generation of dynamic, patterned neuronal activities.
A library of quantitative markers of seizure severity
Authors: Sarah J. Gascoigne, Leonard Waldmann, Mariella Panagiotopoulou, Fahmida Chowdhury, Alison Cronie, Beate Diehl, John S. Duncan, Jennifer Falconer, Yu Guan, Veronica Leach, Shona Livingstone, Christoforos Papasavvas, Ryan Faulder, Jess Blickwedel, Gabrielle M. Schroeder, Rhys H. Thomas, Kevin Wilson, Peter N. Taylor, Yujiang Wang
Date: 2022-06-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure prediction

Purpose: Understanding fluctuations of seizure severity within individuals is important for defining treatment outcomes and response to therapy, as well as developing novel treatments for epilepsy. Current methods for grading seizure severity rely on qualitative interpretations from patients and clinicians. Quantitative measures of seizure severity would complement existing approaches, for EEG monitoring, outcome monitoring, and seizure prediction. Therefore, we developed a library of quantitative electroencephalographic (EEG) markers that assess the spread and intensity of abnormal electrical activity during and after seizures. Methods: We analysed intracranial EEG (iEEG) recordings of 1056 seizures from 63 patients. For each seizure, we computed 16 markers of seizure severity that capture the signal magnitude, spread, duration, and post-ictal suppression of seizures. Results: Quantitative EEG markers of seizure severity distinguished focal vs. subclinical and focal vs. FTBTC seizures across patients. In individual patients, 71% had a moderate to large difference (ranksum r > 0.3) between focal and subclinical seizures in three or more markers. Circadian and longer-term changes in severity were found for 67% and 53% of patients, respectively. Conclusion: We demonstrate the feasibility of using quantitative iEEG markers to measure seizure severity. Our quantitative markers distinguish between seizure types and are therefore sensitive to established qualitative differences in seizure severity. Our results also suggest that seizure severity is modulated over different timescales. We envisage that our proposed seizure severity library will be expanded and updated in collaboration with the epilepsy research community to include more measures and modalities.
Global dynamics of neural mass models
Authors: Gerald Cooray, Richard Rosch, Karl Friston
Date: 2022-06-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Neural mass models are used to simulate cortical dynamics and to explain the electrical and magnetic fields measured using electro- and magnetoencephalography. Simulations evince a complex phase-space structure for these kinds of models; including stationary points and limit cycles and the possibility for bifurcations and transitions among different modes of activity. This complexity allows neural mass models to describe the itinerant features of brain dynamics. However, expressive, nonlinear neural mass models are often difficult to fit to empirical data without additional simplifying assumptions: e.g., that the system can be modelled as linear perturbations around a fixed point. In this study we offer a mathematical analysis of neural mass models, specifically the canonical microcircuit model, providing analytical solutions describing dynamical itinerancy. We derive a perturbation analysis up to second order of the phase flow, together with adiabatic approximations. This allows us to describe amplitude modulations as gradient flows on a potential function of intrinsic connectivity. These results provide analytic proof-of-principle for the existence of semi-stable states of cortical dynamics at the scale of a cortical column. Crucially, this work allows for model inversion of neural mass models, not only around fixed points, but over regions of phase space that encompass transitions among semi or multi-stable states of oscillatory activity. In principle, this formulation of cortical dynamics may improve our understanding of the itinerancy that underwrites measures of cortical activity (through EEG or MEG). Crucially, these theoretical results speak to model inversion in the context of multiple semi-stable brain states, such as onset of seizure activity in epilepsy or beta bursts in Parkinsons disease.
Quadratic growth during the COVID-19 pandemic: merging hotspots and reinfections
Authors: Axel Brandenburg
Date: 2022-06-30 00:00:00

The existence of an exponential growth phase during early stages of a pandemic is often taken for granted. However, for the 2019 novel coronavirus epidemic, the early exponential phase lasted only for about six days, while the quadratic growth prevailed for forty days until it spread to other countries and continued, again quadratically, but with a larger coefficient. Here we show that this rapid phase is followed by a subsequent slow-down where the coefficient is reduced to almost the original value at the outbreak. This can be explained by the merging of previously disconnected sites that occurred after the disease jumped (nonlocally) to a relatively small number of separated sites. Subsequent variations in the slope with continued growth can qualitatively be explained as a result of reinfections and changes in their rate. We demonstrate that the observed behavior can be described by a standard epidemiological model with spatial extent and reinfections included. Time-dependent changes in the spatial diffusion coefficient can also model corresponding variations in the slope.
Extracting Information from Stochastic Trajectories of Gene Expression
Authors: Zachary R Fox
Date: 2022-06-29 00:00:00

Gene expression is a stochastic process in which cells produce biomolecules essential to the function of life. Modern experimental methods allow for the measurement of biomolecules at single-cell and single-molecule resolution over time. Mathematical models are used to make sense of these experiments. The codesign of experiments and models allows one to use models to design optimal experiments, and to find experiments which provide as much information as possible about relevant model parameters. Here, we provide a formulation of Fisher information for trajectories sampled from the continuous time Markov processes often used to model biological systems, and apply the result to potentially correlated measurements of stochastic gene expression. We validate the result on two commonly used models of gene expression and show it can be used to optimize measurement periods for simulated single-cell fluorescence microscopy experiments. Finally, we use a connection between Fisher information and mutual information to derive channel capacities of nonlinearly regulated gene expression.
Most claimed statistical findings in cross-sectional return predictability are likely true
Authors: Andrew Y. Chen
Date: 2022-06-30 00:00:00

I develop simple and intuitive bounds for the false discovery rate (FDR) in cross-sectional return predictability publications. The simplest bounds require only summary statistics from previous papers, and show the FDR is at most 25% in eight out of nine previous studies. A more refined bound shows the FDR is at most 9%. These studies include Harvey, Liu, and Zhu (2016), who ``argue that most claimed findings in financial economics are likely false.'' I demonstrate how Harvey et al.'s own estimates imply an FDR of 9%, and that their conclusion stems from misinterpreting ``insignificant factor'' as ``false discovery.''
AI in Asset Management and Rebellion Research
Authors: Jimei Shen, Yihan Mo, Christopher Plimpton, Mustafa Kaan Basaran
Date: 2022-06-29 00:00:00
Tasks: Asset Management, BIG-bench Machine Learning, Management

On October 30th, 2021, Rebellion Research's CEO announced in a Q3 2021 Letter to Investors that Rebellion's AI Global Equity strategy returned +6.8% gross for the first three quarters of 2021. "It's no surprise", Alex told us, "Our Machine Learning global strategy has a history of outperforming the S&P 500 for 14 years". In 2021, Rebellion's brokerage accounts can be opened in over 70 countries, and Rebellion's research covers over 50 countries. Besides being an AI asset management company, Rebellion also defines itself as a top-tier, global machine learning think tank. Alex planned to build a Rebellion ML & AI ecosystem. Should Rebellion stay in the asset management area or jump into other areas? How could the Rebellion strategically move towards a more broad area? What were Rebellion's new or alternative business models?
Forecasting with Bayesian Grouped Random Effects in Panel Data
Authors: Boyuan Zhang
Date: 2020-07-05 00:00:00

In this paper, we estimate and leverage latent constant group structure to generate the point, set, and density forecasts for short dynamic panel data. We implement a nonparametric Bayesian approach to simultaneously identify coefficients and group membership in the random effects which are heterogeneous across groups but fixed within a group. This method allows us to flexibly incorporate subjective prior knowledge on the group structure that potentially improves the predictive accuracy. In Monte Carlo experiments, we demonstrate that our Bayesian grouped random effects (BGRE) estimators produce accurate estimates and score predictive gains over standard panel data estimators. With a data-driven group structure, the BGRE estimators exhibit comparable accuracy of clustering with the Kmeans algorithm and outperform a two-step Bayesian grouped estimator whose group structure relies on Kmeans. In the empirical analysis, we apply our method to forecast the investment rate across a broad range of firms and illustrate that the estimated latent group structure improves forecasts relative to standard panel data estimators.
Enhancing Targeted Minority Class Prediction in Sentence-Level Relation Extraction
Authors: Hyeong-Ryeol Baek, Yong-Suk Choi
Date: 2022-06-29 00:00:00
Proceeding: Sensors 2022
Tasks: Relation, Relation Classification, Relation Extraction, Sentence

Sentence-level relation extraction (RE) has a highly imbalanced data distribution that about 80% of data are labeled as negative, i.e., no relation; and there exist minority classes (MC) among positive labels; furthermore, some of MC instances have an incorrect label. Due to those challenges, i.e., label noise and low source availability, most of the models fail to learn MC and get zero or very low F1 scores on MCs. Previous studies, however, have rather focused on micro F1 scores and MCs have not been addressed adequately. To tackle high mis-classification errors for MCs, we introduce (1) a minority class attention module (MCAM), and (2) effective augmentation methods specialized in RE. MCAM calculates the confidence scores on MC instances to select reliable ones for augmentation, and aggregates MCs information in the process of training a model. Our experiments show that our methods achieve a state-of-the-art F1 scores on TACRED as well as enhancing minority class F1 score dramatically.