pwc-archive/papers-with-abstracts huggingface dataset:
UHRNet: A Deep Learning-Based Method for Accurate 3D Reconstruction from a Single Fringe-Pattern
Authors: Yixiao Wang, Canlin Zhou, Xingyang Qi, Hui Li
Date: 2023-04-23 00:00:00
Tasks: 3D Reconstruction, Retrieval

The quick and accurate retrieval of an object height from a single fringe pattern in Fringe Projection Profilometry has been a topic of ongoing research. While a single shot fringe to depth CNN based method can restore height map directly from a single pattern, its accuracy is currently inferior to the traditional phase shifting technique. To improve this method's accuracy, we propose using a U shaped High resolution Network (UHRNet). The network uses UNet encoding and decoding structure as backbone, with Multi-Level convolution Block and High resolution Fusion Block applied to extract local features and global features. We also designed a compound loss function by combining Structural Similarity Index Measure Loss (SSIMLoss) function and chunked L2 loss function to improve 3D reconstruction details.We conducted several experiments to demonstrate the validity and robustness of our proposed method. A few experiments have been conducted to demonstrate the validity and robustness of the proposed method, The average RMSE of 3D reconstruction by our method is only 0.443(mm). which is 41.13% of the UNet method and 33.31% of Wang et al hNet method. Our experimental results show that our proposed method can increase the accuracy of 3D reconstruction from a single fringe pattern.
A Novel Low-Rank Tensor Method for Undersampling Artifact Removal in Respiratory Motion-Resolved Multi-Echo 3D Cones MRI
Authors: Seongho Jeong, MungSoo Kang, Gerald Behr, Heechul Jeong, Youngwook Kee
Date: 2023-05-01 00:00:00
Tasks: compressed sensing, Image Reconstruction

We propose a novel low-rank tensor method for respiratory motion-resolved multi-echo image reconstruction. The key idea is to construct a 3-way image tensor (space $\times$ echo $\times$ motion state) from the conventional gridding reconstruction of highly undersampled multi-echo k-space raw data, and exploit low-rank tensor structure to separate it from undersampling artifacts. Healthy volunteers and patients with iron overload were recruited and imaged on a 3T clinical MRI system for this study. Results show that our proposed method Successfully reduced severe undersampling artifacts in respiratory motion-state resolved complex source images, as well as subsequent R2* and quantitative susceptibility mapping (QSM). Compared to conventional respiratory motion-resolved compressed sensing (CS) image reconstruction, the proposed method had a reconstruction time at least three times faster, accounting for signal evolution along the echo dimension in the multi-echo data.
Multi-Species Prey-Predator Dynamics During a Multi-Strain Pandemic
Authors: Ariel Alexi, Ariel Rosenfeld, Teddy Lazebnik
Date: 2023-05-01 00:00:00

Small and large scale pandemics are a natural phenomenon repeatably appearing throughout history, causing ecological and biological shifts in ecosystems and a wide range of their habitats. These pandemics usually start with a single strain but shortly become multi-strain due to a mutation process of the pathogen causing the epidemic. In this study, we propose a novel eco-epidemiological model that captures multi-species prey-predator dynamics with a multi-strain pandemic. The proposed model extends and combines the Lotka-Volterra prey-predator model and the Susceptible-Infectious-Recovered (SIR) epidemiological model. We investigate the ecosystem's sensitivity and stability during such a multi-strain pandemic through extensive simulation relying on both synthetic cases as well as two real-world configurations. Our results are aligned with known ecological and epidemiological findings, thus supporting the adequacy of the proposed model in realistically capturing the complex eco-epidemiological properties of the multi-species multi-strain pandemic dynamics.
Evolutionary Games on Networks: Phase Transition, Quasi-equilibrium, and Mathematical Principles
Authors: Jiangjiang Cheng, Wenjun Mei, Wei Su, Ge Chen
Date: 2023-04-26 00:00:00

The stable cooperation ratio of spatial evolutionary games has been widely studied using simulations or approximate analysis methods. However, sometimes such ``stable'' cooperation ratios obtained via approximate methods might not be actually stable, but correspond to quasi-equilibriums instead. We find that various classic game models, like the evolutionary snowdrift game, evolutionary prisoner's dilemma, and spatial public goods game on square lattices and scale-free networks, exhibit the phase transition in convergence time to the equilibrium state. Moreover, mathematical principles are provided to explain the phase transition of convergence time and quasi-equilibrium of cooperation ratio. The findings explain why and when cooperation and defection have a long-term coexistence.
Estimation and Inference in Threshold Predictive Regression Models with Locally Explosive Regressors
Authors: Christis Katsouris
Date: 2023-05-01 00:00:00
Tasks: regression

In this paper, we study the estimation of the threshold predictive regression model with hybrid stochastic local unit root predictors. We demonstrate the estimation procedure and derive the asymptotic distribution of the least square estimator and the IV based estimator proposed by Magdalinos and Phillips (2009), under the null hypothesis of a diminishing threshold effect. Simulation experiments focus on the finite sample performance of our proposed estimators and the corresponding predictability tests as in Gonzalo and Pitarakis (2012), under the presence of threshold effects with stochastic local unit roots. An empirical application to stock return equity indices, illustrate the usefulness of our framework in uncovering regimes of predictability during certain periods. In particular, we focus on an aspect not previously examined in the predictability literature, that is, the effect of economic policy uncertainty.
Assessing Text Mining and Technical Analyses on Forecasting Financial Time Series
Authors: Ali Lashgari
Date: 2023-04-27 00:00:00
Tasks: Financial Analysis, Time Series

Forecasting financial time series (FTS) is an essential field in finance and economics that anticipates market movements in financial markets. This paper investigates the accuracy of text mining and technical analyses in forecasting financial time series. It focuses on the S&P500 stock market index during the pandemic, which tracks the performance of the largest publicly traded companies in the US. The study compares two methods of forecasting the future price of the S&P500: text mining, which uses NLP techniques to extract meaningful insights from financial news, and technical analysis, which uses historical price and volume data to make predictions. The study examines the advantages and limitations of both methods and analyze their performance in predicting the S&P500. The FinBERT model outperforms other models in terms of S&P500 price prediction, as evidenced by its lower RMSE value, and has the potential to revolutionize financial analysis and prediction using financial news data. Keywords: ARIMA, BERT, FinBERT, Forecasting Financial Time Series, GARCH, LSTM, Technical Analysis, Text Mining JEL classifications: G4, C8
Disturbance Effects on Financial Timberland Returns in Austria
Authors: Petri P. Karenlampi
Date: 2023-05-01 00:00:00
Tasks: Management

Probability theory is applied for the effect of severe disturbances on the return rate on capital within multiannual stands growing crops. Two management regimes are discussed, rotations of even-aged plants on the one hand, and uneven-aged semi-stationary state on the other. The effect of any disturbance appears two-fold, contributing to both earnings and capitalization. Results are illustrated using data from a recently published study, regarding spruce (Picea abies) forests in Austria. The economic results differ from those of the paper where the data is presented, here indicating continuous-cover forestry is financially inferior to rotation forestry. Any severe disturbance may induce a regime shift from continuous-cover to even-aged forestry. If such a regime shift is not accepted, the disturbance losses reduce profits but do not affect capitalization, making continuous-cover forestry financially more sensitive to disturbances. Revenue from carbon rent favors the management regime with higher carbon stock. The methods introduced in this paper can be applied to any dataset, regardless of location and tree species.
Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization
Authors: Achim Ahrens, Alessandra Stampi-Bombelli, Selina Kurer, Dominik Hangartner
Date: 2023-04-30 00:00:00

Research underscores the role of naturalization in enhancing immigrants' socio-economic integration, yet application rates remain low. We estimate a policy rule for a letter-based information campaign encouraging newly eligible immigrants in Zurich, Switzerland, to naturalize. The policy rule assigns one out of three treatment letters to each individual, based on their observed characteristics. We field the policy rule to one-half of 1,717 immigrants, while sending random treatment letters to the other half. Despite only moderate treatment effect heterogeneity, the policy tree yields a larger, albeit insignificant, increase in application rates compared to assigning the same letter to everyone.
Historical trend in educational homophily: U-shaped or not U-shaped? Or, how to set a criterion to choose a criterion?
Authors: Anna Naszodi
Date: 2023-04-29 00:00:00

Measuring changes in overall inequality between different educational groups is often performed by quantifying variations in educational marital homophily across consecutive generations. However, this task becomes challenging when the education level of marriageable individuals is generation-specific. To address this challenge, various indicators have been proposed in the assortative mating literature. In this paper, we review a set of criteria that indicators must satisfy to be considered as suitable measures of homophily and inequality. Our analytical criteria include robustness to the number of educational categories distinguished and the negative association between intergenerational mobility and homophily. Additionally, we also impose an empirical criterion on the identified qualitative historical trend in homophily between 1960 and 2010 in the US at the national and sub-national levels. Our analysis reveals that while a specific cardinal indicator meets all three criteria, many commonly applied indices do not. We propose the application of this well-performing indicator to quantify the trend in overall inequality in any country, including European countries, with available population data on couples' education level.
Political Strategies to Overcome Climate Policy Obstructionism
Authors: Sugandha Srivastav, Ryan Rafaty
Date: 2023-04-28 00:00:00
Tasks: Diversity

Great socio-economic transitions see the demise of certain industries and the rise of others. The losers of the transition tend to deploy a variety of tactics to obstruct change. We develop a political-economy model of interest group competition and garner evidence of tactics deployed in the global climate movement. From this we deduce a set of strategies for how the climate movement competes against entrenched hydrocarbon interests. Five strategies for overcoming obstructionism emerge: (1) Appeasement, which involves compensating the losers; (2) Co-optation, which seeks to instigate change by working with incumbents; (3) Institutionalism, which involves changes to public institutions to support decarbonization; (4) Antagonism, which creates reputational or litigation costs to inaction; and (5) Countervailance, which makes low-carbon alternatives more competitive. We argue that each strategy addresses the problem of obstructionism through a different lens, reflecting a diversity of actors and theories of change within the climate movement. The choice of which strategy to pursue depends on the institutional context.
Greening our Laws: Revising Land Acquisition Law for Coal Mining in India
Authors: Sugandha Srivastav, Tanmay Singh
Date: 2023-04-28 00:00:00
Tasks: valid

Laws that govern land acquisition can lock in old paradigms. We study one such case, the Coal Bearing Areas Act of 1957 (CBAA) which provides minimal social and environmental safegaurds, and deviates in important ways from the Right to Fair Compensation and Transparency in Land Acquisition, Rehabilitation and Resettlement Act 2013 (LARR). The lack of due diligence protocol in the CBAA confers an undue comparative advantage to coal development, which is inconsistent with India's stance to phase down coal use, reduce air pollution, and advance modern sources of energy. We argue that the premise under which the CBAA was historically justified is no longer valid due to a significant change in the local context. Namely, the environmental and social costs of coal energy are far more salient and the market has cleaner energy alternatives that are cost competitive. We recommend updating land acquisition laws to bring coal under the general purview of LARR or, at minimum, amending the CBAA to ensure adequate environmental and social safeguards are in place, both in letter and practice.
Signaling With Commitment
Authors: Raphael Boleslavsky, Mehdi Shadmehr
Date: 2023-05-01 00:00:00

We study the canonical signaling game, endowing the sender with commitment power: before learning the state, sender designs a strategy, which maps the state into a probability distribution over actions. We provide a geometric characterization of the sender's attainable payoffs, described by the topological join of the graphs of the interim payoff functions associated with different sender actions. We then incorporate payoff irrelevant messages into the game, characterizing the attainable payoffs when sender commits to (i) both messages and actions, and (ii) only messages. By studying the tradeoffs between these model variations, we highlight the power of commitment to actions. We apply our results to the design of adjudication procedures and rating systems.
On extensions of partial priorities in school choice
Authors: Minoru Kitahara, Yasunori Okumura
Date: 2023-05-01 00:00:00

We consider a school choice matching model where the priorities for schools are represented by binary relations that may not be weak order. We focus on the (total order) extensions of the binary relations. We introduce a class of algorithms to derive one of the extensions of a binary relation and characterize them by using the class. We show that if the binary relations are the partial orders, then for each stable matching for the profile of the binary relations, there is an extension for which it is also stable. Moreover, if there are multiple stable matchings for the profile of the binary relations that are ranked by Pareto dominance, there is an extension for which all of those matchings are stable. We provide several applications of these results.
Social Preferences and Deliberately Stochastic Behavior
Authors: Yosuke Hashidate, Keisuke Yoshihara
Date: 2023-04-28 00:00:00
Tasks: Fairness

This study proposes a tractable stochastic choice model to identify motivations for prosocial behavior, and to explore alternative motivations of deliberate randomization beyond ex-ante fairness concerns. To represent social preferences, we employ an additively perturbed utility model consisting of the sum of expected utility and a nonlinear cost function, where the utility function is purely selfish while the cost function depends on social preferences. Using the cost function, we study stochastic choice patterns to distinguish between stochastic inequity-averse behavior and stochastic shame-mitigating behavior. Moreover, we discuss how our model can complement recent experimental evidence of ex-post and ex-ante fairness concerns.
Gain-Loss Hedging and Cumulative Prospect Theory
Authors: Lorenzo Bastianello, Alain Chateauneuf, Bernard Cornet
Date: 2023-04-28 00:00:00

Two acts are comonotonic if they yield high payoffs in the same states of nature. The main purpose of this paper is to derive a new characterization of Cumulative Prospect Theory (CPT) through simple properties involving comonotonicity. The main novelty is a concept dubbed gain-loss hedging: mixing positive and negative acts creates hedging possibilities even when acts are comonotonic. This allows us to clarify in which sense CPT differs from Choquet expected utility. Our analysis is performed under the simpler case of (piece-wise) constant marginal utility which allows us to clearly separate the perception of uncertainty from the evaluation of outcomes.
Nontransitive Preferences and Stochastic Rationalizability: A Behavioral Equivalence
Authors: Mogens Fosgerau, John Rehbeck
Date: 2023-04-28 00:00:00

Nontransitive choices have long been an area of curiosity within economics. However, determining whether nontransitive choices represent an individual's preference is a difficult task since choice data is inherently stochastic. This paper shows that behavior from nontransitive preferences under a monotonicity assumption is equivalent to a transitive stochastic choice model. In particular, nontransitive preferences are regularly interpreted as a strength of preference, so we assume alternatives are chosen proportionally to the nontransitive preference. One implication of this result is that one cannot distinguish ``complementarity in attention" and ``complementarity in demand."
A Review of Deep Learning Techniques for Speech Processing
Authors: Ambuj Mehrish, Navonil Majumder, Rishabh Bhardwaj, Rada Mihalcea, Soujanya Poria
Date: 2023-04-30 00:00:00
Tasks: Automatic Speech Recognition, Deep Learning, Emotion Recognition, speech-recognition, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field.
Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR
Authors: Ruchao Fan, Yunzheng Zhu, Jinhan Wang, Abeer Alwan
Date: 2023-04-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Domain Adaptation, Self-Supervised Learning, speech-recognition, Speech Recognition, Transfer Learning

Recently, self-supervised learning (SSL) from unlabelled speech data has gained increased attention in the automatic speech recognition (ASR) community. Typical SSL methods include autoregressive predictive coding (APC), Wav2vec2.0, and hidden unit BERT (HuBERT). However, SSL models are biased to the pretraining data. When SSL models are finetuned with data from another domain, domain shifting occurs and might cause limited knowledge transfer for downstream tasks. In this paper, we propose a novel framework, domain responsible adaptation and finetuning (DRAFT), to reduce domain shifting in pretrained speech models, and evaluate it for a causal and non-causal transformer. For the causal transformer, an extension of APC (E-APC) is proposed to learn richer information from unlabelled data by using multiple temporally-shifted sequences to perform prediction. For the non-causal transformer, various solutions for using the bidirectional APC (Bi-APC) are investigated. In addition, the DRAFT framework is examined for Wav2vec2.0 and HuBERT methods, which use non-causal transformers as the backbone. The experiments are conducted on child ASR (using the OGI and MyST databases) using SSL models trained with unlabelled adult speech data from Librispeech. The relative WER improvements of up to 19.7% on the two child tasks are observed when compared to the pretrained models without adaptation. With the proposed methods (E-APC and DRAFT), the relative WER improvements are even larger (30% and 19% on the OGI and MyST data, respectively) when compared to the models without using pretraining methods.
Quaternion Matrix Completion Using Untrained Quaternion Convolutional Neural Network for Color Image Inpainting
Authors: Jifei Miao, Kit Ian Kou, Liqiao Yang, Juan Han
Date: 2023-04-30 00:00:00
Tasks: Image Inpainting, Matrix Completion

The use of quaternions as a novel tool for color image representation has yielded impressive results in color image processing. By considering the color image as a unified entity rather than separate color space components, quaternions can effectively exploit the strong correlation among the RGB channels, leading to enhanced performance. Especially, color image inpainting tasks are highly beneficial from the application of quaternion matrix completion techniques, in recent years. However, existing quaternion matrix completion methods suffer from two major drawbacks. First, it can be difficult to choose a regularizer that captures the common characteristics of natural images, and sometimes the regularizer that is chosen based on empirical evidence may not be the optimal or efficient option. Second, the optimization process of quaternion matrix completion models is quite challenging because of the non-commutativity of quaternion multiplication. To address the two drawbacks of the existing quaternion matrix completion approaches mentioned above, this paper tends to use an untrained quaternion convolutional neural network (QCNN) to directly generate the completed quaternion matrix. This approach replaces the explicit regularization term in the quaternion matrix completion model with an implicit prior that is learned by the QCNN. Extensive quantitative and qualitative evaluations demonstrate the superiority of the proposed method for color image inpainting compared with some existing quaternion-based and tensor-based methods.