pwc-archive/papers-with-abstracts huggingface dataset:
Semiparametric Inference for Regression-Discontinuity Designs
Authors: Weiwei Jiang, Rong J. B. Zhu
Date: 2024-03-09 00:00:00
Tasks: regression

Treatment effects in regression discontinuity designs (RDDs) are often estimated using local regression methods. \cite{Hahn:01} demonstrated that the identification of the average treatment effect at the cutoff in RDDs relies on the unconfoundedness assumption and that, without this assumption, only the local average treatment effect at the cutoff can be identified. In this paper, we propose a semiparametric framework tailored for identifying the average treatment effect in RDDs, eliminating the need for the unconfoundedness assumption. Our approach globally conceptualizes the identification as a partially linear modeling problem, with the coefficient of a specified polynomial function of propensity score in the linear component capturing the average treatment effect. This identification result underpins our semiparametric inference for RDDs, employing the $P$-spline method to approximate the nonparametric function and establishing a procedure for conducting inference within this framework. Through theoretical analysis, we demonstrate that our global approach achieves a faster convergence rate compared to the local method. Monte Carlo simulations further confirm that the proposed method consistently outperforms alternatives across various scenarios. Furthermore, applications to real-world datasets illustrate that our global approach can provide more reliable inference for practical problems.
Price Gouging or Market Forces? Fairness Perceptions of Price Hikes in the Pandemic
Authors: Avichai Snir, Daniel Levy, Dudi Levy, Haipeng Allan Chen
Date: 2024-03-12 00:00:00
Tasks: Fairness

We report the results of surveys we conducted in the US and Israel in 2020, a time when many prices increased following the spread of the pandemic. To assess respondents perceptions of price increases, we focus on goods whose prices have increased during the pandemic, including some essential goods. Consistent with the principle of dual entitlement, we find that respondents perceive price increases as more acceptable if they are due to cost shocks than if they are due to demand shocks. However, we also find large differences across the two populations, as well as across goods.
Carbon Economics of Different Agricultural Practices for Farming Soil
Authors: Suganthi Pazhanivel Koushika, Anbalagan Krishnaveni, Sellaperumal Pazhanivelan, Alagirisamy Bharani, Venugopal Arunkumar, Perumal Devaki, Narayanan Muthukrishnan
Date: 2024-03-12 00:00:00

The loss of soil organic carbon (SOC) poses a severe danger to agricultural sustainability around the World. This review examines various farming practices and their impact on soil organic carbon storage. After a careful review of the literature, most of the research indicated that different farming practices, such as organic farming, cover crops, conservation tillage, and agroforestry, play vital roles in increasing the SOC content of the soil sustainably. Root exudation from cover crops increases microbial activity and helps break down complex organic compounds into organic carbon. Conservation tillage enhances the soil structure and maintains carbon storage without disturbing the soil. Agroforestry systems boost organic carbon input and fasten nutrient cycling because the trees and crops have symbiotic relationships. Intercropping and crop rotations have a role in changing the composition of plant residues and promoting carbon storage. There were many understanding on the complex interactions between soil organic carbon dynamics and agricultural practices. Based on the study, the paper reveals, the role of different agricultural practices like Carbon storage through cover crops, crop rotation, mulching Conservation tillage, conventional tillage, zero tillage and organic amendments in organic carbon storage in the soil for maximum crop yield to improve the economic condition of the cultivators.
Small Price Changes, Sales Volume, and Menu Cost
Authors: Doron Sayag, Avichai Snir, Daniel Levy
Date: 2024-03-11 00:00:00

The finding of small price changes in many retail price datasets is often viewed as a puzzle. We show that a possible explanation for the presence of small price changes is related to sales volume, an observation that has been overlooked in the existing literature. Analyzing a large retail scanner price dataset that contains information on both prices and sales volume, we find that small price changes are more frequent when products sales volume is high. This finding holds across product categories, within product categories, and for individual products. It is also robust to various sensitivity analyses such as measurement errors, the definition of small price changes, the inclusion of measures of price synchronization, the size of producers, the time horizon used to compute the average sales volume, the revenues, the competition, shoppers characteristics, etc.
A New Testing Method for Justification Bias Using High-Frequency Data of Health and Employment
Authors: Jiayi Wen, Zixi Ye, Xuan Zhang
Date: 2024-03-11 00:00:00

Justification bias, wherein retirees may report poorer health to rationalize their retirement, poses a major concern to the widely-used measure of self-assessed health in retirement studies. This paper introduces a novel method for testing the presence of this bias in the spirit of regression discontinuity. The underlying idea is that any sudden shift in self-assessed health immediately following retirement is more likely attributable to the bias. Our strategy is facilitated by a unique high-frequency data that offers monthly, in contrast to the typical biennial, information on employment, self-assessed health, and objective health conditions. Across a wider post-retirement time frame, we observe a decline in self-assessed health, potentially stemming from both justification bias and changes in actual health. However, this adverse effect diminishes with shorter intervals, indicating no evidence of such bias. Our method also validates a widely-used indirect testing approach.
Reasons behind the Water Crisis and its Potential Health Outcomes
Authors: Md. Galib Ishraq Emran, Rhidi Barma, Akram Hussain Khan, Mrinmoy Roy
Date: 2024-03-09 00:00:00
Tasks: Management

Globally, the water crisis has become a significant problem that affects developing and industrialized nations. Water shortage can harm public health by increasing the chance of contracting water-borne diseases, dehydration, and malnutrition. This study aims to examine the causes of the water problem and its likely effects on human health. The study scrutinizes the reasons behind the water crisis, including population increase, climate change, and inefficient water management techniques. The results of a lack of water on human health, such as the spread of infectious diseases, a higher risk of starvation and dehydration, and psychological stress, are also concealed in the study. The research further suggests several ways to deal with the water situation and lessen its potential outcomes on human health. These remedies include enhanced sanitation and hygiene procedures, water management, and conservation techniques like rainwater gathering and wastewater recycling.
Network formation and efficiency in linear-quadratic games: An experimental study
Authors: Gergely Horvath
Date: 2024-03-09 00:00:00

We experimentally study effort provision and network formation in the linear-quadratic game characterized by positive externality and complementarity of effort choices among network neighbors. We compare experimental outcomes to the equilibrium and efficient allocations and study the impact of group size and linking costs. We find that individuals overprovide effort relative to the equilibrium level on the network they form. However, their payoffs are lower than the equilibrium payoffs because they create fewer links than it is optimal which limits the beneficial spillovers of effort provision. Reducing the linking costs does not significantly increase the connectedness of the network and the welfare loss is higher in larger groups. Individuals connect to the highest effort providers in the group and ignore links to relative low effort providers, even if those links would be beneficial to form. This effect explains the lack of links in the network.
The impact of social status on the formation of collaborative ties and effort provision: An experimental study
Authors: Gergely Horvath, Mofei Jia
Date: 2024-03-09 00:00:00

We study whether competition for social status induces higher effort provision and efficiency when individuals collaborate with their network neighbors. We consider a laboratory experiment in which individuals choose a costly collaborative effort and their network neighbors. They benefit from their neighbors' effort and effort choices of direct neighbors are strategic complements. We introduce two types of social status in a 2x2 factorial design: 1) individuals receive monetary benefits for incoming links representing popularity; 2) they receive feedback on their relative payoff ranking within the group. We find that link benefits induce higher effort provision and strengthen the collaborative ties relative to the Baseline treatment without social status. In contrast, the ranking information induces lower effort as individuals start competing for higher ranking. Overall, we find that social status has no significant impact on the number of links in the network and the efficiency of collaboration in the group.
Investigating Changes of Water Quality in Reservoirs based on Flood and Inflow Fluctuations
Authors: Shabnam Salehi, Mojtaba Ardestani
Date: 2024-03-08 00:00:00

Water temperature and dissolved oxygen are essential indicators of water quality and ecosystem sustainability. Lately, heavy rainfalls are happening frequently and forcefully affecting the thermal structure and mixing layers in depth by sharply increasing the volume of inflow entitled flash flood. It can occur by sudden intense precipitation and develop within minutes or hours. Because of heavy debris load and speedy water, this phenomenon has remarkable effects on water quality. A higher flow during floods may worsens water quality at lakes and reservoirs that are thermally stratified (with separate density layers) and decrease dissolved oxygen content. However, it is unclear how well these parameters represent the response of lakes to changes in volume discharge. To address this question, researchers simulate the thermal structure in two stratified reservoirs, considering the Rajae reservoir as a representative reservoir in the north of Iran and Minab reservoir in the south. In this study, the model realistically represented variations of dissolved oxygen and temperature of dams Lake response to flash floods. The model performance was evaluated using observed data from stations on the dams lake. In this case, the inflow charge considered in a 10-day flash flood from April 6th to April 16th during the yearly normal flow. The complete mixture in a part of the thermal structure has been proved in Rajaee reservoir. The nonpermanent impact of the massive inflow of storm runoff caused an increase in oxygen-consuming, leading to a severe decrease in dissolved oxygen on epilimnion and metalimnion. The situation in Minab reservoir was relatively different from Rajae reservoir. The inflow changes not only cause mixture but also help expanding stratification.
Collusive Outcomes Without Collusion
Authors: Inkoo Cho, Noah Williams
Date: 2024-03-11 00:00:00

We develop a model of algorithmic pricing that shuts down every channel for explicit or implicit collusion while still generating collusive outcomes. We analyze the dynamics of a duopoly market where both firms use pricing algorithms consisting of a parameterized family of model specifications. The firms update both the parameters and the weights on models to adapt endogenously to market outcomes. We show that the market experiences recurrent episodes where both firms set prices at collusive levels. We analytically characterize the dynamics of the model, using large deviation theory to explain the recurrent episodes of collusive outcomes. Our results show that collusive outcomes may be a recurrent feature of algorithmic environments with complementarities and endogenous adaptation, providing a challenge for competition policy.
Success functions in large contests
Authors: Yaron Azrieli, Christopher P. Chambers
Date: 2024-03-11 00:00:00

We consider contests with a large set (continuum) of participants and axiomatize contest success functions that arise when performance is composed of both effort and a random element, and when winners are those whose performance exceeds a cutoff determined by a market clearing condition. A co-monotonicity property is essentially all that is needed for a representation in the general case, but significantly stronger conditions must hold to obtain an additive structure. We illustrate the usefulness of this framework by revisiting some of the classic questions in the contests literature.
Can One Hear the Shape of a Decision Problem?
Authors: Mark Whitmeyer
Date: 2024-03-10 00:00:00

We explore the connection between an agent's decision problem and her ranking of information structures. We find that a finite amount of ordinal data on the agent's ranking of experiments is enough to identify her (finite) set of undominated actions (up to relabeling and duplication) and the beliefs rendering each such action optimal. An additional smattering of cardinal data, comparing the relative value to the agent of finitely many pairs of experiments, identifies her utility function up to an action-independent payoff.
On HRTF Notch Frequency Prediction Using Anthropometric Features and Neural Networks
Authors: Lior Arbel, Ishwarya Ananthabhotla, Zamir Ben-Hur, David Lou Alon, Boaz Rafaely
Date: 2024-03-12 00:00:00
Tasks: Prediction

High fidelity spatial audio often performs better when produced using a personalized head-related transfer function (HRTF). However, the direct acquisition of HRTFs is cumbersome and requires specialized equipment. Thus, many personalization methods estimate HRTF features from easily obtained anthropometric features of the pinna, head, and torso. The first HRTF notch frequency (N1) is known to be a dominant feature in elevation localization, and thus a useful feature for HRTF personalization. This paper describes the prediction of N1 frequency from pinna anthropometry using a neural model. Prediction is performed separately on three databases, both simulated and measured, and then by domain mixing in-between the databases. The model successfully predicts N1 frequency for individual databases and by domain mixing between some databases. Prediction errors are better or comparable to those previously reported, showing significant improvement when acquired over a large database and with a larger output range.
Concurrent Speaker Detection: A multi-microphone Transformer-Based Approach
Authors: Amit Eliav, Sharon Gannot
Date: 2024-03-11 00:00:00

We present a deep-learning approach for the task of Concurrent Speaker Detection (CSD) using a modified transformer model. Our model is designed to handle multi-microphone data but can also work in the single-microphone case. The method can classify audio segments into one of three classes: 1) no speech activity (noise only), 2) only a single speaker is active, and 3) more than one speaker is active. We incorporate a Cost-Sensitive (CS) loss and a confidence calibration to the training procedure. The approach is evaluated using three real-world databases: AMI, AliMeeting, and CHiME 5, demonstrating an improvement over existing approaches.
Aligning Speech to Languages to Enhance Code-switching Speech Recognition
Authors: Hexin Liu, Xiangyu Zhang, Haoyang Zhang, Leibny Paola Garcia, Andy W. H. Khong, Eng Siong Chng, Shinji Watanabe
Date: 2024-03-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Identification, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Code-switching (CS) refers to the switching of languages within a speech signal and results in language confusion for automatic speech recognition (ASR). To address language confusion, we introduce a novel language alignment loss into ASR training to align acoustic features to pseudo-language labels learned from the ASR decoder. This approach enables frame-level language identification without the need for frame-level language annotations. To further tackle the complex token alternatives for language modeling in bilingual scenarios, we propose to employ large language models via a generative error correction method. A linguistic hint, which is derived from LAL outputs and decoded hypotheses, is introduced to guide the prompting and enhance the LLM-based generative error correction for CS-ASR. The proposed methods are evaluated on the SEAME dataset and data from the ASRU 2019 Mandarin-English code-switching speech recognition challenge. The incorporation of the proposed language alignment loss improves the CS-ASR performance for both hybrid CTC/attention and Whisper models on both datasets, with only a negligible increase in the number of parameters. This work also highlights the efficacy of language alignment loss in balancing primary-language-dominant bilingual data during training, with an 8.6% relative improvement on the ASRU dataset compared to the baseline model. Performance evaluation using large language models reveals the advantage of the linguistic hint by achieving 14.1% and 5.5% relative improvement on test sets of the ASRU and SEAME datasets, respectively.
Hybrid Kinetics Embedding Framework for Dynamic PET Reconstruction
Authors: Yubo Ye, Huafeng Liu, Linwei Wang
Date: 2024-03-12 00:00:00

In dynamic positron emission tomography (PET) reconstruction, the importance of leveraging the temporal dependence of the data has been well appreciated. Current deep-learning solutions can be categorized in two groups in the way the temporal dynamics is modeled: data-driven approaches use spatiotemporal neural networks to learn the temporal dynamics of tracer kinetics from data, which relies heavily on data supervision; physics-based approaches leverage \textit{a priori} tracer kinetic models to focus on inferring their parameters, which relies heavily on the accuracy of the prior kinetic model. In this paper, we marry the strengths of these two approaches in a hybrid kinetics embedding (HyKE-Net) framework for dynamic PET reconstruction. We first introduce a novel \textit{hybrid} model of tracer kinetics consisting of a physics-based function augmented by a neural component to account for its gap to data-generating tracer kinetics, both identifiable from data. We then embed this hybrid model at the latent space of an encoding-decoding framework to enable both supervised and unsupervised identification of the hybrid kinetics and thereby dynamic PET reconstruction. Through both phantom and real-data experiments, we demonstrate the benefits of HyKE-Net -- especially in unsupervised reconstructions -- over existing physics-based and data-driven baselines as well as its ablated formulations where the embedded tracer kinetics are purely physics-based, purely neural, or hybrid but with a non-adaptable neural component.
ACMI: An index for exposed coal mapping using Landsat imagery
Authors: Zhen Yang, Jianyong Zhang, Yanchuang Zhao
Date: 2024-03-12 00:00:00

Remotely sensing the spatial distribution of exposed coal (EC) is significant for understanding the footprints of mining activities. However, widely applicable methods for the identification of EC surfaces remain inadequate because the choices of recent methods confront the diverse EC types and backgrounds. Therefore, this study proposed a new Automated Coal Mapping Index (ACMI) which was empirically formulated by an iterative process of identifying parameters that maximize the separability of EC and non-EC surfaces. The performance of ACMI was tested in six study areas worldwide with different landscape types and coal types. Based on the visual inspection, ACMI was more effective in highlighting EC surfaces and suppressing non-EC surfaces than the existing methods. Compared with the sample points obtained through direct interpretation, ACMI obtained better EC mapping results than previous methods with the F1 score and overall accuracy (OA) no less than 0.91 and 93.20% across all the selected Landsat images of the study areas, respectively. In addition, ACMI was demonstrated to have a stable optimal threshold and 0 can serve as its default threshold. The default threshold makes EC mapping using ACMI an automated process. The new index has the potential to support a variety of mining-activity-related studies, such as the identification of mining disturbances and illegal mining detection at multi-spatial-temporal scales.
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression
Authors: Zhi Cao, Youneng Bao, Fanyang Meng, Chao Li, Wen Tan, Genhong Wang, Yongsheng Liang
Date: 2024-03-11 00:00:00
Tasks: Backdoor Attack, Image Compression, Knowledge Distillation

Deep neural network-based image compression (NIC) has achieved excellent performance, but NIC method models have been shown to be susceptible to backdoor attacks. Adversarial training has been validated in image compression models as a common method to enhance model robustness. However, the improvement effect of adversarial training on model robustness is limited. In this paper, we propose a prior knowledge-guided adversarial training framework for image compression models. Specifically, first, we propose a gradient regularization constraint for training robust teacher models. Subsequently, we design a knowledge distillation based strategy to generate a priori knowledge from the teacher model to the student model for guiding adversarial training. Experimental results show that our method improves the reconstruction quality by about 9dB when the Kodak dataset is elected as the backdoor attack object for psnr attack. Compared with Ma2023, our method has a 5dB higher PSNR output at high bitrate points.
OS-FPI: A Coarse-to-Fine One-Stream Network for UAV Geo-Localization
Authors: Jiahao Chen, Enhui Zheng, Ming Dai, Yifu Chen, Yusheng Lu
Date: 2024-03-10 00:00:00
Tasks: geo-localization

The geo-localization and navigation technology of unmanned aerial vehicles (UAVs) in denied environments is currently a prominent research area. Prior approaches mainly employed a two-stream network with non-shared weights to extract features from UAV and satellite images separately, followed by related modeling to obtain the response map. However, the two-stream network extracts UAV and satellite features independently. This approach significantly affects the efficiency of feature extraction and increases the computational load. To address these issues, we propose a novel coarse-to-fine one-stream network (OS-FPI). Our approach allows information exchange between UAV and satellite features during early image feature extraction. To improve the model's performance, the framework retains feature maps generated at different stages of the feature extraction process for the feature fusion network, and establishes additional connections between UAV and satellite feature maps in the feature fusion network. Additionally, the framework introduces offset prediction to further refine and optimize the model's prediction results based on the classification tasks. Our proposed model, boasts a similar inference speed to FPI while significantly reducing the number of parameters. It can achieve better performance with fewer parameters under the same conditions. Moreover, it achieves state-of-the-art performance on the UL14 dataset. Compared to previous models, our model achieved a significant 10.92-point improvement on the RDS metric, reaching 76.25. Furthermore, its performance in meter-level localization accuracy is impressive, with 182.62% improvement in 3-meter accuracy, 164.17% improvement in 5-meter accuracy, and 137.43% improvement in 10-meter accuracy.