pwc-archive/papers-with-abstracts huggingface dataset:
Regret Analysis in Threshold Policy Design
Authors: Federico Crippa
Date: 2024-04-17 00:00:00

Threshold policies are decision rules that assign treatments based on whether an observable characteristic exceeds a certain threshold. They are widespread across multiple domains, including welfare programs, taxation, and clinical medicine. This paper examines the problem of designing threshold policies using experimental data, when the goal is to maximize the population welfare. First, I characterize the regret - a measure of policy optimality - of the Empirical Welfare Maximizer (EWM) policy, popular in the literature. Next, I introduce the Smoothed Welfare Maximizer (SWM) policy, which improves the EWM's regret convergence rate under an additional smoothness condition. The two policies are compared by studying how differently their regrets depend on the population distribution, and investigating their finite sample performances through Monte Carlo simulations. In many contexts, the SWM policy guarantees larger welfare than the EWM. An empirical illustration demonstrates how the treatment recommendations of the two policies may differ in practice.
Testing Mechanisms
Authors: Soonwoo Kwon, Jonathan Roth
Date: 2024-04-17 00:00:00

Economists are often interested in the mechanisms by which a particular treatment affects an outcome. This paper develops tests for the ``sharp null of full mediation'' that the treatment $D$ operates on the outcome $Y$ only through a particular conjectured mechanism (or set of mechanisms) $M$. A key observation is that if $D$ is randomly assigned and has a monotone effect on $M$, then $D$ is a valid instrumental variable for the local average treatment effect (LATE) of $M$ on $Y$. Existing tools for testing the validity of the LATE assumptions can thus be used to test the sharp null of full mediation when $M$ and $D$ are binary. We develop a more general framework that allows one to test whether the effect of $D$ on $Y$ is fully explained by a potentially multi-valued and multi-dimensional set of mechanisms $M$, allowing for relaxations of the monotonicity assumption. We further provide methods for lower-bounding the size of the alternative mechanisms when the sharp null is rejected. An advantage of our approach relative to existing tools for mediation analysis is that it does not require stringent assumptions about how $M$ is assigned; on the other hand, our approach helps to answer different questions than traditional mediation analysis by focusing on the sharp null rather than estimating average direct and indirect effects. We illustrate the usefulness of the testable implications in two empirical applications.
Income Shocks and their Transmission into Consumption
Authors: Edmund Crawley, Alexandros Theloudis
Date: 2024-04-18 00:00:00

This article reviews the economics literature of, primarily, the last 20 years, that studies the link between income shocks and consumption fluctuations at the household level. We identify three broad approaches through which researchers estimate the consumption response to income shocks: 1.) structural methods in which a fully or partially specified model helps identify the consumption response to income shocks from the data; 2.) natural experiments in which the consumption response of one group who receives an income shock is compared to another group who does not; 3.) elicitation surveys in which consumers are asked how they expect to react to various hypothetical events.
Automated Social Science: Language Models as Scientist and Subjects
Authors: Benjamin S. Manning, Kehang Zhu, John J. Horton
Date: 2024-04-17 00:00:00
Tasks: Experimental Design

We present an approach for automatically generating and testing, in silico, social scientific hypotheses. This automation is made possible by recent advances in large language models (LLM), but the key feature of the approach is the use of structural causal models. Structural causal models provide a language to state hypotheses, a blueprint for constructing LLM-based agents, an experimental design, and a plan for data analysis. The fitted structural causal model becomes an object available for prediction or the planning of follow-on experiments. We demonstrate the approach with several scenarios: a negotiation, a bail hearing, a job interview, and an auction. In each case, causal relationships are both proposed and tested by the system, finding evidence for some and not others. We provide evidence that the insights from these simulations of social interactions are not available to the LLM purely through direct elicitation. When given its proposed structural causal model for each scenario, the LLM is good at predicting the signs of estimated effects, but it cannot reliably predict the magnitudes of those estimates. In the auction experiment, the in silico simulation results closely match the predictions of auction theory, but elicited predictions of the clearing prices from the LLM are inaccurate. However, the LLM's predictions are dramatically improved if the model can condition on the fitted structural causal model. In short, the LLM knows more than it can (immediately) tell.
A Comprehensive Study for Multi-Criteria Comparison of EV, ICEV, and HEV
Authors: Tushar Gahlaut, Gourav Dwivedi
Date: 2024-04-17 00:00:00
Tasks: Articles

Electric vehicles (EVs) are increasingly becoming popular as a viable means of transportation for the future. The use of EVs may help to provide better climatic conditions in urban areas with a pocket friendly cost for transportation to the consumers throughout its life. EVs enact as a boon to the society by providing zero tailpipe emissions, better comfort, low lifecycle cost and higher connectivity. The article aims to provide scientific information through the literature across various aspects of EVs in their lifetime and thus, assist the scholarly community and various organisations to understand the impact of EVs. In this study we have gathered information from the articles published in SCOPUS database and through grey literature with the focus of information post 2009. After identification of various factors while purchasing the vehicle, the total cost of ownership (TCO) for vehicles is calculated and their average TCO for each segment is considered for the study. Following that, we investigate the ranking of EVs, vehicles powered by internal combustion engines (ICEVs), and hybrid electric vehicles (HEVs) in a variety of price segments by employing a combination of two different multi-criteria decision making (MCDM) techniques. Initially, best-worst method (BWM) is used to determine the weights of each of the identified criterion, which is thereafter, used in conjunction with the technique for order preference by similarity to ideal solution (TOPSIS) to compute the rank of the available alternatives using BWM. The ranking obtained clearly indicates that EVs should be first purchase choice of the consumers, followed by HEVs and ICEVs respectively. Thus, the results help us conclude that EVs enact as a sustainable means of transport for the future.
Decision making in stochastic extensive form I: Stochastic decision forests
Authors: E. Emanuel Rapsch
Date: 2024-04-18 00:00:00
Tasks: Decision Making, Form

A general theory of stochastic decision forests is developed to bridge two concepts of information flow: decision trees and refined partitions on the one side, filtrations from probability theory on the other. Instead of the traditional "nature" agent, this framework uses a single lottery draw to select a tree of a given decision forest. Each "personal" agent receives dynamic updates from an own oracle on the lottery outcome and makes partition-refining choices adapted to this information. This theory addresses a key limitation of existing approaches in extensive form theory, which struggle to model continuous-time stochastic processes, such as Brownian motion, as outcomes of "nature" decision making. Additionally, a class of stochastic decision forests based on time-indexed action paths is constructed, encompassing a wide range of models from the literature and laying the groundwork for an approximation theory for stochastic differential games in extensive form.
Efficient High-Performance Bark-Scale Neural Network for Residual Echo and Noise Suppression
Authors: Ernst Seidel, Pejman Mowlaee, Tim Fingscheidt
Date: 2024-04-08 00:00:00
Tasks: Speech Enhancement

In recent years, the introduction of neural networks (NNs) into the field of speech enhancement has brought significant improvements. However, many of the proposed methods are quite demanding in terms of computational complexity and memory footprint. For the application in dedicated communication devices, such as speakerphones, hands-free car systems, or smartphones, efficiency plays a major role along with performance. In this context, we present an efficient, high-performance hybrid joint acoustic echo control and noise suppression system, whereby our main contribution is the postfilter NN, performing both noise and residual echo suppression. The preservation of nearend speech is improved by a Bark-scale auditory filterbank for the NN postfilter. The proposed hybrid method is benchmarked with state-of-the-art methods and its effectiveness is demonstrated on the ICASSP 2023 AEC Challenge blind test set. We demonstrate that it offers high-quality nearend speech preservation during both double-talk and nearend speech conditions. At the same time, it is capable of efficient removal of echo leaks, achieving a comparable performance to already small state-of-the-art models such as the end-to-end DeepVQE-S, while requiring only around 10 % of its computational complexity. This makes it easily realtime implementable on a speakerphone device.
Semantic Satellite Communications Based on Generative Foundation Model
Authors: Peiwen Jiang, Chao-Kai Wen, Xiao Li, Shi Jin, Geoffrey Ye Li
Date: 2024-04-18 00:00:00
Tasks: Decoder, model, Semantic Communication

Satellite communications can provide massive connections and seamless coverage, but they also face several challenges, such as rain attenuation, long propagation delays, and co-channel interference. To improve transmission efficiency and address severe scenarios, semantic communication has become a popular choice, particularly when equipped with foundation models (FMs). In this study, we introduce an FM-based semantic satellite communication framework, termed FMSAT. This framework leverages FM-based segmentation and reconstruction to significantly reduce bandwidth requirements and accurately recover semantic features under high noise and interference. Considering the high speed of satellites, an adaptive encoder-decoder is proposed to protect important features and avoid frequent retransmissions. Meanwhile, a well-received image can provide a reference for repairing damaged images under sudden attenuation. Since acknowledgment feedback is subject to long propagation delays when retransmission is unavoidable, a novel error detection method is proposed to roughly detect semantic errors at the regenerative satellite. With the proposed detectors at both the satellite and the gateway, the quality of the received images can be ensured. The simulation results demonstrate that the proposed method can significantly reduce bandwidth requirements, adapt to complex satellite scenarios, and protect semantic information with an acceptable transmission delay.
Environment-aware UAV Communications: CKM Construction and Predictive Beamforming
Authors: Shiqi Zeng, Xiaoli Xu, Yong Zeng
Date: 2024-04-18 00:00:00
Tasks: Benchmarking

Predictive millimeter-wave (mmWave) beamforming is a promising technique to enable low-latency and high-rate ground-air communications for cellular-connected unmanned aerial vehicles (UAVs). However, the high vulnerability of mmWave to blockages poses practical challenges to the implementation of such a technology. In this paper, we tackle the challenges by proposing a channel knowledge map (CKM)-assisted predictive beamforming approach based on the echoed joint communication and sensing signal, whereby the line-of-sight (LoS) link identification is performed via hypothesis testing using prior information provided by CKM. Depending on the identification result, extended Kalman filtering (EKF) is adopted to reliably track the target UAV. Furthermore, if the non-line-of-sight (NLoS) state is identified, the target UAV will be immediately connected to a candidate base station (BS), namely a handover will be triggered to alleviate the communication outage. The simulation results show that the proposed method can significantly enhance the UAV tracking and mmWave communication performance compared to the benchmarking schemes without using CKM or LoS identification.
sEMG-based Fine-grained Gesture Recognition via Improved LightGBM Model
Authors: Xiupeng Qiao, Zekun Chen, Shili Liang
Date: 2024-04-18 00:00:00
Tasks: Gesture Recognition, Transfer Learning

Surface electromyogram (sEMG), as a bioelectrical signal reflecting the activity of human muscles, has a wide range of applications in the control of prosthetics, human-computer interaction and so on. However, the existing recognition methods are all discrete actions, that is, every time an action is executed, it is necessary to restore the resting state before the next action, and it is unable to effectively recognize the gestures of continuous actions. To solve this problem, this paper proposes an improved fine gesture recognition model based on LightGBM algorithm. A sliding window sample segmentation scheme is adopted to replace active segment detection, and a series of innovative schemes such as improved loss function, Optuna hyperparameter search and Bagging integration are adopted to optimize LightGBM model and realize gesture recognition of continuous active segment signals. In order to verify the effectiveness of the proposed algorithm, we used the NinaproDB7 dataset to design the normal data recognition experiment and the disabled data transfer experiment. The results showed that the recognition rate of the proposed model was 89.72% higher than that of the optimal model Bi-ConvGRU for 18 gesture recognition tasks in the open data set, it reached 90.28%. Compared with the scheme directly trained on small sample data, the recognition rate of transfer learning was significantly improved from 60.35% to 78.54%, effectively solving the problem of insufficient data, and proving the applicability and advantages of transfer learning in fine gesture recognition tasks for disabled people.
Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation
Authors: Yang Lu, Yuhang Li, Ruichen Zhang, Wei Chen, Bo Ai, Dusit Niyato
Date: 2024-04-18 00:00:00

Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are shown to be able to learn the structural information about graphs representing the wireless networks to adapt to the time-varying channel state information and dynamics of network topology. This article aims to provide a comprehensive overview of applying GNNs to optimize wireless networks via answering three fundamental questions, i.e., how to input the wireless network data into GNNs, how to improve the performance of GNNs, and how to evaluate GNNs. Particularly, two graph representations are given to transform wireless network parameters into graph-structured data. Then, we focus on the architecture design of the GNN-based models via introducing the basic message passing as well as model improvement methods including multi-head attention mechanism and residual structure. At last, we give task-oriented evaluation metrics for DL-enabled wireless resource allocation. We also highlight certain challenges and potential research directions for the application of GNNs in wireless networks.
AI-Empowered RIS-Assisted Networks: CV-Enabled RIS Selection and DNN-Enabled Transmission
Authors: Conggang Hu, Yang Lu, Hongyang Du, Mi Yang, Bo Ai, Dusit Niyato
Date: 2024-04-18 00:00:00

This paper investigates artificial intelligence (AI) empowered schemes for reconfigurable intelligent surface (RIS) assisted networks from the perspective of fast implementation. We formulate a weighted sum-rate maximization problem for a multi-RIS-assisted network. To avoid huge channel estimation overhead due to activate all RISs, we propose a computer vision (CV) enabled RIS selection scheme based on a single shot multi-box detector. To realize real-time resource allocation, a deep neural network (DNN) enabled transmit design is developed to learn the optimal mapping from channel information to transmit beamformers and phase shift matrix. Numerical results illustrate that the CV module is able to select of RIS with the best propagation condition. The well-trained DNN achieves similar sum-rate performance to the existing alternative optimization method but with much smaller inference time.
Token Space: A Category Theory Framework for AI Computations
Authors: Wuming Pan
Date: 2024-04-11 00:00:00
Tasks: Deep Learning

This paper introduces the Token Space framework, a novel mathematical construct designed to enhance the interpretability and effectiveness of deep learning models through the application of category theory. By establishing a categorical structure at the Token level, we provide a new lens through which AI computations can be understood, emphasizing the relationships between tokens, such as grouping, order, and parameter types. We explore the foundational methodologies of the Token Space, detailing its construction, the role of construction operators and initial categories, and its application in analyzing deep learning models, specifically focusing on attention mechanisms and Transformer architectures. The integration of category theory into AI research offers a unified framework to describe and analyze computational structures, enabling new research paths and development possibilities. Our investigation reveals that the Token Space framework not only facilitates a deeper theoretical understanding of deep learning models but also opens avenues for the design of more efficient, interpretable, and innovative models, illustrating the significant role of category theory in advancing computational models.
A computational scheme connecting gene regulatory network dynamics with heterogeneous stem cell regeneration
Authors: Yakun Li, Xiyin Liang, Jinzhi Lei
Date: 2024-04-17 00:00:00

Stem cell regeneration is a vital biological process in self-renewing tissues, governing development and tissue homeostasis. Gene regulatory network dynamics are pivotal in controlling stem cell regeneration and cell type transitions. However, integrating the quantitative dynamics of gene regulatory networks at the single-cell level with stem cell regeneration at the population level poses significant challenges. This study presents a computational framework connecting gene regulatory network dynamics with stem cell regeneration through a data-driven formulation of the inheritance function. The inheritance function captures epigenetic state transitions during cell division in heterogeneous stem cell populations. Our scheme allows the derivation of the inheritance function based on a hybrid model of cross-cell-cycle gene regulation network dynamics. The proposed scheme enables us to derive the inheritance function based on the hybrid model of cross-cell-cycle gene regulation network dynamics. By explicitly incorporating gene regulatory network structure, it replicates cross-cell-cycling gene regulation dynamics through individual-cell-based modeling. The numerical scheme holds the potential for extension to diverse gene regulatory networks, facilitating a deeper understanding of the connection between gene regulation dynamics and stem cell regeneration.
Understanding the genetic basis of variation in meiotic recombination: past, present, and future
Authors: Susan E. Johnston
Date: 2024-04-18 00:00:00
Tasks: Diversity

Meiotic recombination is a fundamental feature of sexually reproducing species. It is often required for proper chromosome segregation and plays important role in adaptation and the maintenance of genetic diversity. The molecular mechanisms of recombination are remarkably conserved across eukaryotes, yet meiotic genes and proteins show substantial variation in their sequence and function, even between closely related species. Furthermore, the rate and distribution of recombination shows a huge diversity within and between chromosomes, individuals, sexes, populations, and species. This variation has implications for many molecular and evolutionary processes, yet how and why this diversity has evolved is not well understood. A key step in understanding trait evolution is to determine its genetic basis - that is, the number, effect sizes, and distribution of loci underpinning variation. In this perspective, I discuss past and current knowledge on the genetic basis of variation in recombination rate and distribution, explore its evolutionary implications, and present open questions for future research.
Neuropsychological Effects of Rock Steady Boxing in Patients with Parkinson's Disease: A Comprehensive Analysis
Authors: Lorella Bonaccorsi, Ugo Santosuosso, Massimo Gulisano, Luca Sodini
Date: 2024-04-18 00:00:00

This study investigates the efficacy of adapted boxing, specifically Rock Steady Boxing, in mitigating dopamine decline in individuals with Parkinson disease. The research involved 40 participants with confirmed diagnosis of Parkinson disease who underwent biweekly RSB sessions over an 8 week period. Training regimen included activation, core exercise, and a cooldown phase. The findings revealed a significant amelioration in depressive symptoms through the sessions. Assessment using the Beck Depression Inventory demonstrated a progressive decrease in scores associated with depressive symptoms, particularly affective, cognitive, and somatic symptoms. The reduction in more severe symptoms was accompanied by an increase in milder symptoms. Statistical analysis confirmed the significance of the reduction in depressive symptoms over time, suggesting that physical activity, particularly RSB, may contribute to enhancing the quality of life for individuals with Parkinson disease. The positive impact was observed in both motor and depressive symptoms, suggesting an overall beneficial effect of exercise training. It is important to note that six participants withdrew from the study due to organizational reasons, resulting in a reduction in the participant count from 40 to 34. Nonetheless, the overall results suggest that RSB could be an effective approach to addressing depression in Parkinson patients, providing a complementary treatment option to conventional pharmacological therapy.