pwc-archive/papers-with-abstracts huggingface dataset:
On the Robustness of Generative Information Retrieval Models
Authors: Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Changjiang Zhou, Maarten de Rijke, Xueqi Cheng
Date: 2024-12-25 00:00:00
Tasks: Information Retrieval, Retrieval

Generative information retrieval methods retrieve documents by directly generating their identifiers. Much effort has been devoted to developing effective generative IR models. Less attention has been paid to the robustness of these models. It is critical to assess the out-of-distribution (OOD) generalization of generative IR models, i.e., how would such models generalize to new distributions? To answer this question, we focus on OOD scenarios from four perspectives in retrieval problems: (i)query variations; (ii)unseen query types; (iii)unseen tasks; and (iv)corpus expansion. Based on this taxonomy, we conduct empirical studies to analyze the OOD robustness of representative generative IR models against dense retrieval models. Our empirical results indicate that the OOD robustness of generative IR models is in need of improvement. By inspecting the OOD robustness of generative IR models we aim to contribute to the development of more reliable IR models. The code is available at \url{https://github.com/Davion-Liu/GR_OOD}.
Position-aware Graph Transformer for Recommendation
Authors: Jiajia Chen, Jiancan Wu, Jiawei Chen, Chongming Gao, Yong Li, Xiang Wang
Date: 2024-12-25 00:00:00
Tasks: Collaborative Filtering, Position

Collaborative recommendation fundamentally involves learning high-quality user and item representations from interaction data. Recently, graph convolution networks (GCNs) have advanced the field by utilizing high-order connectivity patterns in interaction graphs, as evidenced by state-of-the-art methods like PinSage and LightGCN. However, one key limitation has not been well addressed in existing solutions: capturing long-range collaborative filtering signals, which are crucial for modeling user preference. In this work, we propose a new graph transformer (GT) framework -- \textit{Position-aware Graph Transformer for Recommendation} (PGTR), which combines the global modeling capability of Transformer blocks with the local neighborhood feature extraction of GCNs. The key insight is to explicitly incorporate node position and structure information from the user-item interaction graph into GT architecture via several purpose-designed positional encodings. The long-range collaborative signals from the Transformer block are then combined linearly with the local neighborhood features from the GCN backbone to enhance node embeddings for final recommendations. Empirical studies demonstrate the effectiveness of the proposed PGTR method when implemented on various GCN-based backbones across four real-world datasets, and the robustness against interaction sparsity as well as noise.
Asymptotic Properties of the Maximum Likelihood Estimator for Markov-switching Observation-driven Models
Authors: Frederik Krabbe
Date: 2024-12-27 00:00:00
Tasks: All

A Markov-switching observation-driven model is a stochastic process $((S_t,Y_t))_{t \in \mathbb{Z}}$ where (i) $(S_t)_{t \in \mathbb{Z}}$ is an unobserved Markov process taking values in a finite set and (ii) $(Y_t)_{t \in \mathbb{Z}}$ is an observed process such that the conditional distribution of $Y_t$ given all past $Y$'s and the current and all past $S$'s depends only on all past $Y$'s and $S_t$. In this paper, we prove the consistency and asymptotic normality of the maximum likelihood estimator for such model. As a special case hereof, we give conditions under which the maximum likelihood estimator for the widely applied Markov-switching generalised autoregressive conditional heteroscedasticity model introduced by Haas et al. (2004b) is consistent and asymptotic normal.
Using Ordinal Voting to Compare the Utilitarian Welfare of a Status Quo and A Proposed Policy: A Simple Nonparametric Analysis
Authors: Charles F. Manski
Date: 2024-12-25 00:00:00

The relationship of policy choice by majority voting and by maximization of utilitarian welfare has long been discussed. I consider choice between a status quo and a proposed policy when persons have interpersonally comparable cardinal utilities taking values in a bounded interval, voting is compulsory, and each person votes for a policy that maximizes utility. I show that knowledge of the attained status quo welfare and the voting outcome yields an informative bound on welfare with the proposed policy. The bound contains the value of status quo welfare, so the better utilitarian policy is not known. The minimax-regret decision and certain Bayes decisions choose the proposed policy if its vote share exceeds the known value of status quo welfare. This procedure differs from majority rule, which chooses the proposed policy if its vote share exceeds 1/2.
Sanctions and Venezuelan Migration
Authors: Francisco Rodríguez
Date: 2024-12-26 00:00:00

This paper examines the potential impact of different US economic sanctions policies on Venezuelan migration flows. I consider three possible departures from the current status quo in which selected oil companies are permitted to conduct transactions with Venezuela's state-owned oil sector: a return to maximum pressure, characterized by intensive use of secondary sanctions, a more limited tightening that would revoke only the current Chevron license, and a complete lifting of economic sanctions. I find that sanctions significantly influence migration patterns by disrupting oil revenues, which fund imports critical to productivity in the non-oil sector. Reimposing maximum pressure sanctions would lead to an estimated one million additional Venezuelans emigrating over the next five years compared to a baseline scenario of no economic sanctions. If the US aims to address the Venezuelan migrant crisis effectively, a policy of engagement and lifting economic sanctions appears more likely to stabilize migration flows than a return to maximum pressure strategies.
Nonparametric Estimation of Matching Efficiency and Elasticity in a Spot Gig Work Platform: 2019-2023
Authors: Hayato Kanayama, Suguru Otani
Date: 2024-12-26 00:00:00

This paper provides new evidence on spot gig work platforms for unemployed workers searching for occupations with minimal educational or experience requirements in Japan. Using proprietary data from a private online spot work matching platform, Timee, it examines trends in key variables such as the numbers of unemployed users, vacancies, hires, and labor market tightness. The study compares these trends with part-time worker data from the public employment platform, Hello Work. The private platform shows a significant market expansion from December 2019 to December 2023. Applying a novel nonparametric approach, the paper finds greater variability in efficiency and higher elasticity, with elasticity with respect to the number of users fluctuating from below 0.7 to above 1.5, and elasticity with respect to the number of vacancies often exceeding 1.0, which is higher than Hello Work. Lastly, the study highlights less geographical heterogeneity of the spot work compared to Hello Work.
A Certain Notion of Strategy Freedom under Retail Competition in Claims Problems
Authors: Kentarô Yamamoto
Date: 2024-12-27 00:00:00

A new axiom for rules for claims problems is introduced. It strengthens a condition studied in supply chain literature, which forces rules to disincentivize order inflation under capacity allocation and retail competition. The relevance of the axiom is further demonstrated by one of the main results of the present article: it characterizes the weighted constrained equal awards rule together with known natural axioms.
Market allocations under conflation of goods
Authors: Niccolò Urbinat, Marco LiCalzi
Date: 2024-12-25 00:00:00

We study competitive equilibria in exchange economies when a continuum of goods is conflated into a finite set of commodities. The design of conflation choices affects the allocation of scarce resources among agents, by constraining trading opportunities and shifting competitive pressures. We demonstrate the consequences on relative prices, trading positions, and welfare.
Towards a Single ASR Model That Generalizes to Disordered Speech
Authors: Jimmy Tobin, Katrin Tomanek, Subhashini Venugopalan
Date: 2024-12-26 00:00:00
Tasks: Fairness, speech-recognition, Speech Recognition

This study investigates the impact of integrating a dataset of disordered speech recordings ($\sim$1,000 hours) into the fine-tuning of a near state-of-the-art ASR baseline system. Contrary to what one might expect, despite the data being less than 1% of the training data of the ASR system, we find a considerable improvement in disordered speech recognition accuracy. Specifically, we observe a 33% improvement on prompted speech, and a 26% improvement on a newly gathered spontaneous, conversational dataset of disordered speech. Importantly, there is no significant performance decline on standard speech recognition benchmarks. Further, we observe that the proposed tuning strategy helps close the gap between the baseline system and personalized models by 64% highlighting the significant progress as well as the room for improvement. Given the substantial benefits of our findings, this experiment suggests that from a fairness perspective, incorporating a small fraction of high quality disordered speech data in a training recipe is an easy step that could be done to make speech technology more accessible for users with speech disabilities.
Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained Model for Acoustic Traffic Monitoring
Authors: Shitong Fan, Feiyang Xiao, Wenbo Wang, Shuhan Qi, Qiaoxi Zhu, Wenwu Wang, Jian Guan
Date: 2024-12-26 00:00:00
Tasks: Graph Attention, Sound Source Localization

Microphone array techniques are widely used in sound source localization and smart city acoustic-based traffic monitoring, but these applications face significant challenges due to the scarcity of labeled real-world traffic audio data and the complexity and diversity of application scenarios. The DCASE Challenge's Task 10 focuses on using multi-channel audio signals to count vehicles (cars or commercial vehicles) and identify their directions (left-to-right or vice versa). In this paper, we propose a graph-enhanced dual-stream feature fusion network (GEDF-Net) for acoustic traffic monitoring, which simultaneously considers vehicle type and direction to improve detection. We propose a graph-enhanced dual-stream feature fusion strategy which consists of a vehicle type feature extraction (VTFE) branch, a vehicle direction feature extraction (VDFE) branch, and a frame-level feature fusion module to combine the type and direction feature for enhanced performance. A pre-trained model (PANNs) is used in the VTFE branch to mitigate data scarcity and enhance the type features, followed by a graph attention mechanism to exploit temporal relationships and highlight important audio events within these features. The frame-level fusion of direction and type features enables fine-grained feature representation, resulting in better detection performance. Experiments demonstrate the effectiveness of our proposed method. GEDF-Net is our submission that achieved 1st place in the DCASE 2024 Challenge Task 10.
Neuromorphic Dual-channel Encoding of Luminance and Contrast
Authors: Ernest Greene
Date: 2024-12-26 00:00:00

There is perceptual and physiological evidence that the retina registers and signals luminance and luminance contrast using dual-channel mechanisms. This process begins in the retina, wherein the luminance of a uniform zone and differentials of luminance in neighboring zones determine the degree of brightness or darkness of the zones. The neurons that process the information can be classified as "bright" or "dark" channels. The present paper provides an overview of these retinal mechanisms along with evidence that they provide brightness judgments that are log-linear across roughly seven orders of magnitude.
Multi-population Differential Evolution for RSS based Cooperative Localization in Wireless Sensor Networks with Limited Communication Range
Authors: Lismer Andres Caceres Najarro, Iickho Song, Muhammad Salman, Kiseon Kim
Date: 2024-12-27 00:00:00

This paper presents a novel approach to deal with the cooperative localization problem in wireless sensor networks based on received signal strength measurements. In cooperative scenarios, the cost function of the localization problem becomes increasingly nonlinear and nonconvex due to the heightened interaction between sensor nodes, making the estimation of the positions of the target nodes more challenging. Although most of existing cooperative localization algorithms assure acceptable localization accuracy, their computational complexity increases dramatically, which may restrict their applicability. To reduce the computational complexity and provide competitive localization accuracy at the same time, we propose a localization algorithm based on the differential evolution with multiple populations, opposite-based learning, redirection, and anchoring. In this work, the cooperative localization cost function is split into several simpler cost functions, each of which accounts only for one individual target node. Then, each cost function is solved by a dedicated population of the proposed algorithm. In addition, an enhanced version of the proposed algorithm which incorporates the population midpoint scheme for further improvement in the localization accuracy is devised. Simulation results demonstrate that the proposed algorithms provide comparative localization accuracy with much lower computational complexity compared with the state-of-the-art algorithms.
Movable Antenna Aided Physical Layer Security with No Eavesdropper CSI
Authors: Zhenqiao Cheng, Chongjun Ouyang, Xingqi Zhang
Date: 2024-12-27 00:00:00
Tasks: Position

A novel movable antenna (MA)-aided secure transmission framework is proposed to enhance the secrecy transmission rate without relying on the eavesdropper's channel state information. Within this framework, a joint beamforming and jamming scheme is proposed, where the power of the confidential signal is minimized by optimizing the positions of the MAs, and the residual power is used to jam the eavesdropper. An efficient gradient-based method is employed to solve this non-convex problem. Numerical results are provided to demonstrate the superiority of the MA-based framework over systems using traditional fixed-position antennas in secure transmission.
Real-time Reflectance Generation for UAV Multispectral Imagery using an Onboard Downwelling Spectrometer in Varied Weather Conditions
Authors: Jiayang Xie, YuTao Shen, Haiyan Cen
Date: 2024-12-27 00:00:00

Advancements in unmanned aerial vehicle (UAV) remote sensing with spectral imaging enable efficient assessment of critical agronomic traits. However, existing reflectance calibration or generation methods suffer from limited prediction accuracy and practical flexibility. This study explores reliable and cost-efficient methods for the accurate conversion of digital number values acquired from a multispectral imager into reflectance, leveraging real-time solar spectra as references. To ensure consistent measurements of incident light, an upward gimbal-mounted downwelling spectrometer was attached to the UAV, and a sinusoidal model was developed to correct for solar position variability. Using principal component analysis on the reference solar spectrum for band selection, a multiple linear regression model with four sensitive bands (4-Band MLR) and a 30 nm bandwidth achieved performance comparable to the direct correction method. The root mean square error (RMSE) for reflectance prediction improved by 86.1% compared to the empirical line method under fluctuating cloudy conditions and by 59.6% compared to the downwelling light sensor method averaged across different weather conditions. The RMSE was calculated as 2.24% in a ground-based diurnal validation, and 2.03% in a UAV campaign conducted at various times throughout a sunny day. Implementing the 4-Band MLR model enhanced the consistency of canopy reflectance within a homogeneous vegetation area by 95.0% during spectral imaging in a large rice field under significant cloud fluctuations. Additionally, improvements of 86.0% and 90.3% were noted for two vegetation indices: the normalized difference vegetation index (NDVI; a ratio index) and the difference vegetation index (DVI; a non-ratio index), respectively.
Exploiting Dynamic Sparsity for Near-Field Spatial Non-Stationary XL-MIMO Channel Tracking
Authors: Wenkang Xu, An Liu, Min-Jian Zhao, Giuseppe Caire, Yik-Chung Wu
Date: 2024-12-27 00:00:00
Tasks: Bayesian Inference, Compressive Sensing

This work considers a spatial non-stationary channel tracking problem in broadband extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. In the case of spatial non-stationary, each scatterer has a certain visibility region (VR) over antennas and power change may occur among visible antennas. Concentrating on the temporal correlation of XL-MIMO channels, we design a three-layer Markov prior model and hierarchical two-dimensional (2D) Markov model to exploit the dynamic sparsity of sparse channel vectors and VRs, respectively. Then, we formulate the channel tracking problem as a bilinear measurement process, and a novel dynamic alternating maximum a posteriori (DA-MAP) framework is developed to solve the problem. The DA-MAP contains four basic modules: channel estimation module, VR detection module, grid update module, and temporal correlated module. Specifically, the first module is an inverse-free variational Bayesian inference (IF-VBI) estimator that avoids computational intensive matrix inverse each iteration; the second module is a turbo compressive sensing (Turbo-CS) algorithm that only needs small-scale matrix operations in a parallel fashion; the third module refines the polar-delay domain grid; and the fourth module can process the temporal prior information to ensure high-efficiency channel tracking. Simulations show that the proposed method can achieve a significant channel tracking performance while achieving low computational overhead.
Interference-Robust Broadband Rapidly-Varying MIMO Communications: A Knowledge-Data Dual Driven Framework
Authors: Jingjing Zhao, Jing Su, Kaiquan Cai, YanBo Zhu, Yuanwei Liu, Naofal Al-Dhahir
Date: 2024-12-26 00:00:00

A novel time-efficient framework is proposed for improving the robustness of a broadband multiple-input multiple-output (MIMO) system against unknown interference under rapidly-varying channels. A mean-squared error (MSE) minimization problem is formulated by optimizing the beamformers employed. Since the unknown interference statistics are the premise for solving the formulated problem, an interference statistics tracking (IST) module is first designed. The IST module exploits both the time- and spatial-domain correlations of the interference-plus-noise (IPN) covariance for the future predictions with data training. Compared to the conventional signal-free space sampling approach, the IST module can realize zero-pilot and low-latency estimation. Subsequently, an interference-resistant hybrid beamforming (IR-HBF) module is presented, which incorporates both the prior knowledge of the theoretical optimization method as well as the data-fed training. Taking advantage of the interpretable network structure, the IR-HBF module enables the simplified mapping from the interference statistics to the beamforming weights. The simulations are executed in high-mobility scenarios, where the numerical results unveil that: 1) the proposed IST module attains promising prediction accuracy compared to the conventional counterparts under different snapshot sampling errors; and 2) the proposed IR-HBF module achieves lower MSE with significantly reduced computational complexity.
Advancements in Terahertz Antenna Design
Authors: Sasmita Dash, Amalendu Patnaik
Date: 2024-12-26 00:00:00

The promising way to provide sufficient transmission capacity is by accessing transmission bands at higher carrier frequencies. This desire for higher carrier frequency or more bandwidth led the researchers to take advantage of the terahertz (THz) spectrum. The opportunity for large bandwidth in the THz band leads to the possibility of easy, high data rate transmission. In spite of the advantages, the THz band suffers from large free space path loss. In the development of THz communication systems, the antenna is the most significant component. The focus is especially on designing highly directive antennas because they enhance the performance of the overall system by compensating for the large path loss at THz and thus improving the signal-to-noise ratio. This chapter presents different types of THz antennas, including planar, reflectarray, horn antenna, and lens antenna. Emphasis has been made to present the latest trend of designing THz antennas using carbon-based materials, such as graphene and carbon nanotubes. The performance of these antennas has been compared with that of traditional copper-based THz antennas by critically analyzing their properties. A brief discussion on THz power sources is included in this chapter for completeness. A comprehensive discussion on different fabrication techniques has been provided to appraise the reader of the general fabrication processes of THz components.
Movable Intelligent Surface (MIS) for Wireless Communications: Architecture, Modeling, Algorithm, and Prototyping
Authors: Ziyuan Zheng, Qingqing Wu, Wen Chen, Xiangming Wu, Weiren Zhu
Date: 2024-12-26 00:00:00
Tasks: Position, Scheduling

Reconfigurable intelligent surfaces enhance wireless systems by reshaping propagation environments. However, dynamic metasurfaces (MSs) with numerous phase-shift elements incur undesired control and hardware costs. In contrast, static MSs (SMSs), configured with static phase shifts pre-designed for specific communication demands, offer a cost-effective alternative by eliminating element-wise tuning. Nevertheless, SMSs typically support a single beam pattern with limited flexibility. In this paper, we propose a novel Movable Intelligent Surface (MIS) technology that enables dynamic beamforming while maintaining static phase shifts. Specifically, we design a MIS architecture comprising two closely stacked transmissive MSs: a larger fixed-position MS 1 and a smaller movable MS 2. By differentially shifting MS 2's position relative to MS 1, the MIS synthesizes distinct beam patterns. Then, we model the interaction between MS 2 and MS 1 using binary selection matrices and padding vectors and formulate a new optimization problem that jointly designs the MIS phase shifts and selects shifting positions for worst-case signal-to-noise ratio maximization. This position selection, equal to beam pattern scheduling, offers a new degree of freedom for RIS-aided systems. To solve the intractable problem, we develop an efficient algorithm that handles unit-modulus and binary constraints and employs manifold optimization methods. Finally, extensive validation results are provided. We implement a MIS prototype and perform proof-of-concept experiments, demonstrating the MIS's ability to synthesize desired beam patterns that achieve one-dimensional beam steering. Numerical results show that by introducing MS 2 with a few elements, MIS effectively offers beamforming flexibility for significantly improved performance. We also draw insights into the optimal MIS configuration and element allocation strategy.