pwc-archive/papers-with-abstracts huggingface dataset:
Vulnerabilities and capabilities in the EU Automotive industry: Leveraging Input-Output Analysis and Economic Complexity
Authors: Lorenzo Cresti, Dario Mazzilli, Aurelio Patelli, Angelica Sbardella, Andrea Tacchella
Date: 2025-01-03 00:00:00

This paper investigates the structural vulnerabilities and competitive dynamics of the EU27 automotive sector, with a focus on the complexity and the fragmentation of production processes across global value chains. Employing a mixed-methods approach, our analysis integrates input-output tables to quantify the sector's reliance on non-EU economic branches, alongside an economic complexity framework to assess the underlying productive capabilities of European countries in automotive-related industries. The findings indicate an increasing dependency on extra-EU suppliers, particularly China, for critical components such as lithium-ion batteries, which heightens supply chain risks. Currently, Eastern European countries-most notably Poland, Czechia, and Hungary-have enhanced their competitiveness in the production of automotive components, surpassing traditional leaders such as Germany. The paper advances the literature by providing a novel, granular list of 6-digit products within the automotive supply chain and offers new insights into the challenges posed by the ongoing electric mobility transition in the European Union, particularly in relation to electric accumulators.
Dissertation Paths: Advisors and Students in the Economics Research Production Function
Authors: Joshua Angrist, Marc Diederichs
Date: 2025-01-02 00:00:00

Elite economics PhD programs aim to train graduate students for a lifetime of academic research. This paper asks how advising affects graduate students' post-PhD research productivity. Advising is highly concentrated: at the eight highly-selective schools in our study, a minority of advisors do most of the advising work. We quantify advisor attributes such as an advisor's own research output and aspects of the advising relationship like coauthoring and research field affinity that might contribute to student research success. Students advised by research-active, prolific advisors tend to publish more, while coauthoring has no effect. Student-advisor research affinity also predicts student success. But a school-level aggregate production function provides much weaker evidence of causal effects, suggesting that successful advisors attract students likely to succeed-without necessarily boosting their students' chances of success. Evidence for causal effects is strongest for a measure of advisors' own research output. Aggregate student research output appears to scale linearly with graduate student enrollment, with no evidence of negative class-size effects. An analysis of gender differences in research output shows male and female graduate students to be equally productive in the first few years post-PhD, but female productivity peaks sooner than male productivity.
Agency-Driven Labor Theory: A Framework for Understanding Human Work in the AI Age
Authors: Venkat Ram Reddy Ganuthula
Date: 2024-12-30 00:00:00

This paper introduces Agency-Driven Labor Theory as a new theoretical framework for understanding human work in AI-augmented environments. While traditional labor theories have focused primarily on task execution and labor time, ADLT proposes that human labor value is increasingly derived from agency - the capacity to make informed judgments, provide strategic direction, and design operational frameworks for AI systems. The paper presents a mathematical framework expressing labor value as a function of agency quality, direction effectiveness, and outcomes, providing a quantifiable approach to analyzing human value creation in AI-augmented workplaces. Drawing on recent work in organizational economics and knowledge worker productivity, ADLT explains how human workers create value by orchestrating complex systems that combine human and artificial intelligence. The theory has significant implications for job design, compensation structures, professional development, and labor market dynamics. Through applications across various sectors, the paper demonstrates how ADLT can guide organizations in managing the transition to AI-augmented operations while maximizing human value creation. The framework provides practical tools for policymakers and educational institutions as they prepare workers for a labor market where value creation increasingly centers on agency and direction rather than execution.
Paired Course and Dorm Allocation
Authors: Eric Gao
Date: 2025-01-05 00:00:00

Consider a university administrator who must assign students to both courses and dorms and runs (random) serial dictatorship independently in each market. While serial dictatorship is efficient for each individual matching problem holding the other fixed, Pareto improvements can be found via students jointly trading their allocated course and dorm. To align priorities with students' relative preferences between courses and dorms, we introduce paired serial dictatorship: a novel mechanism where students signal relative preferences to influence their priority in each market. Any deterministic allocation that arises in equilibrium is Pareto efficient, pointing towards ad-hoc tie-breaking as the key barrier to optimality. When students differ only in relative preferences, paired serial dictatorship ex-ante Pareto dominates running random serial dictatorship in each market. Such gains are realized despite students never reporting full preferences, a key advantage over other combinatorial allocation mechanisms that scale poorly with problem size.
An integral transformation approach to differential games: a climate model application
Authors: Raouf Boucekkine, Giorgio Fabbri, Salvatore Federico, Fausto Gozzi, Ted Loch-Temzelides, Cristiano Ricci
Date: 2025-01-03 00:00:00

We develop an Integral Transformation Method (ITM) for the study of suitable optimal control and differential game models. This allows for a solution to such dynamic problems to be found through solving a family of optimization problems parametrized by time. The method is quite flexible, and it can be used in several economic applications where the state equation and the objective functional are linear in a state variable. We illustrate the ITM in the context of a two-country integrated assessment climate model. We characterize emissions, consumption, transfers, and welfare by computing the Nash equilibria of the associated dynamic game. We then compare them to efficiency benchmarks. Further, we apply the ITM in a robust control setup, where we investigate how (deep) uncertainty affects climate outcomes.
Communication Efficient Cooperative Edge AI via Event-Triggered Computation Offloading
Authors: You Zhou, Changsheng You, Kaibin Huang
Date: 2025-01-01 00:00:00
Tasks: Autonomous Driving

Rare events, despite their infrequency, often carry critical information and require immediate attentions in mission-critical applications such as autonomous driving, healthcare, and industrial automation. The data-intensive nature of these tasks and their need for prompt responses, combined with designing edge AI (or edge inference), pose significant challenges in systems and techniques. Existing edge inference approaches often suffer from communication bottlenecks due to high-dimensional data transmission and fail to provide timely responses to rare events, limiting their effectiveness for mission-critical applications in the sixth-generation (6G) mobile networks. To overcome these challenges, we propose a channel-adaptive, event-triggered edge-inference framework that prioritizes efficient rare-event processing. Central to this framework is a dual-threshold, multi-exit architecture, which enables early local inference for rare events detected locally while offloading more complex rare events to edge servers for detailed classification. To further enhance the system's performance, we developed a channel-adaptive offloading policy paired with an online algorithm to dynamically determine the optimal confidence thresholds for controlling offloading decisions. The associated optimization problem is solved by reformulating the original non-convex function into an equivalent strongly convex one. Using deep neural network classifiers and real medical datasets, our experiments demonstrate that the proposed framework not only achieves superior rare-event classification accuracy, but also effectively reduces communication overhead, as opposed to existing edge-inference approaches.
Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging
Authors: Yang Qi, Jiaxin Cai, Jing Lu, Runqing Xiong, Rongshang Chen, Liping Zheng, Duo Ma
Date: 2025-01-01 00:00:00
Tasks: Diagnostic

Prenatal ultrasound evaluates fetal growth and detects congenital abnormalities during pregnancy, but the examination of ultrasound images by radiologists requires expertise and sophisticated equipment, which would otherwise fail to improve the rate of identifying specific types of fetal central nervous system (CNS) abnormalities and result in unnecessary patient examinations. We construct a deep learning model to improve the overall accuracy of the diagnosis of fetal cranial anomalies to aid prenatal diagnosis. In our collected multi-center dataset of fetal craniocerebral anomalies covering four typical anomalies of the fetal central nervous system (CNS): anencephaly, encephalocele (including meningocele), holoprosencephaly, and rachischisis, patient-level prediction accuracy reaches 94.5%, with an AUROC value of 99.3%. In the subgroup analyzes, our model is applicable to the entire gestational period, with good identification of fetal anomaly types for any gestational period. Heatmaps superimposed on the ultrasound images not only provide a visual interpretation for the algorithm but also provide an intuitive visual aid to the physician by highlighting key areas that need to be reviewed, helping the physician to quickly identify and validate key areas. Finally, the retrospective reader study demonstrates that by combining the automatic prediction of the DL system with the professional judgment of the radiologist, the diagnostic accuracy and efficiency can be effectively improved and the misdiagnosis rate can be reduced, which has an important clinical application prospect.
Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions
Authors: Yash Agrawal, Srinidhi Balasubramanian, Rahul Meena, Rohail Alam, Himanshu Malviya, Rohini P
Date: 2024-12-29 00:00:00
Tasks: Data Augmentation, Image Segmentation, Optical Character Recognition, Optical Character Recognition (OCR), Semantic Segmentation, Transfer Learning

This research paper delves into the development of an Optical Character Recognition (OCR) system for the recognition of Ashokan Brahmi characters using Convolutional Neural Networks. It utilizes a comprehensive dataset of character images to train the models, along with data augmentation techniques to optimize the training process. Furthermore, the paper incorporates image preprocessing to remove noise, as well as image segmentation to facilitate line and character segmentation. The study mainly focuses on three pre-trained CNNs, namely LeNet, VGG-16, and MobileNet and compares their accuracy. Transfer learning was employed to adapt the pre-trained models to the Ashokan Brahmi character dataset. The findings reveal that MobileNet outperforms the other two models in terms of accuracy, achieving a validation accuracy of 95.94% and validation loss of 0.129. The paper provides an in-depth analysis of the implementation process using MobileNet and discusses the implications of the findings. The use of OCR for character recognition is of significant importance in the field of epigraphy, specifically for the preservation and digitization of ancient scripts. The results of this research paper demonstrate the effectiveness of using pre-trained CNNs for the recognition of Ashokan Brahmi characters.
Piecewise Beam Training and Channel Estimation for RIS-Aided Near-Field Communications
Authors: Jeongjae Lee, SongNam Hong
Date: 2025-01-06 00:00:00

In this paper, we investigate the channel estimation challenge in reconfigurable intelligent surface (RIS)-aided near-field communication systems. Current channel estimation techniques require substantial pilot overhead and computational complexity, especially when the number of RIS elements is extremely large. To address this issue, we introduce a two-timescale channel estimation strategy that leverages the asymmetric coherence times of both the RIS-base station (BS) channel and the User-RIS channel. We derive a time-scaling property indicating that, for any two effective channels within the longer coherence time, one effective channel can be represented as the product of a vector, termed the small-timescale effective channel, and the other effective channel. By integrating the estimated effective channel from the initial time block with observations from our piecewise beam training, we present an efficient method for estimating subsequent small-timescale effective channels. We theoretically verify the efficacy of the proposed RIS design and demonstrate, through simulations, that our channel estimation method outperforms existing methods in pilot overhead and computational complexity across various realistic channel models.
Fluid Antennas: Reshaping Intrinsic Properties for Flexible Radiation Characteristics in Intelligent Wireless Networks
Authors: Wen-Jun Lu, Chun-Xing He, Yongxu Zhu, Kin-Fai Tong, Kai-Kit Wong, Hyundong Shin, Tie Jun Cui
Date: 2025-01-06 00:00:00

Fluid antennas present a relatively new idea for harnessing the fading and interference issues in multiple user wireless systems, such as 6G. Here, we systematically compare their unique radiation beam forming mechanism to the existing multiple-antenna systems in a wireless system. Subsequently, a unified mathematical model for fluid antennas is deduced based on the eigenmode theory. As mathematically derived from the multimode resonant theory, the spectral expansion model of any antennas which occupy variable spaces and have changeable feeding schemes can be generalized as fluid antennas. Non-liquid and liquid fluid antenna examples are presented, simulated and discussed. The symmetry or modal parity of eigenmodes is explored as an additional degree of freedom to design the fluid antennas for future wireless systems. As conceptually deduced and illustrated, the multi-dimensional and continuously adaptive ability of eigenmodes can be considered as the most fundamental intrinsic characteristic of the fluid antenna systems. It opens an uncharted area in the developments of intelligent antennas (IAs), which brings more flexibility to on-demand antenna beam null manipulating techniques for future wireless applications.
Channel Modeling and Rate Analysis of Optical Inter-Satellite Link (OISL)
Authors: Bodong Shang, Shuo Zhang, Zi Jing Wong
Date: 2025-01-06 00:00:00

Optical inter-satellite links (OISLs) improve connectivity between satellites in space. They offer advantages such as high-throughput data transfer and reduced size, weight, and power requirements compared to traditional radio frequency transmission. However, the channel model and communication performance for long-distance inter-satellite laser transmission still require in-depth study. In this paper, we first develop a channel model for OISL communication within non-terrestrial networks (NTN) by accounting for pointing errors caused by satellite jitter and tracking noise. We derive the distributions of the channel state arising from these pointing errors and calculate their average value. Additionally, we determine the average achievable data rate for OISL communication in NTN and design a cooperative OISL system, highlighting a trade-off between concentrating beam energy and balancing misalignment. We calculate the minimum number of satellites required in cooperative OISLs to achieve a targeted data transmission size while adhering to latency constraints. This involves exploring the balance between the increased data rate of each link and the cumulative latency across all links. Finally, simulation results validate the effectiveness of the proposed analytical model and provide insights into the optimal number of satellites needed for cooperative OISLs and the optimal laser frequency to use.
An Efficient Pre-Processing Method for 6G Dynamic Ray-Tracing Channel Modeling
Authors: Songjiang Yang, Cheng-Xiang Wang, Yinghua Wang, Jie Huang, Yuyang Zhou, el-Hadi M. Aggoune
Date: 2025-01-06 00:00:00

The ray-tracing is often employed in urban areas for channel modeling with high accuracy but encounters a substantial computational complexity for high mobility scenarios. In this paper, we propose a novel pre-processing method for dynamic ray-tracing to reduce the computational burden in high-mobility scenarios by prepending the intersection judgment to the pre-processing stage. The proposed method generates an inter-visibility matrix that establishes visibility relationships among static objects in the environment considering the intersection judgment. Moreover, the inter-visibility matrix can be employed to create the inter-visibility table for mobile transmitters and receivers, which can improve the efficiency of constructing an image tree for the three-dimensional (3D) dynamic ray-tracing method. The results show that the proposed pre-processing method in dynamic ray-tracing has considerable time-saving compared with the traditional method while maintaining the same accuracy. The channel characteristics computed by the proposed method can well match to the channel measurements.
A Large-dimensional Analysis of ESPRIT DoA Estimation: Inconsistency and a Correction via RMT
Authors: Zhengyu Wang, Wei Yang, Xiaoyi Mai, Zenan Ling, Zhenyu Liao, Robert C. Qiu
Date: 2025-01-06 00:00:00

In this paper, we perform asymptotic analyses of the widely used ESPRIT direction-of-arrival (DoA) estimator for large arrays, where the array size $N$ and the number of snapshots $T$ grow to infinity at the same pace. In this large-dimensional regime, the sample covariance matrix (SCM) is known to be a poor eigenspectral estimator of the population covariance. We show that the classical ESPRIT algorithm, that relies on the SCM, and as a consequence of the large-dimensional inconsistency of the SCM, produces inconsistent DoA estimates as $N,T \to \infty$ with $N/T \to c \in (0,\infty)$, for both widely- and closely-spaced DoAs. Leveraging tools from random matrix theory (RMT), we propose an improved G-ESPRIT method and prove its consistency in the same large-dimensional setting. From a technical perspective, we derive a novel bound on the eigenvalue differences between two potentially non-Hermitian random matrices, which may be of independent interest. Numerical simulations are provided to corroborate our theoretical findings.
New Paradigm for Unified Near-Field and Far-Field Wireless Communications
Authors: Zhaocheng Wang, Haochen Wu, Yuanbin Chen, Liyang Lu
Date: 2025-01-06 00:00:00

Current Type I and Type II codebooks in fifth generation (5G) wireless communications are limited in supporting the coexistence of far-field and near-field user equipments, as they are exclusively designed for far-field scenarios. To fill this knowledge gap and encourage relevant proposals by the 3rd Generation Partnership Project (3GPP), this article provides a novel codebook to facilitate a unified paradigm for the coexistence of far-field and near-field contexts. It ensures efficient precoding for all user equipments (UEs), while removing the need for the base station to identify whether one specific UE stays in either near-field or far-field regions. Additionally, our proposed codebook ensures compliance with current 3GPP standards for working flow and reference signals. Simulation results demonstrate the superior performance and versatility of our proposed codebook, validating its effectiveness in unifying near-field and far-field precoding for sixth-generation (6G) multiple-input multiple-output (MIMO) systems.
A Novel Comprehensive Multiport Network Model for Stacked Intelligent Metasurfaces (SIM) Characterization and Optimization
Authors: Andrea Abrardo, Giulio Bartoli, Alberto Toccafondi
Date: 2025-01-05 00:00:00

Reconfigurable Intelligent Surfaces (RIS) represent transformative technologies for next-generation wireless communications, offering advanced control over electromagnetic wave propagation. While RIS have been extensively studied, Stacked Intelligent Metasurfaces (SIM), which extend the RIS concept to multi-layered systems, present significant modeling and optimization challenges. This work addresses these challenges by introducing an optimization framework for SIM that, unlike previous approaches, is based on a comprehensive model without relying on specific assumptions, allowing for broader applicability of the results. We first present a model based on multi-port network theory for characterizing a general electromagnetic collaborative object (ECO) and derive a framework for ECO optimization. We then introduce the SIM as an ECO with a specific architecture, offering insights into SIM optimization for various configurations and discussing the complexities associated with each case. Finally, we demonstrate that the comprehensive model considered in this work simplifies to the model traditionally used in the literature when the assumption of unilateral propagation between the levels of the SIM is made, and mutual coupling between the SIM elements is neglected. To assess the applicability of these assumptions, a case study focused on the realization of a 2D DFT was undertaken. In this context, we highlight that these assumptions introduce a significant mismatch between the SIM model and its behavior as described by the complete model, making these approximations inadequate for optimizing the SIM. Conversely, we show that employing the complete model proposed in this paper can yield excellent performance.
Scaling Achievable Rates in SIM-aided MIMO Systems with Metasurface Layers: A Hybrid Optimization Framework
Authors: Eduard E. Bahingayi, Nemanja Stefan Perović, Le-Nam Tran
Date: 2025-01-05 00:00:00

We investigate the achievable rate (AR) of a stacked intelligent metasurface (SIM)-aided holographic multiple-input multiple-output (HMIMO) system by jointly optimizing the SIM phase shifts and power allocation. Contrary to earlier studies suggesting that the AR decreases when the number of metasurface layers increases past a certain point for \emph{a fixed SIM thickness}, our findings demonstrate consistent increase. To achieve this, we introduce two problem formulations: one based on directly maximizing the AR (RMax) and the other focused on minimizing inter-stream interference (IMin). To solve the RMax problem, we apply Riemannian manifold optimization (RMO) and weighted minimum mean square error (WMMSE) methods to optimize the SIM phase shifts and power allocation alternately. For the IMin problem, we derive an efficient algorithm that iteratively updates each meta-atom's phase shift using a closed-form expression while keeping others fixed. Our key contribution is integrating these two approaches, where the IMin solution initializes the SIM phase shifts in the first algorithm. This hybrid strategy enhances AR performance across varying numbers of metasurface layers. Simulation results demonstrate that the proposed algorithms outperform existing benchmarks. Most importantly, we show that increasing the number of metasurface layers while keeping the SIM thickness fixed leads to significant AR improvements.