pwc-archive/papers-with-abstracts huggingface dataset:
Technostress and Job Performance: Understanding the Negative Impacts and Strategic Responses in the Workplace
Authors: Armita Atrian, Saleh Ghobbeh
Date: 2023-11-13 00:00:00

This study delves into the increasingly pertinent issue of technostress in the workplace and its multifaceted impact on job performance. Technostress, emerging from the rapid integration of technology in professional settings, is identified as a significant stressor affecting employees across various industries. The research primarily focuses on the ways in which technostress influences job performance, both negatively and positively, depending on the context and individual coping mechanisms. Through a blend of qualitative and quantitative methodologies, including surveys and in-depth interviews, the study examines the experiences of employees from diverse sectors. It highlights how technostress manifests in different forms: from anxiety and frustration due to constant connectivity to the pressure of adapting to new technologies. The paper also explores the dual role of technology as both a facilitator and a hindrance in the workplace. Significant findings indicate that technostress adversely impacts job performance, leading to decreased productivity, diminished job satisfaction, and increased turnover intentions. However, the study also uncovers that strategic interventions, such as training programs, supportive leadership, and fostering a positive technological culture, can mitigate these negative effects. These interventions not only help in managing technostress but also in harnessing the potential of technology for enhanced job performance. Furthermore, the research proposes a model outlining the relationship between technostress, coping mechanisms, and job performance. This model serves as a framework for organizations to understand and address the challenges posed by technostress. The study concludes with recommendations for future research, particularly in exploring the long-term effects of technostress and the efficacy of various coping strategies.
Aggregation and Closed-Form Results for Nonhomothetic CES Preferences
Authors: Clement E. Bohr, Martí Mestieri, Emre Enes Yavuz
Date: 2023-11-12 00:00:00
Tasks: Form

We provide four novel results for nonhomothetic Constant Elasticity of Substitution preferences (Hanoch, 1975). First, we derive a closed-form representation of the expenditure function of nonhomothetic CES under relatively flexible distributional assumptions of demand and price distribution parameters. Second, we characterize aggregate demand from heterogeneous households in closed-form, assuming that household total expenditures follow an empirically plausible distribution. Third, we leverage these results to study the Euler equation arising from standard intertemporal consumption-saving problems featuring within-period nonhomothetic CES preferences. Finally, we show that nonhomothetic CES expenditure shares arise as the solution of a discrete-choice logit problem.
Sustainable Development Goal (SDG) 8: New Zealand Prospects while Yield Curve Inverts in Central Bank Digital Currency (CBDC) Era
Authors: Qionghua Chu
Date: 2023-11-12 00:00:00
Tasks: Relation

In the inverted yield curve environment, I intend to assess the feasibility of fulfilling Sustainable Development Goal (SDG) 8, decent work and economic growth, of the United Nations by 2030 in New Zealand. Central Bank Digital Currency (CBDC) issuance supports SDG 8, based on the Cobb-Douglas production function, the growth accounting relation, and the Theory of Aggregate Demand. Bright prospects exist for New Zealand.
Sustainable Development Goals (SDGs): New Zealand Outlook with Central Bank Digital Currency and SDG 8 Realization on the Horizon
Authors: Qionghua Chu
Date: 2023-11-12 00:00:00

Central Bank Digital Currency (CBDC) may assist New Zealand accomplish SDG 8. I aim to evaluate if SDGs could be achieved together because of mutual interactions between SDG 8 and other SDGs. The SDGs are categorized by their shared qualities to affect and effect SDG 8. Also, additional SDGs may help each other achieve. Considering the CBDC as a fundamental stimulus to achieving decent work and economic growth, detailed study and analysis of mutual interactions suggests that SDG 8 and other SDGs can be achieved.
Decision-making under risk: when is utility maximization equivalent to risk minimization?
Authors: Francesco Ruscitti, Ram Sewak Dubey, Giorgio Laguzzi
Date: 2023-11-13 00:00:00
Tasks: Decision Making

Motivated by the analysis of a general optimal portfolio selection problem, which encompasses as special cases an optimal consumption and an optimal debt-arrangement problem, we are concerned with the questions of how a personality trait like risk-perception can be formalized and whether the two objectives of utility-maximization and risk-minimization can be both achieved simultaneously. We address these questions by developing an axiomatic foundation of preferences for which utility-maximization is equivalent to minimizing a utility-based shortfall risk measure. Our axiomatization hinges on a novel axiom in decision theory, namely the risk-perception axiom.
Degree-Weighted DeGroot Learning
Authors: Chen Cheng, Xiao Han, Xin Tong, Yusheng Wu, Yiqing Xing
Date: 2023-11-13 00:00:00
Tasks: Unity

We examine belief formation in large stochastic networks under a degree-weighted DeGroot learning process. In many social settings, agents may place more trust in well-connected individuals or, conversely, discount their influence. Existing research on random networks primarily assumes that agents assign equal weights to their neighbors' opinions. We relax this assumption by allowing agents to weight neighbors based on their degree. Our main technical contribution is to derive the asymptotic properties of learning outcomes, particularly the consensus belief and convergence speed, under this degree-weighted framework. Using this result, we analyze how the weighting rule affects consensus beliefs, societal wisdom, and convergence speed. We find that a more popularity-favoring rule -- i.e., assigning greater weight to higher-degree neighbors -- harms wisdom but has a non-monotonic effect on convergence speed. Whether it accelerates or slows convergence depends on the diversity of views within high- and low-degree groups. This highlights a potential tradeoff between faster convergence and an unwise consensus in networks where agents favor highly connected neighbors.
A non-invariance result for the spatial AK model
Authors: Cristiano Ricci
Date: 2023-11-12 00:00:00

This paper deals with the positivity condition of an infinite-dimensional evolutionary equation, associated with a control problem for the optimal consumption over space. We consider a spatial growth model for capital, with production generating endogenous growth and technology of the form AK. We show that for certain initial data, even in the case of heterogeneous spatial distribution of technology and population, the solution to an auxiliary control problem that is commonly used as a candidate for the original problem is not admissible. In particular, we show that initial conditions that are non-negative, under the auxiliary optimal consumption strategy, may lead to negative capital allocations over time.
PuzzleTuning: Explicitly Bridge Pathological and Natural Image with Puzzles
Authors: Tianyi Zhang, Shangqing Lyu, Yanli Lei, Sicheng Chen, Nan Ying, Yufang He, Yu Zhao, Yunlu Feng, Hwee Kuan Lee, Guanglei Zhang
Date: 2023-11-12 00:00:00
Tasks: Self-Supervised Learning

Pathological image analysis is a crucial field in computer vision. Due to the annotation scarcity in the pathological field, pre-training with self-supervised learning (SSL) is widely applied to learn on unlabeled images. However, the current SSL-based pathological pre-training: (1) does not explicitly explore the essential focuses of the pathological field, and (2) does not effectively bridge with and thus take advantage of the knowledge from natural images. To explicitly address them, we propose our large-scale PuzzleTuning framework, containing the following innovations. Firstly, we define three task focuses that can effectively bridge knowledge of pathological and natural domain: appearance consistency, spatial consistency, and restoration understanding. Secondly, we devise a novel multiple puzzle restoring task, which explicitly pre-trains the model regarding these focuses. Thirdly, we introduce an explicit prompt-tuning process to incrementally integrate the domain-specific knowledge. It builds a bridge to align the large domain gap between natural and pathological images. Additionally, a curriculum-learning training strategy is designed to regulate task difficulty, making the model adaptive to the puzzle restoring complexity. Experimental results show that our PuzzleTuning framework outperforms the previous state-of-the-art methods in various downstream tasks on multiple datasets. The code, demo, and pre-trained weights are available at https://github.com/sagizty/PuzzleTuning.
DUBLINE: A Deep Unfolding Network for B-line Detection in Lung Ultrasound Images
Authors: Tianqi Yang, Nantheera Anantrasirichai, Oktay Karakuş, Marco Allinovi, Hatice Ceylan Koydemir, Alin Achim
Date: 2023-11-11 00:00:00
Tasks: Diagnostic, Line Detection

In the context of lung ultrasound, the detection of B-lines, which are indicative of interstitial lung disease and pulmonary edema, plays a pivotal role in clinical diagnosis. Current methods still rely on visual inspection by experts. Vision-based automatic B-line detection methods have been developed, but their performance has yet to improve in terms of both accuracy and computational speed. This paper presents a novel approach to posing B-line detection as an inverse problem via deep unfolding of the Alternating Direction Method of Multipliers (ADMM). It tackles the challenges of data labelling and model training in lung ultrasound image analysis by harnessing the capabilities of deep neural networks and model-based methods. Our objective is to substantially enhance diagnostic accuracy while ensuring efficient real-time capabilities. The results show that the proposed method runs more than 90 times faster than the traditional model-based method and achieves an F1 score that is 10.6% higher.
Near-Field Sparse Channel Estimation for Extremely Large-Scale RIS-Aided Wireless Communications
Authors: Zixing Tang, Yuanbin Chen, Ying Wang, Tianqi Mao, Qingqing Wu, Marco Di Renzo, Lajos Hanzo
Date: 2023-11-13 00:00:00
Tasks: Denoising

A significant increase in the number of reconfigurable intelligent surface (RIS) elements results in a spherical wavefront in the near field of extremely large-scale RIS (XL-RIS). Although the channel matrix of the cascaded two-hop link may become sparse in the polar-domain representation, their accurate estimation of these polar-domain parameters cannot be readily guaranteed. To tackle this challenge, we exploit the sparsity inherent in the cascaded channel. To elaborate, we first estimate the significant path-angles and distances corresponding to the common paths between the BS and the XL-RIS. Then, the individual path parameters associated with different users are recovered. This results in a two-stage channel estimation scheme, in which distinct learning-based networks are used for channel training at each stage. More explicitly, in stage I, a denoising convolutional neural network (DnCNN) is employed for treating the grid mismatches as noise to determine the true grid index of the angles and distances. By contrast, an iterative shrinkage thresholding algorithm (ISTA) based network is proposed for adaptively adjusting the column coherence of the dictionary matrix in stage II. Finally, our simulation results demonstrate that the proposed two-stage learning-based channel estimation outperforms the state-of-the-art benchmarks.
Time-Frequency Localization Characteristics of the Delay-Doppler Plane Orthogonal Pulse
Authors: Akram Shafie, Jinhong Yuan, Nan Yang, Hai Lin
Date: 2023-11-13 00:00:00

The orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has recently been proposed as a promising solution for ensuring reliable communications in high mobility scenarios. In this work, we investigate the time-frequency (TF) localization characteristics of the DD plane orthogonal pulse (DDOP), which is the prototype pulse of ODDM modulation. The TF localization characteristics examine how concentrated or spread out the energy of a pulse is in the joint TF domain. We first derive the TF localization metric, TF area (TFA), for the DDOP. Based on this result, we provide insights into the energy spread of the DDOP in the joint TF domain. Then, we delve into the potential advantages of the DDOP due to its energy spread, particularly in terms of leveraging both time and frequency diversities, and enabling high-resolution sensing. Furthermore, we determine the TFA for the recently proposed generalized design of the DDOP. Finally, we validate our analysis based on numerical results and show that the energy spread for the generalized design of the DDOP in the joint TF domain exhibits a step-wise increase as the duration of sub-pulses increases.
Cooperative Coherent Multistatic Imaging and Phase Synchronization in Networked Sensing
Authors: Dario Tagliaferri, Marco Manzoni, Marouan Mizmizi, Stefano Tebaldini, Andrea Virgilio Monti-Guarnieri, Claudio Maria Prati, Umberto Spagnolini
Date: 2023-11-13 00:00:00

Coherent multistatic radio imaging represents a pivotal opportunity for forthcoming wireless networks, which involves distributed nodes cooperating to achieve accurate sensing resolution and robustness. This paper delves into cooperative coherent imaging for vehicular radar networks. Herein, multiple radar-equipped vehicles cooperate to improve collective sensing capabilities and address the fundamental issue of distinguishing weak targets in close proximity to strong ones, a critical challenge for vulnerable road users protection. We prove the significant benefits of cooperative coherent imaging in the considered automotive scenario in terms of both probability of correct detection, evaluated considering several system parameters, as well as resolution capabilities, showcased by a dedicated experimental campaign wherein the collaboration between two vehicles enables the detection of the legs of a pedestrian close to a parked car. Moreover, as \textit{coherent} processing of several sensors' data requires very tight accuracy on clock synchronization and sensor's positioning -- referred to as \textit{phase synchronization} -- (such that to predict sensor-target distances up to a fraction of the carrier wavelength), we present a general three-step cooperative multistatic phase synchronization procedure, detailing the required information exchange among vehicles in the specific automotive radar context and assessing its feasibility and performance by hybrid Cram\'er-Rao bound.
Joint Computation and Communication Resource Optimization for Beyond Diagonal UAV-IRS Empowered MEC Networks
Authors: Asad Mahmood, Thang X. Vu, Wali Ullah Khan, Symeon Chatzinotas, Björn Ottersten
Date: 2023-11-13 00:00:00
Tasks: Edge-computing

Recent advancements in 6G systems signal a leap towards universal connectivity and ultra-reliable, low-latency communications for real-time data devices. Yet, these advancements encounter obstacles such as limited device battery life and computational power, along with urban signal blockages. To counter these, Intelligent Reconfigurable Surfaces (IRS) within Mobile Edge Cloud (MEC) infrastructures offer enhanced computing to overcome device limitations and create alternative communication paths. Despite these improvements, connectivity issues remain for remote areas. Our paper presents the Beyond Diagonal IRS (BD-IRS or IRS 2.0), integrated with UAVs in MEC networks (BD-IRS-UAV), providing on-demand links for remote users to offload tasks, tackling resource and battery limitations. We propose a joint optimization strategy to reduce system's worst-case latency and UAV hovering time by optimizing BD-IRS-UAV deployment and resource allocation. This challenge is approached by dividing it into two sub-problems: BD-IRS-UAV Placement and Computational Resource Optimization, and Communication Resource Optimization, each solved iteratively. This design significantly enhances system performance, showing a $17.75\%$ increase over traditional diagonal IRS and a $25.43\%$ improvement over IRS on buildings, with a $13.44\%$ enhancement in worst-case latency compared to binary offloading schemes.
Communication-Assisted Sensing in 6G Networks
Authors: Fuwang Dong, Fan Liu, Shihang Lu, Yifeng Xiong, Qixun Zhang, Zhiyong Feng, Feifei Gao
Date: 2023-11-13 00:00:00
Tasks: Integrated sensing and communication, ISAC

Exploring the mutual benefit and reciprocity of sensing and communication (S\&C) functions is fundamental to realizing deeper integration for integrated sensing and communication (ISAC) systems. This paper investigates a novel communication-assisted sensing (CAS) system within 6G perceptive networks, where the base station actively senses the targets through device-free wireless sensing and simultaneously transmits the estimated information to end-users. In such a CAS system, we first establish an optimal waveform design framework based on the rate-distortion (RD) and source-channel separation (SCT) theorems. After analyzing the relationships between the sensing distortion, coding rate, and communication channel capacity, we propose two distinct waveform design strategies in the scenario of target impulse response estimation. In the separated S\&C waveforms scheme, we equivalently transform the original problem into a power allocation problem and develop a low-complexity one-dimensional search algorithm, shedding light on a notable power allocation tradeoff between the S\&C waveform. In the dual-functional waveform scheme, we conceive a heuristic mutual information optimization algorithm for the general case, alongside a modified gradient projection algorithm tailored for the scenarios with independent sensing sub-channels. Additionally, we identify the presence of both subspace tradeoff and water-filling tradeoff in this scheme. Finally, we validate the effectiveness of the proposed algorithms through numerical simulations.
A Hybrid Joint Source-Channel Coding Scheme for Mobile Multi-hop Networks
Authors: Chenghong Bian, Yulin Shao, Deniz Gunduz
Date: 2023-11-13 00:00:00

We propose a novel hybrid joint source-channel coding (JSCC) scheme for robust image transmission over multi-hop networks. In the considered scenario, a mobile user wants to deliver an image to its destination over a mobile cellular network. We assume a practical setting, where the links between the nodes belonging to the mobile core network are stable and of high quality, while the link between the mobile user and the first node (e.g., the access point) is potentially time-varying with poorer quality. In recent years, neural network based JSCC schemes (called DeepJSCC) have emerged as promising solutions to overcome the limitations of separation-based fully digital schemes. However, relying on analog transmission, DeepJSCC suffers from noise accumulation over multi-hop networks. Moreover, most of the hops within the mobile core network may be high-capacity wireless connections, calling for digital approaches. To this end, we propose a hybrid solution, where DeepJSCC is adopted for the first hop, while the received signal at the first relay is digitally compressed and forwarded through the mobile core network. We show through numerical simulations that the proposed scheme is able to outperform both the fully analog and fully digital schemes. Thanks to DeepJSCC it can avoid the cliff effect over the first hop, while also avoiding noise forwarding over the mobile core network thank to digital transmission. We believe this work paves the way for the practical deployment of DeepJSCC solutions in 6G and future wireless networks.
Dual-Polarized Reconfigurable Intelligent Surface Assisted Broad Beamforming
Authors: Parisa Ramezani, Maksym A. Girnyk, Emil Björnson
Date: 2023-11-12 00:00:00

A reconfigurable intelligent surface (RIS) consists of a large number of low-cost elements that can control the propagation environment seen from a transmitter by intelligently applying phase shifts to impinging signals before reflection. This paper studies an RIS-assisted communication system where a transmitter wants to transmit a common signal to many users residing in a wide angular area. To cover this sector uniformly, the RIS needs to radiate a broad beam with a spatially flat array factor, instead of a narrow beam as normally considered. To achieve this, we propose to use a dual-polarized RIS consisting of elements with orthogonal polarizations and show that the RIS can produce a broad beam if the phase shift configuration vectors in the two polarizations form a so-called Golay complementary sequence pair. By utilizing their properties, we also present a method for constructing configuration for large RISs from smaller ones, while preserving the broad radiation pattern of the smaller RIS. The numerical results corroborate the mathematical analyses and highlight the greatly improved coverage properties.
UAV Formation Optimization for Communication-assisted InSAR Sensing
Authors: Mohamed-Amine Lahmeri, Victor Mustieles-Pérez, Martin Vossiek, Gerhard Krieger, Robert Schober
Date: 2023-11-12 00:00:00

Interferometric synthetic aperture radar (InSAR) is an increasingly important remote sensing technique that enables three-dimensional (3D) sensing applications such as the generation of accurate digital elevation models (DEMs). In this paper, we investigate the joint formation and communication resource allocation optimization for a system comprising two unmanned aerial vehicles (UAVs) to perform InSAR sensing and to transfer the acquired data to the ground. To this end, we adopt as sensing performance metrics the interferometric coherence, i.e., the local correlation between the two co-registered UAV radar images, and the height of ambiguity (HoA), which together are a measure for the accuracy with which the InSAR system can estimate the height of ground objects. In addition, an analytical expression for the coverage of the considered InSAR sensing system is derived. Our objective is to maximize the InSAR coverage while satisfying all relevant InSAR-specific sensing and communication performance metrics. To tackle the non-convexity of the formulated optimization problem, we employ alternating optimization (AO) techniques combined with successive convex approximation (SCA). Our simulation results reveal that the resulting resource allocation algorithm outperforms two benchmark schemes in terms of InSAR coverage while satisfying all sensing and real-time communication requirements. Furthermore, we highlight the importance of efficient communication resource allocation in facilitating real-time sensing and unveil the trade-off between InSAR height estimation accuracy and coverage.