pwc-archive/papers-with-abstracts huggingface dataset:
Digital technologies and performance incentives: Evidence from businesses in the Swiss economy
Authors: Johannes Lehmann, Michael Beckmann
Date: 2024-12-17 00:00:00

Using novel survey data from Swiss firms, this paper empirically examines the relationship between the use of digital technologies and the prevalence of performance incentives. We argue that digital technologies tend to reduce the cost of organizational monitoring through improved measurement of employee behavior and performance, as well as through employee substitution in conjunction with a reduced agency problem. While we expect the former mechanism to increase the prevalence of performance incentives, the latter is likely to decrease it. Our doubly robust ATE estimates show that companies using business software and certain key technologies of Industry 4.0 increasingly resort to performance incentives, suggesting that the improved measurement effect dominates the employee substitution effect. In addition, we find that companies emerging as technology-friendly use performance incentives more frequently than their technology-averse counterparts. Both findings hold for managerial and non-managerial employees. Our estimation results are robust to a variety of sensitivity checks and suggest that Swiss businesses leverage digital technologies to enhance control over production or service processes, allowing them to intensify their management of employees through performance incentives.
Emergence of Power-Law and Other Wealth Distributions in Crowd of Heterogeneous Agents
Authors: Jake J. Xia
Date: 2024-12-16 00:00:00

This study investigates the emergence of power-law and other concentrated distributions through a feedback loop model in crowd interactions. Agents act by their response functions to observations and external forces, while observations change by the aggregated actions of all agents, weighted by their respective influence, i.e. power or wealth. Agents wealth dynamically adjust based on the alignment between an agents actions and observation outcomes: agents gain wealth when their actions align with observed trends and lose wealth otherwise. A reward function, that describes the change of agents wealth at each time step, manifests the differences of response functions of agents to observations. When all agents responses are set to zero and feedback loop is broken, agents wealth follow a normal or lognormal distribution. Otherwise, this response-reward iterative feedback mechanism results in concentrated wealth distributions, characterized by a small number of dominant agents and the marginalization of the majority. Contrasted to past studies, such concentration is not limited only to asymptotic behavior at the upper tail for large variables, nor does it require the reward function to be linear to agents previous wealth as formulated in random growth model and network preferential attachment. Probability density functions for various distributions are more visually distinguishable for small values at the lower tail. In application of this model, key differences in income and wealth distributions in the US vs Japan are attributed to different response functions of agents in the two countries. The model applicability extends beyond social systems to other many-body systems with analogous feedback mechanisms, where power-law distributions represent a rare subset of general concentrated outcomes.
Uncertainty Quantification in Portfolio Temperature Alignment
Authors: Hendrik Weichel, Aleksandr Zinovev, Heikki Haario, Martin Simon
Date: 2024-12-05 00:00:00
Tasks: Computational Efficiency, Decision Making, Management, Uncertainty Quantification

We present a novel Bayesian framework for quantifying uncertainty in portfolio temperature alignment models, leveraging the X-Degree Compatibility (XDC) approach with the scientifically validated Finite Amplitude Impulse Response (FaIR) climate model. This framework significantly advances the widely adopted linear approaches that use the Transient Climate Response to Cumulative CO2 Emissions (TCRE). Developed in collaboration with right{\deg}, one of the pioneering companies in portfolio temperature alignment, our methodology addresses key sources of uncertainty, including parameter variability and input emission data across diverse decarbonization pathways. By employing adaptive Markov Chain Monte Carlo (MCMC) methods, we provide robust parametric uncertainty quantification for the FaIR model. To enhance computational efficiency, we integrate a deep learning-based emulator, enabling near real-time simulations. Through practical examples, we demonstrate how this framework improves climate risk management and decision-making in portfolio construction by treating uncertainty as a critical feature rather than a constraint. Moreover, our approach identifies the primary sources of uncertainty, offering valuable insights for future research.
On Monotone Persuasion
Authors: Anton Kolotilin, Hongyi Li, Andriy Zapechelnyuk
Date: 2024-12-18 00:00:00

We study monotone persuasion in the linear case, where posterior distributions over states are summarized by their mean. We solve the two leading cases where optimal unrestricted signals can be nonmonotone. First, if the objective is s-shaped and the state is discrete, then optimal monotone signals are upper censorship, whereas optimal unrestricted signals may require randomization. Second, if the objective is m-shaped and the state is continuous, then optimal monotone signals are interval disclosure, whereas optimal unrestricted signals may require nonmonotone pooling. We illustrate our results with an application to media censorship.
Obvious manipulations, consistency, and the uniform rule
Authors: R. Pablo Arribillaga, Agustin G. Bonifacio
Date: 2024-12-17 00:00:00

In the problem of fully allocating an infinitely divisible commodity among agents whose preferences are single-peaked, we show that the uniform rule is the only allocation rule that satisfies efficiency, the equal division guarantee, consistency, and non-obvious manipulability.
Greek2MathTex: A Greek Speech-to-Text Framework for LaTeX Equations Generation
Authors: Evangelia Gkritzali, Panagiotis Kaliosis, Sofia Galanaki, Elisavet Palogiannidi, Theodoros Giannakopoulos
Date: 2024-12-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Math, speech-recognition, Speech Recognition, Speech-to-Text

In the vast majority of the academic and scientific domains, LaTeX has established itself as the de facto standard for typesetting complex mathematical equations and formulae. However, LaTeX's complex syntax and code-like appearance present accessibility barriers for individuals with disabilities, as well as those unfamiliar with coding conventions. In this paper, we present a novel solution to this challenge through the development of a novel speech-to-LaTeX equations system specifically designed for the Greek language. We propose an end-to-end system that harnesses the power of Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) techniques to enable users to verbally dictate mathematical expressions and equations in natural language, which are subsequently converted into LaTeX format. We present the architecture and design principles of our system, highlighting key components such as the ASR engine, the LLM-based prompt-driven equations generation mechanism, as well as the application of a custom evaluation metric employed throughout the development process. We have made our system open source and available at https://github.com/magcil/greek-speech-to-math.
Surface-Based Authentication System for Integrated Circuit Chips
Authors: Runze Liu, Prasun Datta, Anirudh Nakra, Chau-Wai Wong, Min Wu
Date: 2024-12-19 00:00:00
Tasks: Sensitivity

The rapid development of the semiconductor industry and the ubiquity of electronic devices have led to a significant increase in the counterfeiting of integrated circuits (ICs). This poses a major threat to public health, the banking industry, and military defense sectors that are heavily reliant on electronic systems. The electronic physically unclonable functions (PUFs) are widely used to authenticate IC chips at the unit level. However, electronic PUFs are limited by their requirement for IC chips to be in working status for measurements and their sensitivity to environmental variations. This paper proposes using optical PUFs for IC chip authentication by leveraging the unique microscopic structures of the packaging surface of individual IC chips. The proposed method relies on color images of IC chip surfaces acquired using a flatbed scanner or mobile camera. Our initial study reveals that these consumer-grade imaging devices can capture meaningful physical features from IC chip surfaces. We then propose an efficient, lightweight verification scheme leveraging specular-reflection-based features extracted from videos, achieving an equal error rate (EER) of 0.0008. We conducted factor, sensitivity, and ablation studies to understand the detailed characteristics of the proposed lightweight verification scheme. This work is the first to apply the optical PUF principle for the authentication of IC chips and has the potential to significantly enhance the security of the semiconductor supply chain.
Blind Deconvolution of Graph Signals: Robustness to Graph Perturbations
Authors: Chang Ye, Gonzalo Mateos
Date: 2024-12-19 00:00:00
Tasks: Denoising

We study blind deconvolution of signals defined on the nodes of an undirected graph. Although observations are bilinear functions of both unknowns, namely the forward convolutional filter coefficients and the graph signal input, a filter invertibility requirement along with input sparsity allow for an efficient linear programming reformulation. Unlike prior art that relied on perfect knowledge of the graph eigenbasis, here we derive stable recovery conditions in the presence of small graph perturbations. We also contribute a provably convergent robust algorithm, which alternates between blind deconvolution of graph signals and eigenbasis denoising in the Stiefel manifold. Reproducible numerical tests showcase the algorithm's robustness under several graph eigenbasis perturbation models.
Cramér-Rao Bound Optimization for Near-Field Sensing with Continuous-Aperture Arrays
Authors: Hao Jiang, Zhaolin Wang, Yuanwei Liu, Arumugam Nallanathan
Date: 2024-12-19 00:00:00

A Cram\'er-Rao bound (CRB) optimization framework for near-field sensing (NISE) with continuous-aperture arrays (CAPAs) is proposed. In contrast to conventional spatially discrete arrays (SPDAs), CAPAs emit electromagnetic (EM) probing signals through continuous source currents for target sensing, thereby exploiting the full spatial degrees of freedom (DoFs). The maximum likelihood estimation (MLE) method for estimating target locations in the near-field region is developed. To evaluate the NISE performance with CAPAs, the CRB for estimating target locations is derived based on continuous transmit and receive array responses of CAPAs. Subsequently, a CRB minimization problem is formulated to optimize the continuous source current of CAPAs. This results in a non-convex, integral-based functional optimization problem. To address this challenge, the optimal structure of the source current is derived and proven to be spanned by a series of basis functions determined by the system geometry. To solve the CRB minimization problem, a low-complexity subspace manifold gradient descent (SMGD) method is proposed, leveraging the derived optimal structure of the source current. Our simulation results validate the effectiveness of the proposed SMGD method and further demonstrate that i)~the proposed SMGD method can effectively solve the CRB minimization problem with reduced computational complexity, and ii)~CAPA achieves a tenfold improvement in sensing performance compared to its SPDA counterpart, due to full exploitation of spatial DoFs.
ElectraSight: Smart Glasses with Fully Onboard Non-Invasive Eye Tracking Using Hybrid Contact and Contactless EOG
Authors: Nicolas Schärer, Federico Villani, Aishwarya Melatur, Steven Peter, Tommaso Polonelli, Michele Magno
Date: 2024-12-19 00:00:00

Smart glasses with integrated eye tracking technology are revolutionizing diverse fields, from immersive augmented reality experiences to cutting-edge health monitoring solutions. However, traditional eye tracking systems rely heavily on cameras and significant computational power, leading to high-energy demand and privacy issues. Alternatively, systems based on electrooculography (EOG) provide superior battery life but are less accurate and primarily effective for detecting blinks, while being highly invasive. The paper introduces ElectraSight, a non-invasive plug-and-play low-power eye tracking system for smart glasses. The hardware-software co-design of the system is detailed, along with the integration of a hybrid EOG (hEOG) solution that incorporates both contact and contactless electrodes. Within 79 kB of memory, the proposed tinyML model performs real-time eye movement classification with 81% accuracy for 10 classes and 92% for 6 classes, not requiring any calibration or user-specific fine-tuning. Experimental results demonstrate that ElectraSight delivers high accuracy in eye movement and blink classification, with minimal overall movement detection latency (90% within 60 ms) and an ultra-low computing time (301 {\mu}s). The power consumption settles down to 7.75 mW for continuous data acquisition and 46 mJ for the tinyML inference. This efficiency enables continuous operation for over 3 days on a compact 175 mAh battery. This work opens new possibilities for eye tracking in commercial applications, offering an unobtrusive solution that enables advancements in user interfaces, health diagnostics, and hands-free control systems.
Generative CKM Construction using Partially Observed Data with Diffusion Model
Authors: Shen Fu, Zijian Wu, Di wu, Yong Zeng
Date: 2024-12-19 00:00:00
Tasks: Benchmarking

Channel knowledge map (CKM) is a promising technique that enables environment-aware wireless networks by utilizing location-specific channel prior information to improve communication and sensing performance. A fundamental problem for CKM construction is how to utilize partially observed channel knowledge data to reconstruct a complete CKM for all possible locations of interest. This problem resembles the long-standing ill-posed inverse problem, which tries to infer from a set of limited observations the cause factors that produced them. By utilizing the recent advances of solving inverse problems with generative artificial intelligence (AI), in this paper, we propose generative CKM construction method using partially observed data by solving inverse problems with diffusion models. Simulation results show that the proposed method significantly improves the performance of CKM construction compared with benchmarking schemes.
An Age of Information Characterization of SPS for V2X Applications
Authors: Maria Bezmenov, Matthias Frey, Zoran Utkovski, Slawomir Stanczak
Date: 2024-12-19 00:00:00
Tasks: Scheduling

We derive a closed-form approximation of the stationary distribution of the Age of Information (AoI) of the semi-persistent scheduling (SPS) protocol which is a core part of NR-V2X, an important standard for vehicular communications. While prior works have studied the average AoI under similar assumptions, in this work we provide a full statistical characterization of the AoI by deriving an approximation of its probability mass function. As result, besides the average AoI, we are able to evaluate the age-violation probability, which is of particular relevance for safety-critical applications in vehicular domains, where the priority is to ensure that the AoI does not exceed a predefined threshold during system operation. The study reveals complementary behavior of the age-violation probability compared to the average AoI and highlights the role of the duration of the reservation as a key parameter in the SPS protocol. We use this to demonstrate how this crucial parameter should be tuned according to the performance requirements of the application.
Estimation in the Gaussian Multiplex Channel
Authors: Daniel R. Fuhrmann, Muhammad Fahad
Date: 2024-12-19 00:00:00

An abstraction for multisensor communication termed the Gaussian Multiplex Channel is presented and analyzed. In this model, the sensor outputs can be added together in any combination through a network of switches, and the combinations can be changed arbitrarily during the observation interval. The sensor output sums are observed in additive Gaussian noise. Using a mean square error cost function and a constraint on the total observation time, an optimal set of combinations (switch positions) and observation times is determined. The solution exhibits high complexity (number of different combinations) even for moderate numbers of sensors. It is then shown that there exists an alternative solution based on Hadamard designs, which achieves the same minimizing MSE cost function and only requires a number of combinations equal to the number of sensors.
Heartbeat Detection from Ballistocardiogram using Transformer Network
Authors: Ruhan Yi, Mihail Popescu, James M. Keller, Grant Scott, Laurel Despins, David Heise, Marjorie Skubic
Date: 2024-12-18 00:00:00
Tasks: Heart Rate Variability, Sleep Quality

Longitudinal monitoring of heart rate (HR) and heart rate variability (HRV) can aid in tracking cardiovascular diseases (CVDs), sleep quality, sleep disorders, and reflect autonomic nervous system activity, stress levels, and overall well-being. These metrics are valuable in both clinical and everyday settings. In this paper, we present a transformer network aimed primarily at detecting the precise timing of heart beats from predicted electrocardiogram (ECG), derived from input Ballistocardiogram (BCG). We compared the performance of segment and subject models across three datasets: a lab dataset with 46 young subjects, an elder dataset with 28 elderly adults, and a combined dataset. The segment model demonstrated superior performance, with correlation coefficients of 0.97 for HR and mean heart beat interval (MHBI) when compared to ground truth. This non-invasive method offers significant potential for long-term, in-home HR and HRV monitoring, aiding in the early indication and prevention of cardiovascular issues.
Uncertainty Awareness in Wireless Communications and Sensing
Authors: Shixiong Wang, Wei Dai, Jianyong Sun, Zongben Xu, Geoffrey Ye Li
Date: 2024-12-18 00:00:00
Tasks: Decision Making, Diversity

Wireless communications and sensing (WCS) establish the backbone of modern information exchange and environment perception. Typical applications range from mobile networks and the Internet of Things to radar and sensor grids. Despite transformative capabilities, wireless systems often face diverse uncertainties in design and operation, such as modeling errors due to incomplete physical knowledge, statistical errors arising from data scarcity, measurement errors caused by sensor imperfections, computational errors owing to resource limitation, and unpredictability of environmental evolution. Once ignored, these uncertainties can lead to severe outcomes, e.g., performance degradation, system untrustworthiness, inefficient resource utilization, and security vulnerabilities. As such, this article reviews mature and emerging architectural, computational, and operational countermeasures, encompassing uncertainty-aware designs of signals and systems (e.g., diversity, adaptivity, modularity), as well as uncertainty-aware modeling and computational frameworks (e.g., risk-informed optimization, robust signal processing, and trustworthy machine learning). Trade-offs to employ these methods, e.g., robustness vs optimality, are also highlighted.
Near-Optimal Cell-Free Beamforming for Physical Layer Multigroup Multicasting
Authors: Mahmoud Zaher, Emil Björnson, Marina Petrova
Date: 2024-12-18 00:00:00

Physical layer multicasting is an efficient transmission technique that exploits the beamforming potential at the transmitting nodes and the broadcast nature of the wireless channel, together with the demand for the same content from several UEs. This paper addresses the max-min fair multigroup multicast beamforming optimization, which is an NP-hard problem. We propose a novel iterative elimination procedure coupled with semidefinite relaxation (SDR) to find the near-global optimum rank-1 beamforming vectors in a cell-free massive MIMO (multiple-input multiple-output) network setup. The proposed optimization procedure shows significant improvements in computational complexity and spectral efficiency performance compared to the SDR followed by the commonly used randomization procedure and the state-of-the-art difference-of-convex approximation algorithm. The significance of the proposed procedure is that it can be utilized as a rank reduction method for any problem in conjunction with SDR.
EOM Minimum Point Bias Voltage Estimation for Application in Quantum Computing
Authors: Frank Obernosterer, Raimund Meyer, Robert Koch, Gerd Kilian, Ewald Hedrich, Christian Kelm
Date: 2024-12-18 00:00:00

In quantum computing systems the quantum states of qubits can be modified among others by applying light pulses. In order to achieve low computing error rates these pulses have to be precisely shaped in magnitude and phase. In practical applications, both acousto-optic (AOM) and electro-optic modulators (EOM) are used for this purpose. The advantages of EOMs, in particular Mach-Zehnder modulators (MZMs), include e.g. higher bandwidth, compactness, good integrability and better noise performance. On the other hand, EOMs are challenging regarding their control voltage regulation as they experience a bias drift, i.e. voltage shifts in the modulator's operating point. Such a shift significantly impairs the quality of the modulation, which is why EOMs usually require a bias control loop to track the DC operating point. This work addresses the estimation of the instantaneous bias voltage using a small pilot tone, where the optical output power of the EOM is tracked by a photodetector feedback signal.
Circuits-Informed Machine Learning Technique for Blind Open-Loop Digital Calibration of SAR ADC
Authors: Sumukh Bhanushali, Debnath Maiti, Phaneendra Bikkina, Esko Mikkola, Arindam Sanyal
Date: 2024-12-18 00:00:00

This work presents a supervised machine-learning (ML) approach for blind digital calibration of SAR ADCs without requiring prior knowledge of errors. A low-speed reference ADC is used to train a shallow neural network (NN) to estimate errors in a high-speed ADC by comparing the outputs of the ADCs when their sampling instants align and subtracting these errors in the back-end. The proposed NN-calibration improves SFDR of a 28nm, 12-bit, 84MHz ADC by >38dB while consuming 25.8fJ/conversion-step.
Antenna Activation for NOMA Assisted Pinching-Antenna Systems
Authors: Kaidi Wang, Zhiguo Ding, Robert Schober
Date: 2024-12-18 00:00:00

In this letter, a non-orthogonal multiple access (NOMA) assisted downlink pinching-antenna system is investigated, where multiple pinching antennas can be activated at pre-configured positions along a dielectric waveguide to serve users via NOMA. In particular, the objective of this letter is to study at what locations and how many pinching antennas should be activated in order to maximize the system throughput. To this end, a sum rate maximization problem with antenna activation is formulated. With the help of matching theory, the formulated problem can be recast as a one-sided one-to-one matching, for which a low-complexity algorithm is developed. Simulation results indicate that the considered NOMA assisted pinching-antenna system can outperform conventional fixed-antenna systems in terms of sum rate, and the proposed matching based antenna activation algorithm yields a significant performance gain over the considered benchmarks.