pwc-archive/papers-with-abstracts huggingface dataset:
Optical hyperdimensional soft sensing: Speckle-based touch interface and tactile sensor
Authors: Kei Kitagawa, Kohei Tsuji, Koyo Sagehashi, Tomoaki Niiyama, Satoshi Sunada
Date: 2024-01-06 00:00:00

Hyperdimensional computing (HDC) is an emerging computing paradigm that exploits the distributed representation of input data in a hyperdimensional space, the dimensions of which are typically between 1,000--10,000. The hyperdimensional distributed representation enables energy-efficient, low-latency, and noise-robust computations with low-precision and basic arithmetic operations. In this study, we propose optical hyperdimensional distributed representations based on laser speckles for adaptive, efficient, and low-latency optical sensor processing. In the proposed approach, sensory information is optically mapped into a hyperdimensional space with >250,000 dimensions, enabling HDC-based cognitive processing. We use this approach for the processing of a soft-touch interface and a tactile sensor and demonstrate to achieve high accuracy of touch or tactile recognition while significantly reducing training data amount and computational burdens, compared with previous machine-learning-based sensing approaches. Furthermore, we show that this approach enables adaptive recalibration to keep high accuracy even under different conditions.
Covariance Function Estimation for High-Dimensional Functional Time Series with Dual Factor Structures
Authors: Chenlei Leng, Degui Li, Hanlin Shang, Yingcun Xia
Date: 2024-01-11 00:00:00
Tasks: Time Series

We propose a flexible dual functional factor model for modelling high-dimensional functional time series. In this model, a high-dimensional fully functional factor parametrisation is imposed on the observed functional processes, whereas a low-dimensional version (via series approximation) is assumed for the latent functional factors. We extend the classic principal component analysis technique for the estimation of a low-rank structure to the estimation of a large covariance matrix of random functions that satisfies a notion of (approximate) functional "low-rank plus sparse" structure; and generalise the matrix shrinkage method to functional shrinkage in order to estimate the sparse structure of functional idiosyncratic components. Under appropriate regularity conditions, we derive the large sample theory of the developed estimators, including the consistency of the estimated factors and functional factor loadings and the convergence rates of the estimated matrices of covariance functions measured by various (functional) matrix norms. Consistent selection of the number of factors and a data-driven rule to choose the shrinkage parameter are discussed. Simulation and empirical studies are provided to demonstrate the finite-sample performance of the developed model and estimation methodology.
Interactions between dynamic team composition and coordination: An agent-based modeling approach
Authors: Darío Blanco-Fernández, Stephan Leitner, Alexandra Rausch
Date: 2024-01-11 00:00:00
Tasks: Decision Making, Sequential Decision Making

This paper examines the interactions between selected coordination modes and dynamic team composition, and their joint effects on task performance under different task complexity and individual learning conditions. Prior research often treats dynamic team composition as a consequence of suboptimal organizational design choices. The emergence of new organizational forms that consciously employ teams that change their composition periodically challenges this perspective. In this paper, we follow the contingency theory and characterize dynamic team composition as a design choice that interacts with other choices such as the coordination mode, and with additional contextual factors such as individual learning and task complexity. We employ an agent-based modeling approach based on the NK framework, which includes a reinforcement learning mechanism, a recurring team formation mechanism based on signaling, and three different coordination modes. Our results suggest that by implementing lateral communication or sequential decision-making, teams may exploit the benefits of dynamic composition more than if decision-making is fully autonomous. The choice of a proper coordination mode, however, is partly moderated by the task complexity and individual learning. Additionally, we show that only a coordination mode based on lateral communication may prevent the negative effects of individual learning.
Follow The Money: Exploring the Key Factors Influencing Investment in African Startups
Authors: Khalil Liouane
Date: 2024-01-11 00:00:00

The African continent has witnessed a notable surge in entrepreneurial activity, with the number of startups and investments made in the ecosystem growing significantly in recent years. Against this backdrop, this paper presents an in-depth analysis of the critical key factors influencing funding amounts in African startup deals. A comprehensive analysis of 2,521 startup investment deals, spanning from January 2019 to March 2023, was conducted using a combination of statistical and several machine learning techniques. The results of this study highlight a significant gender diversity gap, the importance of professional experience, and the impact of founders' academic backgrounds. The study reveals that human capital, a diversified sector approach, and cross-border collaboration strategies are crucial for a robust startup ecosystem. Additionally, we identified the potential positive impact of 'Y combinators' for African startups, the implications of exit strategies on deal amounts, and the heterogeneity as well as the incongruity of investment rounds across the continent. In light of these findings, we propose an assortment of policy recommendations aimed at fostering a propitious milieu for African entrepreneurial ventures, promoting equitable investment distribution, and enhancing cross-border collaboration. By providing a rigorous empirical analysis, this study not only contributes to the existing body of literature but also lays the foundation for future research aimed at promoting investment and catalyzing socio-economic development throughout the African continent.
RIVCoin: an alternative, integrated, CeFi/DeFi-Vaulted Cryptocurrency
Authors: Roberto Rivera, Guido Rocco, Massimiliano Marzo, Enrico Talin, Ammar Elsabe
Date: 2023-12-19 00:00:00

This whitepaper introduces RIV Coin, a cryptocurrency that is fully stabilized by a diversified portfolio of invested reserves that are evaluated by professional independent third parties, and auditable and provable by the protocol. It is born and managed as a decentralized token, minted by a Decentralized Autonomous Organization (DAO). All wealthier Users are then accepting a redistribution of income, to the benefit of those who have purchased less tokens. In cooperative Game Theory, maximization of the economic benefit of the ecosystem is achieved when players' incentives are perfectly aligned. The proposed model allows for alignment of incentives: decreasing the risk exposure by wealthier Users, but implicitly increasing that of smaller ones to a level perceived by them as still sustainable and never creating ultra-speculative positions. In other words, wealthier Users stabilize the risk associated with the market value of portfolios in which the reserves are invested in Centralized and Decentralized Finance, without falling into the bet scheme. Users indirectly benefit from the access to the rewards of sophisticated cryptocurrency portfolios hitherto precluded to them, as well as having access to a real redistribution of wealth, without this turning into a disadvantage for the wealthy User, who benefits from the greater stability created by the huge influx of smaller Users. Therefore, the progressive growth becomes additional value that tends to stabilize over time, optimizing RIV Coin on the systemic risk level.
Income and emotional well-being: Evidence for well-being plateauing around $200,000 per year
Authors: Mikkel Bennedsen
Date: 2023-12-02 00:00:00

Is emotional well-being monotonically increasing in the level of income or does it reach a plateau at some income threshold, whereafter additional income does not contribute to further well-being? Conflicting answers to this question has been suggested in the academic literature. In a recent paper, using an income threshold of $100,000 per year, Killingsworth et al. (2023) appears to have resolved these conflicts, concluding that emotional well-being is monotonically increasing in income for all but the unhappiest individuals. In this paper, we show that this conclusion is sensitive to the placement of the income threshold at which the relationship between emotional well-being and income is allowed to plateau. Using standard econometric methods, we propose a data-driven approach to detect the placement of the threshold. Using this data-driven income threshold, a flat relationship between household income and emotional well-being above a threshold around $200,000 per year is found. While our analysis relaxes the assumption of a pre-specified income threshold, it relies on a number of other assumptions, which we briefly discuss. We conclude that although the analysis of this paper provides some evidence for well-being plateauing around $200,000 per year, more research is needed before any definite conclusions about the relationship between emotional well-being and income can be drawn.
L'utilité de l'échelle opératique pour considérer des stratégies d'intelligence et de guerre économique
Authors: Stéphane Goria
Date: 2023-10-31 00:00:00

The 20th century saw the emergence of an intermediate level of consideration, situated between the tactical and strategic levels: the operational level. This level of scale is that of a large area of operations, that is to say coordinated engagements bringing together forces belonging to different corps over a considerable geographical distance. In this article, we approach operatics and its implementation, called operative art or operational art, first from its contributions to military thinking, then we transpose what can be transposed beyond this domain. We consider operative art as a new point of view generating a particular thought and solutions based on agile systems, but which may have reached their limits. We will limit ourselves here to the question of the potential contributions of the operatic perspective to understand or implement a set of actions related to competitive or economic warfare. To do this, we will begin by providing some answers to the question of what operatic military is in terms of its characteristics. Then, we propose a way to transpose them in the context of companies preferably confronted with a context of competitive or economic warfare by taking as an example the acquisition of the essential activities of the company Alstom by the company General Electric (GE).
Neural Implicit Surface Reconstruction of Freehand 3D Ultrasound Volume with Geometric Constraints
Authors: Hongbo Chen, Logiraj Kumaralingam, Shuhang Zhang, Sheng Song, Fayi Zhang, Haibin Zhang, Thanh-Tu Pham, Edmond H. M. Lou, Kumaradevan Punithakumar, Yuyao Zhang, Lawrence H. Le, Rui Zheng
Date: 2024-01-11 00:00:00
Tasks: Anatomy, Surface Reconstruction

Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging modality that allows non-invasive imaging of medical anatomy without radiation exposure. Surface reconstruction of US volume is vital to acquire the accurate anatomical structures needed for modeling, registration, and visualization. However, traditional methods cannot produce a high-quality surface due to image noise. Despite improvements in smoothness, continuity, and resolution from deep learning approaches, research on surface reconstruction in freehand 3D US is still limited. This study introduces FUNSR, a self-supervised neural implicit surface reconstruction method to learn signed distance functions (SDFs) from US volumes. In particular, FUNSR iteratively learns the SDFs by moving the 3D queries sampled around volumetric point clouds to approximate the surface, guided by two novel geometric constraints: sign consistency constraint and onsurface constraint with adversarial learning. Our approach has been thoroughly evaluated across four datasets to demonstrate its adaptability to various anatomical structures, including a hip phantom dataset, two vascular datasets and one publicly available prostate dataset. We also show that smooth and continuous representations greatly enhance the visual appearance of US data. Furthermore, we highlight the potential of our method to improve segmentation performance, and its robustness to noise distribution and motion perturbation.
Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping
Authors: Dongyi Li, Dong Chu, Xiaobin Guan, wei he, Huanfeng Shen
Date: 2024-01-11 00:00:00
Tasks: Denoising, Hyperspectral Image Denoising, Image Denoising, Tensor Decomposition

Hyperspectral images (HSIs) are inevitably degraded by a mixture of various types of noise, such as Gaussian noise, impulse noise, stripe noise, and dead pixels, which greatly limits the subsequent applications. Although various denoising methods have already been developed, accurately recovering the spatial-spectral structure of HSIs remains a challenging problem to be addressed. Furthermore, serious stripe noise, which is common in real HSIs, is still not fully separated by the previous models. In this paper, we propose an adaptive hyperLaplacian regularized low-rank tensor decomposition (LRTDAHL) method for HSI denoising and destriping. On the one hand, the stripe noise is separately modeled by the tensor decomposition, which can effectively encode the spatial-spectral correlation of the stripe noise. On the other hand, adaptive hyper-Laplacian spatial-spectral regularization is introduced to represent the distribution structure of different HSI gradient data by adaptively estimating the optimal hyper-Laplacian parameter, which can reduce the spatial information loss and over-smoothing caused by the previous total variation regularization. The proposed model is solved using the alternating direction method of multipliers (ADMM) algorithm. Extensive simulation and real-data experiments all demonstrate the effectiveness and superiority of the proposed method.
Wireless Ear EEG to Monitor Drowsiness
Authors: Ryan Kaveh, Carolyn Schwendeman, Leslie Pu, Ana C. Arias, Rikky Muller
Date: 2024-01-11 00:00:00
Tasks: EEG

Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.
Near-Field Communications: A Comprehensive Survey
Authors: Yuanwei Liu, Chongjun Ouyang, Zhaolin Wang, Jiaqi Xu, Xidong Mu, A. Lee Swindlehurst
Date: 2024-01-11 00:00:00
Tasks: Survey

Multiple-antenna technologies are evolving towards larger aperture sizes, extremely high frequencies, and innovative antenna types. This evolution is fostering the emergence of near-field communications (NFC) in future wireless systems. Considerable attention has been directed towards this cutting-edge technology due to its potential to enhance the capacity of wireless networks by introducing increased spatial degrees of freedom (DoFs) in the range domain. Within this context, a comprehensive review of the state of the art on NFC is presented, with a specific focus on its 1) fundamental operating principles, 2) channel modeling, 3) performance analysis, 4) signal processing techniques, and 5) integration with other emerging applications. Specifically, 1) the basic principles of NFC are characterized from both physics and communications perspectives, unveiling its unique properties in contrast to far-field communications. 2) Building on these principles, deterministic and stochastic near-field channel models are explored for spatially-discrete (SPD) and continuous-aperture (CAP) arrays. 3) Based on these models, existing contributions to near-field performance analysis are reviewed in terms of DoFs/effective DoFs (EDoFs), the power scaling law, and transmission rate. 4) Existing signal processing techniques for NFC are systematically surveyed, which include channel estimation, beamforming design, and low-complexity beam training. 5) Major issues and research opportunities in incorporating near-field models into other promising technologies are identified to advance NFC's deployment in next-generation networks. Throughout this paper, promising directions are highlighted to inspire future research endeavors in the realm of NFC, underscoring its significance in the advancement of wireless communication technologies.
Enhancing Sensing-Assisted Communications in Cluttered Indoor Environments through Background Subtraction
Authors: Andrea Ramos, Musa Furkan Keskin, Henk Wymeersch, Saul Inca, Jose F. Monserrat
Date: 2024-01-11 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) is poised to be a native technology for the forthcoming Sixth Generation (6G) era, with an emphasis on its potential to enhance communications performance through the integration of sensing information, i.e., sensing-assisted communications (SAC). Nevertheless, existing research on SAC has predominantly confined its focus to scenarios characterized by minimal clutter and obstructions, largely neglecting indoor environments, particularly those in industrial settings, where propagation channels involve high clutter density. To address this research gap, background subtraction is proposed on the monostatic sensing echoes, which effectively addresses clutter removal and facilitates detection and tracking of user equipments (UEs) in cluttered indoor environments with SAC. A realistic evaluation of the introduced SAC strategy is provided, using ray tracing (RT) data with the scenario layout following Third Generation Partnership Project (3GPP) indoor factory (InF) channel models. Simulation results show that the proposed approach enables precise predictive beamforming largely unaffected by clutter echoes, leading to significant improvements in effective data rate over the existing SAC benchmarks and exhibiting performance very close to the ideal case where perfect knowledge of UE location is available.
End-to-End Learning for SLP-Based ISAC Systems
Authors: Yixian Zheng, Rang Liu, Ming Li, Qian Liu
Date: 2024-01-11 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is an encouraging wireless technology which can simultaneously perform both radar and communication functionalities by sharing the same transmit waveform, spectral resource, and hardware platform. Recently emerged symbol-level precoding (SLP) technique exhibits advancement in ISAC systems by leveraging the waveform design degrees of freedom (DoFs) in both temporal and spatial domains. However, traditional SLP-based ISAC systems are designed in a modular paradigm, which potentially limits the overall performance of communication and radar sensing. The high complexity of existing SLP design algorithms is another issue that hurdles the practical deployment. To break through the bottleneck of these approaches, in this paper we propose an end-to-end approach to jointly design the SLP-based dual-functional transmitter and receivers of communication and radar sensing. In particular, we aim to utilize deep learning-based methods to minimize the symbol error rate (SER) of communication users, maximize the detection probability, and minimize the root mean square error (RMSE) of the target angle estimation. Multi-layer perceptron (MLP) networks and a long short term memory (LSTM) network are respectively applied to the transmitter, communication users and radar receiver. Simulation results verify the feasibility of the proposed deep-learning-based end-to-end optimization for ISAC systems and reveal the effectiveness of the proposed neural networks for the end-to-end design.
Weiss-Weinstein bound of frequency estimation error for very weak GNSS signals
Authors: Xin Zhang, Xingqun Zhan, Jihong Huang, Jiahui Liu, Yingchao Xiao
Date: 2024-01-11 00:00:00

Tightness remains the center quest in all modern estimation bounds. For very weak signals, this is made possible with judicial choices of prior probability distribution and bound family. While current bounds in GNSS assess performance of carrier frequency estimators under Gaussian or uniform assumptions, the circular nature of frequency is overlooked. In addition, of all bounds in Bayesian framework, Weiss-Weinstein bound (WWB) stands out since it is free from regularity conditions or requirements on the prior distribution. Therefore, WWB is extended for the current frequency estimation problem. A divide-and-conquer type of hyperparameter tuning method is developed to level off the curse of computational complexity for the WWB family while enhancing tightness. Synthetic results show that with von Mises as prior probability distribution, WWB provides a bound up to 22.5% tighter than Ziv-Zaka\"i bound (ZZB) when SNR varies between -3.5 dB and -20 dB, where GNSS signal is deemed extremely weak.