pwc-archive/papers-with-abstracts huggingface dataset:
Selective linear segmentation for detecting relevant parameter changes
Authors: Arnaud Dufays, Aristide Houndetoungan, Alain Coën
Date: 2024-02-08 00:00:00
Tasks: Model Selection, Point Processes, Time Series

Change-point processes are one flexible approach to model long time series. We propose a method to uncover which model parameter truly vary when a change-point is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of 14 Hedge funds (HF) strategies, using an asset based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.
Monetary Policy and the Gendered Labor Market Dynamics: Evidence from Developing Economies
Authors: Marjan Petreski, Stefan Tanevski, Alejandro D. Jacobo
Date: 2024-02-08 00:00:00

Using a Taylor rule amended with official reserves movements, we derive country-specific monetary shocks and employ a local projections-estimator for tracking gender-disaggregated labor-market responses in 99 developing economies from 2009 to 2021. Results show that women experience more negative post-shock employment responses than men, contributing to a deepening of the gender gaps on the labor market. After the shock, women leave the labor market more so than men, which results in an apparently intact or even improved unemployment outcome for women. We find limited evidence of sector-specific reaction to interest rates. Additionally, we identify an intense worsening of women-s position on the labor market in high-growth environments as well under monetary policy tightening. Developing Asia and Latin America experience the most significant detrimental effects on women's employment, Africa exhibits a slower manifestation of the monetary shocks-impact and developing Europe shows the mildest effects.
Heterogeneous drivers of overnight and same-day visits
Authors: Francesco Scotti, Andrea Flori, Piercesare Secchi, Marika Arena, Giovanni Azzone
Date: 2024-02-08 00:00:00

This paper aims to explore the factors stimulating different tourism behaviours, with specific reference to same-day visits and overnight stays. To this aim, we employ mobile network data referred to the area of Lombardy. The paper highlights that larger availability of tourism accommodations, cultural and natural endowments are relevant factors explaining overnight stays. Conversely, temporary entertainment and transportation facilities increase municipalities attractiveness for same-day visits. The results also highlight a trade-off in the capability of municipalities of being attractive in connection to both the tourism behaviours, with higher overnight stays in areas with more limited same-day visits. Mobile data offer a spatial and temporal granularity allowing to detect relevant patterns and support the design of tourism precision policies.
XLR (piXel Loss Rate): a Lightweight Indicator to Measure Video QoE in IP Networks
Authors: César Díaz, Pablo Pérez, Julián Cabrera, Jaime Ruiz, Narciso García
Date: 2024-02-08 00:00:00

A novel Key Quality Indicator for video delivery applications, XLR (piXel Loss Rate), is defined, characterized, and evaluated. The proposed indicator is an objective measure that captures the effects of transmission errors in the received video, has a good correlation with subjective Mean Opinion Scores, and provides comparable results with state-of-the-art Full-Reference metrics. Moreover, XLR can be estimated using only a lightweight analysis on the compressed bitstream, thus allowing a No-Reference operational method. Therefore, XLR can be used for measuring the quality of experience without latency at any network location. Thus, it is a relevant tool for network planning, specially in new high-demanding scenarios. The experiments carried out show the outstanding performance of its linear-dimension score and the reliability of the bitstream-based estimation.
Performance analysis of Deep Learning-based Lossy Point Cloud Geometry Compression Coding Solutions
Authors: Joao Prazeres, Rafael Rodrigues, Manuela Pereira, Antonio M. G. Pinheiro
Date: 2024-02-07 00:00:00

The quality evaluation of three deep learning-based coding solutions for point cloud geometry, notably ADLPCC, PCC GEO CNNv2, and PCGCv2, is presented. The MPEG G-PCC was used as an anchor. Furthermore, LUT SR, which uses multi-resolution Look-Up tables, was also considered. A set of six point clouds representing landscapes and objects were used. As point cloud texture has a great influence on the perceived quality, two different subjective studies that differ in the texture addition model are reported and statistically compared. In the first experiment, the dataset was first encoded with the identified codecs. Then, the texture of the original point cloud was mapped to the decoded point cloud using the Meshlab software, resulting in a point cloud with both geometry and texture information. Finally, the resulting point cloud was encoded with G-PCC using the lossless-geometry-lossy-atts mode, while in the second experiment the texture was mapped directly onto the distorted geometry. Moreover, both subjective evaluations were used to benchmark a set of objective point cloud quality metrics. The two experiments were shown to be statistically different, and the tested metrics revealed quite different behaviors for the two sets of data. The results reveal that the preferred method of evaluation is the encoding of texture information with G-PCC after mapping the texture of the original point cloud to the distorted point cloud. The results suggest that current objective metrics are not suitable to evaluate distortions created by machine learning-based codecs.
A Survey on Detection, Classification, and Tracking of UAVs using Radar and Communications Systems
Authors: Wahab Khawaja, Martins Ezuma, Vasilii Semkin, Fatih Erden, Ozgur Ozdemir, Ismail Guvenc
Date: 2024-02-08 00:00:00
Tasks: Survey

The use of unmanned aerial vehicles (UAVs) for a variety of commercial, civilian, and defense applications has increased many folds in recent years. While UAVs are expected to transform future air operations, there are instances where they can be used for malicious purposes. In this context, the detection, classification, and tracking (DCT) of UAVs (DCT-U) for safety and surveillance of national air space is a challenging task when compared to DCT of manned aerial vehicles. In this survey, we discuss the threats and challenges from malicious UAVs and we subsequently study three radio frequency (RF)-based systems for DCT-U. These RF-based systems include radars, communication systems, and RF analyzers. Radar systems are further divided into conventional and modern radar systems, while communication systems can be used for joint communications and sensing (JC&S) in active mode and act as a source of illumination to passive radars for DCT-U. The limitations of the three RF-based systems are also provided. The survey briefly discusses non-RF systems for DCT-U and their limitations. Future directions based on the lessons learned are provided at the end of the survey.
Reconfigurable Intelligent Surface-Aided Dual-Function Radar and Communication Systems With MU-MIMO Communication
Authors: Yasheng Jin, Hong Ren, Cunhua Pan, Zhiyuan Yu, Ruisong Weng, Boshi Wang, Gui Zhou, Yongchao He, Maged Elkashlan
Date: 2024-02-08 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we investigate an reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system. Our objective is to maximize the achievable sum rate of the multi-antenna communication users through the joint active and passive beamforming. {Specifically}, the weighted minimum mean-square error (WMMSE) method is { first} used to reformulate the original problem into an equivalent one. Then, we utilize an alternating optimization (AO) { algorithm} to decouple the optimization variables and decompose this challenging problem into two subproblems. Given reflecting coefficients, a penalty-based algorithm is utilized to deal with the non-convex radar signal-to-noise ratio (SNR) constraints. For the given beamforming matrix of the BS, we apply majorization-minimization (MM) to transform the problem into a quadratic constraint quadratic programming (QCQP) problem, which is ultimately solved using a semidefinite relaxation (SDR)-based algorithm. Simulation results illustrate the advantage of deploying RIS in the considered multi-user MIMO (MU-MIMO) ISAC systems.
RF Energy Absorption in Human Bodies Due to Wearable Antennas in the 2.4 GHz Frequency Band
Authors: Marta Fernandez, Hugo G. Espinosa, David Guerra, Ivan Pena, David V. Thiel, Amaia Arrinda
Date: 2024-02-08 00:00:00

Human exposure to electromagnetic fields produced by two wearable antennas operating in the 2.4 GHz frequency band was assessed by computational tools. Both antennas were designed to be attached to the skin, but they were intended for different applications. The first antenna was designed for off-body applications, i.e. to communicate with a device placed outside the body, while the second antenna model was optimized to communicate with a device located inside the body. The power absorption in human tissues was determined at several locations of adult male and female body models. The maximum specific absorption rate (SAR) value obtained with the off-body antenna was found on the torso of the woman model and was equal to 0.037 W/kg at 2.45 GHz. SAR levels increased significantly for the antenna transmitting inside the body. In this case, SAR values ranged between 0.23 and 0.45 W/kg at the same body location. The power absorbed in different body tissues and total power absorbed in the body were also calculated; the maximum total power absorbed was equal to 5.2 mW for an antenna input power equal to 10 mW.
A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions
Authors: Mohammad Cheraghinia, Adnan Shahid, Stijn Luchie, Gert-Jan Gordebeke, Olivier Caytan, Jaron Fontaine, Ben Van Herbruggen, Sam Lemey, Eli de Poorter
Date: 2024-02-08 00:00:00
Tasks: Activity Recognition, Survey

Due to their large bandwidth, relatively low cost, and robust performance, UWB radio chips can be used for a wide variety of applications, including localization, communication, and radar. This article offers an exhaustive survey of recent progress in UWB radar technology. The goal of this survey is to provide a comprehensive view of the technical fundamentals and emerging trends in UWB radar. Our analysis is categorized into multiple parts. Firstly, we explore the fundamental concepts of UWB radar technology from a technology and standardization point of view. Secondly, we examine the most relevant UWB applications and use cases, such as device-free localization, activity recognition, presence detection, and vital sign monitoring, discussing each time the bandwidth requirements, processing techniques, algorithms, latest developments, relevant example papers, and trends. Next, we steer readers toward relevant datasets and available radio chipsets. Finally, we discuss ongoing challenges and potential future research avenues. As such, this overview paper is designed to be a cornerstone reference for researchers charting the course of UWB radar technology over the last decade.
Fast Transition-Aware Reconfiguration of Liquid Crystal-based RISs
Authors: Mohamadreza Delbari, Robin Neuder, Alejandro Jiménez-Sáez, Arash Asadi, Vahid Jamali
Date: 2024-02-08 00:00:00

Liquid crystal (LC) technology offers a cost-effective, scalable, energy-efficient, and continuous phase tunable realization of extremely large reconfigurable intelligent surfaces (RISs). However, LC response time to achieve a desired differential phase is significantly higher compared to competing silicon-based technologies (RF switches, PIN diodes, etc). The slow response time can be the performance bottleneck for applications where frequent reconfiguration of the RIS (e.g., to serve different users) is needed. In this paper, we develop an RIS phase-shift design that is aware of the transition behavior and aims to minimize the time to switch among multiple RIS configurations each serving a mobile user in a time-division multiple-access (TDMA) protocol. Our simulation results confirm that the proposed algorithm significantly reduces the time required for the users to achieve a threshold signal quality. This leads to a considerable improvement in the achievable throughput for applications, where the length of the TDMA time intervals is comparable with the RIS reconfiguration time.
Measurement Methodology for Determining the Optimal Frequency Domain Configuration to Accurately Record WiFi Exposure Levels
Authors: M. Fernandez, D. Guerra, Unai Gil, I. Pena, A. Arrinda
Date: 2024-02-07 00:00:00

Radiofrequency fields are usually measured in order to be compared with electromagnetic exposure limits defined by international standardization organizations with the aim of preserving the human health. However, in the case of WiFi technology, accurate measurement of the radiation coming from user terminals and access points is a great challenge due to the nature of these emissions, which are noncontinuous signals transmitted in the form of pulses of short duration. Most of the methodologies defined up to now for determining WiFi exposure levels use or take as reference exposimeters, broadband probes, and spectrum analyzers without taking into account that WiFi signals are not continuously transmitted. This leads to an overestimation of the radiation level that cannot be considered negligible when data of the actual exposure are needed. To avoid this, other procedures apply empirical weighting factors that account for the actual duration of burst transmissions. However, this implies the implementation of additional measurements for calculating the weighting factors, and thus, increases the complexity of the work. According to this, it was still necessary to define the frequency domain measurement setup that is optimal for obtaining realistic WiFi signal values, without requiring the performance of additional recordings. Thus, the definition of an appropriate methodology to achieve this goal was established as the main objective of this paper. The set of tasks carried out to identify such a configuration, as well as the limitations obtained for other measurement settings, are deeply explained in this paper.
The impact of high frequency-based stability on the onset of action potentials in neuron models
Authors: Eduardo Cerpa, Nathaly Corrales, Matías Courdurier, Leonel E. Medina, Esteban Paduro
Date: 2024-02-08 00:00:00

This paper studies the phenomenon of conduction block in model neurons using high-frequency biphasic stimulation (HFBS). The focus is investigating the triggering of undesired onset action potentials when the HFBS is turned on. The approach analyzes the transient behavior of an averaged system corresponding to the FitzHugh-Nagumo neuron model using Lyapunov and quasi-static methods. The first result provides a more comprehensive understanding of the onset activation through a mathematical proof of how to avoid it using a ramp in the amplitude of the oscillatory source. The second result tests the response of the blocked system to a piecewise linear stimulus, providing a quantitative description of how the HFBS strength translates into conduction block robustness. The results of this work can provide insights for the design of electrical neurostimulation therapies.
Predicting concentration changes via discrete sampling
Authors: Age J. Tjalma, Pieter Rein ten Wolde
Date: 2024-02-08 00:00:00
Tasks: Navigate

To successfully navigate chemical gradients, microorganisms need to predict how the ligand concentration changes in space. Due to their limited size, they do not take a spatial derivative over their body length but rather a temporal derivative, comparing the current signal with that in the recent past, over the so-called adaptation time. This strategy is pervasive in biology, but it remains unclear what determines the accuracy of such measurements. Using a generalized version of the previously established sampling framework, we investigate how resource limitations and the statistics of the input signal set the optimal design of a well-characterized network that measures temporal concentration changes: the Escherichia coli chemotaxis network. Our results show how an optimal adaptation time arises from the trade-off between the sampling error, caused by the stochastic nature of the network, and the dynamical error, caused by uninformative fluctuations in the input. A larger resource availability reduces the sampling error, which allows for a smaller adaptation time, thereby simultaneously decreasing the dynamical error. Similarly, we find that the optimal adaptation time scales inversely with the gradient steepness, because steeper gradients lift the signal above the noise and reduce the sampling error. These findings shed light on the principles that govern the optimal design of the E. coli chemotaxis network specifically, and any system measuring temporal changes more broadly.
Neuronal functional connectivity graph estimation with the R package neurofuncon
Authors: Lauren Miako Beede, Giuseppe Vinci
Date: 2024-02-08 00:00:00
Tasks: Functional Connectivity

Researchers continue exploring neurons' intricate patterns of activity in the cerebral visual cortex in response to visual stimuli. The way neurons communicate and optimize their interactions with each other under different experimental conditions remains a topic of active investigation. Probabilistic Graphical Models are invaluable tools in neuroscience research, as they let us identify the functional connections, or conditional statistical dependencies, between neurons. Graphical models represent these connections as a graph, where nodes represent neurons and edges indicate the presence of functional connections between them. We developed the R package neurofuncon for the computation and visualization of functional connectivity graphs from large-scale data based on the Graphical lasso. We illustrate the use of this package with publicly available two-photon calcium microscopy imaging data from approximately 10000 neurons in a 1mm cubic section of a mouse visual cortex.
Effects of kappa-opioid agonist U-50488 and p38 MAPK inhibitor SB203580 on the spike activity of pyramidal neurons in the basolateral amygdala
Authors: Konstantin Y. Kalitin, Alexander A. Spasov, Olga Y. Mukha
Date: 2024-02-08 00:00:00

Introduction: Kappa-opioid receptor (KOR) signaling in the basolateral amygdala (BLA) underlies KOR agonist-induced aversion. In this study, we aimed to understand the individual and combined effects of KOR agonist U-50488 and p38 MAPK inhibitor SB203580 on the spiking activity of pyramidal neurons in the BLA to shed light on the complex interplay between KORs, the p38 MAPK, and neuronal excitability. Materials and Methods: Electrophysiological experiments were performed using the patch-clamp technique in the whole-cell configuration. Rat brain slices containing the amygdala were prepared, and pyramidal neurons within the BLA were visually patched and recorded in the current clamp mode. The neurons were identified by their accommodation properties and neural activity signals were amplified and analyzed. Using local perfusion, we obtained three dose-response curves for: a) U-50488 (0.001-10 {\mu}M); b) U-50488 (0.001-10 {\mu}M) in the presence of SB203580 (1 {\mu}M); and c) U-50488 (0.01-10 {\mu}M) in the presence of SB203580 (5 {\mu}M). Results: After the application of U-50488, pyramidal neurons had a higher action potential firing rate in response to a current injection than control neurons (p<0.001). The dose-dependent curves we obtained indicate that the combination of U-50488 and SB203580 results in non-competitive antagonism. This conclusion is supported by the observed change in the curve`s slope with reduction in the maximum effect of U-50488. Thus, it can be assumed that the increase in spike activity of pyramidal neurons of the amygdala is mediated through the beta-arrestin pathway. When this pathway is blocked, the spike activity reverts to its baseline level. Conclusion: Our study found that the KOR agonist-induced spiking activity of the BLA pyramidal neurons is mediated by the beta-arrestin pathway and can be suppressed by the application of the p38 MAPK inhibitor SB203580.
A multiscale sensorimotor model of experience-dependent behavior in a minimal organism
Authors: Maria Sol Vidal-Saez, Oscar Vilarroya, Jordi Garcia-Ojalvo
Date: 2024-02-07 00:00:00

To survive in ever-changing environments, living organisms need to continuously combine the ongoing external inputs they receive, representing present conditions, with their dynamical internal state, which includes influences of past experiences. It is still unclear in general, however, (i) how this happens at the molecular and cellular levels, and (ii) how the corresponding molecular and cellular processes are integrated with the behavioral responses of the organism. Here we address these issues by modeling mathematically a particular behavioral paradigm in a minimal model organism, namely chemotaxis in the nematode C. elegans. Specifically, we use a long-standing collection of elegant experiments on salt chemotaxis in this animal, in which the migration direction varies depending on its previous experience. Our model integrates the molecular, cellular and organismal levels to reproduce the experimentally observed experience-dependent behavior. The model proposes specific molecular mechanisms for the encoding of current conditions and past experiences in key neurons associated with this response, predicting the behavior of various mutants associated with those molecular circuits.
Biological computation through recurrence
Authors: Maria Sol Vidal-Saez, Oscar Vilarroya, Jordi Garcia-Ojalvo
Date: 2024-02-07 00:00:00

One of the defining features of living systems is their adaptability to changing environmental conditions. This requires organisms to extract temporal and spatial features of their environment, and use that information to compute the appropriate response. In the last two decades, a growing body or work, mainly coming from the machine learning and computational neuroscience fields, has shown that such complex information processing can be performed by recurrent networks. In those networks, temporal computations emerge from the interaction between incoming stimuli and the internal dynamic state of the network. In this article we review our current understanding of how recurrent networks can be used by biological systems, from cells to brains, for complex information processing. Rather than focusing on sophisticated, artificial recurrent architectures such as long short-term memory (LSTM) networks, here we concentrate on simpler network structures and learning algorithms that can be expected to have been found by evolution. We also review studies showing evidence of naturally occurring recurrent networks in living organisms. Lastly, we discuss some relevant evolutionary aspects concerning the emergence of this natural computation paradigm.