pwc-archive/papers-with-abstracts huggingface dataset:
PI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information
Authors: Liqiu Dong, Marta Zagorowska, Tong Liu, Alex Durkin, Mehmet Mercangöz
Date: 2024-02-21 00:00:00
Tasks: Active Learning, Bayesian Optimization, Bilevel Optimization

Physics informed neural networks (PINNs) have recently been proposed as surrogate models for solving process optimization problems. However, in an active learning setting collecting enough data for reliably training PINNs poses a challenge. This study proposes a broadly applicable method for incorporating physics information into existing machine learning (ML) models of any type. The proposed method - referred to as PI-CoF for Physics-Informed Correction Factors - introduces additive or multiplicative correction factors for pointwise inference, which are identified by solving a regularized unconstrained optimization problem for reconciliation of physics information and ML model predictions. When ML models are used in an optimization context, using the proposed approach translates into a bilevel optimization problem, where the reconciliation problem is solved as an inner problem each time before evaluating the objective and constraint functions of the outer problem. The utility of the proposed approach is demonstrated through a numerical example, emphasizing constraint satisfaction in a safe Bayesian optimization (BO) setting. Furthermore, a simulation study is carried out by using PI-CoF for the real-time optimization of a fuel cell system. The results show reduced fuel consumption and better reference tracking performance when using the proposed PI-CoF approach in comparison to a constrained BO algorithm not using physics information.
Delay-Aware Semantic Sampling in Power Electronic Systems
Authors: Kirti Gupta, Subham Sahoo, Bijaya Ketan Panigrahi
Date: 2024-02-21 00:00:00

In power electronic systems (PES), attacks on data availability such as latency attacks, data dropouts, and time-synchronization attacks (TSAs) continue to pose significant threats to both the communication network and the control system performance. As per the conventional norms of communication engineering, PES still rely on time synchronized sampling, which translates every received message with equal importance. In this paper, we go beyond event-triggered sampling/estimation to integrate semantic principles into the sampling process for each distributed energy resource (DER), which not only compensates for delayed communicated signals by reconstruction of a new signal from the inner control layer dynamics, but also evaluates the reconstruction stage using key semantic requirements, namely Freshness, Relevance and Priority for good dynamic performance. As a result, the sparsity provided by event-driven sampling of internal control loop dynamics translates as semantics in PES. The proposed scheme has been extensively tested and validated on a modified IEEE 37-bus AC distribution system, under many operating conditions and noisy environment in OPAL-RT environment to establish its robustness, model-free design ability and adaptive behavior to dynamic cyber graph topologies.
Global Tropical Cyclone Intensity Forecasting with Multi-modal Multi-scale Causal Autoregressive Model
Authors: Xinyu Wang, Kang Chen, Lei Liu, Tao Han, Bin Li, Lei Bai
Date: 2024-02-16 00:00:00
Tasks: Tropical Cyclone Intensity Forecasting

Accurate forecasting of Tropical cyclone (TC) intensity is crucial for formulating disaster risk reduction strategies. Current methods predominantly rely on limited spatiotemporal information from ERA5 data and neglect the causal relationships between these physical variables, failing to fully capture the spatial and temporal patterns required for intensity forecasting. To address this issue, we propose a Multi-modal multi-Scale Causal AutoRegressive model (MSCAR), which is the first model that combines causal relationships with large-scale multi-modal data for global TC intensity autoregressive forecasting. Furthermore, given the current absence of a TC dataset that offers a wide range of spatial variables, we present the Satellite and ERA5-based Tropical Cyclone Dataset (SETCD), which stands as the longest and most comprehensive global dataset related to TCs. Experiments on the dataset show that MSCAR outperforms the state-of-the-art methods, achieving maximum reductions in global and regional forecast errors of 9.52% and 6.74%, respectively. The code and dataset are publicly available at https://anonymous.4open.science/r/MSCAR.
Magnetic Nanoparticles for Neural Engineering
Authors: Gerardo F. Goya, Vittoria Raffa
Date: 2024-02-05 00:00:00

Magnetic nanoparticles (MNPs) are the foundation of several new strategies for neural repair and neurological therapies. The fact that a remote force can act on MNPs at the cytoplasmic space constitutes the essence of many new neurotherapeutic concepts. MNPs with a predesigned physicochemical characteristic can interact with external magnetic fields to apply mechanical forces in definite areas of the cell to modulate cellular behaviour. Magnetic actuation to direct the outgrowth on neurons after nerve injury has already demonstrated the therapeutic potential for neural repair. When these magnetic cores are functionalized with molecules such as nerve growth factors or neuroprotective molecules, multifunctional devices can be developed. This chapter will review some of these new nanotechnology-based solutions for neurological diseases, specifically those based on the use of engineered MNPs used for neuroprotection and neuroregeneration. These include the use of MNPs as magnetic actuators to guide neural cells, modulate intracellular transport and stimulate axonal growth after nerve injury.
Visual acuity and contrast sensitivity under monochromatic yellow light
Authors: Elisabetta Baldanzi, Paolo Antonino Grasso, Clara Gori, Massimo Gurioli, Federcio Tommasi, Alessandro Farini
Date: 2024-01-22 00:00:00
Tasks: Sensitivity

This study investigates the impact of monochromatic lighting on visual acuity (VA) and contrast sensitivity (CS). Traditional assessments of VA and CS are typically conducted under the illumination of ``white'' light, but variations in color temperature can influence outcomes. Utilizing data from an exhibition by Olafur Eliasson, where a room was illuminated with low-pressure sodium lamps, creating an almost monochromatic yellow light, we compared visual assessments in the yellow room with conventional lighting in a white room. For VA, the results show no significant differences between the two lighting conditions, while for CS, a more nuanced situation is observed. The bias in CS measurements is clinically relevant, and the p-value suggests that further investigation with a larger, more diverse sample may be worthwhile. Despite limitations, such as higher illumination conditions than standard protocols, the unique ``laboratory'' offered by the exhibition facilitated measurements not easily achievable in a traditional setting.
Offshoring Emissions through Used Vehicle Exports
Authors: S. J. Newman, K. Schulte, M. M. Morellini, C. Rahal, D. Leasure
Date: 2024-02-21 00:00:00

Policies to reduce transport emissions often overlook the international flow of used vehicles. We quantify the rate at which used vehicles generated CO2 and pollution for all used vehicles exported from Great Britain; a globally leading used vehicle exporter across 2005-2021. Destined for low-middle-income countries, exported vehicles fail roadworthiness standards and, even under extremely optimistic functioning as new assumptions, generate at least 13-53 percent more emissions than scrapped or on-road vehicles.
Vulnerability Webs: Systemic Risk in Software Networks
Authors: Cornelius Fritz, Co-Pierre Georg, Angelo Mele, Michael Schweinberger
Date: 2024-02-20 00:00:00

Modern software development is a collaborative effort that re-uses existing code to reduce development and maintenance costs. This practice exposes software to vulnerabilities in the form of undetected bugs in direct and indirect dependencies, as demonstrated by the Crowdstrike and HeartBleed bugs. The economic costs resulting from such vulnerabilities can be staggering. We study a directed network of 52,897 software dependencies across 16,102 Python repositories, guided by a strategic model of network formation that incorporates both observable and unobservable heterogeneity. Using a scalable variational approximation of the conditional distribution of unobserved heterogeneity, we show that outsourcing code to other software packages by creating dependencies generates negative externalities. Modeling the propagation of risk in networks of software packages as an epidemiological process, we show that increasing protection of dependencies based on popular heuristics is ineffective at reducing systemic risk. By contrast, AI-assisted coding enables developers to replace dependencies with in-house code and reduces systemic risk.
The seasonality of air ticket prices before and after the pandemic
Authors: Alessandro V. M. Oliveira
Date: 2024-02-21 00:00:00

This study investigates price seasonality in the Brazilian air transport industry, emphasizing the impact of the COVID-19 pandemic on domestic airline pricing strategies. Given potential shifts in demand patterns following the global health crisis, this study explores possible long-term structural changes in the seasonality of Brazilian airfare. We analyze an open dataset of domestic city pairs from 2013 to 2023, employing an econometric model developed using Stata software. Our findings indicate alterations in seasonal patterns and long-term trends in the post-pandemic era. These changes underscore potential shifts in the composition of leisure and business travelers, along with the cost pressures faced by airlines.
Estimating Demand for Lamb, Beef, Pork, and Poultry in Canada
Authors: Zakary Rodrigue Diakité
Date: 2024-02-21 00:00:00

This paper investigates the demand for lamb, beef, pork, and poultry in Canada, both at the national level and in disaggregated provinces, to identify meat consumption patterns in different provinces. Meat consumption plays a significant role in Canada's economy and is an important source of calories for the population. However, meat demand faces several consumption challenges due to logistic constraints, as a significant portion of the supply is imported from other countries. Therefore, there is a need for a better understanding of the causal relationships underlying lamb, beef, pork, and poultry consumption in Canada. Until recently, there have been no attempts to estimate meat consumption at the provincial level in Canada. Different Almost Ideal Demand System (AIDS) models have been applied for testing specifications to circumvent several econometric and theoretical problems. In particular, generalized AIDS and its Quadratic extension QUAIDS methods have been estimated across each province using the Iterative Linear Least Squares Estimator (ILLE) estimation Method. Weekly retail meat consumption price and quantity data from 2019 to 2022 have been used for Canada and for each province namely Quebec, Maritime provinces (New Brunswick, Nova Scotia, and Prince Edward Island), Ontario, total West (Yukon, Northwest Territory and Nunavut), Alberta, Manitoba-Saskatchewan and Manitoba as well as British Columbia. Consistent coefficients and demand elasticities estimates reveal patterns of substitution and/or complementarity between the four categories of meat. Meat consumption patterns differ across each province. Results show that the demand for the four categories of meat is responsive to price changes. Overall, lamb expenditure was found to be elastic and thus considered a luxury good during the study period, while the other three categories are considered normal goods across Canada.
Privatização de aeroportos: motivações, regulação e eficiência operacional
Authors: Igor R. S. Brito, Alessandro V. M. Oliveira
Date: 2024-02-17 00:00:00

In this study, we will address some topics related to the privatization of airports in the scientific literature, in an attempt to provide an answer to the following question: does the privatization of airports bring positive results? Firstly, we turn our attention to the motivations leading to privatization, considering the two main parties involved, the government and the private sector. After all, the success of such a decision will be relative to the reasons that justify it. In a second moment, we will consider the regulatory issue, whose influence on the airport's financial performance is consolidated in the literature. In the third part, we address the main documented results of privatization, with special attention to productive efficiency, whose improvement is the most popular motivation for privatization.
Determinantes do planejamento estratégico da rede de uma companhia aérea
Authors: Bruno F. Oliveira, Alessandro V. M. Oliveira
Date: 2024-02-17 00:00:00

This work focuses on trying to understand how the construction of an airline's network is made. For this purpose, the case of Azul was studied, investigating which and how factors affect the decision of this airline to enter domestic routes, in addition to analyzing how the merger of Azul with the regional airline Trip affected the company's network planning. For this, an academic study was conducted using an econometric model to understand the airline's entry model. The results show that Azul's business model is based on connecting new destinations, not yet served by its competitors, to one of its hubs, and consistently avoiding routes or airports dominated by other airlines. Regarding the effects of the merger, the results suggest that Azul moved away from its original entry model, based on JetBlue, to a model more oriented towards regional aviation, entering shorter routes and regional airports.
Multidimensional Signaling with a Resource Constraint
Authors: Seungjin Han, Alex Sam
Date: 2024-02-21 00:00:00

We study multidimensional signaling (cognitive/non-cognitive) as a sender's portfolio choice with a resource constraint. We establish the existence of a unique monotone D1 equilibrium where the cognitive (non-cognitive) signal increases (decreases) in sender type and the sum of the two increases in sender type. The equilibrium is characterized by two threshold sender types. The low threshold is one where a kink occurs in signaling. The constraint is binding only for sender types above it. The high threshold is the other one, above which all types spend all the resources in cognitive signal with pooling and discontinuity on the top.
Mechanism Design with Sequential-Move Games: Revelation Principle
Authors: Siyang Xiong
Date: 2024-02-21 00:00:00

Traditionally, mechanism design focuses on simultaneous-move games (e.g., Myerson (1981)). In this paper, we study mechanism design with sequential-move games, and provide two results on revelation principles for general solution concepts (e.g., perfect Bayesian equilibrium, obvious dominance, strong-obvious dominance). First, if a solution concept is additive, implementation in sequential-move games is equivalent to implementation in simultaneous-move games. Second, for any solution concept \r{ho} and any social choice function f, we identify a canonical operator {\gamma}^{(\r{ho},f)}, which is defined on primitives. We prove that, if \r{ho} is monotonic, f can be implemented by a sequential-move game if and only if {\gamma}^{(\r{ho},f)} is achievable, which translates a complicated mechanism design problem into checking some conditions defined on primitives. Most of the existing solution concepts are either additive or monotonic.
HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays
Authors: Federico Miotello, Paolo Ostan, Mirco Pezzoli, Luca Comanducci, Alberto Bernardini, Fabio Antonacci, Augusto Sarti
Date: 2024-02-21 00:00:00

In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to model a remote attendance teleconferencing scenario. Specifically, measurements were performed in a seminar room, where a 64-microphone ULA was used as a multichannel audio acquisition system in the proximity of the speakers, while HOMs were used to model 25 attendees actually present in the seminar room. The HOMs cover a wide area of the room, making the dataset suitable also for applications of virtual acoustics. Through the measurement of the reverberation time and clarity index, and sample applications such as source localization and separation, we demonstrate the effectiveness of the HOMULA-RIR dataset.
Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR
Authors: Rui Zhou, Xian Li, Ying Fang, Xiaofei Li
Date: 2024-02-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Denoising, Speech Enhancement, speech-recognition, Speech Recognition

In this work, we propose Mel-FullSubNet, a single-channel Mel-spectrogram denoising and dereverberation network for improving both speech quality and automatic speech recognition (ASR) performance. Mel-FullSubNet takes as input the noisy and reverberant Mel-spectrogram and predicts the corresponding clean Mel-spectrogram. The enhanced Mel-spectrogram can be either transformed to speech waveform with a neural vocoder or directly used for ASR. Mel-FullSubNet encapsulates interleaved full-band and sub-band networks, for learning the full-band spectral pattern of signals and the sub-band/narrow-band properties of signals, respectively. Compared to linear-frequency domain or time-domain speech enhancement, the major advantage of Mel-spectrogram enhancement is that Mel-frequency presents speech in a more compact way and thus is easier to learn, which will benefit both speech quality and ASR. Experimental results demonstrate a significant improvement in both speech quality and ASR performance achieved by the proposed model.
Performance Evaluation and Analysis of Thresholding-based Interference Mitigation for Automotive Radar Systems
Authors: Jun Li, Jihwan Youn, Ryan Wu, Jeroen Overdevest, Shunqiao Sun
Date: 2024-02-21 00:00:00

In automotive radar, time-domain thresholding (TD-TH) and time-frequency domain thresholding (TFD-TH) are crucial techniques underpinning numerous interference mitigation methods. Despite their importance, comprehensive evaluations of these methods in dense traffic scenarios with different types of interference are limited. In this study, we segment automotive radar interference into three distinct categories. Utilizing the in-house traffic scenario and automotive radar simulator, we evaluate interference mitigation methods across multiple metrics: probability of detection, signal-to-interference-plus-noise ratio, and phase error involving hundreds of targets and dozens of interfering radars. The numerical results highlight that TFD-TH is more effective than TD-TH, particularly as the density and signal correlation of interfering radars escalate.
A Ray Launching Approach for Computing Exact Paths with Point Clouds
Authors: Niklas Vaara, Pekka Sangi, Miguel Bordallo López, Janne Heikkilä
Date: 2024-02-21 00:00:00

Ray tracing is a deterministic method that produces propagation paths between a transmitter and a receiver. The simulation accuracy is significantly influenced by the environment details. One way to capture the environment with great precision is the utilization of depth sensors and cameras. Such reconstructed environment is in the form of a point cloud. However, utilizing such data directly in ray tracing, a key aspect on vision-aided wireless communications, often involves a significant trade-off between accuracy and execution time. In this paper, we propose an open source novel and fast point cloud-based ray launching algorithm that produces exact paths, which provide a good basis for accurate modeling of radio channel characteristics. In experiments, preliminary validation of the ray tracer output is obtained with the aid of a commercial ray tracer.
A Framework of RIS-assisted ICSC User-centric Based Systems: Latency Optimization and Design
Authors: Jiahua Wan, Hong Ren, Zhiyuan Yu, Zhenkun Zhang, Yang Zhang, Cunhua Pan, Jiangzhou Wang
Date: 2024-02-21 00:00:00
Tasks: Edge-computing, Form

This paper studies a comprehensive framework for reconfigurable intelligent surface (RIS)-assisted integrated communication, sensing, and computation (ICSC) systems with a User-centric focus. The study encompasses two scenarios: the general multi-user equipment (UE) scenario and the simplified single-UE scenario. To satisfy the critical need for time-efficient sensing, we investigate the latency minimization problem, subject to constraints on UEs' transmit power, radar signal-to-interference-plus-noise-ratio (SINR), RIS phase shift, and computation capability. To address the formulated non-convex problem in the multi-UE scenario, we decouple the original problem into two subproblems, where the computational and beamforming settings are optimized alternately. Specifically, for the computational settings, we derive a closed-form solution for the offloading volume and propose a low-complexity algorithm based on the bisection search method to optimize the edge computing resource allocation. Additionally, we employ two equivalent transformations to address the challenge posed by the non-convex sum-of-ratios form in the objective function (OF) of the subproblem related to active and passive beamforming. Several techniques are then combined to address these subproblems. Furthermore, a low-complexity algorithm that offers closed-form solutions is developed for the simplified single UE scenario. Finally, simulation results substantiate the effectiveness of the proposed framework.
Malicious Reconfigurable Intelligent Surfaces: How Impactful can Destructive Beamforming be?
Authors: Steven Rivetti, Ozlem Tugfe Demir, Emil Bjornson, Mikael Skoglund
Date: 2024-02-20 00:00:00

Reconfigurable intelligent surfaces (RISs) have demonstrated significant potential for enhancing communication system performance if properly configured. However, a RIS might also pose a risk to the network security. In this letter, we explore the impact of a malicious RIS on a multi-user multiple-input single-output (MISO) system when the system is unaware of the RIS's malicious intentions. The objective of the malicious RIS is to degrade the \ac{SNR} of a specific \ac{UE}, with the option of preserving the SNR of the other UEs, making the attack harder to detect. To achieve this goal, we derive the optimal RIS phase-shift pattern, assuming perfect channel state information (CSI) at the hacker. We then relax this assumption by introducing CSI uncertainties and subsequently determine the RIS's phase-shift pattern using a robust optimization approach. Our simulations reveal a direct proportionality between the performance degradation caused by the malicious RIS and the number of reflective elements, along with resilience toward CSI uncertainties.