pwc-archive/papers-with-abstracts huggingface dataset:
Robust Model Predictive Techno-Economic Control of Active Distribution Networks
Authors: Salish Maharjan, Prashant Tiwari, Rui Cheng, Zhaoyu Wang
Date: 2023-05-05 00:00:00
Tasks: continuous-control, Continuous Control

Stochastic controllers are perceived as a promising solution for techno-economic operation of distribution networks having higher generation uncertainties at large penetration of renewables. These controllers are supported by forecasters capable of predicting generation uncertainty by means of lower/upper bounds rather than by probability density function (PDF). Hence, the stochastic controller assumes a suitable PDF for scenario creation and optimization, requiring validation of the assumption. To effectively bridge the forecaster's capability and resolve the assumption issues, the paper proposes a robust model prediction-based techno-economic controller, which essentially utilizes only the lower/upper bounds of the forecast, eliminating the necessity of PDF. Both discrete and continuous control resources such as tap-changers and DERs are utilized for regulating the lower/upper bounds of the network states and robustly minimizing the cost of energy import. The proposed controller is implemented for UKGDS network and validated by comparing performance at various confidence levels of lower/upper bound forecast.
A small-gain theorem for 2-contraction of nonlinear interconnected systems
Authors: David Angeli, Davide Martini, Giacomo Innocenti, Alberto Tesi
Date: 2023-05-04 00:00:00

This paper introduces small-gain sufficient conditions for $2$-contraction of feedback interconnected systems, on the basis of individual gains of suitable subsystems arising from a modular decomposition of the second additive compound equation. The condition applies even to cases when individual subsystems might fail to be contractive (due to the extra margin of contraction afforded by the second additive compound matrix). Examples of application are provided to illustrate the theory and show its degree of conservatism and scope of applicability.
TelecomTM: A Fine-Grained and Ubiquitous Traffic Monitoring System Using Pre-Existing Telecommunication Fiber-Optic Cables as Sensors
Authors: Jingxiao Liu, Siyuan Yuan, Yiwen Dong, Biondo Biondi, Hae Young Noh
Date: 2023-05-04 00:00:00
Tasks: vehicle detection

We introduce the TelecomTM system that uses pre-existing telecommunication fiber-optic cables as virtual strain sensors to sense vehicle-induced ground vibrations for fine-grained and ubiquitous traffic monitoring and characterization. Here we call it a virtual sensor because it is a software-based representation of a physical sensor. Due to the extensively installed telecommunication fiber-optic cables at the roadside, our system using redundant dark fibers enables to monitor traffic at low cost with low maintenance. Many existing traffic monitoring approaches use cameras, piezoelectric sensors, and smartphones, but they are limited due to privacy concerns and/or deployment requirements. Previous studies attempted to use telecommunication cables for traffic monitoring, but they were only exploratory and limited to simple tasks at a coarse granularity, e.g., vehicle detection, due to their hardware constraints and real-world challenges. In particular, those challenges are 1) unknown and heterogeneous properties of virtual sensors and 2) large and complex noise conditions. To this end, our TelecomTM system first characterizes the geographic location and analyzes the signal pattern of each virtual sensor through driving tests. We then develop a spatial-domain Bayesian filtering and smoothing algorithm to detect, track, and characterize each vehicle. Our approach uses the spatial dependency of multiple virtual sensors and Newton's laws of motion to combine the distributed sensor data to reduce uncertainties in vehicle detection and tracking. In our real-world evaluation on a two-way traffic road with 1120 virtual sensors, TelecomTM achieved 90.18% vehicle detection accuracy, 27$\times$ and 5$\times$ error reduction for vehicle position and speed tracking compared to a baseline method, and $\pm$3.92% and $\pm$11.98% percent error for vehicle wheelbase and weight estimation, respectively.
Impact of reproduction-mobility trade-off on biodiversity in rock-paper-scissors models in changing environmental conditions
Authors: J. Menezes, E. Rangel
Date: 2023-05-04 00:00:00

We investigate a tritrophic system in which organisms' energy depletion, resulting from failed selection attempts, leads to a partial loss of capacity to win the cyclic spatial game. The energy required to maintain optimal organism fitness may be impacted by changes in environmental conditions, increasing the death risk due to accelerated deterioration of health. We simulate the evolutionary behavioural strategy performed by individuals of one species, which consists in balancing efforts dedicated to reproduction and mobility to minimise the chances of death by lack of energy. We show that the unevenness introduced by the trade-off strategy unbalances the rock-paper-scissors model, with the predominant species profiting from enemies' lower birth rate of enemies. Quantifying the spatial patterns, we demonstrate that the characteristic length scale of single-species domains decreases as energy loss accelerates due to environmental changes. The erosion in the spatial patterns provoked by the reproduction-mobility trade-off benefits biodiversity, with coexistence probability rising for faster energy depletion and higher trade-off factors. The findings have implications for ecologists seeking to understand the impact of survival behaviour on biodiversity promotion.
Assessing Rate limits Using Behavioral and Neural Responses of Interaural-Time-Difference Cues in Fine-Structure and Envelope
Authors: Hongmei Hu, Stephan Ewert, Birger Kollmeier, Deborah Vickers
Date: 2023-05-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The objective was to determine the effect of pulse rate on the sensitivity to use interaural-time-difference (ITD) cues and to explore the mechanisms behind rate-dependent degradation in ITD perception in bilateral cochlear implant (CI) listeners using CI simulations and electroencephalogram (EEG) measures. To eliminate the impact of CI stimulation artifacts and to develop protocols for the ongoing bilateral CI studies, upper-frequency limits for both behavior and EEG responses were obtained from normal hearing (NH) listeners using sinusoidal-amplitude-modulated (SAM) tones and filtered clicks with changes in either fine structure ITD or envelope ITD. Multiple EEG responses were recorded, including the subcortical auditory steady-state responses (ASSRs) and cortical auditory evoked potentials (CAEPs) elicited by stimuli onset, offset, and changes. Results indicated that acoustic change complex (ACC) responses elicited by envelope ITD changes were significantly smaller or absent compared to those elicited by fine structure ITD changes. The ACC morphologies evoked by fine structure ITD changes were similar to onset and offset CAEPs, although smaller than onset CAEPs, with the longest peak latencies for ACC responses and shortest for offset CAEPs. The study found that high-frequency stimuli clearly elicited subcortical ASSRs, but smaller than those evoked by lower carrier frequency SAM tones. The 40-Hz ASSRs decreased with increasing carrier frequencies. Filtered clicks elicited larger ASSRs compared to high-frequency SAM tones, with the order being 40-Hz-ASSR>160-Hz-ASSR>80-Hz-ASSR>320-Hz-ASSR for both stimulus types. Wavelet analysis revealed a clear interaction between detectable transient CAEPs and 40-Hz-ASSRs in the time-frequency domain for SAM tones with a low carrier frequency.
Debiased Inference for Dynamic Nonlinear Panels with Multi-dimensional Heterogeneities
Authors: Xuan Leng, Jiaming Mao, Yutao Sun
Date: 2023-05-04 00:00:00
Tasks: Vocal Bursts Valence Prediction

We introduce a generic class of dynamic nonlinear heterogeneous parameter models that incorporate individual and time fixed effects in both the intercept and slope. These models are subject to the incidental parameter problem, in that the limiting distribution of the point estimator is not centered at zero, and that test statistics do not follow their standard asymptotic distributions as in the absence of the fixed effects. To address the problem, we develop an analytical bias correction procedure to construct a bias-corrected likelihood. The resulting estimator follows an asymptotic normal distribution with mean zero. Moreover, likelihood-based tests statistics -- including likelihood-ratio, Lagrange-multiplier, and Wald tests -- follow the limiting chi-squared distribution under the null hypothesis. Simulations demonstrate the effectiveness of the proposed correction method, and an empirical application on the labor force participation of single mothers underscores its practical importance.
Rankings-Dependent Preferences: A Real Goods Matching Experiment
Authors: Andrew Kloosterman, Peter Troyan
Date: 2023-05-05 00:00:00
Tasks: Experimental Design

We investigate whether preferences for objects received via a matching mechanism are influenced by how highly agents rank them in their reported rank order list. We hypothesize that all else equal, agents receive greater utility for the same object when they rank it higher. The addition of rankings-dependent utility implies that it may not be a dominant strategy to submit truthful preferences to a strategyproof mechanism, and that non-strategyproof mechanisms that give more agents objects they \emph{report} as higher ranked may increase market welfare. We test these hypotheses with a matching experiment in a strategyproof mechanism, the random serial dictatorship, and a non-strategyproof mechanism, the Boston mechanism. A novel feature of our experimental design is that the objects allocated in the matching markets are real goods, which allows us to directly measure rankings-dependence by eliciting values for goods both inside and outside of the mechanism. The experimental results are mixed, with stronger evidence for rankings-dependence in the RSD treatment than the Boston treatment. We find no differences between the two mechanisms for the rates of truth-telling and the final welfare.
Empirical Evidence for the New Definitions in Financial Markets and Equity Premium Puzzle
Authors: Atilla Aras
Date: 2023-05-05 00:00:00

This study presents empirical evidence to support the validity of new definitions in financial markets. The author develops a new method to determine investors' risk attitudes in financial markets. The risk attitudes of investors in US financial markets from 1889-1978 are analyzed and the results indicate that equity investors who invested in the composite S&P 500 index were risk-averse in 1977. Conversely, risk-free asset investors who invested in US Treasury bills were found to exhibit not enough risk-loving behavior, which can be considered a type of risk-averse behavior. These findings suggest that the new definitions in financial markets accurately reflect the behavior of investors and should be considered in investment strategies.
Algorithmic Decision Processes
Authors: Carlo Baldassi, Fabio Maccheroni, Massimo Marinacci, Marco Pirazzini
Date: 2023-05-05 00:00:00

We develop a full-fledged analysis of an algorithmic decision process that, in a multialternative choice problem, produces computable choice probabilities and expected decision times.
Disclosure and Incentives in Teams
Authors: Paula Onuchic, João Ramos
Date: 2023-05-05 00:00:00

We consider a team-production environment where all participants are motivated by career concerns, and where a team's joint productive outcome may have different reputational implications for different team members. In this context, we characterize equilibrium disclosure of team-outcomes when team-disclosure choices aggregate individual decisions through some deliberation protocol. In contrast with individual disclosure problems, we show that equilibria often involve partial disclosure. Furthermore, we study the effort-incentive properties of equilibrium disclosure strategies implied by different deliberation protocols; and show that the partial disclosure of team outcomes may improve individuals' incentives to contribute to the team. Finally, we study the design of deliberation protocols, and characterize productive environments where effort-incentives are maximized by unilateral decision protocols or more consensual deliberation procedures.
Games Under Network Uncertainty
Authors: Promit K. Chaudhuri, Matthew O. Jackson, Sudipta Sarangi, Hector Tzavellas
Date: 2023-05-04 00:00:00

We examine settings in which agents choose behaviors and care about their neighbors' behaviors, but have incomplete information about the network in which they are embedded. We develop a model in which agents use local knowledge of their direct (and in some cases indirect) connections to make inferences about the complementarity strength of their actions with other agents in the society. Consequently, an agent's position in the network and identity plays a crucial role in determining their strategic behavior in ways that we can quantify. Our main results characterize equilibrium behaviors under various beliefs. In particular, we analyze the role of heterogeneous network information among agents in detail. For instance, we show how having people update about the network can lead those with lower degree to systematically underestimate the complementarities in the society, while those with higher degree overestimate the complementarities. We also study the inferences that agents make and the resulting behaviors in several prominent families of network architectures.
Deep Unsupervised Learning for 3D ALS Point Cloud Change Detection
Authors: Iris de Gélis, Sudipan Saha, Muhammad Shahzad, Thomas Corpetti, Sébastien Lefèvre, Xiao Xiang Zhu
Date: 2023-05-05 00:00:00
Tasks: Change Detection, Contrastive Learning, Deep Clustering, Self-Supervised Learning

Change detection from traditional \added{2D} optical images has limited capability to model the changes in the height or shape of objects. Change detection using 3D point cloud \added{from photogrammetry or LiDAR surveying} can fill this gap by providing critical depth information. While most existing machine learning based 3D point cloud change detection methods are supervised, they severely depend on the availability of annotated training data, which is in practice a critical point. To circumnavigate this dependence, we propose an unsupervised 3D point cloud change detection method mainly based on self-supervised learning using deep clustering and contrastive learning. The proposed method also relies on an adaptation of deep change vector analysis to 3D point cloud via nearest point comparison. Experiments conducted on \added{an aerial LiDAR survey dataset} show that the proposed method obtains higher performance in comparison to the traditional unsupervised methods, with a gain of about 9\% in mean accuracy (to reach more than 85\%). Thus, it appears to be a relevant choice in scenario where prior knowledge (labels) is not ensured.
Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising
Authors: Elvira Fleig, Erik Bochinski, Thomas Sikora
Date: 2023-05-05 00:00:00
Tasks: Decoder, Denoising, Mixture-of-Experts, Super-Resolution

Research in the past years introduced Steered Mixture-of-Experts (SMoE) as a framework to form sparse, edge-aware models for 2D- and higher dimensional pixel data, applicable to compression, denoising, and beyond, and capable to compete with state-of-the-art compression methods. To circumvent the computationally demanding, iterative optimization method used in prior works an autoencoder design is introduced that reduces the run-time drastically while simultaneously improving reconstruction quality for block-based SMoE approaches. Coupling a deep encoder network with a shallow, parameter-free SMoE decoder enforces an efficent and explainable latent representation. Our initial work on the autoencoder design presented a simple model, with limited applicability to compression and beyond. In this paper, we build on the foundation of the first autoencoder design and improve the reconstruction quality by expanding it to models of higher complexity and different block sizes. Furthermore, we improve the noise robustness of the autoencoder for SMoE denoising applications. Our results reveal that the newly adapted autoencoders allow ultra-fast estimation of parameters for complex SMoE models with excellent reconstruction quality, both for noise free input and under severe noise. This enables the SMoE image model framework for a wide range of image processing applications, including compression, noise reduction, and super-resolution.
Dynamic DH-MBIR for Phase-Error Estimation from Streaming Digital-Holography Data
Authors: Ali G. Sheikh, Casey J. Pellizzari, Sherman J. Kisner, Gregery T. Buzzard, Charles A. Bouman
Date: 2023-05-05 00:00:00

Directed energy applications require the estimation of digital-holographic (DH) phase errors due to atmospheric turbulence in order to accurately focus the outgoing beam. These phase error estimates must be computed with very low latency to keep pace with changing atmospheric parameters, which requires that phase errors be estimated in a single shot of DH data. The digital holography model-based iterative reconstruction (DH-MBIR) algorithm is capable of accurately estimating phase errors in a single shot using the expectation maximization (EM) algorithm. However, existing implementations of DH-MBIR require hundreds of iterations, which is not practical for real-time applications. In this paper, we present the Dynamic DH-MBIR (DDH-MBIR) algorithm for estimating isoplanatic phase errors from streaming single-shot data with extremely low latency. The Dynamic DH-MBIR algorithm reduces the computation and latency by orders of magnitude relative to conventional DH-MBIR, making real-time throughput and latency feasible in applications. Using simulated data that models frozen flow of atmospheric turbulence, we show that our algorithm can achieve a consistently high Strehl ratio with realistic simulation parameters using only 1 iteration per timestep.
Mining fMRI Dynamics with Parcellation Prior for Brain Disease Diagnosis
Authors: Xiaozhao Liu, Mianxin Liu, Lang Mei, Yuyao Zhang, Feng Shi, Han Zhang, Dinggang Shen
Date: 2023-05-04 00:00:00
Tasks: Graph Learning, Multiple Instance Learning

To characterize atypical brain dynamics under diseases, prevalent studies investigate functional magnetic resonance imaging (fMRI). However, most of the existing analyses compress rich spatial-temporal information as the brain functional networks (BFNs) and directly investigate the whole-brain network without neurological priors about functional subnetworks. We thus propose a novel graph learning framework to mine fMRI signals with topological priors from brain parcellation for disease diagnosis. Specifically, we 1) detect diagnosis-related temporal features using a "Transformer" for a higher-level BFN construction, and process it with a following graph convolutional network, and 2) apply an attention-based multiple instance learning strategy to emphasize the disease-affected subnetworks to further enhance the diagnosis performance and interpretability. Experiments demonstrate higher effectiveness of our method than compared methods in the diagnosis of early mild cognitive impairment. More importantly, our method is capable of localizing crucial brain subnetworks during the diagnosis, providing insights into the pathogenic source of mild cognitive impairment.
Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer's Disease Classification
Authors: Davide Coluzzi, Valentina Bordin, Massimo Walter Rivolta, Igor Fortel, Liang Zhang, Alex Leow, Giuseppe Baselli
Date: 2023-05-03 00:00:00
Tasks: Classification, Diagnostic, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Alzheimer's Disease (AD) is the world leading cause of dementia, a progressively impairing condition leading to high hospitalization rates and mortality. To optimize the diagnostic process, numerous efforts have been directed towards the development of deep learning approaches (DL) for the automatic AD classification. However, their typical black box outline has led to low trust and scarce usage within clinical frameworks. In this work, we propose two state-of-the art DL models, trained respectively on structural MRI (ResNet18) and brain connectivity matrixes (BC-GCN-SE) derived from diffusion data. The models were initially evaluated in terms of classification accuracy. Then, results were analyzed using an Explainable Artificial Intelligence (XAI) approach (Grad-CAM) to measure the level of interpretability of both models. The XAI assessment was conducted across 132 brain parcels, extracted from a combination of the Harvard-Oxford and AAL brain atlases, and compared to well-known pathological regions to measure adherence to domain knowledge. Results highlighted acceptable classification performance as compared to the existing literature (ResNet18: TPRmedian = 0.817, TNRmedian = 0.816; BC-GCN-SE: TPRmedian = 0.703, TNRmedian = 0.738). As evaluated through a statistical test (p < 0.05) and ranking of the most relevant parcels (first 15%), Grad-CAM revealed the involvement of target brain areas for both the ResNet18 and BC-GCN-SE models: the medial temporal lobe and the default mode network. The obtained interpretabilities were not without limitations. Nevertheless, results suggested that combining different imaging modalities may result in increased classification performance and model reliability. This could potentially boost the confidence laid in DL models and favor their wide applicability as aid diagnostic tools.
A 3D Modeling Method for Scattering on Rough Surfaces at the Terahertz Band
Authors: Ben Chen, Ke Guan, Danping He, Pengxiang Xie, Zhangdui Zhong, Jianwu Dou, Shahid Mumtaz, Wael Bazzi
Date: 2023-05-05 00:00:00

The terahertz (THz) band (0.1-10 THz) is widely considered to be a candidate band for the sixth-generation mobile communication technology (6G). However, due to its short wavelength (less than 1 mm), scattering becomes a particularly significant propagation mechanism. In previous studies, we proposed a scattering model to characterize the scattering in THz bands, which can only reconstruct the scattering in the incidence plane. In this paper, a three-dimensional (3D) stochastic model is proposed to characterize the THz scattering on rough surfaces. Then, we reconstruct the scattering on rough surfaces with different shapes and under different incidence angles utilizing the proposed model. Good agreements can be achieved between the proposed model and full-wave simulation results. This stochastic 3D scattering model can be integrated into the standard channel modeling framework to realize more realistic THz channel data for the evaluation of 6G.