pwc-archive/papers-with-abstracts huggingface dataset:
Unified Control Scheme for Optimal Allocation of GFM and GFL Inverters in Power Networks
Authors: Sushobhan Chatterjee, Sijia Geng
Date: 2024-12-19 00:00:00

With the rapid adoption of emerging inverter-based resources, it is crucial to understand their dynamic interactions across the network and ensure stability. This paper proposes a systematic and efficient method to determine the optimal allocation of grid-forming and grid-following inverters in power networks. The approach leverages a novel unified grid-forming/following inverter control and formulates an optimization problem to ensure stability and maximal energy dissipation during transient periods. An iterative algorithm is developed to solve the optimization problem. The resulting optimal droop gains for the unified inverters provide insights into the needs for grid-forming and grid-following resources in the network. A three-bus system is used to demonstrate the optimality and dynamic performance of the proposed method.
Safety-Critical Control of Discontinuous Systems with Nonsmooth Safe Sets
Authors: Mohammed Alyaseen, Nikolay Atanasov, Jorge Cortes
Date: 2024-12-19 00:00:00

This paper studies the design of controllers for discontinuous dynamics that ensure the safety of non-smooth sets. The safe set is represented by arbitrarily nested unions and intersections of 0-superlevel sets of differentiable functions. We show that any optimization-based controller that satisfies only the point-wise active safety constraints is generally un-safe, ruling out the standard techniques developed for safety of continuous dynamics. This motivates the introduction of the notion of transition functions, which allow us to incorporate even the inactive safety constraints without falling into unnecessary conservatism. These functions allow system trajectories to leave a component of the nonsmooth safe set to transition to a different one. The resulting controller is then defined as the solution to a convex optimization problem, which we show is feasible and continuous wherever the system dynamics is continuous. We illustrate the effectiveness of the proposed design approach in a multi-agent reconfiguration control problem.
Optimizing Photovoltaic Panel Quantity for Water Distribution Networks
Authors: Mirhan Ürkmez, Carsten Kallesøe, Jan Dimon Bendtsen, John Leth
Date: 2024-12-19 00:00:00

The paper introduces a procedure for determining an approximation of the optimal amount of photovoltaics (PVs) for powering water distribution networks (WDNs) through grid-connected PVs. The procedure aims to find the PV amount minimizing the total expected cost of the WDN over the lifespan of the PVs. The approach follows an iterative process, starting with an initial estimate of the PV quantity, and then calculating the total cost of WDN operation. To calculate the total cost of the WDN, we sample PV power profiles that represent the future production based on a probabilistic PV production model. Simulations are conducted assuming these sampled PV profiles power the WDN, and pump flow rates are determined using a control method designed for PV-powered WDNs. Following the simulations, the overall WDN cost is calculated. Since we lack access to derivative information, we employ the derivative-free Nelder-Mead method for iteratively adjusting the PV quantity to find an approximation of the optimal value. The procedure is applied for the WDN of Randers, a Danish town. By determining an approximation of the optimal quantity of PVs, we observe a 14.5\% decrease in WDN costs compared to the scenario without PV installations, assuming a 25 year lifespan for the PV panels.
Autonomous Vehicle Security: A Deep Dive into Threat Modeling
Authors: Amal Yousseef, Shalaka Satam, Banafsheh Saber Latibari, Jesus Pacheco, Soheil Salehi, Salim Hariri, Partik Satam
Date: 2024-12-19 00:00:00
Tasks: Autonomous Vehicles, Survey

Autonomous vehicles (AVs) are poised to revolutionize modern transportation, offering enhanced safety, efficiency, and convenience. However, the increasing complexity and connectivity of AV systems introduce significant cybersecurity challenges. This paper provides a comprehensive survey of AV security with a focus on threat modeling frameworks, including STRIDE, DREAD, and MITRE ATT\&CK, to systematically identify and mitigate potential risks. The survey examines key components of AV architectures, such as sensors, communication modules, and electronic control units (ECUs), and explores common attack vectors like wireless communication exploits, sensor spoofing, and firmware vulnerabilities. Through case studies of real-world incidents, such as the Jeep Cherokee and Tesla Model S exploits, the paper highlights the critical need for robust security measures. Emerging technologies, including blockchain for secure Vehicle-to-Everything (V2X) communication, AI-driven threat detection, and secure Over-The-Air (OTA) updates, are discussed as potential solutions to mitigate evolving threats. The paper also addresses legal and ethical considerations, emphasizing data privacy, user safety, and regulatory compliance. By combining threat modeling frameworks, multi-layered security strategies, and proactive defenses, this survey offers insights and recommendations for enhancing the cybersecurity of autonomous vehicles.
A Hybrid Three-Port Topology for Urban Charging Stations
Authors: Mohammadreza Khodaparast Klidbari, Naser Souri, Zahra Sadat Habibolahi, Hamid Montazeri Hedeshi
Date: 2024-12-19 00:00:00

Electric vehicles are rapidly gaining popularity as a sustainable alternative to conventional gasoline. In urban areas, chargers with different ratings can accommodate the diverse needs of electric vehicles. However, the available multiport topologies have variable switching frequencies. This paper introduces a hybrid multiport isolated DC-DC converter for urban charging stations, incorporating fast and slow charging ports with a fixed switching frequency. It provides isolation and enables soft switching on the primary side of the converter without circulating current on its secondary side. The primary side does not need feedback, which reduces complexity. The second stage generates a wide output voltage range to charge the electric vehicle battery by employing a switch. In addition, the proposed topology offers reduced component count and simple control with fixed-frequency operation. This paper provides the concept and the operation modes. Experimental results are provided to validate its features. The prototype converter achieves 96\% peak efficiency.
Does the brain behave like a (complex) network? I. Dynamics
Authors: D. Papo, J. M. Buldú
Date: 2024-12-20 00:00:00
Tasks: Anatomy

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.
Synaptic plasticity alters the nature of chaos transition in neural networks
Authors: Wenkang Du, Haiping Huang
Date: 2024-12-20 00:00:00

In realistic neural circuits, both neurons and synapses are coupled in dynamics with separate time scales. The circuit functions are intimately related to these coupled dynamics. However, it remains challenging to understand the intrinsic properties of the coupled dynamics. Here, we develop the neuron-synapse coupled quasi-potential method to demonstrate how learning induces the qualitative change in macroscopic behaviors of recurrent neural networks. We find that under the Hebbian learning, a large Hebbian strength will alter the nature of the chaos transition, from a continuous type to a discontinuous type, where the onset of chaos requires a smaller synaptic gain compared to the non-plastic counterpart network. In addition, our theory predicts that under feedback and homeostatic learning, the location and type of chaos transition are retained, and only the chaotic fluctuation is adjusted. Our theoretical calculations are supported by numerical simulations.
Decoding fairness: a reinforcement learning perspective
Authors: Guozhong Zheng, Jiqiang Zhang, Xin Ou, Shengfeng Deng, Li Chen
Date: 2024-12-20 00:00:00
Tasks: Fairness, Imitation Learning, Q-Learning, reinforcement-learning, Reinforcement Learning

Behavioral experiments on the ultimatum game (UG) reveal that we humans prefer fair acts, which contradicts the prediction made in orthodox Economics. Existing explanations, however, are mostly attributed to exogenous factors within the imitation learning framework. Here, we adopt the reinforcement learning paradigm, where individuals make their moves aiming to maximize their accumulated rewards. Specifically, we apply Q-learning to UG, where each player is assigned two Q-tables to guide decisions for the roles of proposer and responder. In a two-player scenario, fairness emerges prominently when both experiences and future rewards are appreciated. In particular, the probability of successful deals increases with higher offers, which aligns with observations in behavioral experiments. Our mechanism analysis reveals that the system undergoes two phases, eventually stabilizing into fair or rational strategies. These results are robust when the rotating role assignment is replaced by a random or fixed manner, or the scenario is extended to a latticed population. Our findings thus conclude that the endogenous factor is sufficient to explain the emergence of fairness, exogenous factors are not needed.
Exploring the Multifractal Behavior of the Human Genome T2T-CHM13v2.0: Graphical Representations and Cytogenetics
Authors: Yulián A. Alvarez-Ballesteros, Mario A. Quiroz-Juarez, José L. Del-Rio-Correa, Adrian M. Escobar-Ruiz
Date: 2024-12-21 00:00:00

In this work, we applied the Chaos Game Representation (CGR) to the complete human genomic sequence T2T-CHM13v2.0, analyzing the entire chromosome assembly and each chromosome separately, including mitochondrial DNA. Multifractal spectra were determined using two types of box-counting coverage, revealing slight variations across most chromosomes. While the geometric support remained consistent, distinct distributions were observed for each chromosome. Chromosomes 9 and Y exhibited the greatest differences in singularity (H\"older exponent), with minor variations in their fractal support. The CGR distributions generally demonstrated an approximate separation between coding and non-coding sections, as well as CpG or GpC islands. A base-by-base analysis of the fractal support of the CGR uncovered characteristic structural bands in chromosome sequences, which align with patterns identified in cytogenetic studies. Using the complete assembly as a reference, we compared two alternative representations: the Binary Genomic Representation (RGB) and the Markov Chain (MC) representation. Both methods tended toward the same fractal support but displayed differing distributions based on the assigned length parameter. Multifractal analysis highlighted quantitative differences between these representations: RGB aligned more closely with high-frequency components, while MC showed better correspondence with low frequencies. The optimal fit was achieved using MC for twelve-base chains, yielding an average percentage error of 2% relative to the full genomic assembly.
Optimal signal transmission and timescale diversity in a model of human brain operating near criticality
Authors: Yang Qi, Jiexiang Wang, Weiyang Ding, Gustavo Deco, Viktor Jirsa, Wenlian Lu, Jianfeng Feng
Date: 2024-12-22 00:00:00
Tasks: Diversity

Cortical neurons exhibit a hierarchy of timescales across brain regions in response to input stimuli, which is thought to be crucial for information processing of different temporal scales. Modeling studies suggest that both intra-regional circuit dynamics as well as cross-regional connectome may contribute to this timescale diversity. Equally important to diverse timescales is the ability to transmit sensory signals reliably across the whole brain. Therefore, the brain must be able to generate diverse timescales while simultaneously minimizing signal attenuation. To understand the dynamical mechanism behind these phenomena, we develop a second-order mean field model of the human brain by applying moment closure and coarse-graining to a digital twin brain model endowed with whole brain structural connectome. Cross-regional coupling strength is found to induced a phase transition from asynchronous activity to synchronous oscillation. By analyzing the input-response properties of the model, we reveal criticality as a unifying mechanism for enabling simultaneously optimal signal transmission and timescales diversity. We show how structural connectome and criticality jointly shape intrinsic timescale hierarchy across the brain.
Is AI Robust Enough for Scientific Research?
Authors: Jun-Jie Zhang, Jiahao Song, Xiu-Cheng Wang, Fu-Peng Li, Zehan Liu, Jian-Nan Chen, Haoning Dang, Shiyao Wang, Yiyan Zhang, Jianhui Xu, Chunxiang Shi, Fei Wang, Long-Gang Pang, Nan Cheng, Weiwei Zhang, Duo Zhang, Deyu Meng
Date: 2024-12-19 00:00:00
Tasks: Weather Forecasting

We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs. Through an analysis of five diverse application areas -- weather forecasting, chemical energy and force calculations, fluid dynamics, quantum chromodynamics, and wireless communication -- we demonstrate that this vulnerability is a broad and general characteristic of AI systems. This revelation exposes a hidden risk in relying on neural networks for essential scientific computations, calling further studies on their reliability and security.
A Generalizable 3D Diffusion Framework for Low-Dose and Few-View Cardiac SPECT
Authors: Huidong Xie, Weijie Gan, Wei Ji, Xiongchao Chen, Alaa Alashi, Stephanie L. Thorn, Bo Zhou, Qiong Liu, Menghua Xia, Xueqi Guo, Yi-Hwa Liu, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Albert J. Sinusas, Chi Liu
Date: 2024-12-21 00:00:00
Tasks: Diagnostic

Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize across different acquisition settings, limiting their applicability in reality. This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning. Using both image and projection data, a consistency strategy is proposed to ensure that diffusion sampling at each step aligns with the low-dose/few-view projection measurements, the image data, and the scanner geometry, thus enabling generalization to different low-dose/few-view settings. Incorporating anatomical spatial information from CT and total variation constraint, we proposed a 2.5D conditional strategy to allow the DiffSPECT-3D to observe 3D contextual information from the entire image volume, addressing the 3D memory issues in diffusion model. We extensively evaluated the proposed method on 1,325 clinical 99mTc tetrofosmin stress/rest studies from 795 patients. Each study was reconstructed into 5 different low-count and 5 different few-view levels for model evaluations, ranging from 1% to 50% and from 1 view to 9 view, respectively. Validated against cardiac catheterization results and diagnostic comments from nuclear cardiologists, the presented results show the potential to achieve low-dose and few-view SPECT imaging without compromising clinical performance. Additionally, DiffSPECT-3D could be directly applied to full-dose SPECT images to further improve image quality, especially in a low-dose stress-first cardiac SPECT imaging protocol.
Performance limits of spectro-temporal unitary transformations for coherent modulation
Authors: Callum Deakin, Xi Chen
Date: 2024-12-20 00:00:00

We analyse the performance limits of coherent modulation based on lossless unitary transformations, demonstrating that they can achieve high ($>$30~dB) SINAD and outperform conventional IQ modulators at equivalent transmitter laser powers.
A large non-Gaussian structural VAR with application to Monetary Policy
Authors: Jan Prüser
Date: 2024-12-23 00:00:00

We propose a large structural VAR which is identified by higher moments without the need to impose economically motivated restrictions. The model scales well to higher dimensions, allowing the inclusion of a larger number of variables. We develop an efficient Gibbs sampler to estimate the model. We also present an estimator of the deviance information criterion to facilitate model comparison. Finally, we discuss how economically motivated restrictions can be added to the model. Experiments with artificial data show that the model possesses good estimation properties. Using real data we highlight the benefits of including more variables in the structural analysis. Specifically, we identify a monetary policy shock and provide empirical evidence that prices and economic output respond with a large delay to the monetary policy shock.
Advanced Models for Hourly Marginal CO2 Emission Factor Estimation: A Synergy between Fundamental and Statistical Approaches
Authors: Souhir Ben Amor, Smaranda Sgarciu, Taimyra BatzLineiro, Felix Muesgens
Date: 2024-12-23 00:00:00
Tasks: regression

Global warming is caused by increasing concentrations of greenhouse gases, particularly carbon dioxide (CO2). A metric used to quantify the change in CO2 emissions is the marginal emission factor, defined as the marginal change in CO2 emissions resulting from a marginal change in electricity demand over a specified period. This paper aims to present two methodologies to estimate the marginal emission factor in a decarbonized electricity system with high temporal resolution. First, we present an energy systems model that incrementally calculates the marginal emission factors. Second, we examine a Markov Switching Dynamic Regression model, a statistical model designed to estimate marginal emission factors faster and use an incremental marginal emission factor as a benchmark to assess its precision. For the German electricity market, we estimate the marginal emissions factor time series historically (2019, 2020) using Agora Energiewende and for the future (2025, 2030, and 2040) using estimated energy system data. The results indicate that the Markov Switching Dynamic Regression model is more accurate in estimating marginal emission factors than the Dynamic Linear Regression models, which are frequently used in the literature. Hence, the Markov Switching Dynamic Regression model is a simpler alternative to the computationally intensive incremental marginal emissions factor, especially when short-term marginal emissions factor estimation is needed. The results of the marginal emission factor estimation are applied to an exemplary low-emission vehicle charging scenario to estimate CO2 savings by shifting the charge hours to those corresponding to the lower marginal emissions factor. By implementing this emission-minimized charging approach, an average reduction of 31% in the marginal emission factor was achieved over the 5 years.
Counting Defiers in Health Care: A Design-Based Model of an Experiment Can Reveal Evidence Against Monotonicity
Authors: Neil Christy, Amanda Ellen Kowalski
Date: 2024-12-20 00:00:00

We show that a design-based model of an experiment with a binary intervention and outcome can reveal empirical evidence against a ``monotonicity'' assumption that the intervention affects all subjects in weakly the same direction. A canonical sampling-based model cannot, but we show that other sampling-based models can. Using statistical decision theory, we propose a maximum likelihood decision rule that does not assume monotonicity and provide conditions for its optimality. Under these conditions, we calculate the exact performance of our rule in small samples and show that the gains relative to a rule that assumes monotonicity grow with the sample size. In a real experiment in health care, we use visualizations of potential outcomes to illustrate evidence against monotonicity, which we quantify with a likelihood ratio. Despite a large and statistically significant average effect, our rule reveals positive counts of compilers affected in one direction and defiers affected in the other.
The impact of Egypt's accession to the BRICS group on the foreign exchange crisis in Egypt
Authors: Yasmeen Fekery Yaseen El Khodary, Mousa Gowfal Selmey Gowfal Selmey, Elsayed Farrag Elsaid Mohamed Elsayed
Date: 2024-12-22 00:00:00

This paper investigated the impact of Egypt's accession to the BRICS bloc by studying the participation rate of BRICS countries in Egypt, as well as the exposure rate and the ratio of foreign investment of BRICS countries in Egypt to the total foreign investment in Egypt, as well as Egypt's export opportunities in the BRICS markets and the impact of these variables on the ratio of foreign assets in Egypt during the period from 2000 to 2024, In order to predict the impact of Egypt's accession to the BRICS bloc in the coming years as well. The study used the ARDL model to determine the impact of the four independent variables on the dependent variable (foreign assets), which is the ratio of foreign assets. It also used automatic ARIMA model to predict the values of the study variables during the period from 2024 to 2030.
Data-Driven Economic Agent-Based Models
Authors: Marco Pangallo, R. Maria del Rio-Chanona
Date: 2024-12-21 00:00:00
Tasks: Time Series

Economic agent-based models (ABMs) are becoming more and more data-driven, establishing themselves as increasingly valuable tools for economic research and policymaking. We propose to classify the extent to which an ABM is data-driven based on whether agent-level quantities are initialized from real-world micro-data and whether the ABM's dynamics track empirical time series. This paper discusses how making ABMs data-driven helps overcome limitations of traditional ABMs and makes ABMs a stronger alternative to equilibrium models. We review state-of-the-art methods in parameter calibration, initialization, and data assimilation, and then present successful applications that have generated new scientific knowledge and informed policy decisions. This paper serves as a manifesto for data-driven ABMs, introducing a definition and classification and outlining the state of the field, and as a guide for those new to the field.