pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Power Flow for Integrated Primary-Secondary Distribution Networks with Center-Tapped Service Transformers
Authors: Rui Cheng, Naihao Shi, Zhaoyu Wang, Zixiao Ma
Date: 2023-06-23 00:00:00
Tasks: Decision Making

Secondary distribution networks (SDNets) play an increasingly important role in smart grids due to a high proliferation of distributed energy resources (DERs) in SDNets. However, most existing optimal power flow (OPF) problems do not take into account SDNets with center-tapped service transformers. Handling the nonlinear and nonconvex SDNet power flow constraints is still an outstanding problem. To meet this gap, we first utilize the second-order cone programming relaxation and linearization to make center-tapped service transformer constraints convex, respectively. Then, a linearized triplex service line power flow model, including its compact matrix-vector form, is further developed to compose the SDNet OPF model with our proposed center-tapped service transformer model. This proposed SDNet OPF model can be easily embedded into existing primary distribution network (PDNet) OPF models, resulting in a holistic power system decision-making solution for integrated primary-secondary distribution networks. Case studies are presented for two different integrated primary-secondary distribution networks that demonstrate the effectiveness and superiority of this model.
Communication-Free Distributed Charging Control for Electric Vehicle Group
Authors: Heyang Yu, Chuanzi Xu, Weifeng Wang, Guangchao Geng, Quanyuan Jiang
Date: 2023-06-22 00:00:00

The disordered charging of electric vehicles (EVs) in residential areas leads to a rapid increase of the peak load, causing transformer overload, but the charging control of EV group can effectively alleviate this phenomenon. However, existing charging control methods need reliable two-way communication infrastructure, which brings high operation costs and security risks. To offer a backup strategy for charging control of EVs after communication facilities fail, this paper proposes a communication-free charging control scheme to provide a decentralized on-site charging strategy for EV group. First, an uncontrollable EV group baseline estimation considering charging behaviors enabled by Gaussian mixture model (GMM) is proposed to acquire the capacity margin forecasting for controllable EVs. Next, this paper proposes a probabilistic distributed control method to assist users formulate the charging plan autonomously. Here, the charging behavior of EV group is regulated from an optimization with uncertain boundary conditions to a sampling with uncertain feasible regions expressed by a probability distribution. Finally, the scheme is verified via real-world EV charging data from a residential area in Hangzhou, China. The results show that this method can reduce the probability of transformer overload caused by out-of-order EV charging after a communication failure.
Many-species ecological fluctuations as a jump process from the brink of extinction
Authors: Thibaut Arnoulx de Pirey, Guy Bunin
Date: 2023-06-23 00:00:00

Highly-diverse ecosystems exhibit a broad distribution of population sizes and species turnover, where species at high and low abundances are exchanged over time. We show that these two features generically emerge in the fluctuating phase of many-variable model ecosystems with disordered species interactions, when species are supported by migration from outside the system at a small rate. We show that these and other phenomena can be understood through the existence of a scaling regime in the limit of small migration, in which large fluctuations and long timescales emerge. We construct an exact analytical theory for this asymptotic regime, that provides scaling predictions on timescales and abundance distributions that are verified exactly in simulations. In this regime, a clear separation emerges between rare and abundant species at any given time, despite species moving back and forth between the rare and abundant subsets. The number of abundant species is found to lie strictly below a well-known stability bound, maintaining the system away from marginality. At the same time, other measures of diversity, which also include some of the rare species, go above this bound. In the asymptotic limit where the migration rate goes to zero, trajectories of individual species abundances are described by non-Markovian jump-diffusion processes, which proceeds as follows: A rare species remains so for some time, then experiences a jump in population sizes after which it becomes abundant (a species turnover event) and later sees its population size gradually decreasing again until rare, due to the competition with other species. The asymmetry of abundance trajectories under time-reversal is maintained at small but finite migration rate. These features may serve as fingerprints of endogenous fluctuations in highly-diverse ecosystems.
Large system population dynamics with non-Gaussian interactions
Authors: Sandro Azaele, Amos Maritan
Date: 2023-06-23 00:00:00

We investigate the Generalized Lotka-Volterra (GLV) equations, a central model in theoretical ecology, where species interactions are assumed to be fixed over time and heterogeneous (quenched noise). Recent studies have suggested that the stability properties and abundance distributions of large disordered GLV systems depend, in the simplest scenario, solely on the mean and variance of the distribution of species interactions. However, empirical communities deviate from this level of universality. In this article, we present a generalized version of the dynamical mean field theory for non-Gaussian interactions that can be applied to various models, including the GLV equations. Our results show that the generalized mean field equations have solutions which depend on all cumulants of the distribution of species interactions, leading to a breakdown of universality. We leverage on this informative breakdown to extract microscopic interaction details from the macroscopic distribution of densities which are in agreement with empirical data. Specifically, in the case of sparse interactions, which we analytically investigate, we establish a simple relationship between the distribution of interactions and the distribution of species population densities.
Exact solution to a generalised Lillo-Mike-Farmer model with heterogeneous order-splitting strategies
Authors: Yuki Sato, Kiyoshi Kanazawa
Date: 2023-06-23 00:00:00

The Lillo-Mike-Farmer (LMF) model is an established econophysics model describing the order-splitting behaviour of institutional investors in financial markets. In the original article (LMF, Physical Review E 71, 066122 (2005)), LMF assumed the homogeneity of the traders' order-splitting strategy and derived a power-law asymptotic solution to the order-sign autocorrelation function (ACF) based on several heuristic reasonings. This report proposes a generalised LMF model by incorporating the heterogeneity of traders' order-splitting behaviour that is exactly solved without heuristics. We find that the power-law exponent in the order-sign ACF is robust for arbitrary heterogeneous intensity distributions. On the other hand, the prefactor in the ACF is very sensitive to heterogeneity in trading strategies and is shown to be systematically underestimated in the original homogeneous LMF model. Our work highlights that the ACF prefactor should be more carefully interpreted than the ACF power-law exponent in data analyses.
Worst-case analysis of array beampatterns using interval arithmetic
Authors: Håvard Kjellmo Arnestad, Gábor Geréb, Tor Inge Birkenes Lønmo, Jan Egil Kirkebø, Andreas Austeng, Sven Peter Näsholm
Date: 2023-06-19 00:00:00

Over the past decade, interval arithmetic (IA) has been utilized to determine tolerance bounds of phased array beampatterns. IA only requires that the errors of the array elements are bounded, and can provide reliable beampattern bounds even when a statistical model is missing. However, previous research has not explored the use of IA to find the error realizations responsible for achieving specific bounds. In this study, the capabilities of IA are extended by introducing the concept of ``backtracking'', which provides a direct way of addressing how specific bounds can be attained. Backtracking allows for the recovery of both the specific error realization and the corresponding beampattern, enabling the study and verification of which errors result in the worst-case array performance in terms of the peak sidelobe level. Moreover, IA is made applicable to a wider range of arrays by adding support for arbitrary array geometries with directive elements and mutual coupling, in addition to element amplitude, phase, and positioning errors. Lastly, a simple formula for approximate bounds of uniformly bounded errors is derived and numerically verified. This formula gives insights into how array size and apodization cannot reduce the worst-case peak sidelobe level beyond a certain limit.
Artificial Neural Network Prediction of COVID-19 Daily Infection Count
Authors: Ning Jiang, Charles Kolozsvary, Yao Li
Date: 2023-06-23 00:00:00

It is well known that the confirmed COVID-19 infection is only a fraction of the true fraction. In this paper we use an artificial neural network to learn the connection between the confirmed infection count, the testing data, and the true infection count. The true infection count in the training set is obtained by backcasting from the death count and the infection fatality ratio (IFR). Multiple factors are taken into consideration in the estimation of IFR. We also calibrate the recovered true COVID-19 case count with an SEIR model.
Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects
Authors: Simon Hirsch, Florian Ziel
Date: 2023-06-23 00:00:00

Intraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross-product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high-dimensional intraday price return vector. We model the marginal distribution as a zero-inflated Johnson's $S_U$ distribution with location, scale and shape parameters that depend on market and fundamental data. The dependence structure is modelled using latent beta regression to account for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days. We allow the dependence parameter to be time-varying. We validate our approach in a simulation study for the German intraday electricity market and find that modelling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling (SIDC) on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets.
Factor-augmented sparse MIDAS regressions with an application to nowcasting
Authors: Jad Beyhum, Jonas Striaukas
Date: 2023-06-23 00:00:00
Tasks: Dimensionality Reduction, regression, Time Series

This article investigates factor-augmented sparse MIDAS (Mixed Data Sampling) regressions for high-dimensional time series data, which may be observed at different frequencies. Our novel approach integrates sparse and dense dimensionality reduction techniques. We derive the convergence rate of our estimator under misspecification, $\tau$-mixing dependence, and polynomial tails. Our method's finite sample performance is assessed via Monte Carlo simulations. We apply the methodology to nowcasting U.S. GDP growth and demonstrate that it outperforms both sparse regression and standard factor-augmented regression during the COVID-19 pandemic. To ensure the robustness of these results, we also implement factor-augmented sparse logistic regression, which further confirms the superior accuracy of our nowcast probabilities during recessions. These findings indicate that recessions are influenced by both idiosyncratic (sparse) and common (dense) shocks.
Does Environmental Attention by Governments Promote Carbon Reductions
Authors: Yichuan Tian
Date: 2023-06-23 00:00:00

The carbon-reducing effect of attention is scarcer than that of material resources, and when the government focuses its attention on the environment, resources will be allocated in a direction that is conducive to reducing carbon. Using panel data from 30 Chinese provinces from 2007 to 2019, this study revealed the impact of governments' environmental attention on carbon emissions and the synergistic mechanism between governments' environmental attention and informatization level. The findings suggested that (1)the environmental attention index of local governments in China showed an overall fluctuating upward trend; (2)governments' environmental atten-tion had the effect of reducing carbon emissions; (3)the emission-reducing effect of governments' environmental attention is more significant in the western region but not in the central and eastern regions; (4)informatization level plays a positive moderating role in the relationship between governments' environmental attention and carbon emissions; (5)there is a significant threshold effect on the carbon reduction effect of governments' environmental attention. Based on the findings, this study proposed policy implications from the perspectives of promoting the sustainable enhancement of environmental attention, bringing institutional functions into play, emphasizing the ecological benefits and strengthening the disclosure of information.
Consumption Partial Insurance in the Presence of Tail Income Risk
Authors: Anisha Ghosh, Alexandros Theloudis
Date: 2023-06-22 00:00:00

We measure consumption insurance against income shocks accounting for high-order moments of the income distribution. We derive a nonlinear consumption function, where the extent of insurance varies with the shocks' sign and magnitude. Using PSID data, we estimate an asymmetric pass-through of bad versus good permanent shocks - 17% of a 3 sigma negative shock transmits to consumption versus 9% of an equal-sized positive shock - with greater pass-through as shocks worsen. Households would sacrifice over 1/8 of lifetime consumption to eliminate tail income risk. Our results align with survey responses to hypothetical events and suggest that tail risk matters substantially for consumption.
Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation
Authors: Anton Vasiliuk, Daria Frolova, Mikhail Belyaev, Boris Shirokikh
Date: 2023-06-23 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Out-of-Distribution Detection, Out of Distribution (OOD) Detection, Segmentation, Semantic Segmentation

Deep Learning models perform unreliably when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-distribution (OOD) detection methods help to identify such data samples, preventing erroneous predictions. In this paper, we further investigate the OOD detection effectiveness when applied to 3D medical image segmentation. We design several OOD challenges representing clinically occurring cases and show that none of these methods achieve acceptable performance. Methods not dedicated to segmentation severely fail to perform in the designed setups; their best mean false positive rate at 95% true positive rate (FPR) is 0.59. Segmentation-dedicated ones still achieve suboptimal performance, with the best mean FPR of 0.31 (lower is better). To indicate this suboptimality, we develop a simple method called Intensity Histogram Features (IHF), which performs comparable or better in the same challenges, with a mean FPR of 0.25. Our findings highlight the limitations of the existing OOD detection methods on 3D medical images and present a promising avenue for improving them. To facilitate research in this area, we release the designed challenges as a publicly available benchmark and formulate practical criteria to test the OOD detection generalization beyond the suggested benchmark. We also propose IHF as a solid baseline to contest the emerging methods.
One-Bit Spectrum Sensing for Cognitive Radio
Authors: Pei-Wen Wu, Lei Huang, David Ramírez, Yu-Hang Xiao, Hing Cheung So
Date: 2023-06-23 00:00:00

Spectrum sensing in cognitive radio necessitates effective monitoring of wide bandwidths, which requires high-rate sampling. Traditional spectrum sensing methods employing high-precision analog-to-digital converters (ADCs) result in increased power consumption and expensive hardware costs. In this paper, we explore blind spectrum sensing utilizing one-bit ADCs. We derive a closed-form detector based on Rao's test and demonstrate its equivalence with the second-order eigenvalue-moment-ratio test. Furthermore, a near-exact distribution based on the moment-based method, and an approximate distribution in the low signal-to-noise ratio (SNR) regime with the use of the central limit theorem, are obtained. Theoretical analysis is then performed and our results show that the performance loss of the proposed detector is approximately $2$ dB ($\pi/2$) compared to detectors employing $\infty$-bit ADCs when SNR is low. This loss can be compensated for by using approximately $2.47$ ($\pi^2/4$) times more samples. In addition, we unveil that the efficiency of incoherent accumulation in one-bit detection is the square root of that of coherent accumulation. Simulation results corroborate the correctness of our theoretical calculations.
Semantic-aware Transmission for Robust Point Cloud Classification
Authors: Tianxiao Han, Kaiyi Chi, Qianqian Yang, Zhiguo Shi
Date: 2023-06-23 00:00:00
Tasks: Classification, Decoder, Point Cloud Classification, Scene Understanding

As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing. In this study, we introduce a semantic-aware communication system for robust point cloud classification that capitalizes on the advantages of pre-trained Point-BERT models. Our proposed method comprises four main components: the semantic encoder, channel encoder, channel decoder, and semantic decoder. By employing a two-stage training strategy, our system facilitates efficient and adaptable learning tailored to the specific classification tasks. The results show that the proposed system achieves classification accuracy of over 89\% when SNR is higher than 10 dB and still maintains accuracy above 66.6\% even at SNR of 4 dB. Compared to the existing method, our approach performs at 0.8\% to 48\% better across different SNR values, demonstrating robustness to channel noise. Our system also achieves a balance between accuracy and speed, being computationally efficient while maintaining high classification performance under noisy channel conditions. This adaptable and resilient approach holds considerable promise for a wide array of 3D scene understanding applications, effectively addressing the challenges posed by channel noise.
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments
Authors: Qiushuo Hou, Mengyuan Lee, Guanding Yu, Yunlong Cai
Date: 2023-06-23 00:00:00
Tasks: image-classification, Image Classification, Meta-Learning, speech-recognition, Speech Recognition

With the great success of deep learning (DL) in image classification, speech recognition, and other fields, more and more studies have applied various neural networks (NNs) to wireless resource allocation. Generally speaking, these artificial intelligent (AI) models are trained under some special learning hypotheses, especially that the statistics of the training data are static during the training stage. However, the distribution of channel state information (CSI) is constantly changing in the real-world wireless communication environment. Therefore, it is essential to study effective dynamic DL technologies to solve wireless resource allocation problems. In this paper, we propose a novel framework, named meta-gating, for solving resource allocation problems in an episodically dynamic wireless environment, where the CSI distribution changes over periods and remains constant within each period. The proposed framework, consisting of an inner network and an outer network, aims to adapt to the dynamic wireless environment by achieving three important goals, i.e., seamlessness, quickness and continuity. Specifically, for the former two goals, we propose a training method by combining a model-agnostic meta-learning (MAML) algorithm with an unsupervised learning mechanism. With this training method, the inner network is able to fast adapt to different channel distributions because of the good initialization. As for the goal of continuity, the outer network can learn to evaluate the importance of inner network's parameters under different CSI distributions, and then decide which subset of the inner network should be activated through the gating operation. Additionally, we theoretically analyze the performance of the proposed meta-gating framework.
EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks
Authors: Jinpei Han, Xiaoxi Wei, A. Aldo Faisal
Date: 2023-06-20 00:00:00
Tasks: Brain Computer Interface, Domain Adaptation, EEG, Eeg Decoding, Motor Imagery, Transfer Learning

Brain-Machine Interfacing (BMI) has greatly benefited from adopting machine learning methods for feature learning that require extensive data for training, which are often unavailable from a single dataset. Yet, it is difficult to combine data across labs or even data within the same lab collected over the years due to the variation in recording equipment and electrode layouts resulting in shifts in data distribution, changes in data dimensionality, and altered identity of data dimensions. Our objective is to overcome this limitation and learn from many different and diverse datasets across labs with different experimental protocols. To tackle the domain adaptation problem, we developed a novel machine learning framework combining graph neural networks (GNNs) and transfer learning methodologies for non-invasive Motor Imagery (MI) EEG decoding, as an example of BMI. Empirically, we focus on the challenges of learning from EEG data with different electrode layouts and varying numbers of electrodes. We utilise three MI EEG databases collected using very different numbers of EEG sensors (from 22 channels to 64) and layouts (from custom layouts to 10-20). Our model achieved the highest accuracy with lower standard deviations on the testing datasets. This indicates that the GNN-based transfer learning framework can effectively aggregate knowledge from multiple datasets with different electrode layouts, leading to improved generalization in subject-independent MI EEG classification. The findings of this study have important implications for Brain-Computer-Interface (BCI) research, as they highlight a promising method for overcoming the limitations posed by non-unified experimental setups. By enabling the integration of diverse datasets with varying electrode layouts, our proposed approach can help advance the development and application of BMI technologies.