pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Objective Planning of Community Energy Storage Systems Under Uncertainty
Authors: K. B. J. Anuradha, Jose Iria, Chathurika P. Mediwaththe
Date: 2023-01-04 00:00:00
Tasks: energy trading, Scheduling, Stochastic Optimization

This paper evaluates how the planning of a community energy storage (CES) system under different energy trading schemes (ETSs) can benefit low voltage (LV) prosumers and the CES provider equitably. First, we consider an ETS where the CES provider trades energy with prosumers at the average grid energy trading price, second, an ETS where the CES provider trades energy at a higher price than the grid energy trading price, and third, an ETS where the CES provider trades energy at a lower price than the grid energy trading price. To this end, we present a multi-objective stochastic optimization framework to minimize the investment and annual operating costs of the CES provider and annual operating costs of prosumers, taking into account the uncertainties of real and reactive energy consumption and photovoltaic (PV) generation of prosumers. The uncertainties are modeled using the normal probability density function. Then, the roulette wheel mechanism (RWM) is exploited to formulate a scenario-based stochastic program. The initial scenarios obtained from the RWM, are then reduced using the K-Means clustering algorithm, to make the problem tractable. Our experiments show that the ETS where the CES provider trades energy at the average grid energy trading price benefits prosumers and the CES provider more equitably than the other two ETSs.
Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems
Authors: Yiting Chen, Ana M. Ospina, Fabio Pasqualetti, Emiliano Dall'Anese
Date: 2023-01-04 00:00:00
Tasks: Federated Learning, Multi-Task Learning

This paper presents a system identification framework -- inspired by multi-task learning -- to estimate the dynamics of a given number of linear time-invariant (LTI) systems jointly by leveraging structural similarities across the systems. In particular, we consider LTI systems that model networked systems with similar connectivity, or LTI systems with small differences in their matrices. The system identification task involves the minimization of the least-squares (LS) fit for individual systems, augmented with a regularization function that enforces structural similarities. The proposed method is particularly suitable for cases when the recorded trajectories for one or more LTI systems are not sufficiently rich, leading to ill-conditioning of LS methods. We analyze the performance of the proposed method when the matrices of the LTI systems feature a common sparsity pattern (i.e., similar connectivity), and provide simulations based on real data for the estimation of the brain dynamics. We show that the proposed method requires a significantly smaller number of fMRI scans to achieve similar error levels of the LS.
Quantitative Planning with Action Deception in Concurrent Stochastic Games
Authors: Chongyang Shi, Shuo Han, Jie Fu
Date: 2023-01-03 00:00:00
Tasks: Motion Planning

We study a class of two-player competitive concurrent stochastic games on graphs with reachability objectives. Specifically, player 1 aims to reach a subset $F_1$ of game states, and player 2 aims to reach a subset $F_2$ of game states where $F_2\cap F_1=\emptyset$. Both players aim to satisfy their reachability objectives before their opponent does. Yet, the information players have about the game dynamics is asymmetric: P1 has a (set of) hidden actions unknown to P2 at the beginning of their interaction. In this setup, we investigate P1's strategic planning of action deception that decides when to deviate from the Nash equilibrium in P2's game model and employ a hidden action, so that P1 can maximize the value of action deception, which is the additional payoff compared to P1's payoff in the game where P2 has complete information. Anticipating that P2 may detect his misperception about the game and adapt his strategy during interaction in unpredictable ways, we construct a planning problem for P1 to augment the game model with an incomplete model about the theory of mind of the opponent P2. While planning in the augmented game, P1 can effectively influence P2's perception so as to entice P2 to take actions that benefit P1. We prove that the proposed deceptive planning algorithm maximizes a lower bound on the value of action deception and demonstrate the effectiveness of our deceptive planning algorithm using a robot motion planning problem inspired by soccer games.
Locally adaptive aggregation of organisms under death risk in rock-paper-scissors models
Authors: J. Menezes, E. Rangel
Date: 2023-01-04 00:00:00

We run stochastic simulations of the spatial version of the rock-paper-scissors game, considering that individuals use sensory abilities to scan the environment to detect the presence of enemies. If the local dangerousness level is above a tolerable threshold, individuals aggregate instead of moving randomly on the lattice. We study the impact of the locally adaptive aggregation on the organisms' spatial organisation by measuring the characteristic length scale of the spatial domains occupied by organisms of a single species. Our results reveal that aggregation is beneficial if triggered when the local density of opponents does not exceed $30\%$; otherwise, the behavioural strategy may harm individuals by increasing the average death risk. We show that if organisms can perceive further distances, they can accurately scan and interpret the signals from the neighbourhood, maximising the effects of the locally adaptive aggregation on the death risk. Finally, we show that the locally adaptive aggregation behaviour promotes biodiversity independently of the organism's mobility. The coexistence probability rises if organisms join conspecifics, even in the presence of a small number of enemies. We verify that our conclusions hold for more complex systems by simulating the generalised rock-paper-scissors models with five and seven species. Our discoveries may be helpful to ecologists in understanding systems where organisms' self-defence behaviour adapts to local environmental cues.
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification
Authors: Yuxuan Du, Yibo Yang, DaCheng Tao, Min-Hsiu Hsieh
Date: 2022-12-29 00:00:00
Tasks: Multi-class Classification

Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Some QNNs with specific encoding methods can be efficiently simulated by classical surrogates, while others with quantum memory may perform better than classical classifiers. Here we systematically investigate the problem-dependent power of quantum neural classifiers (QCs) on multi-class classification tasks. Through the analysis of expected risk, a measure that weighs the training loss and the generalization error of a classifier jointly, we identify two key findings: first, the training loss dominates the power rather than the generalization ability; second, QCs undergo a U-shaped risk curve, in contrast to the double-descent risk curve of deep neural classifiers. We also reveal the intrinsic connection between optimal QCs and the Helstrom bound and the equiangular tight frame. Using these findings, we propose a method that uses loss dynamics to probe whether a QC may be more effective than a classical classifier on a particular learning task. Numerical results demonstrate the effectiveness of our approach to explain the superiority of QCs over multilayer Perceptron on parity datasets and their limitations over convolutional neural networks on image datasets. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
Complex dynamics of knowledgeable monopoly models with gradient mechanisms
Authors: Xiaoliang Li, Jiacheng Fu, Wei Niu
Date: 2023-01-04 00:00:00

In this paper, we explore the dynamics of two monopoly models with knowledgeable players. The first model was initially introduced by Naimzada and Ricchiuti, while the second one is simplified from a famous monopoly introduced by Puu. We employ several tools based on symbolic computations to analyze the local stability and bifurcations of the two models. To the best of our knowledge, the complete stability conditions of the second model are obtained for the first time. We also investigate periodic solutions as well as their stability. Most importantly, we discover that the topological structure of the parameter space of the second model is much more complex than that of the first one. Specifically, in the first model, the parameter region for the stability of any periodic orbit with a fixed order constitutes a connected set. In the second model, however, the stability regions for the 3-cycle, 4-cycle, and 5-cycle orbits are disconnected sets formed by many disjoint portions. Furthermore, we find that the basins of the two stable equilibria in the second model are disconnected and also have complicated topological structures. In addition, the existence of chaos in the sense of Li-Yorke is rigorously proved by finding snapback repellers and 3-cycle orbits in the two models, respectively.
First Demonstration of 512-Color Shift Keying Signal Demodulation Using Neural Equalization for Optical Camera Communication
Authors: Yukito Onodera, Daisuke Hisano, Kazuki Maruta, Yu Nakayama
Date: 2022-12-29 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

This paper experimentally demonstrates 512 color shift keying (CSK) signal transmission for optical camera communication (OCC). We achieved error-free operation with a CMOS image sensor module and a multi-label classification neural network-based equalizer.
Super-resolution with Sparse Arrays: A Non-Asymptotic Analysis of Spatio-temporal Trade-offs
Authors: Pulak Sarangi, Mehmet Can Hucumenoglu, Robin Rajamaki, Piya Pal
Date: 2023-01-04 00:00:00
Tasks: Super-Resolution

Sparse arrays have emerged as a popular alternative to the conventional uniform linear array (ULA) due to the enhanced degrees of freedom (DOF) and superior resolution offered by them. In the passive setting, these advantages are realized by leveraging correlation between the received signals at different sensors. This has led to the belief that sparse arrays require a large number of temporal measurements to reliably estimate parameters of interest from these correlations, and therefore they may not be preferred in the sample-starved regime. In this paper, we debunk this myth by performing a rigorous non-asymptotic analysis of the Coarray ESPRIT algorithm. This seemingly counter-intuitive result is a consequence of the scaling of the singular value of the coarray manifold, which compensates for the potentially large covariance estimation error in the limited snapshot regime. Specifically, we show that for a nested array operating in the regime of fewer sources than sensors ($S=O(1)$), it is possible to bound the matching distance error between the estimated and true directions of arrival (DOAs) by an arbitrarily small quantity ($\epsilon$) with high probability, provided (i) the number of temporal snapshots ($L$) scales only logarithmically with the number of sensors ($P$), i.e. $L=\Omega(\ln(P)/\epsilon^2)$, and (ii) a suitable separation condition is satisfied. Our results also formally prove the well-known empirical resolution benefits of sparse arrays, by establishing that the minimum separation between sources can be $\Omega(1/P^2)$, as opposed to separation $\Omega(1/P)$ required by a ULA with the same number of sensors. Our sample complexity expression reveals the dependence on other key model parameters such as SNR and the dynamic range of the source powers. This enables us to establish the superior noise-resilience of nested arrays both theoretically and empirically.
Super-resolution with Binary Priors: Theory and Algorithms
Authors: Pulak Sarangi, Ryoma Hattori, Takaki Komiyama, Piya Pal
Date: 2023-01-04 00:00:00
Tasks: Super-Resolution

The problem of super-resolution is concerned with the reconstruction of temporally/spatially localized events (or spikes) from samples of their convolution with a low-pass filter. Distinct from prior works which exploit sparsity in appropriate domains in order to solve the resulting ill-posed problem, this paper explores the role of binary priors in super-resolution, where the spike (or source) amplitudes are assumed to be binary-valued. Our study is inspired by the problem of neural spike deconvolution, but also applies to other applications such as symbol detection in hybrid millimeter wave communication systems. This paper makes several theoretical and algorithmic contributions to enable binary super-resolution with very few measurements. Our results show that binary constraints offer much stronger identifiability guarantees than sparsity, allowing us to operate in "extreme compression" regimes, where the number of measurements can be significantly smaller than the sparsity level of the spikes. To ensure exact recovery in this "extreme compression" regime, it becomes necessary to design algorithms that exactly enforce binary constraints without relaxation. In order to overcome the ensuing computational challenges, we consider a first order auto-regressive filter (which appears in neural spike deconvolution), and exploit its special structure. This results in a novel formulation of the super-resolution binary spike recovery in terms of binary search in one dimension. We perform numerical experiments that validate our theory and also show the benefits of binary constraints in neural spike deconvolution from real calcium imaging datasets.
ESPRIT-Oriented Precoder Design for mmWave Channel Estimation
Authors: Musa Furkan Keskin, Alessio Fascista, Fan Jiang, Angelo Coluccia, Gonzalo Seco-Granados, Henk Wymeersch
Date: 2023-01-04 00:00:00

We consider the problem of ESPRIT-oriented precoder design for beamspace angle-of-departure (AoD) estimation in downlink mmWave multiple-input single-output communications. Standard precoders (i.e., directional/sum beams) yield poor performance in AoD estimation, while Cramer-Rao bound-optimized precoders undermine the so-called shift invariance property (SIP) of ESPRIT. To tackle this issue, the problem of designing ESPRIT-oriented precoders is formulated to jointly optimize over the precoding matrix and the SIP-restoring matrix of ESPRIT. We develop an alternating optimization approach that updates these two matrices under unit-modulus constraints for analog beamforming architectures. Simulation results demonstrate the validity of the proposed approach while providing valuable insights on the beampatterns of the ESPRIT-oriented precoders.
PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse Spikes
Authors: Paul Zheng, Emilie Chouzenoux, Laurent Duval
Date: 2023-01-04 00:00:00
Tasks: Denoising

Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peak-like signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can somewhat be separated by low-pass filtering. We combine the generalized quasi-norm ratio SOOT/SPOQ sparse penalties $\ell_p/\ell_q$ with the BEADS ternary assisted source separation algorithm. This results in a both convergent and efficient tool, with a novel Trust-Region block alternating variable metric forward-backward approach. It outperforms comparable methods, when applied to typically peaked analytical chemistry signals. Reproducible code is provided.
Radio Frequency Fingerprints Extraction for LTE-V2X: A Channel Estimation Based Methodology
Authors: Tianshu Chen, Hong Shen, Aiqun Hu, Weihang He, Jie Xu, Hongxing Hu
Date: 2023-01-04 00:00:00
Tasks: Denoising

The vehicular-to-everything (V2X) technology has recently drawn a number of attentions from both academic and industrial areas. However, the openness of the wireless communication system makes it more vulnerable to identity impersonation and information tampering. How to employ the powerful radio frequency fingerprint (RFF) identification technology in V2X systems turns out to be a vital and also challenging task. In this paper, we propose a novel RFF extraction method for Long Term Evolution-V2X (LTE-V2X) systems. In order to conquer the difficulty of extracting transmitter RFF in the presence of wireless channel and receiver noise, we first estimate the wireless channel which excludes the RFF. Then, we remove the impact of the wireless channel based on the channel estimate and obtain initial RFF features. Finally, we conduct RFF denoising to enhance the quality of the initial RFF. Simulation and experiment results both demonstrate that our proposed RFF extraction scheme achieves a high identification accuracy. Furthermore, the performance is also robust to the vehicle speed.
A compositional account of motifs, mechanisms, and dynamics in biochemical regulatory networks
Authors: Rebekah Aduddell, James Fairbanks, Amit Kumar, Pablo S. Ocal, Evan Patterson, Brandon T. Shapiro
Date: 2023-01-04 00:00:00

Regulatory networks depict promoting or inhibiting interactions between molecules in a biochemical system. We introduce a category-theoretic formalism for regulatory networks, using signed graphs to model the networks and signed functors to describe occurrences of one network in another, especially occurrences of network motifs. With this foundation, we establish functorial mappings between regulatory networks and other mathematical models in biochemistry. We construct a functor from reaction networks, modeled as Petri nets with signed links, to regulatory networks, enabling us to precisely define when a reaction network could be a physical mechanism underlying a regulatory network. Turning to quantitative models, we associate a regulatory network with a Lotka-Volterra system of differential equations, defining a functor from the category of signed graphs to a category of parameterized dynamical systems. We extend this result from closed to open systems, demonstrating that Lotka-Volterra dynamics respects not only inclusions and collapsings of regulatory networks, but also the process of building up complex regulatory networks by gluing together simpler pieces. Formally, we use the theory of structured cospans to produce a lax double functor from the double category of open signed graphs to that of open parameterized dynamical systems. Throughout the paper, we ground the categorical formalism in examples inspired by systems biology.
Pattern Recognition Experiments on Mathematical Expressions
Authors: David Naccache, Ofer Yifrach-Stav
Date: 2022-12-21 00:00:00

We provide the results of pattern recognition experiments on mathematical expressions. We give a few examples of conjectured results. None of which was thoroughly checked for novelty. We did not attempt to prove all the relations found and focused on their generation.
Power Spectral Density-Based Resting-State EEG Classification of First-Episode Psychosis
Authors: Sadi Md. Redwan, Md Palash Uddin, Anwaar Ulhaq, Muhammad Imran Sharif
Date: 2022-11-23 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Specificity

Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with First-Episode Psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian Process Classifier (GPC), to demonstrate the practicality of resting-state Power Spectral Density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.
Optimal Liquidation with High Risk Aversion and Small Linear Price Impact
Authors: Leonid Dolinskyi, Yan Dolinsky
Date: 2023-01-04 00:00:00
Tasks: Position

We consider the Bachelier model with linear price impact. Exponential utility indifference prices are studied for vanilla European options in the case where the investor is required to liquidate her position. Our main result is establishing a non-trivial scaling limit for a vanishing price impact which is inversely proportional to the risk aversion. We compute the limit of the corresponding utility indifference prices and find explicitly a family of portfolios which are asymptotically optimal.
Optimization code with weighting function for the reconstruction of coronal magnetic fields
Authors: T. Wiegelmann
Date: 2008-02-01 00:00:00

We developed a code for the reconstruction of nonlinear force-free and non-force-free coronal magnetic fields. The 3D magnetic field is computed numerically with the help of an optimization principle. The force-free and non-force-free codes are compiled in one program. The force-free approach needs photospheric vector magnetograms as input. The non-force-free code additionally requires the line-of-sight integrated coronal density distribution in combination with a tomographic inversion code. Previously the optimization approach has been used to compute magnetic fields using all six boundaries of a computational box. Here we extend this method and show how the coronal magnetic field can be reconstructed only from the bottom boundary, where the boundary conditions are measured with vector magnetographs. The program is planed for use within the Stereo mission.