pwc-archive/papers-with-abstracts huggingface dataset:
Parameterized Linear Power Flow for High Fidelity Voltage Solutions in Distribution Systems
Authors: Marija Marković, Bri-Mathias Hodge
Date: 2022-09-09 00:00:00

This paper introduces a new model for highly accurate distribution voltage solutions, coined as a parameterized linear power flow model. The proffered model is grounded on a physical model of linear power flow equations, and uses learning-aided parameterization to increase the fidelity of voltage solutions over a wide range of operating points. To this end, the closed-form analytic solution of the parameterization approach is obtained via a Gaussian Process using a deliberately small input sample and without the need for recomputation. The resulting "self-adjusting" parameter is system-specific and controls how accurate the proposed power flow equations are according to loading conditions. Under a certain value of the resulting parameter, the proposed model can fully recover the linearized formulation of a specialized branch flow model for radial distribution systems, the so-called simplified DistFlow model. Numerical examples are provided to illustrate the effectiveness of the proposed model as well as the improvement in solution accuracy for voltage magnitudes over the simplified DistFlow model and several other linear power flow models, at multiple loading levels. Simulations were carried out on six small- and medium-sized test systems.
Robust Policy Optimization in Continuous-time Mixed $\mathcal{H}_2/\mathcal{H}_\infty$ Stochastic Control
Authors: Leilei Cui, Lekan Molu
Date: 2022-09-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Following the recent resurgence in establishing linear control theoretic benchmarks for reinforcement leaning (RL)-based policy optimization (PO) for complex dynamical systems with continuous state and action spaces, an optimal control problem for a continuous-time infinite-dimensional linear stochastic system possessing additive Brownian motion is optimized on a cost that is an exponent of the quadratic form of the state, input, and disturbance terms. We lay out a model-based and model-free algorithm for RL-based stochastic PO. For the model-based algorithm, we establish rigorous convergence guarantees. For the sampling-based algorithm, over trajectory arcs that emanate from the phase space, we find that the Hamilton-Jacobi Bellman equation parameterizes trajectory costs -- resulting in a discrete-time (input and state-based) sampling scheme accompanied by unknown nonlinear dynamics with continuous-time policy iterates. The need for known dynamics operators is circumvented and we arrive at a reinforced PO algorithm (via policy iteration) where an upper bound on the $\mathcal{H}_2$ norm is minimized (to guarantee stability) and a robustness metric is enforced by maximizing the cost with respect to a controller that includes the level of noise attenuation specified by the system's $H_\infty$ norm. Rigorous robustness analyses is prescribed in an input-to-state stability formalism. Our analyses and contributions are distinguished by many natural systems characterized by additive Wiener process, amenable to \^Ito's stochastic differential calculus in dynamic game settings.
Fast-Response Variable Frequency DC-DC Converters Using Switching Cycle Event-Driven Digital Control
Authors: Xiaofan Cui, Al-Thaddeus Avestruz
Date: 2022-09-09 00:00:00

This paper investigates a new method to model and control variable-frequency power converters in a switching-synchronized sampled-state space for cycle-by-cycle digital control. There are a number of significant benefits in comparison to other methods including fast dynamic performance together with ease of design and implementation. Theoretical results are presented and verified through hardware, and simulations of a current-mode buck converter with constant on-time and a current-mode boost converter with constant off-time. Dynamic voltage scaling for microprocessors and LiDAR are among the applications that can benefit.
Analysis of an Induction Machine Fed by a Space Vector Modulated VSI
Authors: Fnu Nishanth
Date: 2022-09-06 00:00:00

This paper describes the analysis and simulation performed on a three phase squirrel cage induction motor, fed by a Voltage Source Inverter (VSI), modulated using the space vector modulation scheme. The study mainly focused on analyzing the motor performance when fed by a space vector modulated voltage source inverter. The effect of switching frequency on the motor current ripple as well as the frequency spectra of motor current, voltage and torque were studied. The transient response of the inverter-fed motor for a sudden load change was studied and compared to that of a motor fed with an ideal AC source. Results from these studies performed as computer simulations are presented and analyzed.
Computing brain networks with complex dynamics
Authors: Anca Radulescu, Johan Nakuci, Simone Evans, Sarah Muldoon
Date: 2022-09-12 00:00:00

One important question in neuroscience is how global behavior in a brain network emerges from the interplay between network connectivity and the neural dynamics of individual nodes. To better understand this theoretical relationship, we have been exploring a simplified modeling approach in which we equip each node with discrete quadratic dynamics in the complex plane, and we study the emerging behavior of the resulting complex quadratic network (CQN). The long-term behavior of CQNs can be represented by asymptotic fractal sets with specific topological signatures going far beyond those described in traditional single map iterations. In this study, we illustrate how topological measures of these asymptotic sets can be used efficiently as comprehensive descriptors and classifiers of dynamics in tractography-derived connectomes for human subjects. We investigate to what extent the complex geometry of these sets is tied to network architecture (on one hand) and to the network behavior (on the other). This helps us understand the mechanics of the relationship between the subject's brain function, physiology and behavior and their underlying connectivity architecture.
Simultaneously Transmitting and Reflecting (STAR)-RISs: Are they Applicable to Dual-Sided Incidence?
Authors: Jiaqi Xu, Xidong Mu, Joey Tianyi Zhou, Yuanwei Liu
Date: 2022-09-12 00:00:00

A hardware model and a signal model are proposed for dual-sided simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), where the signal simultaneously incident on both sides of the surface. Based on the proposed hardware model, signal models for dual-sided STAR-RISs are developed. For elements with scalar surface impedance, it is proved that their transmission and reflection coefficients on both sides are identical. Based on the obtained symmetrical dual-sided STAR model, a STAR-RIS-aided two-user uplink communication system is investigated for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes. Analytical results for the outage probabilities for users are derived in the high transmit signal-to-noise ratio (SNR) regime. Numerical results demonstrate the performance gain of NOMA over OMA and reveal that the outage probability error floor can be lowered by adjusting the ratio between the amplitudes of transmission and reflection signals.
A detail-enhanced sampling strategy in Hadamard single-pixel imaging
Authors: Yan Cai, Shijian Li, Wei zhang, Hao Wu, Xu-Ri Yao, Qing Zhao
Date: 2022-09-09 00:00:00
Tasks: compressed sensing, Image Reconstruction

Hadamard single-pixel imaging (HSI) is an appealing imaging technique due to its features of low hardware complexity and industrial cost. To improve imaging efficiency, many studies have focused on sorting Hadamard patterns to obtain reliable reconstructed images with very few samples. In this study, we present an efficient HSI imaging method that employs an exponential probability function to sample Hadamard spectra along a direction with better energy concentration for obtaining Hadamard patterns. We also propose an XY order to further optimize the pattern-selection method with extremely fast Hadamard order generation while retaining the original performance. We used the compressed sensing algorithm for image reconstruction. The simulation and experimental results show that these pattern-selection method reliably reconstructs objects and preserves the edge and details of images.
Testing the martingale difference hypothesis in high dimension
Authors: Jinyuan Chang, Qing Jiang, Xiaofeng Shao
Date: 2022-09-11 00:00:00
Tasks: Time Series, Time Series Analysis, valid, Vocal Bursts Intensity Prediction

In this paper, we consider testing the martingale difference hypothesis for high-dimensional time series. Our test is built on the sum of squares of the element-wise max-norm of the proposed matrix-valued nonlinear dependence measure at different lags. To conduct the inference, we approximate the null distribution of our test statistic by Gaussian approximation and provide a simulation-based approach to generate critical values. The asymptotic behavior of the test statistic under the alternative is also studied. Our approach is nonparametric as the null hypothesis only assumes the time series concerned is martingale difference without specifying any parametric forms of its conditional moments. As an advantage of Gaussian approximation, our test is robust to the cross-series dependence of unknown magnitude. To the best of our knowledge, this is the first valid test for the martingale difference hypothesis that not only allows for large dimension but also captures nonlinear serial dependence. The practical usefulness of our test is illustrated via simulation and a real data analysis. The test is implemented in a user-friendly R-function.
Rethinking Generalized Beta Family of Distributions
Authors: Jiong Liu, R. A. Serota
Date: 2022-09-05 00:00:00

We approach the Generalized Beta (GB) family of distributions using a mean-reverting stochastic differential equation (SDE) for a power of the variable, whose steady-state (stationary) probability density function (PDF) is a modified GB (mGB) distribution. The SDE approach allows for a lucid explanation of Generalized Beta Prime (GB2) and Generalized Beta (GB1) limits of GB distribution and, further down, of Generalized Inverse Gamma (GIGa) and Generalized Gamma (GGa) limits, as well as describe the transition between the latter two. We provide an alternative form to the "traditional" GB PDF to underscore that a great deal of usefulness of GB distribution lies in its allowing a long-range power-law behavior to be ultimately terminated at a finite value. We derive the cumulative distribution function (CDF) of the "traditional" GB, which belongs to the family generated by the regularized beta function and is crucial for analysis of the tails of the distribution. We analyze fifty years of historical data on realized market volatility, specifically for S\&P500, as a case study of the use of GB/mGB distributions and show that its behavior is consistent with that of negative Dragon Kings.
Exploitees vs. Exploiters: Dynamics of Exploitation
Authors: Davood Qorbani
Date: 2022-09-12 00:00:00

Either saying that the market is structured to promote workforce exploitation in some sections or the people who participate there benefit from the existing structure and exploit, exploitation happens - systematically or opportunistically. This research presents a perspective on how workforce exploitation may occur when vulnerable groups voluntarily seek employment in the labor market.
Analyzing Linear DSGE models: the Method of Undetermined Markov States
Authors: Jordan Roulleau-Pasdeloup
Date: 2022-09-12 00:00:00

I show that a class of Linear DSGE models with one endogenous state variable can be represented as a three-state Markov chain. I develop a new analytical solution method based on this representation, which amounts to solving for a vector of Markov states and one transition probability. These two objects constitute sufficient statistics to compute in closed form objects that have routinely been computed numerically: impulse response function, cumulative sum, present discount value multiplier. I apply the method to a standard New Keynesian model that features optimal monetary policy with commitment.
Good News Is Not a Sufficient Condition for Motivated Reasoning
Authors: Michael Thaler
Date: 2020-12-02 00:00:00

People often receive good news that makes them feel better about the world around them, or bad news that makes them feel worse about it. This paper studies how the valence of news affects belief updating, absent functional and ego-relevant factors. Using experiments with over 1,500 participants and 5,600 observations, I test whether people engage in motivated reasoning to overly trust good news versus bad news on valence-relevant issues like cancer survival rates, others' happiness, and infant mortality. The estimate for motivated reasoning towards good news is a precisely-estimated null. Modest effects, of one-third the size of motivated reasoning in politics and performance, can be ruled out. Complementary survey evidence shows that most people expect good news to increase happiness, but to not systematically lead to motivated reasoning. These results suggest that belief-based utility is not sufficient in leading people to distort belief updating in order to favor those beliefs.
The economic and health impacts of contact tracing and quarantine programs
Authors: Darija Barak, Edoardo Gallo, Alastair Langtry
Date: 2022-09-11 00:00:00

Contact tracing and quarantine programs have been one of the leading Non-Pharmaceutical Interventions against COVID-19. Some governments have relied on mandatory programs, whereas others embrace a voluntary approach. However, there is limited evidence on the relative effectiveness of these different approaches. In an interactive online experiment conducted on 731 subjects representative of the adult US population in terms of sex and region of residence, we find there is a clear ranking. A fully mandatory program is better than an optional one, and an optional system is better than no intervention at all. The ranking is driven by reductions in infections, while economic activity stays unchanged. We also find that political conservatives have higher infections and levels of economic activity, and they are less likely to participate in the contact tracing program.
A Mathematical Analysis of the 2022 Alaska Special Election for US House
Authors: Adam Graham-Squire, David McCune
Date: 2022-09-11 00:00:00

The August 2022 Alaska Special Election for US House contained many interesting features from the perspective of social choice theory. This election used instant runoff voting (often referred to as ranked choice voting) to elect a winner, and many of the weaknesses of this voting method were on display in this election. For example, the Condorcet winner is different from the instant runoff winner, and the election demonstrated a monotonicity paradox. The election also demonstrated a no show paradox; as far as we are aware, this election represents the first document American ranked choice election to demonstrate this paradox.
On the Evaluation of Skill in Binary Forecast
Authors: Thitithep Sitthiyot, Kanyarat Holasut
Date: 2022-09-10 00:00:00
Tasks: Prediction

A good prediction is very important for scientific, economic, and administrative purposes. It is therefore necessary to know whether a predictor is skillful enough to predict the future. Given the increased reliance on predictions in various disciplines, prediction skill index (PSI) is devised. Twenty-four numerical examples are used to demonstrate how the PSI method works. The results show that the PSI awards not only the same score for random prediction and always predicting the same value but also nontrivial scores for correct prediction of rare or extreme events. Moreover, the PSI can distinguish the difference between the perfect forecast of rare or extreme events and that of random events by awarding different skill scores while other conventional methods cannot and award the same score. The data on growth of real gross domestic product forecast of the Bank of Thailand between 2000 and 2019 are also used to demonstrate how the PSI evaluates skill of the forecaster in practice.
Revisiting QUAD ambition in the Indo Pacific leveraging Space and Cyber Domain
Authors: Mandeep Singh Rai
Date: 2022-09-10 00:00:00

In the backdrop of growing power transitions and rise of strategic high tide in the world, the Indo Pacific Region (IPR) has emanated as an area which is likely to witness increased development & enhanced security cooperation through militarization. With China trying to be at the seat of the Global leadership, US & its allies in the Indo pacific are aiming at working together, finding mutually beneficial areas of functional cooperation and redefining the canvas of security. This purpose of this paper is to analyze an informal alliance, Quadrilateral Security dialogue (QUAD) in its present form with the geostrategic landscape in Indo pacific and present recommendations to strengthen collaboration & response capability.
The 2022 Far-field Speaker Verification Challenge: Exploring domain mismatch and semi-supervised learning under the far-field scenario
Authors: Xiaoyi Qin, Ming Li, Hui Bu, Shrikanth Narayanan, Haizhou Li
Date: 2022-09-12 00:00:00
Tasks: Speaker Verification

FFSVC2022 is the second challenge of far-field speaker verification. FFSVC2022 provides the fully-supervised far-field speaker verification to further explore the far-field scenario and proposes semi-supervised far-field speaker verification. In contrast to FFSVC2020, FFSVC2022 focus on the single-channel scenario. In addition, a supplementary set for the FFSVC2020 dataset is released this year. The supplementary set consists of more recording devices and has the same data distribution as the FFSVC2022 evaluation set. This paper summarizes the FFSVC 2022, including tasks description, trial designing details, a baseline system and a summary of challenge results. The challenge results indicate substantial progress made in the field but also present that there are still difficulties with the far-field scenario.
How Much Does Prosody Help Turn-taking? Investigations using Voice Activity Projection Models
Authors: Erik Ekstedt, Gabriel Skantze
Date: 2022-09-12 00:00:00
Proceeding: SIGDIAL (ACL) 2022 9

Turn-taking is a fundamental aspect of human communication and can be described as the ability to take turns, project upcoming turn shifts, and supply backchannels at appropriate locations throughout a conversation. In this work, we investigate the role of prosody in turn-taking using the recently proposed Voice Activity Projection model, which incrementally models the upcoming speech activity of the interlocutors in a self-supervised manner, without relying on explicit annotation of turn-taking events, or the explicit modeling of prosodic features. Through manipulation of the speech signal, we investigate how these models implicitly utilize prosodic information. We show that these systems learn to utilize various prosodic aspects of speech both on aggregate quantitative metrics of long-form conversations and on single utterances specifically designed to depend on prosody.
Stochastic Geometry-based Analysis of Multi-Purpose UAVs for Package and Data Delivery
Authors: Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2022-09-12 00:00:00

Using drones for communications and transportation is drawing great attention in many practical scenarios, such as package delivery and providing additional wireless coverage. However, the increasing demand for UAVs from industry and academia will cause aerial traffic conflicts in the future. This, in turn, motivates the idea of this paper: multi-purpose UAVs, acting as aerial wireless data relays and means of aerial transportation simultaneously, to deliver packages and data at the same time. This paper aims to analyze the feasibility of using drones to collect and deliver data from the Internet of Things (IoT) devices to terrestrial base stations (TBSs) while delivering packages from warehouses to residential areas. We propose an algorithm to optimize the trajectory of UAVs to maximize the size of collected/delivered data while minimizing the total round trip time subject to the limited onboard battery of UAVs. Specifically, we use tools from stochastic geometry to model the locations of the IoT clusters and the TBSs and study the system performance with respect to energy efficiency, average size of collected/delivered data, and package delivery time. Our numerical results reveal that multi-functional UAVs have great potential to enhance the efficiency of both communication and transportation networks.
Manifold Rewiring for Unlabeled Imaging
Authors: Valentin Debarnot, Vinith Kishore, Cheng Shi, Ivan Dokmanić
Date: 2022-09-12 00:00:00
Tasks: Denoising, Link Prediction

Geometric data analysis relies on graphs that are either given as input or inferred from data. These graphs are often treated as "correct" when solving downstream tasks such as graph signal denoising. But real-world graphs are known to contain missing and spurious links. Similarly, graphs inferred from noisy data will be perturbed. We thus define and study the problem of graph denoising, as opposed to graph signal denoising, and propose an approach based on link-prediction graph neural networks. We focus in particular on neighborhood graphs over point clouds sampled from low-dimensional manifolds, such as those arising in imaging inverse problems and exploratory data analysis. We illustrate our graph denoising framework on regular synthetic graphs and then apply it to single-particle cryo-EM where the measurements are corrupted by very high levels of noise. Due to this degradation, the initial graph is contaminated by noise, leading to missing or spurious edges. We show that our proposed graph denoising algorithm improves the state-of-the-art performance of multi-frequency vector diffusion maps.
RIS-aided Wireless Communication with $1$-bit Discrete Optimization for Signal Enhancement
Authors: Rujing Xiong, Xuehui Dong, Tiebin Mi, Robert Caiming Qiu
Date: 2022-09-12 00:00:00

In recent years, a brand-new technology, reconfigurable intelligent surface (RIS) has been widely studied for reconfiguring the wireless propagation environment. RIS is an artificial surface of electromagnetic material that is capable of customizing the propagation of the wave impinging upon it. Utilizing RIS for communication service like signal enhancement usually lead to non-convex optimization problems. Existing optimization methods either suffers from scalability issues for $N$ number of RIS elements large, or may lead to suboptimal solutions in some scenario. In this paper, we propose a divide-and-sort (DaS) discrete optimization approach, that is guaranteed to find the global optimal phase shifts for $1$-bit RIS, and has time complexity $\mathcal{O}(N \log(N))$. Numerical experiments show that the proposed approach achieves a better ``performance--complexity tradeoff'' over other methods for $1$-bit RIS.
Dynamic Hybrid Beamforming Design for Dual-Function Radar-Communication Systems
Authors: Bowen Wang, Hongyu Li, Ziyang Cheng
Date: 2022-09-11 00:00:00

This paper investigates dynamic hybrid beamforming (HBF) for a dual-function radar-communication (DFRC) system, where the DFRC base station (BS) simultaneously serves multiple single-antenna users and senses a target in the presence of multiple clutters. Particularly, we apply a HBF architecture with dynamic subarrays and double phase shifters in the DFRC BS. Aiming at maximizing the radar mutual information, we consider jointly designing the dynamic HBF of the DFRC system, subject to the constraints of communication quality of service (QoS), transmit power, and analog beamformer. To solve the complicated non-convex optimization, an efficient alternating optimization algorithm based on the majorization-minimization methods is developed. Simulation results verify the advancement of the considered HBF architecture and the effectiveness of the proposed design method.