pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation
Authors: HongYu Zhou, Xin Zhou, Lingzi Zhang, Zhiqi Shen
Date: 2023-01-28 00:00:00
Tasks: Graph Learning, Multimodal Recommendation, Recommendation Systems

User interaction data in recommender systems is a form of dyadic relation that reflects the preferences of users with items. Learning the representations of these two discrete sets of objects, users and items, is critical for recommendation. Recent multimodal recommendation models leveraging multimodal features (e.g., images and text descriptions) have been demonstrated to be effective in improving recommendation accuracy. However, state-of-the-art models enhance the dyadic relations between users and items by considering either user-user or item-item relations, leaving the high-order relations of the other side (i.e., users or items) unexplored. Furthermore, we experimentally reveal that the current multimodality fusion methods in the state-of-the-art models may degrade their recommendation performance. That is, without tainting the model architectures, these models can achieve even better recommendation accuracy with uni-modal information. On top of the finding, we propose a model that enhances the dyadic relations by learning Dual RepresentAtions of both users and items via constructing homogeneous Graphs for multimOdal recommeNdation. We name our model as DRAGON. Specifically, DRAGON constructs the user-user graph based on the commonly interacted items and the item-item graph from item multimodal features. It then utilizes graph learning on both the user-item heterogeneous graph and the homogeneous graphs (user-user and item-item) to obtain the dual representations of users and items. To capture information from each modality, DRAGON employs a simple yet effective fusion method, attentive concatenation, to derive the representations of users and items. Extensive experiments on three public datasets and seven baselines show that DRAGON can outperform the strongest baseline by 22.03% on average. Various ablation studies are conducted on DRAGON to validate its effectiveness.
The persistent homology of genealogical networks
Authors: Zachary M. Boyd, Nick Callor, Taylor Gledhill, Abigail Jenkins, Robert Snellman, Benjamin Z. Webb, Raelynn Wonnacott
Date: 2023-01-27 00:00:00

Genealogical networks (i.e. family trees) are of growing interest, with the largest known data sets now including well over one billion individuals. Interest in family history also supports an 8.5 billion dollar industry whose size is projected to double within 7 years (FutureWise report HC1137). Yet little mathematical attention has been paid to the complex network properties of genealogical networks, especially at large scales. The structure of genealogical networks is of particular interest due to the practice of forming unions, e.g. marriages, that are typically well outside one's immediate family. In most other networks, including other social networks, no equivalent restriction exists on the distance at which relationships form. To study the effect this has on genealogical networks we use persistent homology to identify and compare the structure of 101 genealogical and 31 other social networks. Specifically, we introduce the notion of a network's persistence curve, which encodes the network's set of persistence intervals. We find that the persistence curves of genealogical networks have a distinct structure when compared to other social networks. This difference in structure also extends to subnetworks of genealogical and social networks suggesting that, even with incomplete data, persistent homology can be used to meaningfully analyze genealogical networks. Here we also describe how concepts from genealogical networks, such as common ancestor cycles, are represented using persistent homology. We expect that persistent homology tools will become increasingly important in genealogical exploration as popular interest in ancestry research continues to expand.
Deep Quantum Error Correction
Authors: Yoni Choukroun, Lior Wolf
Date: 2023-01-27 00:00:00

Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. QECC, as its classical counterpart (ECC), enables the reduction of error rates, by distributing quantum logical information across redundant physical qubits, such that errors can be detected and corrected. In this work, we efficiently train novel {\emph{end-to-end}} deep quantum error decoders. We resolve the quantum measurement collapse by augmenting syndrome decoding to predict an initial estimate of the system noise, which is then refined iteratively through a deep neural network. The logical error rates calculated over finite fields are directly optimized via a differentiable objective, enabling efficient decoding under the constraints imposed by the code. Finally, our architecture is extended to support faulty syndrome measurement, by efficient decoding of repeated syndrome sampling. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy, outperforming {for small distance topological codes,} the existing {end-to-end }neural and classical decoders, which are often computationally prohibitive.
Low-Carbon Economic Dispatch of Bulk Power Systems Using Nash Bargaining Game
Authors: Xuyang Li, Guangchun Ruan, Haiwang Zhong
Date: 2023-01-30 00:00:00
Tasks: Computational Efficiency

Decarbonization of power systems plays a crucial role in achieving carbon neutral goals across the globe, but there exists a sharp contradiction between the emission reduction and levelized generation cost. Therefore, it is of great importance for power system operators to take economic as well as low-carbon factors into account. This paper establishes a low-carbon economic dispatch model of bulk power systems based on Nash bargaining game, which derives a Nash bargaining solution making a reasonable trade-off between economic and low-carbon objectives. Because the Nash bargaining solution satisfies Pareto effectiveness, we analyze the computational complexity of Pareto frontiers with parametric linear programming and interpret the inefficiency of the method. Instead, we assign a group of dynamic weights in the objective function of the proposed low-carbon economic dispatch model so as to improve the computational efficiency by decoupling time periods and avoiding the complete computation of Pareto frontiers. In the end, we validate the proposed model and the algorithm by a realistic nationwide simulation in mainland China.
Online coalitional games for real-time payoff distribution with applications to energy markets
Authors: Aitazaz Ali Raja, Sergio Grammatico
Date: 2023-01-28 00:00:00

Motivated by the markets operating on fast time scales, we present a framework for online coalitional games with time-varying coalitional values and propose real-time payoff distribution mechanisms. Specifically, we design two online distributed algorithms to track the Shapley value and the core, the two most widely studied payoff distribution criteria in coalitional game theory. We show that the payoff distribution trajectory resulting from our proposed algorithms converges to a neighborhood of the time-varying solutions. We adopt an operator-theoretic perspective to show the convergence of our algorithms. Numerical simulations of a real-time local electricity market and cooperative energy forecasting market illustrate the performance of our algorithms: {the difference between online payoffs and static payoffs (Shapley and the core) to the participants is little; online algorithms considerably improve the scalability of the mechanism with respect to the number of market participants.
Robust Precoding via Characteristic Functions for VSAT to Multi-Satellite Uplink Transmission
Authors: Maik Röper, Bho Matthiesen, Dirk Wübben, Petar Popovski, Armin Dekorsy
Date: 2023-01-30 00:00:00
Tasks: Position

The uplink from a very small aperture terminal (VSAT) towards multiple satellites is considered, in this paper. VSATs can be equipped with multiple antennas, allowing parallel transmission to multiple satellites. A low-complexity precoder based on imperfect positional information of the satellites is presented. The probability distribution of the position uncertainty and the statistics of the channel elements are related by the characteristic function of the position uncertainty. This knowledge is included in the precoder design to maximize the mean signal-to-leakage-and-noise ratio (SLNR) at the satellites. Furthermore, the performance w.r.t. the inter-satellite distance is numerically evaluated. It is shown that the proposed approach achieves the capacity for perfect position knowledge and sufficiently large inter-satellite distances. In case of imperfect position knowledge, the performance degradation of the robust precoder is relatively small.
Unbiased estimators for the Heston model with stochastic interest rates
Authors: Chao Zheng, Jiangtao Pan
Date: 2023-01-28 00:00:00

We combine the unbiased estimators in Rhee and Glynn (Operations Research: 63(5), 1026-1043, 2015) and the Heston model with stochastic interest rates. Specifically, we first develop a semi-exact log-Euler scheme for the Heston model with stochastic interest rates. Then, under mild assumptions, we show that the convergence rate in the $L^2$ norm is $O(h)$, where $h$ is the step size. The result applies to a large class of models, such as the Heston-Hull-While model, the Heston-CIR model and the Heston-Black-Karasinski model. Numerical experiments support our theoretical convergence rate.
NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation
Authors: Yixuan Zhang, Meng Yu, Hao Zhang, Dong Yu, DeLiang Wang
Date: 2023-01-29 00:00:00
Tasks: Acoustic echo cancellation

The robustness of the Kalman filter to double talk and its rapid convergence make it a popular approach for addressing acoustic echo cancellation (AEC) challenges. However, the inability to model nonlinearity and the need to tune control parameters cast limitations on such adaptive filtering algorithms. In this paper, we integrate the frequency domain Kalman filter (FDKF) and deep neural networks (DNNs) into a hybrid method, called NeuralKalman, to leverage the advantages of deep learning and adaptive filtering algorithms. Specifically, we employ a DNN to estimate nonlinearly distorted far-end signals, a transition factor, and the nonlinear transition function in the state equation of the FDKF algorithm. Experimental results show that the proposed NeuralKalman improves the performance of FDKF significantly and outperforms strong baseline methods.
Cross-domain Neural Pitch and Periodicity Estimation
Authors: Max Morrison, Caedon Hsieh, Nathan Pruyne, Bryan Pardo
Date: 2023-01-28 00:00:00
Tasks: CPU, GPU, Music Transcription, Singing Voice Synthesis

Pitch is a foundational aspect of our perception of audio signals. Pitch contours are commonly used to analyze speech and music signals and as input features for many audio tasks, including music transcription, singing voice synthesis, and prosody editing. In this paper, we describe a set of techniques for improving the accuracy of widely-used neural pitch and periodicity estimators to achieve state-of-the-art performance on both speech and music. We also introduce a novel entropy-based method for extracting periodicity and per-frame voiced-unvoiced classifications from statistical inference-based pitch estimators (e.g., neural networks), and show how to train a neural pitch estimator to simultaneously handle both speech and music data (i.e., cross-domain estimation) without performance degradation. Our estimator implementations run 11.2x faster than real-time on a Intel i9-9820X 10-core 3.30 GHz CPU$\unicode{x2014}$approaching the speed of state-of-the-art DSP-based pitch estimators$\unicode{x2014}$or 408x faster than real-time on a NVIDIA GeForce RTX 3090 GPU. We release all of our code and models as Pitch-Estimating Neural Networks (penn), an open-source, pip-installable Python module for training, evaluating, and performing inference with pitch- and periodicity-estimating neural networks. The code for penn is available at https://github.com/interactiveaudiolab/penn.
Graph Neural Network Framework for Security Assessment Informed by Topological Measures
Authors: Mojtaba Dezvarei, Kevin Tomsovic, Jinyuan Stella Sun, Seddik M. Djouadi
Date: 2023-01-30 00:00:00
Tasks: Graph Neural Network

In the power system, security assessment (SA) plays a pivotal role in determining the safe operation in a normal situation and some contingencies scenarios. Electrical variables as input variables of the model are mainly considered to indicate the power system operation as secure or insecure, according to the reliability criteria for contingency scenarios. In this approach, the features are in grid format data, where the relation between features and any knowledge of network topology is absent. Moreover, the traditional and common models, such as neural networks (NN), are not applicable if the input variables are in the graph format structure. Therefore, this paper examines the security analysis in the graph neural network (GNN) framework such that the GNN model incorporates the network connection and node's neighbors' influence for the assessment. Here the input features are separated graphs representing different network conditions in electrical and structural statuses. Topological characteristics defined by network centrality measures are added in the feature vector representing the structural properties of the network. The proposed model is simulated in the IEEE 118-Bus system for the voltage static security assessment (SSA). The performance indices validate the efficiency of the GNN-based model compared to the traditional NN model denoting that the information enclosed in graph data boosts the classifier performance since the GNN model benefits the neighbors' features. Moreover, outperforming of GNN-based model is determined when robustness and sensitivity analyzes are carried out. The proposed method is not limited to a specific task and can be extended for other security assessments with different critical variables, such as dynamic analysis and frequency criteria, respectively.
A bi-level co-design approach for multicopters
Authors: Jordan Mabboux, Scott Delbecq, Valérie Pommier-Budinger, Joël Bordeneuve-Guibe, Ana Truc-Hermel, Jean-Marie Kai
Date: 2023-01-30 00:00:00

Over the last few decades, multicopters have become significantly popular because of their various applications. However, multicopters suffer from poor endurance performance as compared to other aircraft configurations with lifting surfaces. In this paper, a co-design approach for the multicopter structure and propulsive system is presented with the objective of improving the energetic efficiency of the vehicle, while respecting dynamic performance requirements. For this purpose, the approach uses a bi-level formulation to optimize concurrently the design and the control. The methodology offers the possibility to activate or not a passive fault tolerant strategy in the controller synthesis to be tolerant to single rotor failure. In contrast to conventional design approaches that sequentially design each part, the co-design approach leads to a better optimal solution due to the consideration of the interactions and couplings between design and control. With the proposed co-design approach, the results show the soundness of the proposed design methodology and a significant reduction in the consumed energy compared to a reference non-optimal design, while maintaining the same requirements in terms of dynamic performance.
Measuring and Analyzing Effects of HEMP Simulation on Synthetic Power Grids
Authors: Carson L. May, Arthur K. Barnes, Jose E. Tabarez, Adam Mate, Eric M. Nelson, Ross Guttromson
Date: 2023-01-30 00:00:00

There is significant uncertainty about the potential effects of a high-altitude electromagnetic pulse (HEMP) detonation on the bulk electric system. This study attempts to account for such uncertainty, in using Monte-Carlo methods to account for speculated range of effect of HEMP contingency. Through task parallelism and asynchronous processing techniques implemented throughout simulation, this study measure the effects of 700 large-scale HEMP simulations on a 7173 bus synthetic power grid. Analysis explores how contingency severity varies, depending on initial contingency parameters. Severity indices were captured throughout simulation to measure and quantify the cascading nature of an HEMP event. Further development of HEMP simulation modeling is explored as well, which could augment forecasts of potential contingency events as well.
Optimal Planning for Electrical Collector System of Offshore Wind Farm with Double-sided Ring Topology
Authors: Xinwei Shen, Qiuwei Wu, Hongcai Zhang, Liming Wang
Date: 2023-01-30 00:00:00

We propose a planning method for offshore wind farm electrical collector system (OWF-ECS) with double-sided ring topology meeting the "N-1" criterion on cable faults, in which the submarine cables layout of OWF is optimized considering cable length and power losses. The proposed mixed-integer quadratic programming (MIQP) model is based on the Capacitated Vehicle Routing Problem (CVRP) formulation and power network expansion planning, which could approximate the power losses in OWF-ECS. In addition, cross-avoidance constraints are proposed to avoid crossing cables, and the minimum k-degree center tree model is included to improve the convergence. Case studies on OWFs with 30 and 62 WTs demonstrate the effectiveness of the proposed method. Considering the potential outage cost in the radial topology, the total cost of the planning result is reduced by up to 25.9% with reliability improvement. The cable investment is reduced by 4%~8% with the proposed method compared with conventional heuristic methods and Google OR-tools. The proposed method/model can also achieve acceptable computation efficiency and OWF-ECS planning results with good optimality. Moreover, it could be solved by modern commercial solvers/optimization software, thus it's easy to use even for large-scale OWF.
Non-Asymptotic State-Space Identification of Closed-Loop Stochastic Linear Systems using Instrumental Variables
Authors: Szabolcs Szentpéteri, Balázs Csanád Csáji
Date: 2023-01-29 00:00:00

The paper suggests a generalization of the Sign-Perturbed Sums (SPS) finite sample system identification method for the identification of closed-loop observable stochastic linear systems in state-space form. The solution builds on the theory of matrix-variate regression and instrumental variable methods to construct distribution-free confidence regions for the state-space matrices. Both direct and indirect identification are studied, and the exactness as well as the strong consistency of the construction are proved. Furthermore, a new, computationally efficient ellipsoidal outer-approximation algorithm for the confidence regions is proposed. The new construction results in a semidefinite optimization problem which has an order-of-magnitude smaller number of constraints, as if one applied the ellipsoidal outer-approximation after vectorization. The effectiveness of the approach is also demonstrated empirically via a series of numerical experiments.
Autonomous Satellite Docking via Adaptive Optimal Output Rregulation: A Reinforcement Learning Approach
Authors: Omar Qasem, Madhur Tiwari, Hector Gutierrez
Date: 2023-01-29 00:00:00
Tasks: Position, reinforcement-learning, Reinforcement Learning (RL)

This paper describes an online off-policy data-driven reinforcement learning based-algorithm to regulate and control the relative position of a deputy satellite in an autonomous satellite docking problem. The optimal control policy is learned under the framework of output regulation problem and adaptive dynamic programming (ADP) by considering the continuous-time linearized model of the satellite. The linearized model of relative motion is used to describe the motion between satellites, and the satellite docking problem is formulated as a linear optimal output regulation problem, in which the feedback-forward optimal controller is used to track a class of references and rejecting a class of disturbances while maintaining the overall system's closed-loop stability. The optimal control problem is presented using a data-driven reinforcement learning based method to regulate the relative position and velocity of the deputy to safely dock with the chief. Using the adaptive optimal output regulation framework, the learned optimal feedback-feedforward gains guarantee optimal transient and steady state performances without any prior knowledge of the dynamics of the studied system. {The states/input information of the underlying dynamical system are instead used to compute the approximated optimal feedback-feedforward control gain matrices.} Reference tracking and disturbance rejection are achieved in an optimal sense without using any modelling information of the physics of the satellites. {Simulation results are presented and demonstrate the efficacy of the proposed method.
Topological entropy of switched nonlinear and interconnected systems
Authors: Guosong Yang, Daniel Liberzon, João P. Hespanha
Date: 2023-01-29 00:00:00

A general upper bound for topological entropy of switched nonlinear systems is constructed, using an asymptotic average of upper limits of the matrix measures of Jacobian matrices of strongly persistent individual modes, weighted by their active rates. A general lower bound is constructed as well, using a similar weighted average of lower limits of the traces of these Jacobian matrices. In a case of interconnected structure, the general upper bound is readily applied to derive upper bounds for entropy that depend only on "network-level" information. In a case of block-diagonal structure, less conservative upper and lower bounds for entropy are constructed. In each case, upper bounds for entropy that require less information about the switching signal are also derived. The upper bounds for entropy and their relations are illustrated by numerical examples of a switched Lotka-Volterra ecosystem model.
Stochastic Wasserstein Gradient Flows using Streaming Data with an Application in Predictive Maintenance
Authors: Nicolas Lanzetti, Efe C. Balta, Dominic Liao-McPherson, Florian Dörfler
Date: 2023-01-29 00:00:00
Tasks: Decision Making

We study estimation problems in safety-critical applications with streaming data. Since estimation problems can be posed as optimization problems in the probability space, we devise a stochastic projected Wasserstein gradient flow that keeps track of the belief of the estimated quantity and can consume samples from online data. We show the convergence properties of our algorithm. Our analysis combines recent advances in the Wasserstein space and its differential structure with more classical stochastic gradient descent. We apply our methodology for predictive maintenance of safety-critical processes: Our approach is shown to lead to superior performance when compared to classical least squares, enabling, among others, improved robustness for decision-making.