pwc-archive/papers-with-abstracts huggingface dataset:
An Assessment of PC-mer's Performance in Alignment-Free Phylogenetic Tree Construction
Authors: Saeedeh Akbari Rokn Abadi, Melika Honarmand, Ali Hajialinaghi, Somayyeh Koohi
Date: 2023-11-21 00:00:00
Tasks: Drug Discovery

Background: Sequence comparison is essential in bioinformatics, serving various purposes such as taxonomy, functional inference, and drug discovery. The traditional method of aligning sequences for comparison is time-consuming, especially with large datasets. To overcome this, alignment-free methods have emerged as an alternative approach, prioritizing comparison scores over alignment itself. These methods directly compare sequences without the need for alignment. However, accurately representing the relationships between sequences is a significant challenge in the design of these tools. Methods:One of the alignment-free comparison approaches utilizes the frequency of fixed-length substrings, known as K-mers, which serves as the foundation for many sequence comparison methods. However, a challenge arises in these methods when increasing the length of the substring (K), as it leads to an exponential growth in the number of possible states. In this work, we explore the PC-mer method, which utilizes a more limited set of words that experience slower growth 2^k instead of 4^k compared to K. We conducted a comparison of sequences and evaluated how the reduced input vector size influenced the performance of the PC-mer method. Results: For the evaluation, we selected the Clustal Omega method as our reference approach, alongside three alignment-free methods: kmacs, FFP, and alfpy (word count). These methods also leverage the frequency of K-mers. We applied all five methods to 9 datasets for comprehensive analysis. The results were compared using phylogenetic trees and metrics such as Robinson-Foulds and normalized quartet distance (nQD). Conclusion: Our findings indicate that, unlike reducing the input features in other alignment-independent methods, the PC-mer method exhibits competitive performance when compared to the aforementioned methods especially when input sequences are very varied.
High order universal portfolios
Authors: Gabriel Turinici
Date: 2023-11-22 00:00:00

The Cover universal portfolio (UP from now on) has many interesting theoretical and numerical properties and was investigated for a long time. Building on it, we explore what happens when we add this UP to the market as a new synthetic asset and construct by recurrence higher order UPs. We investigate some important theoretical properties of the high order UPs and show in particular that they are indeed different from the Cover UP and are capable to break the time permutation invariance. We show that under some perturbation regime the second high order UP has better Sharp ratio than the standard UP and briefly investigate arbitrage opportunities thus created. Numerical experiences on a benchmark from the literature confirm that high order UPs improve Cover's UP performances.
Spatial Audio and Individualized HRTFs using a Convolutional Neural Network (CNN)
Authors: Ludovic Pirard
Date: 2023-11-22 00:00:00
Tasks: Anatomy

Spatial audio and 3-Dimensional sound rendering techniques play a pivotal and essential role in immersive audio experiences. Head-Related Transfer Functions (HRTFs) are acoustic filters which represent how sound interacts with an individual's unique head and ears anatomy. The use of HRTFs compliant to the subjects anatomical traits is crucial to ensure a personalized and unique spatial experience. This work proposes the implementation of an HRTF individualization method based on anthropometric features automatically extracted from ear images using a Convolutional Neural Network (CNN). Firstly, a CNN is implemented and tested to assess the performance of machine learning on positioning landmarks on ear images. The I-BUG dataset, containing ear images with corresponding 55 landmarks, was used to train and test the neural network. Subsequently, 12 relevant landmarks were selected to correspond to 7 specific anthropometric measurements established by the HUTUBS database. These landmarks serve as a reference for distance computation in pixels in order to retrieve the anthropometric measurements from the ear images. Once the 7 distances in pixels are extracted from the ear image, they are converted in centimetres using conversion factors, a best match method vector is implemented computing the Euclidean distance for each set in a database of 116 ears with their corresponding 7 anthropometric measurements provided by the HUTUBS database. The closest match of anthropometry can be identified and the corresponding set of HRTFs can be obtained for personnalized use. The method is evaluated in its validity instead of the accuracy of the results. The conceptual scope of each stage has been verified and substantiated to function correctly. The various steps and the available elements in the process are reviewed and challenged to define a greater algorithm entity designed for the desired task.
Cramér-Rao Bounds for the Simultaneous Estimation of Power System Electromechanical Modes and Forced Oscillations
Authors: Luke Dosiek
Date: 2023-11-22 00:00:00

In this paper, the Cram\'{e}r-Rao Bounds (CRB) for the simultaneous estimation of power system electromechanical modes and forced oscillations (FO) are derived. Two cases are considered; in the first case only the steady-state response to the FO is present in the measured system output used by estimation algorithms. In the second, the startup transient of the FO is present in addition to the steady-state response. The CRBs are analyzed numerically to explore sensitivities to FO frequency, signal-to-noise ratio (SNR) and observation window length. It is demonstrated that 1) the CRB of FO parameters is not affected by the presence of the transient response, 2) the CRB of the system modes is not affected by the presence of an FO in steady-state and 3) the CRB of the system modes can be drastically reduced by the presence of a FO startup transient.
Learning from similar systems and online data-driven LQR using iterative randomised data compression
Authors: Vatsal Kedia, Sneha Susan George, Debraj Chakraborty
Date: 2023-11-22 00:00:00
Tasks: Data Compression

The problem of data-driven recursive computation of receding horizon LQR control through a randomized combination of online/current and historical/recorded data is considered. It is assumed that large amounts of historical input-output data from a system, which is similar but not identical to the current system under consideration, is available. This (possibly large) data set is compressed through a novel randomized subspace algorithm to directly synthesize an initial solution of the standard LQR problem, which however is sub-optimal due to the inaccuracy of the historical model. The first instance of this input is used to actuate the current system and the corresponding instantaneous output is used to iteratively re-solve the LQR problem through a computationally inexpensive randomized rank-one update of the old compressed data. The first instance of the re-computed input is applied to the system at the next instant, output recorded and the entire procedure is repeated at each subsequent instant. As more current data becomes available, the algorithm learns automatically from the new data while simultaneously controlling the system in near optimal manner. The proposed algorithm is computationally inexpensive due to the initial and repeated compression of old and newly available data. Moreover, the simultaneous learning and control makes this algorithm particularly suited for adapting to unknown, poorly modeled and time-varying systems without any explicit exploration stage. Simulations demonstrate the effectiveness of the proposed algorithm vs popular exploration/exploitation approaches to LQR control.
Machine Learning based Post Event Analysis for Cybersecurity of Cyber-Physical System
Authors: Kuchan Park, Junho Hong, Wencong Su, HyoJong Lee
Date: 2023-11-22 00:00:00

As Information and Communication Technology (ICT) equipment continues to be integrated into power systems, issues related to cybersecurity are increasingly emerging. Particularly noteworthy is the transition to digital substations, which is shifting operations from traditional hardwired-based systems to communication-based Supervisory Control and Data Acquisition (SCADA) system operations. These changes in the power system have increased the vulnerability of the system to cyber-attacks and emphasized its importance. This paper proposes a machine learning (ML) based post event analysis of the power system in order to respond to these cybersecurity issues. An artificial neural network (ANN) and other ML models are trained using transient fault measurements and cyber-attack data on substations. The trained models can successfully distinguish between power system faults and cyber-attacks. Furthermore, the results of the proposed ML-based methods can also identify 10 different fault types and the location where the event occurred.
A Novel Dynamic Event-triggered Mechanism for Dynamic Average Consensus
Authors: Tao Xu, Zhisheng Duan, Guanghui Wen, Zhiyong Sun
Date: 2023-11-22 00:00:00

This paper studies a challenging issue introduced in a recent survey, namely designing a distributed event-based scheme to solve the dynamic average consensus (DAC) problem. First, a robust adaptive distributed event-based DAC algorithm is designed without imposing specific initialization criteria to perform estimation task under intermittent communication. Second, a novel adaptive distributed dynamic event-triggered mechanism is proposed to determine the triggering time when neighboring agents broadcast information to each other. Compared to the existing event-triggered mechanisms, the novelty of the proposed dynamic event-triggered mechanism lies in that it guarantees the existence of a positive and uniform minimum inter-event interval without sacrificing any accuracy of the estimation, which is much more practical than only ensuring the exclusion of the Zeno behavior or the boundedness of the estimation error. Third, a composite adaptive law is developed to update the adaptive gain employed in the distributed event-based DAC algorithm and dynamic event-triggered mechanism. Using the composite adaptive update law, the distributed event-based solution proposed in our work is implemented without requiring any global information. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.
Feedback control of plant-soil autotoxicity via pulse-width modulation
Authors: Tancredi Rino, Francesco Giannino, Davide Fiore
Date: 2023-11-22 00:00:00

Plant-soil negative feedback (PSNF) is the rise in soil of negative conditions for plant performance induced by the plants themselves, limiting the full potential yield and thus representing a loss for the agricultural industry. It has been recently shown that detrimental effects the PSNF has on the growth of plant's biomass can be mitigated by periodically intervening on the plant/soil system, for example by washing the soil. The periodic control inputs were computed by using an average model of the system and then applied in open-loop. In this paper we present two feedback control strategies, namely a PI and a MPC-based controllers, that, by adapting online the duty-cycle of the periodic control input, guarantee precise regulation of the biomass yield and at the same time robustness to unavoidable modeling errors and perturbations acting on the system. The performance of the proposed control strategies is then validated by means of extensive numerical simulations.
Passivity-Preserving, Balancing-Based Model Reduction for Interconnected Systems
Authors: Luuk Poort, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw
Date: 2023-11-22 00:00:00

This paper proposes a balancing-based model reduction approach for an interconnection of passive dynamic subsystems. This approach preserves the passivity and stability of both the subsystems and the interconnected system. Hereto, one Linear Matrix Inequality (LMI) per subsystem and a single Lyapunov equation for the entire interconnected system needs to be solved, the latter of which warrants the relevance of the reduction of the subsystems for the accurate reduction of the interconnected system, while preserving the modularity of the reduction approach. In a numerical example from structural dynamics, the presented approach displays superior accuracy with respect to an approach in which the individual subsystems are reduced independently.
Optimal trajectory planning meets network-level routing: Integrated control framework for emerging mobility systems
Authors: Heeseung Bang, Andreas A. Malikopoulos
Date: 2023-11-22 00:00:00
Tasks: Decision Making, Trajectory Planning

In this paper, we introduce a hierarchical decision-making framework for emerging mobility systems. Despite numerous studies focusing on optimizing vehicle flow, practical feasibility has often been overlooked. To address this gap, we present a route-recovery method and energy-optimal trajectory planning tailored for connected and automated vehicles (CAVs) to ensure the realization of optimal flow. Our approach identifies the optimal vehicle flow to minimize total travel time while considering consistent mobility demands in urban settings. We deploy a heuristic route-recovery algorithm that assigns routes to CAVs and departure/arrival time at each road segment. Furthermore, we propose an efficient coordination method that rapidly solves constrained optimization problems by flexibly piecing together unconstrained energy-optimal trajectories. The proposed method has the potential to effectively generate optimal vehicle flow, contributing to the reduction of travel time and energy consumption in urban areas.
Enhancing Microgrid Resilience with Green Hydrogen Storage
Authors: Shreshtha Dhankar, Cong Chen, Lang Tong
Date: 2023-11-22 00:00:00
Tasks: Model Predictive Control, Scheduling

We consider the problem of hydrogen storage integration in microgrids to improve the electricity supply resilience. Nonlinear effects from electrochemical models of electrolyzers and fuel cells for hydrogen storage are considered, making scheduling under the nonlinear model intractable and the conventional linear approximation infeasible. A piecewise linear model approximation with feasibility projection is proposed, resulting in a computationally efficient model predictive control for hydrogen storage operation. Several resilience performance measures, such as loss-of-load, duration-of-outage, and system cost, are used in performance evaluation. Simulations for the proposed optimization demonstrated a 13%-48% reduction in duration-of-outage, a 6.4%-21.7% reduction in system cost, and a 95% reduction in loss-of-load for critical loads compared to the scheduling algorithm involving linear model approximation. The performance gap of the proposed optimization to the benchmark involving the accurate nonlinear electrochemical model is less than 1% in most metrics.
AC Power Flow Informed Parameter Learning for DC Power Flow Network Equivalents
Authors: Babak Taheri, Daniel K. Molzahn
Date: 2023-11-22 00:00:00

This paper presents an algorithm to optimize the parameters of power systems equivalents to enhance the accuracy of the DC power flow approximation in reduced networks. Based on a zonal division of the network, the algorithm produces a reduced power system equivalent that captures inter-zonal flows with aggregated buses and equivalent transmission lines. The algorithm refines coefficient and bias parameters for the DC power flow model of the reduced network, aiming to minimize discrepancies between inter-zonal flows in DC and AC power flow results. Using optimization methods like BFGS, L-BFGS, and TNC in an offline training phase, these parameters boost the accuracy of online DC power flow computations. In contrast to existing network equivalencing methods, the proposed algorithm optimizes accuracy over a specified range of operation as opposed to only considering a single nominal point. Numerical tests demonstrate substantial accuracy improvements over traditional equivalencing and approximation methods.
High-Speed Voltage Control in Active Distribution Systems with Smart Inverter Coordination and Deep Reinforcement Learning
Authors: Mohammad Golgol, Anamitra Pal
Date: 2023-11-22 00:00:00
Tasks: Deep Reinforcement Learning, State Estimation

The increasing penetration of renewable energy resources in distribution systems necessitates high-speed monitoring and control of voltage for ensuring reliable system operation. However, existing voltage control algorithms often make simplifying assumptions in their formulation, such as real-time availability of smart meter measurements (for monitoring), or real-time knowledge of every power injection information(for control).This paper leverages the recent advances made in highspeed state estimation for real-time unobservable distribution systems to formulate a deep reinforcement learning-based control algorithm that utilizes the state estimates alone to control the voltage of the entire system. The results obtained for a modified (renewable-rich) IEEE34-nodedistributionfeeder indicate that the proposed approach excels in monitoring and controlling voltage of active distribution systems.
Terminal Phase Navigation for AUV Docking: An Innovative Electromagnetic Approach
Authors: Yevgeni Gutnik, Morel Groper
Date: 2023-11-22 00:00:00

This study introduces a groundbreaking approach for real-time 3D localization, specifically focusing on achieving seamless and precise localization during an AUV's terminal guidance phase as it approaches an omnidirectional docking component in an automated Launch and Recovery System (LARS). Through the use of the AUV's magnetometer, an economical electromagnetic beacon embedded in the docking station, and an advanced signal processing algorithm, this novel approach ensures the accurate localization of the docking component in three dimensions without the need for direct line-of-sight contact. The method's real-time capabilities were rigorously evaluated via simulations, prototype experiments in a controlled lab setting, and extensive full-scale pool experiments. These assessments consistently demonstrated an exceptional average positioning accuracy of under 3 cm., marking a significant advancement in AUV guidance systems.
Composite Adaptive Lyapunov-Based Deep Neural Network (Lb-DNN) Controller
Authors: Omkar Sudhir Patil, Emily J. Griffis, Wanjiku A. Makumi, Warren E. Dixon
Date: 2023-11-21 00:00:00
Tasks: parameter estimation

Recent advancements in adaptive control have equipped deep neural network (DNN)-based controllers with Lyapunov-based adaptation laws that work across a range of DNN architectures to uniquely enable online learning. However, the adaptation laws are based on tracking error, and offer convergence guarantees on only the tracking error without providing conclusions on the parameter estimation performance. Motivated to provide guarantees on the DNN parameter estimation performance, this paper provides the first result on composite adaptation for adaptive Lyapunov-based DNN controllers, which uses the Jacobian of the DNN and a prediction error of the dynamics that is computed using a novel method involving an observer of the dynamics. A Lyapunov-based stability analysis is performed which guarantees the tracking, observer, and parameter estimation errors are uniformly ultimately bounded (UUB), with stronger performance guarantees when the DNN's Jacobian satisfies the persistence of excitation (PE) condition. Comparative simulation results demonstrate a significant performance improvement with the developed composite adaptive Lb-DNN controller in comparison to the tracking error-based Lb-DNN.
Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control
Authors: Songyuan Zhang, Kunal Garg, Chuchu Fan
Date: 2023-11-21 00:00:00
Tasks: Collision Avoidance

We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agents are required to maintain safety using only local information and reach their goals. This paper addresses the problem of collision avoidance, scalability, and generalizability by introducing graph control barrier functions (GCBFs) for distributed control. The newly introduced GCBF is based on the well-established CBF theory for safety guarantees but utilizes a graph structure for scalable and generalizable decentralized control. We use graph neural networks to learn both neural a GCBF certificate and distributed control. We also extend the framework from handling state-based models to directly taking point clouds from LiDAR for more practical robotics settings. We demonstrated the efficacy of GCBF in a variety of numerical experiments, where the number, density, and traveling distance of agents, as well as the number of unseen and uncontrolled obstacles increase. Empirical results show that GCBF outperforms leading methods such as MAPPO and multi-agent distributed CBF (MDCBF). Trained with only 16 agents, GCBF can achieve up to 3 times improvement of success rate (agents reach goals and never encountered in any collisions) on <500 agents, and still maintain more than 50% success rates for >1000 agents when other methods completely fail.
Terrestrial-Satellite Spectrum Sharing in the Upper Mid-Band with Interference Nulling
Authors: Seongjoon Kang, Giovanni Geraci, Marco Mezzavilla, Sundeep Rangan
Date: 2023-11-21 00:00:00

The growing demand for broader bandwidth in cellular networks has turned the upper mid-band (7-24 GHz) into a focal point for expansion. However, the integration of terrestrial cellular and incumbent satellite services, particularly in the 12 GHz band, poses significant interference challenges. This paper investigates the interference dynamics in terrestrial-satellite coexistence scenarios and introduces a novel beamforming approach that leverages available ephemeris data for dynamic interference mitigation. By establishing spatial radiation nulls directed towards visible satellites, our technique ensures the protection of satellite uplink communications without markedly compromising terrestrial downlink quality. Through a practical case study, we demonstrate that our approach maintains the satellite uplink signal-to-noise ratio (SNR) degradation under 0.1 dB and incurs only a negligible SNR penalty for the terrestrial downlink. Our findings offer a promising pathway for efficient spectrum sharing in the upper mid-band, fostering a concurrent enhancement in both terrestrial and satellite network capacity.
Training Deep 3D Convolutional Neural Networks to Extract BSM Physics Parameters Directly from HEP Data: a Proof-of-Concept Study Using Monte Carlo Simulations
Authors: S. Dubey, T. E. Browder, S. Kohani, R. Mandal, A. Sibidanov, R. Sinha
Date: 2023-11-21 00:00:00

We report on a novel application of computer vision techniques to extract beyond the Standard Model parameters directly from high energy physics flavor data. We propose a simple but novel data representation that transforms the angular and kinematic distributions into "quasi-images", which are used to train a convolutional neural network to perform regression tasks, similar to fitting. As a proof-of-concept, we train a 34-layer Residual Neural Network to regress on these images and determine information about the Wilson Coefficient $C_{9}$ in Monte Carlo simulations of $B^0 \rightarrow K^{*0}\mu^{+}\mu^{-}$ decays. The method described here can be generalized and may find applicability across a variety of experiments.