pwc-archive/papers-with-abstracts huggingface dataset:
Bi-Encoder Cascades for Efficient Image Search
Authors: Robert Hönig, Jan Ackermann, Mingyuan Chi
Date: 2023-03-27 00:00:00
Tasks: Image Retrieval, Retrieval

Modern neural encoders offer unprecedented text-image retrieval (TIR) accuracy, but their high computational cost impedes an adoption to large-scale image searches. To lower this cost, model cascades use an expensive encoder to refine the ranking of a cheap encoder. However, existing cascading algorithms focus on cross-encoders, which jointly process text-image pairs, but do not consider cascades of bi-encoders, which separately process texts and images. We introduce the small-world search scenario as a realistic setting where bi-encoder cascades can reduce costs. We then propose a cascading algorithm that leverages the small-world search scenario to reduce lifetime image encoding costs of a TIR system. Our experiments show cost reductions by up to 6x.
ASCNet-ECG: Deep Autoencoder based Attention aware Skip Connection network for ECG filtering
Authors: Raghavendra Badiger, M. Prabhakar
Date: 2023-03-28 00:00:00
Tasks: Decoder, Deep Learning

Currently, the telehealth monitoring field has gained huge attention due to its noteworthy use in day-to-day life. This advancement has led to an increase in the data collection of electrophysiological signals. Due to this advancement, electrocardiogram (ECG) signal monitoring has become a leading task in the medical field. ECG plays an important role in the medical field by analysing cardiac physiology and abnormalities. However, these signals are affected due to numerous varieties of noises, such as electrode motion, baseline wander and white noise etc., which affects the diagnosis accuracy. Therefore, filtering ECG signals became an important task. Currently, deep learning schemes are widely employed in signal-filtering tasks due to their efficient architecture of feature learning. This work presents a deep learning-based scheme for ECG signal filtering, which is based on the deep autoencoder module. According to this scheme, the data is processed through the encoder and decoder layer to reconstruct by eliminating noises. The proposed deep learning architecture uses a modified ReLU function to improve the learning of attributes because standard ReLU cannot adapt to huge variations. Further, a skip connection is also incorporated in the proposed architecture, which retains the key feature of the encoder layer while mapping these features to the decoder layer. Similarly, an attention model is also included, which performs channel and spatial attention, which generates the robust map by using channel and average pooling operations, resulting in improving the learning performance. The proposed approach is tested on a publicly available MIT-BIH dataset where different types of noise, such as electrode motion, baseline water and motion artifacts, are added to the original signal at varied SNR levels.
On de novo Bridging Paired-end RNA-seq Data
Authors: Xiang Li, Mingfu Shao
Date: 2023-03-27 00:00:00

The high-throughput short-reads RNA-seq protocols often produce paired-end reads, with the middle portion of the fragments being unsequenced. We explore if the full-length fragments can be computationally reconstructed from the sequenced two ends in the absence of the reference genome - a problem here we refer to as de novo bridging. Solving this problem provides longer, more informative RNA-seq reads, and benefits downstream RNA-seq analysis such as transcript assembly, expression quantification, and splicing differential analysis. However, de novo bridging is a challenging and complicated task owing to alternative splicing, transcript noises, and sequencing errors. It remains unclear if the data provides sufficient information for accurate bridging, let alone efficient algorithms that determine the true bridges. Methods have been proposed to bridge paired-end reads in the presence of reference genome (called reference-based bridging), but the algorithms are far away from scaling for de novo bridging as the underlying compacted de Bruijn graph(cdBG) used in the latter task often contains millions of vertices and edges. We designed a new truncated Dijkstra's algorithm for this problem, and proposed a novel algorithm that reuses the shortest path tree to avoid running the truncated Dijkstra's algorithm from scratch for all vertices for further speeding up. These innovative techniques result in scalable algorithms that can bridge all paired-end reads in a cdBG with millions of vertices. Our experiments showed that paired-end RNA-seq reads can be accurately bridged to a large extent. The resulting tool is freely available at https://github.com/Shao-Group/rnabridge-denovo.
Optimal Intermittent Sensing for Pursuit-Evasion Games
Authors: Dipankar Maity
Date: 2023-03-27 00:00:00

We consider a class of pursuit-evasion differential games in which the evader has continuous access to the pursuer's location, but not vice-versa. There is a remote sensor (e.g., a radar station) that can sense the evader's location upon a request from the pursuer and communicate that sensed location to the pursuer. The pursuer has a budget on the total number of sensing requests. The outcome of the game is determined by the sensing and motion strategies of the players. We obtain an equilibrium sensing strategy for the pursuer and an equilibrium motion strategy for the evader. We quantify the degradation in the pursuer's pay-off due to its sensing limitations.
Continuum Swarm Tracking Control: A Geometric Perspective in Wasserstein Space
Authors: Max Emerick, Bassam Bamieh
Date: 2023-03-27 00:00:00
Tasks: Model Predictive Control

We consider a setting in which one swarm of agents is to service or track a second swarm, and formulate an optimal control problem which trades off between the competing objectives of servicing and motion costs. We consider the continuum limit where large-scale swarms are modeled in terms of their time-varying densities, and where the Wasserstein distance between two densities captures the servicing cost. We show how this non-linear infinite-dimensional optimal control problem is intimately related to the geometry of Wasserstein space, and provide new results in the case of absolutely continuous densities and constant-in-time references. Specifically, we show that optimal swarm trajectories follow Wasserstein geodesics, while the optimal control tradeoff determines the time-schedule of travel along these geodesics. We briefly describe how this solution provides a basis for a model-predictive control scheme for tracking time-varying and real-time reference trajectories as well.
Non-planar snake gaits: from Stigmatic-starts to Sidewinding
Authors: N. Charles, R. Chelakkot, M. Gazzola, B. Young, L. Mahadevan
Date: 2023-03-26 00:00:00

Of the vast variety of animal gaits, one of the most striking is the non-planar undulating motion of a sidewinder. But non-planar gaits are not limited to sidewinders. Here we report a new non-planar mode used as an escape strategy in juvenile anacondas (Eunectes notaeus). In the S-start, named for its eponymous shape, transient locomotion arises when the snake writhes and bends out of the plane while rolling forward about its midsection without slippage. To quantify our observations, we present a mathematical model for an active non-planar filament that interacts anisotropically with a frictional substrate and show that locomotion is due to a propagating localized pulse of a topological quantity, the link density. A two-dimensional phase space characterized by scaled body weight and muscular torque shows that relatively light juveniles are capable of S-starts but heavy adults are not, consistent with our experiments. Finally, we show that a periodic sequence of S-starts naturally leads to a sidewinding gait. All together, our characterization of a novel escape strategy in snakes highlights the role of topology in locomotion, provides a phase diagram for mode feasibility as a function of body size, and suggests a role for the S-start in the evolution of sidewinding.
Spatial Active Noise Control Method Based On Sound Field Interpolation From Reference Microphone Signals
Authors: Kazuyuki Arikawa, Shoichi Koyama, Hiroshi Saruwatari
Date: 2023-03-28 00:00:00

A spatial active noise control (ANC) method based on the interpolation of a sound field from reference microphone signals is proposed. In most current spatial ANC methods, a sufficient number of error microphones are required to reduce noise over the target region because the sound field is estimated from error microphone signals. However, in practical applications, it is preferable that the number of error microphones is as small as possible to keep a space in the target region for ANC users. We propose to interpolate the sound field from reference microphones, which are normally placed outside the target region, instead of the error microphones. We derive a fixed filter for spatial noise reduction on the basis of the kernel ridge regression for sound field interpolation. Furthermore, to compensate for estimation errors, we combine the proposed fixed filter with multichannel ANC based on a transition of the control filter using the error microphone signals. Numerical experimental results indicate that regional noise can be sufficiently reduced by the proposed methods even when the number of error microphones is particularly small.
Bridging the Gap: Applying Assurance Arguments to MIL-HDBK-516C Certification of a Neural Network Control System with ASIF Run Time Assurance Architecture
Authors: Jonathan Rowanhill, Ashlie B. Hocking, Aditya Zutshi, Kerianne L. Hobbs
Date: 2023-03-27 00:00:00

Recent advances in artificial intelligence and machine learning may soon yield paradigm-shifting benefits for aerospace systems. However, complexity and possible continued on-line learning makes neural network control systems (NNCS) difficult or impossible to certify under the United States Military Airworthiness Certification Criteria defined in MIL-HDBK-516C. Run time assurance (RTA) is a control system architecture designed to maintain safety properties regardless of whether a primary control system is fully verifiable. This work examines how to satisfy compliance with MIL-HDBK-516C while using active set invariance filtering (ASIF), an advanced form of RTA not envisaged by the 516c committee. ASIF filters the commands from a primary controller, passing on safe commands while optimally modifying unsafe commands to ensure safety with minimal deviation from the desired control action. This work examines leveraging the core theory behind ASIF as assurance argument explaining novel satisfaction of 516C compliance criteria. The result demonstrates how to support compliance of novel technologies with 516C as well as elaborate how such standards might be updated for emerging technologies.
Research on Efficiency Analysis of Microservices
Authors: Abel C. H. Chen
Date: 2023-03-21 00:00:00
Tasks: Cloud Computing

With the maturity of web services, containers, and cloud computing technologies, large services in traditional systems (e.g. the computation services of machine learning and artificial intelligence) are gradually being broken down into many microservices to increase service reusability and flexibility. Therefore, this study proposes an efficiency analysis framework based on queuing models to analyze the efficiency difference of breaking down traditional large services into n microservices. For generalization, this study considers different service time distributions (e.g. exponential distribution of service time and fixed service time) and explores the system efficiency in the worst-case and best-case scenarios through queuing models (i.e. M/M/1 queuing model and M/D/1 queuing model). In each experiment, it was shown that the total time required for the original large service was higher than that required for breaking it down into multiple microservices, so breaking it down into multiple microservices can improve system efficiency. It can also be observed that in the best-case scenario, the improvement effect becomes more significant with an increase in arrival rate. However, in the worst-case scenario, only slight improvement was achieved. This study found that breaking down into multiple microservices can effectively improve system efficiency and proved that when the computation time of the large service is evenly distributed among multiple microservices, the best improvement effect can be achieved. Therefore, this study's findings can serve as a reference guide for future development of microservice architecture.
A Harmonic-based Fault detection algorithm for Microgrids
Authors: Wael Al Hanaineh, Jose Matas, Jorge. El mariachet, Josep. M. Guerrero
Date: 2023-03-28 00:00:00
Tasks: Fault Detection

The trend toward Microgrids (MGs) is significantly increasing by employing Distributed Generators (DGs) which leads to new challenges, especially in the fault detection. This paper proposes an algorithm based on the Total Harmonic Distortion (THD) of the grid voltages to detect the events of faults in MGs. The algorithm uses the THD together with the estimate amplitude voltages and the zero-sequence component for the detection and identification of the faults. The performance is evaluated by using MATLAB/Simulink simulations to validate the capability for detecting different fault types in the least possible time.
Globally Intelligent Adaptive Finite-/Fixed- Time Tracking Control for Strict-Feedback Nonlinear Systems via Composite Learning Approaches
Authors: Xidong Wang
Date: 2023-03-28 00:00:00

This article focuses on the globally composite adaptive law-based intelligent finite-/fixed- time (FnT/FxT) tracking control issue for a family of uncertain strict-feedback nonlinear systems. First, intelligent approximators with new composite updating laws are developed to model uncertain nonlinear terms, which encompass prediction errors to enhance intelligent approximators' learning behaviors and fewer online learning parameters to diminish computational burden. Then, a novel smooth switching function coupled with robust controllers is designed to pull system states back when the transients are out of the approximators' active domain. After that, a modified FnT/FxT backstepping technique is constructed to render output to follow the reference trajectory, and an adaptive law is employed to alleviate the impact of external disturbances. Moreover, input nonlinearities are considered in the design of control laws. In view of the known actuator magnitude and rate saturations, a novel adaptive fast FxT auxiliary variable system is established to effectively diminish saturation time, and further alleviate the impact of the auxiliary signal on the tracking error. Furthermore, when the input is subject to unknown magnitude saturation, rate saturation and dead-zone simultaneously, a smooth function is introduced to approximate the non-smooth input nonlinearities, where the unknown parameters are modeled via the presented global intelligent approximators. It is theoretically confirmed that the proposed control strategies ensure globally FnT/FxT boundedness of all the closed-loop variables. Finally, the validity of theoretical results is testified via a simulation case.
On Optimal Synchronization of Diffusively Coupled Heterogeneous Van der Pol Oscillators
Authors: Tabea Trummel, Zonglin Liu, Olaf Stursberg
Date: 2023-03-28 00:00:00

This paper proposes a novel method to achieve and preserve synchronization for a set of connected heterogeneous Van der Pol oscillators. Unlike the state-of-the-art synchronization methods, in which a large coupling gain is applied to couple any pair of connected oscillators, the proposed method first casts the whole synchronization process into two phases. The first one considers the period from the beginning to the first instant of synchronization, while the second phase covers the following time in which synchronization must be preserved. It is shown that a large coupling gain is adopted for the first phase, while the averaged coupling gain to preserve the synchronization in the second phase can be reduced significantly by using an offline optimized coupling law. Efficiency and performance of this method are confirmed by a set of numerical tests with different graphs and system dynamics.
Satellite Dynamics Toolbox Library: a tool to model multi-body space systems for robust control synthesis and analysis
Authors: Francesco Sanfedino, Daniel Alazard, Ervan Kassarian, Franca Somers
Date: 2023-03-28 00:00:00
Tasks: Robust Design

The level of maturity reached by robust control theory techniques nowadays contributes to a considerable minimization of the development time of an end-to-end control design of a spacecraft system. The advantage offered by this framework is twofold: all system uncertainties can be included from the very beginning of the design process; the validation and verification (V\&V) process is improved by fast detection of worst-case configurations that could escape to a classical sample-based Monte Carlo simulation campaign. Before proceeding to the control synthesis and analysis, a proper uncertain plant model has to be available in order to push these techniques to their limits of performance. In this spirit, the Satellite Dynamics Toolbox Library (SDTlib) offers many features to model a spacecraft system in a multi-body fashion on SIMULINK. Parametric models can be easily built in a Linear Fractional Transformation (LFT) form by including uncertainties and varying parameters with minimal number of repetitions. Uncertain Linear Time Invariant (LTI) and uncertain Linear Parameter-Varying (LPV) controllers can then be synthesized and analyzed in a straightforward way. The authors present in this article a tutorial, that can be downloaded at https://nextcloud.isae.fr/index.php/s/XDfRfHntejHTmmp, to show how to deal with an end-to-end robust design of a spacecraft mission and to provide to researchers a benchmark to test their own algorithms.
On the Use of Reinforcement Learning for Attacking and Defending Load Frequency Control
Authors: Amr S. Mohamed, Deepa Kundur
Date: 2023-03-28 00:00:00
Tasks: Deep Reinforcement Learning

The electric grid is an attractive target for cyberattackers given its critical nature in society. With the increasing sophistication of cyberattacks, effective grid defense will benefit from proactively identifying vulnerabilities and attack strategies. We develop a deep reinforcement learning-based method that recognizes vulnerabilities in load frequency control, an essential process that maintains grid security and reliability. We demonstrate how our method can synthesize a variety of attacks involving false data injection and load switching, while specifying the attack and threat models - providing insight into potential attack strategies and impact. We discuss how our approach can be employed for testing electric grid vulnerabilities. Moreover our method can be employed to generate data to inform the design of defense strategies and develop attack detection methods. For this, we design and compare a (deep learning-based) supervised attack detector with an unsupervised anomaly detector to highlight the benefits of developing defense strategies based on identified attack strategies.
Synergistic Potential Functions from Single Modified Trace Function on SO(3)
Authors: Xin Tong, Shing Shin Cheng
Date: 2023-03-28 00:00:00

This paper is about the construction of a family of centrally synergistic potential functions from a single modified trace function on SO(3). First, we demonstrate that it is possible to complete the construction through angular warping with multiple directions, particularly effective in the unresolved cases in the literature. Second, it can be shown that for each potential function in the family, there exists a subset of the family such that the synergistic gap is positive at the unwanted critical points. This allows the switching condition to be checked within the selected subsets while implementing synergistic hybrid control. Furthermore, the positive lower bound of synergistic gap is explicitly expressed by selecting a traditional warping angle function. Finally, we apply the proposed synergistic potential functions to obtain robust global attitude tracking.
Minimization of Sensor Activation in Discrete-Event Systems with Control Delays and Observation Delays
Authors: Yunfeng Hou, Ching-Yen Weng, Peng Li
Date: 2023-03-28 00:00:00

In discrete-event systems, to save sensor resources, the agent continuously adjusts sensor activation decisions according to a sensor activation policy based on the changing observations. However, new challenges arise for sensor activations in networked discrete-event systems, where observation delays and control delays exist between the sensor systems and the agent. In this paper, a new framework for activating sensors in networked discrete-event systems is established. In this framework, we construct a communication automaton that explicitly expresses the interaction process between the agent and the sensor systems over the observation channel and the control channel. Based on the communication automaton, we can define dynamic observations of a communicated string. To guarantee that a sensor activation policy is physically implementable and insensitive to non-deterministic control delays and observation delays, we further introduce the definition of delay feasibility. We show that a delay feasible sensor activation policy can be used to dynamically activate sensors even if control delays and observation delays exist. A set of algorithms are developed to minimize sensor activations in a transition-based domain while ensuring a given specification condition is satisfied. A practical example is also provided to show the application of the proposed framework. Finally, we briefly discuss how to extend the proposed framework to a decentralized observation setting.
Switched Moving Boundary Modeling of Phase Change Thermal Energy Storage Systems
Authors: Trent J. Sakakini, Justin P. Koeln
Date: 2023-03-28 00:00:00

Thermal Energy Storage (TES) devices, which leverage the constant-temperature thermal capacity of the latent heat of a Phase Change Material (PCM), provide benefits to a variety of thermal management systems by decoupling the absorption and rejection of thermal energy. While performing a role similar to a battery in an electrical system, it is critical to know when to charge (freeze) and discharge (melt) the TES to maximize the capabilities and efficiency of the overall system. Therefore, control-oriented models of TES are needed to predict the behavior of the TES and make informed control decisions. While existing modeling approaches divide the TES in to multiple sections using a Fixed Grid (FG) approach, this paper proposes a switched Moving Boundary (MB) model that captures the key dynamics of the TES with significantly fewer dynamic states. Specifically, a graph-based modeling approach is used to model the heat flow through the TES and a MB approach is used to model the time-varying liquid and solid regions of the TES. Additionally, a Finite State Machine (FSM) is used to switch between four different modes of operation based on the State-of-Charge (SOC) of the TES. Numerical simulations comparing the proposed approach with a more traditional FG approach show that the MB model is capable of accurately modeling the behavior of the FG model while using far fewer states, leading to five times faster simulations.