pwc-archive/papers-with-abstracts huggingface dataset:
The Impact of Environment Configurations on the Stability of AI-Enabled Systems
Authors: Musfiqur Rahman, SayedHassan Khatoonabadi, Ahmad Abdellatif, Haya Samaana, Emad Shihab
Date: 2024-08-05 00:00:00
Tasks: CPU

Nowadays, software systems tend to include Artificial Intelligence (AI) components. Changes in the operational environment have been known to negatively impact the stability of AI-enabled software systems by causing unintended changes in behavior. However, how an environment configuration impacts the behavior of such systems has yet to be explored. Understanding and quantifying the degree of instability caused by different environment settings can help practitioners decide the best environment configuration for the most stable AI systems. To achieve this goal, we performed experiments with eight different combinations of three key environment variables (operating system, Python version, and CPU architecture) on $30$ open-source AI-enabled systems using the Travis CI platform. We determine the existence and the degree of instability introduced by each configuration using three metrics: the output of an AI component of the system (model performance), the time required to build and run the system (processing time), and the cost associated with building and running the system (expense). Our results indicate that changes in environment configurations lead to instability across all three metrics; however, it is observed more frequently with respect to processing time and expense rather than model performance. For example, between Linux and MacOS, instability is observed in 23\%, 96.67\%, and 100\% of the studied projects in model performance, processing time, and expense, respectively. Our findings underscore the importance of identifying the optimal combination of configuration settings to mitigate drops in model performance and reduce the processing time and expense before deploying an AI-enabled system.
NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing
Authors: Shide Zhou, Tianlin Li, Yihao Huang, Ling Shi, Kailong Wang, Yang Liu, Haoyu Wang
Date: 2024-07-26 00:00:00
Tasks: Model Compression, Semantic Similarity, Semantic Textual Similarity

Deep Neural networks (DNNs), extensively applied across diverse disciplines, are characterized by their integrated and monolithic architectures, setting them apart from conventional software systems. This architectural difference introduces particular challenges to maintenance tasks, such as model restructuring (e.g., model compression), re-adaptation (e.g., fitting new samples), and incremental development (e.g., continual knowledge accumulation). Prior research addresses these challenges by identifying task-critical neuron layers, and dividing neural networks into semantically-similar sequential modules. However, such layer-level approaches fail to precisely identify and manipulate neuron-level semantic components, restricting their applicability to finer-grained model maintenance tasks. In this work, we implement NeuSemSlice, a novel framework that introduces the semantic slicing technique to effectively identify critical neuron-level semantic components in DNN models for semantic-aware model maintenance tasks. Specifically, semantic slicing identifies, categorizes and merges critical neurons across different categories and layers according to their semantic similarity, enabling their flexibility and effectiveness in the subsequent tasks. For semantic-aware model maintenance tasks, we provide a series of novel strategies based on semantic slicing to enhance NeuSemSlice. They include semantic components (i.e., critical neurons) preservation for model restructuring, critical neuron tuning for model re-adaptation, and non-critical neuron training for model incremental development. A thorough evaluation has demonstrated that NeuSemSlice significantly outperforms baselines in all three tasks.
Model-free optimal controller for discrete-time Markovian jump linear systems: A Q-learning approach
Authors: Ehsan Badfar, Babak Tavassoli
Date: 2024-08-06 00:00:00
Tasks: Q-Learning, Reinforcement Learning (RL)

This research paper introduces a model-free optimal controller for discrete-time Markovian jump linear systems (MJLSs), employing principles from the methodology of reinforcement learning (RL). While Q-learning methods have demonstrated efficacy in determining optimal controller gains for deterministic systems, their application to systems with Markovian switching remains unexplored. To address this research gap, we propose a Q-function involving the Markovian mode. Subsequently, a Q-learning algorithm is proposed to learn the unknown kernel matrix using raw input-state information from the system. Notably, the study proves the convergence of the proposed Q-learning optimal controller gains to the model-based optimal controller gains after proving the convergence of a value iteration algorithm as the first step. Addition of excitation noise to input which is required to ensure the leaning performance does not lead to any bias. Unlike the conventional optimal controller, the proposed method does not require any knowledge on system dynamics and eliminates the need for solving coupled algebraic Riccati equations arising in optimal control of MJLSs. Finally, the efficiency of the proposed method is demonstrated through a simulation study.
Joint Design of Probabilistic Constellation Shaping and Precoding for Multi-user VLC Systems
Authors: Thang K. Nguyen, Thanh V. Pham, Hoang D. Le, Chuyen T. Nguyen, Anh T. Pham
Date: 2024-08-06 00:00:00

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar $M$-pulse amplitude modulation ($M$-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex non-convex problem due to the non-convexity of the objective function. To tackle the problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima to the original non-convex optimization problem. The FA-based approach, however, suffers from high computational complexity. Therefore, we propose a low-complexity design based on zero-forcing (ZF) precoding, which is solved using an alternating optimization (AO) approach. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. Some insights into the optimal symbol distributions of the two joint design approaches are also provided.
Advancing Nonlinear System Stability Analysis with Hessian Matrix Analysis
Authors: Samaneh Alsadat Saeedinia, Mojtaba Sharifi, Seyed Mohammad Hosseindokht, Hedieh Jafarpourdavatgar
Date: 2024-08-06 00:00:00

This paper introduces an innovative method for ensuring global stability in a broad array of nonlinear systems. The novel approach enhances the traditional analysis based on Jacobian matrices by incorporating the Taylor series boundary error of estimation and the eigenvalues of the Hessian matrix, resulting in a fresh criterion for global stability. The main strength of this methodology lies in its unrestricted nature regarding the number of equilibrium points or the system's dimension, giving it a competitive edge over alternative methods for global stability analysis. The efficacy of this method has been validated through its application to two established benchmark systems within the industrial domain. The results suggest that the expanded Jacobian stability analysis can ensure global stability under specific circumstances, which are thoroughly elaborated upon in the manuscript. The proposed approach serves as a robust tool for assessing the global stability of nonlinear systems and holds promise for advancing the realms of nonlinear control and optimization.
Practical Design of Probabilistic Constellation Shaping for Physical Layer Security in Visible Light Communications
Authors: Thanh V. Pham, Susumu Ishihara
Date: 2024-08-06 00:00:00

This paper studies a practical design of probabilistic constellation shaping (PCS) for physical layer security in visible light communications (VLC). In particular, we consider a wiretap VLC channel employing a probabilistically shaped $M$-ary pulse amplitude modulation (PAM) constellation. Considering the requirements for reliability of the legitimate user's channel, flickering-free transmission, and symmetric constellation distribution, the optimal constellation distributions to maximize modulation-constrained secrecy capacity or the bit error rate (BER) of eavesdropper's channel are investigated for both scenarios of known and unknown eavesdropper's channel state information (CSI). To formulate the constraint on the channel reliability, tractable closed-form expressions for the upper bound and approximate BER of $M$-ary PAM under an arbitrary symbol probability are derived. The design problem is shown to be non-convex due to the non-convex BER constraint. By proving that the upper bound BER is a concave function of the constellation distribution, a suboptimal solution based on the convex-concave procedure (CCCP) is presented. Our findings reveal that while the uniform signaling can only satisfy the BER constraint when the optical power is beyond a certain value, the proposed PCS design works in the entire region of the optical power. Some insights into the optimal constellation distribution with respect to the emitted optical power are also discussed.
Dirichlet Logistic Gaussian Processes for Evaluation of Black-Box Stochastic Systems under Complex Requirements
Authors: Ryohei Oura, Yuji Ito
Date: 2024-08-06 00:00:00
Tasks: Gaussian Processes

The requirement-driven performance evaluation of a black-box cyber-physical system (CPS) that utilizes machine learning methods has proven to be an effective way to assess the quality of the CPS. However, the distributional evaluation of the performance has been poorly considered. Although many uncertainty estimation methods have been advocated, they have not successfully estimated highly complex performance distributions under small data. In this paper, we propose a method to distributionally evaluate the performance under complex requirements using small input-trajectory data. To handle the unknown complex probability distributions under small data, we discretize the corresponding performance measure, yielding a discrete random process over an input region. Then, we propose a semiparametric Bayesian model of the discrete process based on a Dirichlet random field whose parameter function is represented by multiple logistic Gaussian processes (LGPs). The Dirichlet posterior parameter function is estimated through the LGP posteriors in a reasonable and conservative fashion. We show that the proposed Bayesian model converges to the true discrete random process as the number of data becomes large enough. We also empirically demonstrate the effectiveness of the proposed method by simulation.
Narrowband Electromagnetic Coupling Matrix in Coupled-Resonator Microwave Circuits
Authors: Valentin de la Rubia, David Young
Date: 2024-08-06 00:00:00

A novel methodology to unleash electromagnetic coupling matrix information in coupled-resonator microwave circuits has been recently proposed [1]. This information is derived from Maxwell's equations and the natural language of electromagnetics is employed. As a result, the coupling matrix coefficients stand only for electromagnetics. In this work, we enhance this approach to reveal valuable design information for microwave engineering, showing the electromagnetic (EM) coupling among all EM resonators and ports. By the same token, the similarities with the well-known classical coupling matrix theory are addressed. We bridge this gap since the classical theory is the preferential language among microwave engineers. Classical coupling matrix theory is a narrowband model for electromagnetics. Thus, we carry out a narrowband approximation in the electromagnetic coupling matrix. This makes it possible to describe the EM coupling coefficients in the same framework as classical circuits. As a result, proper comparison between both coupling matrices is allowed. Finally, both coupling matrix approaches have a common ground, namely, get physical insight and valuable information for design purposes in coupled-resonator microwave circuits. However, only the electromagnetic coupling matrix details all EM behavior, including parasitic and leakage couplings, and the higher-order mode influence in the microwave circuit. Several microwave circuits, such as filters and diplexers, will show the possibilities of this new technique and its relation to classical coupling matrix theory.
Designing model predictive control strategies for grid-interactive water heaters for load shifting applications
Authors: Elizabeth Buechler, Aaron Goldin, Ram Rajagopal
Date: 2024-08-05 00:00:00
Tasks: energy management, Management, Model Predictive Control

Model predictive control (MPC) strategies allow residential water heaters to shift load in response to dynamic price signals. Crucially, the performance of such strategies is sensitive to various algorithm design choices. In this work, we develop a framework for implementing model predictive controls on residential water heaters for load shifting applications. We use this framework to analyze how four different design factors affect control performance and thermal comfort: (i) control model fidelity, (ii) temperature sensor configuration, (iii) water draw estimation methodology, and (iv) water draw forecasting methodology. We propose new methods for estimating water draw patterns without the use of a flow meter. MPC strategies are compared under two different time-varying price signals through simulations using a high-fidelity tank model and real-world draw data. Results show that control model fidelity and the number of temperature sensors have the largest impact on electricity costs, while the water draw forecasting methodology has a significant impact on thermal comfort and the frequency of runout events. Results provide practical insight into effective MPC design for water heaters in home energy management systems.
On the Impact of High-Order Harmonic Generation in Electrical Distribution Systems
Authors: Aaqib Peerzada, Bhaskar Mitra, Soumya Kundu, James P. Ogle
Date: 2024-07-31 00:00:00

The modern power grid has seen a rise in the integration of non-linear loads, presenting a significant concern for operators. These loads introduce unwanted harmonics, leading to potential issues such as overheating and improper functioning of circuit breakers. In pursuing a more sustainable grid, the adoption of electric vehicles (EVs) and photovoltaic (PV) systems in residential networks has increased. Understanding and examining the effects of high-order harmonic frequencies beyond $1.5$ kHz is crucial to understanding their impact on the operation and planning of electrical distribution systems under varying nonlinear loading conditions. This study investigates a diverse set of critical power electronic loads within a household modeled using PSCAD/EMTdc, analyzing their unique harmonic spectra. This information is utilized to run the time-series harmonic analysis program in OpenDSS on a modified IEEE 34 bus test system model. The impact of high-order harmonics is quantified using metrics that evaluate total harmonic distortion (THD), transformer harmonic-driven eddy current loss component, and propagation of harmonics from the source to the substation transformer.
DUST: A Framework for Data-Driven Density Steering
Authors: Joshua Pilipovsky, Panagiotis Tsiotras
Date: 2024-08-05 00:00:00
Tasks: Uncertainty Quantification

We consider the problem of data-driven stochastic optimal control of an unknown LTI dynamical system. Assuming the process noise is normally distributed, we pose the problem of steering the state's mean and covariance to a target normal distribution, under noisy data collected from the underlying system, a problem commonly referred to as covariance steering (CS). A novel framework for Data-driven Uncertainty quantification and density STeering (DUST) is presented that simultaneously characterizes the noise affecting the measured data and designs an optimal affine-feedback controller to steer the density of the state to a prescribed terminal value. We use both indirect and direct data-driven design approaches based on the notions of persistency of excitation and subspace predictors to exactly represent the mean and covariance dynamics of the state in terms of the data and noise realizations. Since both the mean and the covariance steering sub-problems are plagued with distributional uncertainty arising from noisy data collection, we first estimate the noise realization from this dataset and subsequently compute tractable upper bounds on the estimation errors. The moment steering problems are then solved to optimality using techniques from robust control and robust optimization. Lastly, we present an alternative control design approach based on the certainty equivalence principle and interpret the problem as one of CS under multiplicative uncertainties. We analyze the performance and efficacy of each of these data-driven approaches using a case study and compare them with their model-based counterparts.
Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation
Authors: Zhirui Liang, Qi Li, Joshua Comden, Andrey Bernstein, Yury Dvorkin
Date: 2024-08-05 00:00:00

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.
An Artificial Neural Network based approach for Harmonic Component Prediction in a Distribution Line
Authors: Dixant Bikal Sapkota, Puskar Neupane, Kajal Pokharel, Shahabuddin Khan
Date: 2024-08-05 00:00:00

With the increasing use of nonlinear devices in both generation and consumption of power, it is essential that we develop accurate and quick control for active filters to suppress harmonics. Time delays between input and output are catastrophic for such filters which rely on real-time operation. Artificial Neural Networks (ANNs) are capable of modeling complex nonlinear systems through adjustments in their learned parameters. Once properly trained, they can produce highly accurate predictions at an instantaneous time frame. Leveraging these qualities, various complex control systems may be replaced or aided by neural networks to provide quick and precise responses. This paper proposes an ANN-based approach for the prediction of individual harmonic components using minimal inputs. By extracting and analyzing the nature of harmonic component magnitudes obtained from the survey of a particular area through real-time measurements, a sequential pattern in their occurrence is observed. Various neural network architectures are trained using the collected data and their performances are evaluated. The best-performing model, whose losses are minimal, is then used to observe the harmonic cancellation for multiple unseen cases through a simplified simulation in hardware-in-the-loop. These neural network structures, which produce instantaneous and accurate outputs, are effective in harmonic filtering.
Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning
Authors: Hao Zhou, Chengming Hu, Dun Yuan, Ye Yuan, Di wu, Xue Liu, Zhu Han, Charlie Zhang
Date: 2024-08-05 00:00:00
Tasks: In-Context Learning

Generative artificial intelligence (GAI) is a promising technique towards 6G networks, and generative foundation models such as large language models (LLMs) have attracted considerable interest from academia and telecom industry. This work considers a novel edge-cloud deployment of foundation models in 6G networks. Specifically, it aims to minimize the service delay of foundation models by radio resource allocation and task offloading, i.e., offloading diverse content generation tasks to proper LLMs at the network edge or cloud. In particular, we first introduce the communication system model, i.e., allocating radio resources and calculating link capacity to support generated content transmission, and then we present the LLM inference model to calculate the delay of content generation. After that, we propose a novel in-context learning method to optimize the task offloading decisions. It utilizes LLM's inference capabilities, and avoids the difficulty of dedicated model training or fine-tuning as in conventional machine learning algorithms. Finally, the simulations demonstrate that the proposed edge-cloud deployment and in-context learning task offloading method can achieve satisfactory generation service quality without dedicated model training or fine-tuning.
Global Observability Analysis of a Growth Model for Insects Farming
Authors: Rania Tafat, Jaime A. Moreno, Stefan Streif
Date: 2024-08-05 00:00:00

The Hermetia illucens insects or the black soldier fly has been attracting a growing interest in the food and feed industry. For its high nutritional value on the one hand, and because it is an adequate species for insects in controlled environmental agriculture systems, on the other. Therefore, several models describing this larvae's behaviour have been developed in the literature. Due to the complex nature of living organisms, systems of controlled environment agriculture are characterised by their strong nonlinearities. In this paper, we present a three dimensional nonlinear model describing the black soldier fly dry biomass weight dynamic changes due to the temperature's influence. In practice, this biomass weight is not measured in real time. This becomes problematic for applying feedback control strategies that assume full information of the states. Thus, this work investigates the observability of the dry biomass of a Hermetia illucens farming batch. The instantaneous and global observability of the aforementioned model is proven by constructing an injective transformation between the state space and a higher dimensional space where the transformed states are observable.