pwc-archive/papers-with-abstracts huggingface dataset:
Energy Optimized Congestion Control-Based Temperature Aware Routing Algorithm for Software Defined Wireless Body Area Networks
Authors: Omar Ahmed, Fuji Ren, Ammar Hawbani, Yaser Al-Sharabi
Date: 2020-02-27 00:00:00
Proceeding: journal 2020 2

Wireless Body Area Network (WBAN) is a promising cost-effective technology for the privacy confined military applications and healthcare applications like remote health monitoring, telemedicine, and e-health services. The use of a Software-Defined Network (SDN) approach improves the control and management processes of the complex structured WBANs and also provides higher flexibility and dynamic network structure. To seamless routing performance in SDN-based WBAN, the energy-efficiency problems must be tackled effectively. The main contribution of this paper is to develop a novel Energy Optimized Congestion Control based on Temperature Aware Routing Algorithm (EOCC-TARA) using Enhanced Multi-objective Spider Monkey Optimization (EMSMO) for SDN-based WBAN. This algorithm overcomes the vital challenges, namely energy-efficiency, congestion-free communication, and reducing adverse thermal effects in WBAN routing. First, the proposed EOCC-TARA routing algorithm considers the effects of temperature due to the thermal dissipation of sensor nodes and formulates a strategy to adaptively select the forwarding nodes based on temperature and energy. Then the congestion avoidance concept is added with the energy-efficiency, link reliability, and path loss for modeling the cost function based on which the EMSMO provides the optimal routing. Simulations were performed, and the evaluation results showed that the proposed EOCC-TARA routing algorithm has superior performance than the traditional routing approaches in terms of energy consumption, network lifetime, throughput, temperature control, congestion overhead, delay, and successful transmission rate.
H2O Open Ecosystem for State-of-the-art Large Language Models
Authors: Arno Candel, Jon McKinney, Philipp Singer, Pascal Pfeiffer, Maximilian Jeblick, Chun Ming Lee, Marcos V. Conde
Date: 2023-10-17 00:00:00

Large Language Models (LLMs) represent a revolution in AI. However, they also pose many significant risks, such as the presence of biased, private, copyrighted or harmful text. For this reason we need open, transparent and safe solutions. We introduce a complete open-source ecosystem for developing and testing LLMs. The goal of this project is to boost open alternatives to closed-source approaches. We release h2oGPT, a family of fine-tuned LLMs of diverse sizes. We also introduce H2O LLM Studio, a framework and no-code GUI designed for efficient fine-tuning, evaluation, and deployment of LLMs using the most recent state-of-the-art techniques. Our code and models are fully open-source. We believe this work helps to boost AI development and make it more accessible, efficient and trustworthy. The demo is available at: https://gpt.h2o.ai/
Iterative Prediction-and-Optimization for E-Logistics Distribution Network Design
Authors: Junming Liu, Weiwei Chen, Jingyuan Yang, Hui Xiong, Can Chen
Date: 2021-09-27 00:00:00
Proceeding: INFORMS Journal 2021 9

The emergence of online retailers has brought new opportunities to the design
of their distribution networks. Notably, for online retailers that do not operate offline
stores, their target customers are more sensitive to the quality of logistic services, such as
delivery speed and reliability. This paper is motivated by a leading online retailer for cosmetic products on Taobao.com that aimed to improve its logistics efficiency by redesigning
its centralized distribution network into a multilevel one. The multilevel distribution network consists of a layer of primary facilities to hold stocks from suppliers and transshipment and a layer of secondary facilities to provide last-mile delivery. There are two major
challenges of designing such a facility network. First, online customers can respond significantly to the change of logistics efficiency with the redesigned network, thereby rendering
the network optimized under the original demand distribution suboptimal. Second, because
online retailers have relatively small sales volumes and are very flexible in choosing facility
locations, the facility candidate set can be large, causing the facility location optimization
challenging to solve. To this end, we propose an iterative prediction-and-optimization strategy for distribution network design. Specifically, we first develop an artificial neural network (ANN) to predict customer demands, factoring in the logistic service quality given the
network and the city-level purchasing power based on demographic statistics. Then, a
mixed integer linear programming (MILP) model is formulated to choose facility locations
with minimum transportation, facility setup, and package processing costs. We further develop an efficient two-stage heuristic for computing high-quality solutions to the MILP
model, featuring an agglomerative hierarchical clustering algorithm and an expectation and
maximization algorithm. Subsequently, the ANN demand predictor and two-stage heuristic
are integrated for iterative network design. Finally, using a real-world data set, we validate
the demand prediction accuracy and demonstrate the mutual interdependence between the
demand and network design.
Summary of Contribution: We propose an iterative prediction-and-optimization algorithm for multilevel distribution network design for e-logistics and evaluate its operational
value for online retailers. We address the issue of the interplay between distribution network design and the demand distribution using an iterative framework. Further, combining the idea in operational research and data mining, our paper provides an end-to-end solution that can provide accurate predictions of online sales distribution, subsequently
solving large-scale optimization problems for distribution network design problems
Progressively Efficient Learning
Authors: Ruijie Zheng, Khanh Nguyen, Hal Daumé III, Furong Huang, Karthik Narasimhan
Date: 2023-10-13 00:00:00
Tasks: Decision Making, Imitation Learning, Minecraft

Assistant AI agents should be capable of rapidly acquiring novel skills and adapting to new user preferences. Traditional frameworks like imitation learning and reinforcement learning do not facilitate this capability because they support only low-level, inefficient forms of communication. In contrast, humans communicate with progressive efficiency by defining and sharing abstract intentions. Reproducing similar capability in AI agents, we develop a novel learning framework named Communication-Efficient Interactive Learning (CEIL). By equipping a learning agent with an abstract, dynamic language and an intrinsic motivation to learn with minimal communication effort, CEIL leads to emergence of a human-like pattern where the learner and the teacher communicate progressively efficiently by exchanging increasingly more abstract intentions. CEIL demonstrates impressive performance and communication efficiency on a 2D MineCraft domain featuring long-horizon decision-making tasks. Agents trained with CEIL quickly master new tasks, outperforming non-hierarchical and hierarchical imitation learning by up to 50% and 20% in absolute success rate, respectively, given the same number of interactions with the teacher. Especially, the framework performs robustly with teachers modeled after human pragmatic communication behavior.
Active Sensing for Localization with Reconfigurable Intelligent Surface
Authors: Zhongze Zhang, Tao Jiang, Wei Yu
Date: 2023-10-19 00:00:00

This paper addresses an uplink localization problem in which the base station (BS) aims to locate a remote user with the aid of reconfigurable intelligent surface (RIS). This paper proposes a strategy in which the user transmits pilots over multiple time frames, and the BS adaptively adjusts the RIS reflection coefficients based on the observations already received so far in order to produce an accurate estimate of the user location at the end. This is a challenging active sensing problem for which finding an optimal solution involves a search through a complicated functional space whose dimension increases with the number of measurements. In this paper, we show that the long short-term memory (LSTM) network can be used to exploit the latent temporal correlation between measurements to automatically construct scalable information vectors (called hidden state) based on the measurements. Subsequently, the state vector can be mapped to the RIS configuration for the next time frame in a codebook-free fashion via a deep neural network (DNN). After all the measurements have been received, a final DNN can be used to map the LSTM cell state to the estimated user equipment (UE) position. Numerical result shows that the proposed active RIS design results in lower localization error as compared to existing active and nonactive methods. The proposed solution produces interpretable results and is generalizable to early stopping in the sequence of sensing stages.
Foundational Techniques for Wireless Communications: Channel Coding, Modulation, and Equalization
Authors: Solomon McKiernan
Date: 2023-10-20 00:00:00

This paper analyses foundational techniques for improving wireless communication systems, including coding methods, modulation schemes, and channel equalization. Using industry-standard simulation tools, the paper evaluates the performance of these techniques under different channel conditions. Convolutional codes, punctured and unpunctured, are assessed for reliable data transfer. The suitability of various modulation schemes, such as Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM), are examined. Linear and decision-feedback equalization techniques are evaluated for mitigating the effects of channel impairments. The paper provides practical insights into the implementation of these techniques, emphasizing their importance in modern wireless communication systems.
Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching
Authors: Junliang Luo, Yi Tian Xu, Di wu, Michael Jenkin, Xue Liu, Gregory Dudek
Date: 2023-10-05 00:00:00

Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental and regulatory concerns, and potential energy crises arising from geopolitical tensions. In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics. We use a multilayer perceptron (MLP) given each state-action pair to predict the power consumption to approximate the value function in ADP for selecting the action with optimal expected power saved. To save the largest possible power consumption without deteriorating QoS, we include another MLP to predict QoS and a long short-term memory (LSTM) for predicting handovers, incorporated into an online optimization algorithm producing an adaptive QoS threshold for filtering cell switching actions based on the overall QoS history. The performance of the method is evaluated using a practical network simulator with various real-world scenarios with dynamic traffic patterns.
Metacognitive threshold: a computational account
Authors: Brendan Conway-Smith, Robert L. West
Date: 2023-10-14 00:00:00

This paper will explore ways of computationally accounting for the metacognitive threshold -- the minimum amount of stimulus needed for a mental state to be perceived -- and discuss potential cognitive mechanisms by which this threshold can be influenced through metacognitive training and meditation.
Neural domain alignment for spoken language recognition based on optimal transport
Authors: Xugang Lu, Peng Shen, Yu Tsao, Hisashi Kawai
Date: 2023-10-20 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Domain shift poses a significant challenge in cross-domain spoken language recognition (SLR) by reducing its effectiveness. Unsupervised domain adaptation (UDA) algorithms have been explored to address domain shifts in SLR without relying on class labels in the target domain. One successful UDA approach focuses on learning domain-invariant representations to align feature distributions between domains. However, disregarding the class structure during the learning process of domain-invariant representations can result in over-alignment, negatively impacting the classification task. To overcome this limitation, we propose an optimal transport (OT)-based UDA algorithm for a cross-domain SLR, leveraging the distribution geometry structure-aware property of OT. An OT-based discrepancy measure on a joint distribution over feature and label information is considered during domain alignment in OT-based UDA. Our previous study discovered that completely aligning the distributions between the source and target domains can introduce a negative transfer, where classes or irrelevant classes from the source domain map to a different class in the target domain during distribution alignment. This negative transfer degrades the performance of the adaptive model. To mitigate this issue, we introduce coupling-weighted partial optimal transport (POT) within our UDA framework for SLR, where soft weighting on the OT coupling based on transport cost is adaptively set during domain alignment. A cross-domain SLR task was used in the experiments to evaluate the proposed UDA. The results demonstrated that our proposed UDA algorithm significantly improved the performance over existing UDA algorithms in a cross-channel SLR task.
Adaptive Robust Control Contraction Metrics: Transient Bounds in Adaptive Control with Unmatched Uncertainties
Authors: Samuel G. Gessow, Brett T. Lopez
Date: 2023-10-20 00:00:00
Tasks: Model Predictive Control

This work presents a new sufficient condition for synthesizing nonlinear controllers that yield bounded closed-loop tracking error transients despite the presence of unmatched uncertainties that are concurrently being learned online. The approach utilizes contraction theory and addresses fundamental limitations of existing approaches by allowing the contraction metric to depend on the unknown model parameters. This allows the controller to incorporate new model estimates generated online without sacrificing its strong convergence and bounded transients guarantees. The approach is specifically designed for trajectory tracking so the approach is more broadly applicable to adaptive model predictive control as well. Simulation results on a nonlinear system with unmatched uncertainties demonstrates the approach.
Open-Loop Control Co-Design of Semisubmersible Floating Offshore Wind Turbines using Linear Parameter-Varying Models
Authors: Athul Krishna Sundarrajan, Yong Hoon Lee, James T Allison, Daniel Zalkind, Daniel Herber
Date: 2023-10-20 00:00:00

This paper discusses a framework to design elements of the plant and control systems for floating offshore wind turbines in an integrated manner using linear parameter-varying models. Multiple linearized models derived from aeroelastic simulation software in different operating regions characterized by the incoming wind speed are combined to construct an approximate low-fidelity model of the system. The combined model is then used to generate open-loop, optimal control trajectories as part of a nested control co-design strategy that explores the system's power production and stability using the platform pitch tilt as a proxy in the context of crucial plant and control design decisions. The radial distance between the central and outer columns and the diameter of the outer columns of the semisubmersible platform are the plant design variables. The platform stability and power production are studied for different plant design decisions. The effect of plant decisions on subsequent power production and stability response of the floating wind turbine is quantified in terms of the levelized cost of energy. The results show that the inner-loop constraints and the plant design decisions affect the turbine's power and, subsequently, the cost of the system.
Cooperative Multi-Agent Deep Reinforcement Learning for Adaptive Decentralized Emergency Voltage Control
Authors: Ying Zhang, Meng Yue
Date: 2023-10-20 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning

Under voltage load shedding (UVLS) for power grid emergency control builds the last defensive perimeter to prevent cascade outages and blackouts in case of contingencies. This letter proposes a novel cooperative multi-agent deep reinforcement learning (MADRL)-based UVLS algorithm in an adaptive decentralized way. With well-designed input signals reflecting the voltage deviation, newly structured neural networks are developed as intelligent agents to obtain control actions and their probabilities to accommodate high uncertainties in volatile power system operations. Moreover, the interaction among the agents for coordinated control is implemented and refined by a state-of-the-art attention mechanism, which helps agents concentratively learn effective interacted information. The proposed method realizes decentralized coordinated control, adapting to extremely high uncertainties. Case studies on an IEEE benchmark system indicate the superior performance of the proposed algorithm.
Distributed Optimal Coverage Control in Multi-agent Systems: Known and Unknown Environments
Authors: Mohammadhasan Faghihi, Meysam Yadegar, Mohammadhosein Bakhtiaridoust, Nader Meskin, Javad Sharifi, Peng Shi
Date: 2023-10-20 00:00:00

This paper introduces a novel approach to solve the coverage optimization problem in multi-agent systems. The proposed technique offers an optimal solution with a lower cost with respect to conventional Voronoi-based techniques by effectively handling the issue of agents remaining stationary in regions void of information using a ranking function. The proposed approach leverages a novel cost function for optimizing the agents coverage and the cost function eventually aligns with the conventional Voronoi-based cost function. Theoretical analyses are conducted to assure the asymptotic convergence of agents towards the optimal configuration. A distinguishing feature of this approach lies in its departure from the reliance on geometric methods that are characteristic of Voronoi-based approaches; hence can be implemented more simply. Remarkably, the technique is adaptive and applicable to various environments with both known and unknown information distributions. Lastly, the efficacy of the proposed method is demonstrated through simulations, and the obtained results are compared with those of Voronoi-based algorithms.
Distributed Continuous-Time Optimization with Uncertain Time-Varying Quadratic Cost Functions
Authors: Liangze Jiang, Zheng-Guang Wu, Lei Wang
Date: 2023-10-20 00:00:00
Tasks: Distributed Optimization

This paper studies distributed continuous-time optimization for time-varying quadratic cost functions with uncertain parameters. We first propose a centralized adaptive optimization algorithm using partial information of the cost function. It can be seen that even if there are uncertain parameters in the cost function, exact optimization can still be achieved. To solve this problem in a distributed manner when different local cost functions have identical Hessians, we propose a novel distributed algorithm that cascades the fixed-time average estimator and the distributed optimizer. We remove the requirement for the upper bounds of certain complex functions by integrating state-based gains in the proposed design. We further extend this result to address the distributed optimization where the time-varying cost functions have nonidentical Hessians. We prove the convergence of all the proposed algorithms in the global sense. Numerical examples verify the proposed algorithms.
Secure Event-Triggered Control for Vehicle Platooning in the Presence of Modification Attacks
Authors: Ali Nikoutadbir, Sajjad Torabi, Sadegh Bolouki
Date: 2023-10-20 00:00:00

This paper addresses the problem of achieving secure consensus in a vehicular platoon using event-triggered control. The platoon consists of a leader and multiple follower vehicles exchanging their position and velocity information discretely to maintain stability. The paper focuses on the issue of gain modification attacks, where a malicious actor attempts to alter the controller gains to destabilize the platoon. To tackle this problem, the paper proposes a resilient event-triggered control scheme that ensures secure consensus while considering constraints on the duration and frequency of attacks. The paper also introduces an attack mitigation strategy through a topology-switching procedure, which is employed when the attack frequency and duration constraints are not met. First, sufficient consensus conditions for distributed static and dynamic event-triggered control schemes are derived under sequential gain modification attacks. The impact of the system matrices and triggering parameters on the attack constraints is also discussed. Second, the paper derives conditions to broaden the range of controller gain stability using the Schur stability criterion to mitigate attacks and maintain platoon stability. Finally, the effectiveness of the proposed methodology is demonstrated through simulation scenarios in various case studies.
Electrical Fault Localisation Over a Distributed Parameter Transmission Line
Authors: Daniel Selvaratnam, Amritam Das, Henrik Sandberg
Date: 2023-10-20 00:00:00
Tasks: parameter estimation

Motivated by the need to localise faults along electrical power lines, this paper adopts a frequency-domain approach to parameter estimation for an infinite-dimensional linear dynamical system with one spatial variable. Since the time of the fault is unknown, and voltages and currents are measured at only one end of the line, distance information must be extracted from the post-fault transients. To properly account for high-frequency transient behaviour, the line dynamics is modelled directly by the Telegrapher's equation, rather than the more commonly used lumped-parameter approximations. First, the governing equations are non-dimensionalised to avoid ill-conditioning. A closed-form expression for the transfer function is then derived. Finally, nonlinear least-squares optimisation is employed to search for the fault location. Requirements on fault bandwidth, sensor bandwidth and simulation time-step are also presented. The result is a novel end-to-end algorithm for data generation and fault localisation, the effectiveness of which is demonstrated via simulation.