pwc-archive/papers-with-abstracts huggingface dataset:
Minimizing Sensor Allocation Cost for Crowdsensing On-street Parking Availability
Authors: Boyu Pang, Ruizhi Liao, Yinyu Ye
Date: 2023-10-12 00:00:00

In recent years, innovative roadside parking vacancy crowdsensing solutions have emerged as a cost-effective alternative to traditional methods, which can significantly reduce sensor installation and maintenance expenses. This crowdsensing scheme relies on vehicles equipped with sensors, such as buses and taxis, roaming around urban streets to detect on-street parking availability. Therefore, the accuracy of this scheme strongly depends on the vehicles' routes and the frequency of their passage through parking spots. This paper presents an integer programming-based optimal sensor allocation model to ensure the detection accuracy of the scheme while using the minimum number of sensing kits or probing vehicles. Moreover, a customized heuristic algorithm is proposed to hasten the solution process. Numerical simulations using the street dataset from San Francisco confirm the model's ability to reduce probing vehicle usage while ensuring detection accuracy. Thus, our approach represents an effective means of optimizing roadside parking detection in a crowdsensing way.
CLExtract: Recovering Highly Corrupted DVB/GSE Satellite Stream with Contrastive Learning
Authors: Minghao Lin, Minghao Cheng, Dongsheng Luo, Yueqi Chen
Date: 2023-10-12 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Decoder

Since satellite systems are playing an increasingly important role in our civilization, their security and privacy weaknesses are more and more concerned. For example, prior work demonstrates that the communication channel between maritime VSAT and ground segment can be eavesdropped on using consumer-grade equipment. The stream decoder GSExtract developed in this prior work performs well for most packets but shows incapacity for corrupted streams. We discovered that such stream corruption commonly exists in not only Europe and North Atlantic areas but also Asian areas. In our experiment, using GSExtract, we are only able to decode 2.1\% satellite streams we eavesdropped on in Asia. Therefore, in this work, we propose to use a contrastive learning technique with data augmentation to decode and recover such highly corrupted streams. Rather than rely on critical information in corrupted streams to search for headers and perform decoding, contrastive learning directly learns the features of packet headers at different protocol layers and identifies them in a stream sequence. By filtering them out, we can extract the innermost data payload for further analysis. Our evaluation shows that this new approach can successfully recover 71-99\% eavesdropped data hundreds of times faster speed than GSExtract. Besides, the effectiveness of our approach is not largely damaged when stream corruption becomes more severe.
Causality-based Cost Allocation for Peer-to-Peer Energy Trading in Distribution System
Authors: Hyun Joong Kim, Yong Hyun Song, Jip Kim
Date: 2023-10-12 00:00:00
Tasks: energy trading

While peer-to-peer energy trading has the potential to harness the capabilities of small-scale energy resources, a peer-matching process often overlooks power grid conditions, yielding increased losses, line congestion, and voltage problems. This imposes a great challenge on the distribution system operator (DSO), which can eventually limit peer-to-peer energy trading. To align the peer-matching process with the physical grid conditions, this paper proposes a cost causality-based network cost allocation method and the grid-aware peer-matching process. Building on the cost causality principle, the proposed model utilizes the network cost (loss, congestion, and voltage) as a signal to encourage peers to adjust their preferences ensuring that matches are more in line with grid conditions, leading to enhanced social welfare. Additionally, this paper presents mathematical proof showing the superiority of the causality-based cost allocation over existing methods.
Online Feedback Optimization and Singular Perturbation via Contraction Theory
Authors: Liliaokeawawa Cothren, Francesco Bullo, Emiliano Dall'Anese
Date: 2023-10-12 00:00:00

In this paper, we provide a novel contraction-theoretic approach to analyze two-time scale systems, including those commonly encountered in Online Feedback Optimization (OFO). Our framework endows these systems with several robustness properties, enabling a more comprehensive characterization of their behaviors. The primary assumptions are the contractivity of the fast sub-system and the reduced model, along with an explicit upper bound on the time-scale parameter. For two-time scale systems subject to disturbances, we show that the distance between solutions of the nominal system and solutions of its reduced model is uniformly upper bounded by a function of contraction rates, Lipschitz constants, the time-scale parameter, and the variability of the disturbances over time. Applying these general results to the OFO context, we establish new individual tracking error bounds, showing that solutions converge to their time-varying optimizer, provided the plant and steady-state feedback controller exhibit contractivity and the controller gain is suitably bounded. Finally, we explore two special cases: for autonomous nonlinear systems, we derive sharper bounds than those in the general results, and for linear time-invariant systems, we present novel bounds based on induced matrix norms and induced matrix log norms.
Power Tracking Control of Heterogeneous Populations of TCLs with Partially Measured States
Authors: Zhenhe Zhang, Jun Zheng, Guchuan Zhu
Date: 2023-10-11 00:00:00

This paper presents a new aggregate power tracking control scheme for populations of thermostatically controlled loads (TCLs). The control design is performed in the framework of partial differential equations (PDEs) based on a late-lumping procedure without truncating the infinite-dimensional model describing the dynamics of the TCL population. An input-output linearization control scheme, which is independent of system parameters and uses only partial state measurement, is derived, and a sliding model-like control is applied to achieve finite-time input-to-state stability for tracking error dynamics. Such a control strategy can ensure robust performance in the presence of modeling uncertainties, while considerably reducing the communication burden in large scale distributed systems similar to that considered in the present work. A rigorous analysis of the closed-loop stability of the underlying PDE system was conducted, which guaranteed the validity of the developed control scheme. Simulation studies were performed while considering two TCL populations with a significant difference in their size, and the results show that the developed control scheme performs well in both cases, thereby confirming the effectiveness of the proposed solution.
Correction to Euler Lagrange Multirotor Model with Euler Angles Generalized Coordinates
Authors: Simone Martini, Kimon P. Valavanis, Margareta Stefanovic, Matthew J. Rutherford, Alessandro Rizzo
Date: 2023-10-11 00:00:00
Tasks: Position

This technical note proves analytically how the exact equivalence of the Newton-Euler and Euler-Lagrange modeling formulations as applied to multirotor UAVs is achieved. This is done by deriving a revised Euler-Lagrange multirotor attitude dynamics model. A review of the published literature reveals that the commonly adopted Euler-Lagrange multirotor dynamics model is equivalent to the Newton-Euler model only when it comes to the position dynamics, but not in the attitude dynamics. Step-by-step derivations and calculations are provided to show how modeling equivalence to the Newton-Euler formulation is proven. The modeling equivalence is then verified by obtaining identical results in numerical simulation studies. Simulation results also illustrate that when using the revised model for feedback linearization, controller stability at high gains is improved.
Agent-Based simulation reveals localized isolation key to saving lives and resources
Authors: Mintu Karmakar
Date: 2023-10-17 00:00:00

In the realm of pandemic dynamics, understanding the intricate interplay between disease transmission, interventions, and immunity is pivotal for effective control strategies. Through a rigorous agent-based computer simulation, we embarked on a comprehensive exploration, traversing unmitigated spread, lockdown scenarios, and the transformative potential of vaccination. We unveil that while quarantine unquestionably delays the pandemic peak, it does not act as an impenetrable barrier to halt the progression of infectious diseases. Vaccination factor revealed a potent weapon against outbreaks higher vaccination percentage not only delayed infection peaks but also substantially curtailed their impact. Our investigation into bond dilution below the percolation threshold presents an additional dimension to pandemic control. We observed that localized isolation through bond dilution offers a more resource-efficient targeted control strategy than blanket lockdowns or large-scale vaccination campaigns.
Universal characterization of epitope immunodominance from a multi-scale model of clonal competition in germinal centers
Authors: Federica Ferretti, Mehran Kardar
Date: 2023-10-17 00:00:00

We introduce a novel, multi-scale model for affinity maturation, which aims to capture the intra-clonal, inter-clonal and epitope-specific organization of the B cell population in a germinal center. We describe the evolution of the B cell population via a quasispecies dynamics, with species corresponding to unique B cell receptors (BCRs), where the desired multi-scale structure is reflected on the mutational connectivity of the accessible BCR space, and on the statistical properties of its fitness landscape. Within this mathematical framework, we study the competition among classes of BCRs targeting different antigen epitopes, and construct an effective \emph{immunogenic space} where epitope immunodominance relations can be universally characterized. We finally study how varying the relative composition of a mixture of antigens with variable and conserved domains allows for a parametric exploration of this space, and identify general principles for the rational design of two-antigen cocktails.
Noise driven phase transitions in eco-evolutionary systems
Authors: Jim Wu, David J. Schwab, Trevor GrandPre
Date: 2023-10-12 00:00:00

In complex ecosystems such as microbial communities, there is constant ecological and evolutionary feedback between the residing species and the environment occurring on concurrent timescales. Species respond and adapt to their surroundings by modifying their phenotypic traits, which in turn alters their environment and the resources available. To study this interplay between ecological and evolutionary mechanisms, we develop a consumer-resource model that incorporates phenotypic mutations. In the absence of noise, we find that phase transitions require finely-tuned interaction kernels. Additionally, we quantify the effects of noise on frequency dependent selection by defining a time-integrated mutation current, which accounts for the rate at which mutations and speciation occurs. We find three distinct phases: homogeneous, patterned, and patterned traveling waves. The last phase represents one way in which co-evolution of species can happen in a fluctuating environment. Our results highlight the principal roles that noise and non-reciprocal interactions between resources and consumers play in phase transitions within eco-evolutionary systems.
Characterizing the Conditions for Indefinite Growth in Open Chemical Reaction Networks
Authors: Shesha Gopal Marehalli Srinivas, Francesco Avanzini, Massimiliano Esposito
Date: 2023-10-12 00:00:00
Tasks: Mathematical Proofs

The thermodynamic and dynamical conditions necessary to observe indefinite growth in homogeneous open chemical reaction networks (CRNs) satisfying mass action kinetics were presented in Srinivas et al. (2023): Unimolecular CRNs can only accumulate equilibrium concentrations of species while multimolecular CRNs are needed to produce indefinite growth with nonequilibrium concentrations. Within multimolecular CRNs, pseudo-unimolecular CRNs produce nonequilibrium concentrations with zero efficiencies. Nonequilibrium growth with finite efficiencies requires dynamically nonlinear CRNs. In this paper, we provide a detailed analysis supporting these results. Mathematical proofs are provided for growth in unimolecular and pseudo-unimolecular CRNs. For multimolecular CRNs, four models displaying very distinctive topological properties are extensively studied, both numerically and partly analytically.
Thermodynamics of Growth in Open Chemical Reaction Networks
Authors: Shesha Gopal Marehalli Srinivas, Francesco Avanzini, Massimiliano Esposito
Date: 2023-10-12 00:00:00

We identify the thermodynamic conditions necessary to observe indefinite growth in homogeneous open chemical reaction networks (CRNs) satisfying mass action kinetics. We also characterize the thermodynamic efficiency of growth by considering the fraction of the chemical work supplied from the surroundings that is converted into CRN free energy. We find that indefinite growth cannot arise in CRNs chemostatted by fixing the concentration of some species at constant values, or in continuous-flow stirred tank reactors. Indefinite growth requires a constant net influx from the surroundings of at least one species. In this case, unimolecular CRNs always generate equilibrium linear growth, i.e., a continuous linear accumulation of species with equilibrium concentrations and efficiency one. Multimolecular CRNs are necessary to generate nonequilibrium growth, i.e., the continuous accumulation of species with nonequilibrium concentrations. Pseudo-unimolecular CRNs - a subclass of multimolecular CRNs - always generate asymptotic linear growth with zero efficiency. Our findings demonstrate the importance of the CRN topology and the chemostatting procedure in determining the dynamics and thermodynamics of growth.
Tree size distribution as the stationary limit of an evolutionary master equation
Authors: Szabolcs Kelemen, Máté Józsa, Tibor Hartel, György Csóka, Zoltán Néda
Date: 2023-10-14 00:00:00

The diameter distribution of a given species of deciduous trees in mature, temperate zone forests is well approximated by a Gamma distribution. Here we give new experimental evidence for this conjecture by analyzing deciduous tree size data in mature semi-natural forest and ancient, traditionally managed wood-pasture from Central Europe. These distribution functions collapse on a universal shape if the tree sizes are normalized to the mean value in the considered sample. A novel evolutionary master equation is used to model the observed distribution. The model incorporates three probabilistic processes: tree growth, mortality and diversification. By using simple, and realistic state dependent kernel functions for the growth and reset rates together with an assumed multiplicative dilution due to diversification, the stationary solution of the master equation yields the experimentally observed Gamma distribution. The model as it is formulated allows analytically compact solution and has only two fitting parameters whose values are consistent with the experimental data for the growth and reset processes. Our results suggest also that tree size statistics can be used to infer woodland naturalness.
Hybrid Quantum-Classical Machine Learning for Sentiment Analysis
Authors: Abu Kaisar Mohammad Masum, Anshul Maurya, Dhruthi Sridhar Murthy, Pratibha, Naveed Mahmud
Date: 2023-10-08 00:00:00
Tasks: Dimensionality Reduction, Sentiment Analysis

The collaboration between quantum computing and classical machine learning offers potential advantages in natural language processing, particularly in the sentiment analysis of human emotions and opinions expressed in large-scale datasets. In this work, we propose a methodology for sentiment analysis using hybrid quantum-classical machine learning algorithms. We investigate quantum kernel approaches and variational quantum circuit-based classifiers and integrate them with classical dimension reduction techniques such as PCA and Haar wavelet transform. The proposed methodology is evaluated using two distinct datasets, based on English and Bengali languages. Experimental results show that after dimensionality reduction of the data, performance of the quantum-based hybrid algorithms were consistent and better than classical methods.
Towards Emotion-Based Synthetic Consciousness: Using LLMs to Estimate Emotion Probability Vectors
Authors: David Sinclair, Willem Pye
Date: 2023-10-09 00:00:00
Tasks: Emotion Recognition

This paper shows how LLMs (Large Language Models) may be used to estimate a summary of the emotional state associated with piece of text. The summary of emotional state is a dictionary of words used to describe emotion together with the probability of the word appearing after a prompt comprising the original text and an emotion eliciting tail. Through emotion analysis of Amazon product reviews we demonstrate emotion descriptors can be mapped into a PCA type space. It was hoped that text descriptions of actions to improve a current text described state could also be elicited through a tail prompt. Experiment seemed to indicate that this is not straightforward to make work. This failure put our hoped for selection of action via choosing the best predict ed outcome via comparing emotional responses out of reach for the moment.
Creation Of A ChatBot Based On Natural Language Proccesing For Whatsapp
Authors: Valderrama Jonatan, Aguilar-Alonso Igor
Date: 2023-10-10 00:00:00
Tasks: Chatbot, Natural Language Understanding

In the era of digital transformation, customer service is of paramount importance to the success of organizations, and to meet the growing demand for immediate responses and personalized assistance 24 hours a day, chatbots have become a promising tool to solve these problems. Currently, there are many companies that need to provide these solutions to their customers, which motivates us to study this problem and offer a suitable solution. The objective of this study is to develop a chatbot based on natural language processing to improve customer satisfaction and improve the quality of service provided by the company through WhatsApp. The solution focuses on creating a chatbot that efficiently and effectively handles user queries. A literature review related to existing chatbots has been conducted, analyzing methodological approaches, artificial intelligence techniques and quality attributes used in the implementation of chatbots. The results found highlight that chatbots based on natural language processing enable fast and accurate responses, which improves the efficiency of customer service, as chatbots contribute to customer satisfaction by providing accurate answers and quick solutions to their queries at any time. Some authors point out that artificial intelligence techniques, such as machine learning, improve the learning and adaptability of chatbots as user interactions occur, so a good choice of appropriate natural language understanding technologies is essential for optimal chatbot performance. The results of this study will provide a solid foundation for the design and development of effective chatbots for customer service, ensuring a satisfactory user experience and thus meeting the needs of the organization.
LLMs as Potential Brainstorming Partners for Math and Science Problems
Authors: Sophia Gu
Date: 2023-10-10 00:00:00
Tasks: Math

With the recent rise of widely successful deep learning models, there is emerging interest among professionals in various math and science communities to see and evaluate the state-of-the-art models' abilities to collaborate on finding or solving problems that often require creativity and thus brainstorming. While a significant chasm still exists between current human-machine intellectual collaborations and the resolution of complex math and science problems, such as the six unsolved Millennium Prize Problems, our initial investigation into this matter reveals a promising step towards bridging the divide. This is due to the recent advancements in Large Language Models (LLMs). More specifically, we conduct comprehensive case studies to explore both the capabilities and limitations of the current state-of-the-art LLM, notably GPT-4, in collective brainstorming with humans.
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification
Authors: Jacopo Talpini, Fabio Sartori, Marco Savi
Date: 2023-09-05 00:00:00
Tasks: Active Learning, Classification, Intrusion Detection, Network Intrusion Detection, open-set classification, Out of Distribution (OOD) Detection, Uncertainty Quantification

The evolution of Internet and its related communication technologies have consistently increased the risk of cyber-attacks. In this context, a crucial role is played by Intrusion Detection Systems (IDSs), which are security devices designed to identify and mitigate attacks to modern networks. Data-driven approaches based on Machine Learning (ML) have gained more and more popularity for executing the classification tasks required by signature-based IDSs. However, typical ML models adopted for this purpose do not properly take into account the uncertainty associated with their prediction. This poses significant challenges, as they tend to produce misleadingly high classification scores for both misclassified inputs and inputs belonging to unknown classes (e.g. novel attacks), limiting the trustworthiness of existing ML-based solutions. In this paper, we argue that ML-based IDSs should always provide accurate uncertainty quantification to avoid overconfident predictions. In fact, an uncertainty-aware classification would be beneficial to enhance closed-set classification performance, would make it possible to carry out Active Learning, and would help recognize inputs of unknown classes as truly unknowns, unlocking open-set classification capabilities and Out-of-Distribution (OoD) detection. To verify it, we compare various ML-based methods for uncertainty quantification and for open-set classification, either specifically designed for or tailored to the domain of network intrusion detection. Moreover, we develop a custom model based on Bayesian Neural Networks to ensure reliable uncertainty estimates and improve the OoD detection capabilities, thus showing how proper uncertainty quantification can be exploited to significantly enhance the trustworthiness of ML-based IDSs.