pwc-archive/papers-with-abstracts huggingface dataset:
Large-Area Conductor-Loaded PDMS Dielectric Composites for High-Sensitivity Wireless and Chipless Electromagnetic Temperature Sensors
Authors: Benjamin King, Nikolas Bruce, Mahmoud Wagih
Date: 2024-09-05 00:00:00
Tasks: Sensitivity

Wireless electromagnetic sensing is a passive, non-destructive technique for measuring physical and chemical changes through permittivity or conductivity changes. However, the readout is limited by the sensitivity of the materials, often requiring complex sampling, particularly in the GHz range. We report capacitive dielectric temperature sensors based on polydimethylsiloxne (PDMS) loaded with 10 vol% of inexpensive, commercially available conductive fillers including copper powder (Cu), graphite powder (GP) and milled carbon fibre powder (CF). The sensors are tested in the range of 20{\deg}C to 110{\deg}C, with enhanced sensitivity from 20 to 60{\deg}C, and relative response of up to 85.5% at 200 MHz for CF loaded capacitors. Additionally, we demonstrate that operating frequency influences the relative sensing response by as much as 15.0% in loaded composite capacitors. Finally, we demonstrate the suitability of PDMS-CF capacitors as a sensing element in wirelessly coupled chipless resonant coils tuned to 6.78 MHz with a readout response, in the resonant frequency of the sensor. The wireless readout of the PDMS-CF chipless system exhibited an average sensitivity of 0.38 %{\deg}C-1 which is a 40x improvement over a pristine PDMS-based capacitive sensor and outperforms state of the art frequency-domain radio frequency (RF) temperature sensors including carbon-based composites at higher loadings. Exploiting the high sensitivity, we interrogate the sensor wirelessly using a low-cost and portable open-source NanoVNA demonstrating a relative response in the resonant frequency of the reader coil of 48.5%, with the response in good agreement with the instrumentation-grade vector network analyzers (VNAs), demonstrating that the sensors could be integrated into an inexpensive and portable measurement setup that does not rely on specialty equipment or highly trained operators.
Optimal position-building strategies in competition
Authors: Neil A. Chriss
Date: 2024-09-05 00:00:00
Tasks: Position

This paper develops a mathematical framework for building a position in a stock over a fixed period of time while in competition with one or more other traders doing the same thing. We develop a game-theoretic framework that takes place in the space of trading strategies where action sets are trading strategies and traders try to devise best-response strategies to their adversaries. In this setup trading is guided by a desire to minimize the total cost of trading arising from a mixture of temporary and permanent market impact caused by the aggregate level of trading including the trader and the competition. We describe a notion of equilibrium strategies, show that they exist and provide closed-form solutions.
Dynamic Hybrid Beamforming Designs for ELAA Near-Field Communications
Authors: Mengzhen Liu, Ming Li, Rang Liu, Qian Liu
Date: 2024-09-05 00:00:00

Extremely large-scale antenna array (ELAA) is a key candidate technology for the sixth generation (6G) mobile networks. Nevertheless, using substantial numbers of antennas to transmit high-frequency signals in ELAA systems significantly exacerbates the near-field effect. Unfortunately, traditional hybrid beamforming schemes are highly vulnerable to ELAA near-field communications. To effectively mitigate severe near-field effect, we propose a novel dynamic hybrid beamforming architecture for ELAA systems, in which each antenna is either adaptively connected to one radio frequency (RF) chain for signal transmission or deactivated for power saving. For the case that instantaneous channel state information (CSI) is available during each channel coherence time, a real-time dynamic hybrid beamforming design is developed to maximize the achievable sum rate under the constraints of the constant modulus of phase-shifters (PSs), non-overlapping dynamic connection network and total transmit power. When instantaneous CSI cannot be easily obtained in real-time, we propose a two-timescale dynamic hybrid beamforming design, which optimizes analog beamformer in long-timescale and digital beamformer in short-timescale, with the goal of maximizing ergodic sum-rate under the same constraints. Simulation results demonstrate the advantages of the proposed dynamic hybrid beamforming architecture and the effectiveness of the developed algorithms for ELAA near-field communications.
Robust synchronization and policy adaptation for networked heterogeneous agents
Authors: Miguel F. Arevalo-Castiblanco, Eduardo Mojica-Nava and, César A. Uribe
Date: 2024-09-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a robust adaptive online synchronization method for leader-follower networks of nonlinear heterogeneous agents with system uncertainties and input magnitude saturation. Synchronization is achieved using a Distributed input Magnitude Saturation Adaptive Control with Reinforcement Learning (DMSAC-RL), which improves the empirical performance of policies trained on off-the-shelf models using Reinforcement Learning (RL) strategies. The leader observes the performance of a reference model, and followers observe the states and actions of the agents they are connected to, but not the reference model. The leader and followers may differ from the reference model in which the RL control policy was trained. DMSAC-RL uses an internal loop that adjusts the learned policy for the agents in the form of augmented input to solve the distributed control problem, including input-matched uncertainty parameters. We show that the synchronization error of the heterogeneous network is Uniformly Ultimately Bounded (UUB). Numerical analysis of a network of Multiple Input Multiple Output (MIMO) systems supports our theoretical findings.
Reinforcement-Learning-Enabled Beam Alignment for Water-Air Direct Optical Wireless Communications
Authors: Jiayue Liu, Tianqi Mao, Dongxuan He, Yang Yang, Zhen Gao, Dezhi Zheng, Jun Zhang
Date: 2024-09-05 00:00:00
Tasks: Deep Reinforcement Learning

The escalating interests on underwater exploration/reconnaissance applications have motivated high-rate data transmission from underwater to airborne relaying platforms, especially under high-sea scenarios. Thanks to its broad bandwidth and superior confidentiality, Optical wireless communication has become one promising candidate for water-air transmission. However, the optical signals inevitably suffer from deviations when crossing the highly-dynamic water-air interfaces in the absence of relaying ships/buoys. To address the issue, this article proposes one novel beam alignment strategy based on deep reinforcement learning (DRL) for water-air direct optical wireless communications. Specifically, the dynamic water-air interface is mathematically modeled using sea-wave spectrum analysis, followed by characterization of the propagation channel with ray-tracing techniques. Then the deep deterministic policy gradient (DDPG) scheme is introduced for DRL-based transceiving beam alignment. A logarithm-exponential (LE) nonlinear reward function with respect to the received signal strength is designed for high-resolution rewarding between different actions. Simulation results validate the superiority of the proposed DRL-based beam alignment scheme.
A multi-scale analysis of the CzrA transcription repressor highlights the allosteric changes induced by metal ion binding
Authors: Marta Rigoli, Raffaello Potestio, Roberto Menichetti
Date: 2024-09-05 00:00:00

Allosteric regulation is a widespread strategy employed by several proteins to transduce chemical signals and perform biological functions. Metal sensor proteins are exemplary in this respect, e.g., in that they selectively bind and unbind DNA depending on the state of a distal ion coordination site. In this work, we carry out an investigation of the structural and mechanical properties of the CzrA transcription repressor through the analysis of microsecond-long molecular dynamics (MD) trajectories; the latter are processed through the mapping entropy optimisation workflow (MEOW), a recently developed information-theoretical method that highlights, in an unsupervised manner, residues of particular mechanical, functional, and biological importance. This approach allows us to unveil how differences in the properties of the molecule are controlled by the state of the zinc coordination site, with particular attention to the DNA binding region. These changes correlate with a redistribution of the conformational variability of the residues throughout the molecule, in spite of an overall consistency of its architecture in the two (ion-bound and free) coordination states. The results of this work corroborate previous studies, provide novel insight into the fine details of the mechanics of CzrA, and showcase the MEOW approach as a novel instrument for the study of allosteric regulation and other processes in proteins through the analysis of plain MD simulations.
CrowdGrader: Crowdsourcing the Evaluation of Homework Assignments
Authors: Luca de Alfaro, Michael Shavlovsky
Date: 2013-08-24 00:00:00

Crowdsourcing offers a practical method for ranking and scoring large amounts of items. To investigate the algorithms and incentives that can be used in crowdsourcing quality evaluations, we built CrowdGrader, a tool that lets students submit and collaboratively grade solutions to homework assignments. We present the algorithms and techniques used in CrowdGrader, and we describe our results and experience in using the tool for several computer-science assignments. CrowdGrader combines the student-provided grades into a consensus grade for each submission using a novel crowdsourcing algorithm that relies on a reputation system. The algorithm iterativerly refines inter-dependent estimates of the consensus grades, and of the grading accuracy of each student. On synthetic data, the algorithm performs better than alternatives not based on reputation. On our preliminary experimental data, the performance seems dependent on the nature of review errors, with errors that can be ascribed to the reviewer being more tractable than those arising from random external events. To provide an incentive for reviewers, the grade each student receives in an assignment is a combination of the consensus grade received by their submissions, and of a reviewing grade capturing their reviewing effort and accuracy. This incentive worked well in practice.
Reconstructing unsteady flows from sparse, noisy measurements with a physics-constrained convolutional neural network
Authors: Yaxin Mo, Luca Magri
Date: 2024-08-30 00:00:00

Data from fluid flow measurements are typically sparse, noisy, and heterogeneous, often from mixed pressure and velocity measurements, resulting in incomplete datasets. In this paper, we develop a physics-constrained convolutional neural network, which is a deterministic tool, to reconstruct the full flow field from incomplete data. We explore three loss functions, both from machine learning literature and newly proposed: (i) the softly-constrained loss, which allows the prediction to take any value; (ii) the snapshot-enforced loss, which constrains the prediction at the sensor locations; and (iii) the mean-enforced loss, which constrains the mean of the prediction at the sensor locations. The proposed methods do not require the full flow field during training, making it suitable for reconstruction from incomplete data. We apply the method to reconstruct a laminar wake of a bluff body and a turbulent Kolmogorov flow. First, we assume that measurements are not noisy and reconstruct both the laminar wake and the Kolmogorov flow from sensors located at fewer than 1% of all grid points. The snapshot-enforced loss reduces the reconstruction error of the Kolmogorov flow by approximately 25% compared to the softly-constrained loss. Second, we assume that measurements are noisy and propose the mean-enforced loss to reconstruct the laminar wake and the Kolmogorov flow at three different signal-to-noise ratios. We find that, across the ratios tested, the loss functions with harder constraints are more robust to both the random initialization of the networks and the noise levels in the measurements. At high noise levels, the mean-enforced loss can recover the instantaneous snapshots accurately, making it the suitable choice when reconstructing flows from data corrupted with an unknown amount of noise. The proposed method opens opportunities for physical flow reconstruction from sparse, noisy data.
Force-Limited Control of Wave Energy Converters using a Describing Function Linearization
Authors: Rebecca McCabe, Maha Haji
Date: 2024-09-04 00:00:00

Actuator saturation is a common nonlinearity. In wave energy conversion, force saturation conveniently limits drivetrain size and cost with minimal impact on energy generation. However, such nonlinear dynamics typically demand numerical simulation, which increases computational cost and diminishes intuition. This paper instead uses describing functions to approximate a force saturation nonlinearity as a linear impedance mismatch. In the frequency domain, the impact of controller impedance mismatch (such as force limit, finite bandwidth, or parameter error) on electrical power production is shown analytically and graphically for a generic nondimensionalized single degree of freedom wave energy converter in regular waves. Results are visualized with Smith charts. Notably, systems with a specific ratio of reactive to real mechanical impedance are least sensitive to force limits, a criteria which conflicts with resonance and bandwidth considerations. The describing function method shows promise to enable future studies such as large-scale design optimization and co-design.
OSL𝛼: Online Structure Learning Using Background Knowledge Axiomatization
Authors: Evangelos Michelioudakis, Anastasios Skarlatidis, Georgios Paliouras, Alexander Artikis
Date: 2016-09-04 00:00:00
Proceeding: European Conference on Machine Learning and Knowledge Discovery in Databases 2016 9
Tasks: Activity Recognition, Relational Reasoning

We present OSL𝛼 — an online structure learner for Markov Logic Networks (MLNs) that exploits background knowledge axiomatization in order to constrain the space of possible structures. Many domains of interest are characterized by uncertainty and complex relational structure. MLNs is a state-of-the-art Statistical Relational Learning framework that can naturally be applied to domains governed by these characteristics. Learning MLNs from data is challenging, as their relational structure increases the complexity of the learning process. In addition, due to the dynamic nature of many real-world applications, it is desirable to incrementally learn or revise the model’s structure and parameters. Experimental results are presented in activity recognition using a probabilistic variant of the Event Calculus (MLN−EC) as background knowledge and a benchmark dataset for video surveillance.
Online Semi-Supervised Learning of Composite Event Rules by Combining Structure and Mass-Based Predicate Similarity
Authors: Evangelos Michelioudakis, Alexander Artikis, Georgios Paliouras
Date: 2023-12-15 00:00:00
Proceeding: Machine Learning 2023 12
Tasks: Activity Recognition, feature selection, graph construction, Human Activity Recognition, Missing Labels

Symbolic event recognition systems detect event occurrences using first-order logic rules. Although existing online structure learning approaches ease the discovery of such rules in noisy data streams, they assume the existence of fully labelled training data. SPLICE is a recent online graph-based approach that estimates the labels of unlabelled data and makes it possible to learn such rules from semi-supervised training sequences of logical interpretations. However, SPLICE labelling depends significantly on the metric used to compute the distances of unlabelled examples to their labelled counterparts. Moreover, there is no guarantee about the quality of the labelling found in the local graphs that are built as the data stream in. In this paper, we propose a new online learning method, which includes an enhanced hybrid measure that combines an optimised structural distance, and a data-driven one. The former is guided by feature selection targeted to kNN classification, while the latter is a mass-based dissimilarity. Additionally, the enhanced SPLICE method, improves the graph construction process, by storing a synopsis of the past, in order to achieve more informed labelling on the local graphs. We evaluate our approach by learning Event Calculus theories for the tasks of human activity recognition, maritime monitoring, and fleet management. The evaluation suggests that our approach outperforms its predecessor, in terms of inferring the missing labels and improving the predictive accuracy of the underlying structure learning system.
A Framework to Evaluate Early Time-Series Classification Algorithms
Authors: Charilaos Akasiadis, Evgenios Kladis, Petro-Foti Kamberi, Evangelos Michelioudakis, Elias Alevizos, Alexander Artikis
Date: 2024-03-25 00:00:00
Proceeding: Extending Database Technology 2024 3
Tasks: Classification, Time Series, Time Series Classification

Early Time-Series Classification (ETSC) is the task of predicting the class of incoming time-series by observing as few measurements as possible. Such methods can be employed to obtain classification forecasts in many time-critical applications. However, available techniques are not equally suitable for every problem, since differentiations in the data characteristics can impact performance in terms of earliness, accuracy, F1-score, or training time. We evaluate five existing ETSC algorithms on publicly available data, as well as on two newly introduced datasets originating from the life sciences and maritime domains. Existing ETSC algorithms are also compared against a method that selectively truncates time-series and incorporates state-of-the-art algorithms for full time-series classification. Our main goal is to provide a framework for the evaluation and comparison of ETSC algorithms and to obtain intuition on how such approaches perform on real-life applications. The presented framework can serve as a benchmark for new related approaches.
Online Learning of Weighted Relational Rules for Complex Event Recognition
Authors: Nikos Katzouris, Evangelos Michelioudakis, Alexander Artikis, Georgios Paliouras
Date: 2019-01-23 00:00:00
Proceeding: European Conference on Machine Learning and Knowledge Discovery in Databases 2019 1
Tasks: Activity Recognition

Systems for symbolic complex event recognition detect occurrences of events in time using a set of event definitions in the form of logical rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing, among others, connections to techniques for learning such rules from data. We advance the state-of-the-art by combining an existing online algorithm for learning crisp relational structure with an online method for weight learning in Markov Logic Networks (MLN). The result is an algorithm that learns complex event patterns in the form of Event Calculus theories in the MLN semantics. We evaluate our approach on a challenging real-world application for activity recognition and show that it outperforms both its crisp predecessor and competing online MLN learners in terms of predictive performance, at the price of a small increase in training time.
TDv2: A Novel Tree-Structured Decoder for Offline Mathematical Expression Recognition
Authors: Changjie Wu, Jun Du, Yunqing Li, Jianshu Zhang, Chen Yang, Bo Ren, Yiqing Hu
Date: 2022-06-28 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2022 6
Tasks: Decoder, Handwritten Mathmatical Expression Recognition

In recent years, tree decoders become more popular than LaTeX string decoders in the field of handwritten mathematical expression recognition (HMER) as they can capture the hierarchical tree structure of mathematical expressions. However previous tree decoders converted the tree structure labels into a fixed and ordered sequence, which could not make full use of the diversified expression of tree labels. In this study, we propose a novel tree decoder (TDv2) to fully utilize the tree structure labels. Compared with previous tree decoders, this new model does not require a fixed priority for different branches of a node during training and inference, which can effectively improve the model generalization capability. The input and output of the model make full use of the tree structure label, so that there is no need to find the parent node in the decoding process, which simplifies the decoding process and adds a prior information to help predict the node. We verified the effectiveness of each part of the model through comprehensive ablation experiments and attention visualization analysis. On the authoritative CROHME 14/16/19 datasets, our method achieves the state-of-the-art results.
Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation
Authors: Yiling Fan, Zhuang Ma, Wanwei Tang, Jing Liang, and Pengfei Xu
Date: 2024-02-12 00:00:00
Proceeding: Energy Res 2024 2
Tasks: energy management, Prediction, Scheduling

Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power
generation faces significant challenges in energy grid integration. To address these challenges,
current research mainly focuses on developing more efficient energy management systems and
prediction technologies. Through optimizing scheduling and integration in PV power generation, the
stability and reliability of the power grid can be further improved. In this study, a new prediction
model is introduced that combines the strengths of convolutional neural networks (CNNs), long
short-term memory (LSTM) networks, and attention mechanisms, so we call this algorithm CNN-
LSTM-Attention (CLA). In addition, the Crested Porcupine Optimizer (CPO) algorithm is utilized to
solve the short-term prediction problem in photovoltaic power generation. This model is abbreviated
as CPO-CLA. This is the first time that the CPO algorithm has been introduced into the LSTM
algorithm for parameter optimization. To effectively capture univariate and multivariate time series
patterns, multiple relevant and target variables prediction patterns (MRTPPs) are employed in the
CPO-CLA model. The results show that the CPO-CLA model is superior to traditional methods and
recent popular models in terms of prediction accuracy and stability, especially in the 13 h timestep.
The integration of attention mechanisms enables the model to adaptively focus on the most relevant
historical data for future power prediction. The CPO algorithm further optimizes the LSTM network
parameters, which ensures the robust generalization ability of the model. The research results are
of great significance for energy generation scheduling and establishing trust in the energy market.
Ultimately, it will help integrate renewable energy into the grid more reliably and efficiently.