pwc-archive/papers-with-abstracts huggingface dataset:
Comparison of pipeline, sequence-to-sequence, and GPT models for end-to-end relation extraction: experiments with the rare disease use-case
Authors: Shashank Gupta, Xuguang Ai, Ramakanth Kavuluru
Date: 2023-11-22 00:00:00
Tasks: NER, Relation Extraction

End-to-end relation extraction (E2ERE) is an important and realistic application of natural language processing (NLP) in biomedicine. In this paper, we aim to compare three prevailing paradigms for E2ERE using a complex dataset focused on rare diseases involving discontinuous and nested entities. We use the RareDis information extraction dataset to evaluate three competing approaches (for E2ERE): NER $\rightarrow$ RE pipelines, joint sequence to sequence models, and generative pre-trained transformer (GPT) models. We use comparable state-of-the-art models and best practices for each of these approaches and conduct error analyses to assess their failure modes. Our findings reveal that pipeline models are still the best, while sequence-to-sequence models are not far behind; GPT models with eight times as many parameters are worse than even sequence-to-sequence models and lose to pipeline models by over 10 F1 points. Partial matches and discontinuous entities caused many NER errors contributing to lower overall E2E performances. We also verify these findings on a second E2ERE dataset for chemical-protein interactions. Although generative LM-based methods are more suitable for zero-shot settings, when training data is available, our results show that it is better to work with more conventional models trained and tailored for E2ERE. More innovative methods are needed to marry the best of the both worlds from smaller encoder-decoder pipeline models and the larger GPT models to improve E2ERE. As of now, we see that well designed pipeline models offer substantial performance gains at a lower cost and carbon footprint for E2ERE. Our contribution is also the first to conduct E2ERE for the RareDis dataset.
Dynamic Fault Analysis in Substations Based on Knowledge Graphs
Authors: Weiwei Li, Xing Liu, Wei Wang, Lu Chen, Sizhe Li, Hui Fan
Date: 2023-11-22 00:00:00
Tasks: Knowledge Graphs

To address the challenge of identifying hidden danger in substations from unstructured text, a novel dynamic analysis method is proposed. We first extract relevant information from the unstructured text, and then leverages a flexible distributed search engine built on Elastic-Search to handle the data. Following this, the hidden Markov model is employed to train the data within the engine. The Viterbi algorithm is integrated to decipher the hidden state sequences, facilitating the segmentation and labeling of entities related to hidden dangers. The final step involves using the Neo4j graph database to dynamically create a knowledge graph that visualizes hidden dangers in the substation. The effectiveness of the proposed method is demonstrated through a case analysis from a specific substation with hidden dangers revealed in the text records.
Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
Authors: Jens E. Pedersen, Steven Abreu, Matthias Jobst, Gregor Lenz, Vittorio Fra, Felix C. Bauer, Dylan R. Muir, Peng Zhou, Bernhard Vogginger, Kade Heckel, Gianvito Urgese, Sadasivan Shankar, Terrence C. Stewart, Sadique Sheik, Jason K. Eshraghian
Date: 2023-11-24 00:00:00

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available at neuroir.org
Evolution of Neural Architectures for Financial Forecasting: A Note on Data Incompatibility during Crisis Periods
Authors: Faizal Hafiz, Jan Broekaert, Akshya Swain
Date: 2023-11-24 00:00:00

This note focuses on the optimization of neural architectures for stock index movement forecasting following a major market disruption or crisis. Given that such crises may introduce a shift in market dynamics, this study aims to investigate whether the training data from market dynamics prior to the crisis are compatible with the data during the crisis period. To this end, two distinct learning environments are designed to evaluate and reconcile the effects of possibly different market dynamics. These environments differ principally based on the role assigned to the pre-crisis data. In both environments, a set of non-dominated architectures are identified to satisfy the multi-criteria co-evolution problem, which simultaneously addresses the selection issues related to features and hidden layer topology. To test the hypothesis of pre-crisis data incompatibility, the day-ahead movement prediction of the NASDAQ index is considered during two recent and major market disruptions; the 2008 financial crisis and the COVID-19 pandemic. The results of a detailed comparative evaluation convincingly support the incompatibility hypothesis and highlight the need to select re-training windows carefully.
RFI Detection with Spiking Neural Networks
Authors: Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson
Date: 2023-11-24 00:00:00
Tasks: Astronomy, Semantic Segmentation

Detecting and mitigating Radio Frequency Interference (RFI) is critical for enabling and maximising the scientific output of radio telescopes. The emergence of machine learning methods has led to their application in radio astronomy, and in RFI detection. Spiking Neural Networks (SNNs), inspired by biological systems, are well-suited for processing spatio-temporal data. This study introduces the first exploratory application of SNNs to an astronomical data-processing task, specifically RFI detection. We adapt the nearest-latent-neighbours (NLN) algorithm and auto-encoder architecture proposed by previous authors to SNN execution by direct ANN2SNN conversion, enabling simplified downstream RFI detection by sampling the naturally varying latent space from the internal spiking neurons. Our subsequent evaluation aims to determine whether SNNs are viable for future RFI detection schemes. We evaluate detection performance with the simulated HERA telescope and hand-labelled LOFAR observation dataset the original authors provided. We additionally evaluate detection performance with a new MeerKAT-inspired simulation dataset that provides a technical challenge for machine-learnt RFI detection methods. This dataset focuses on satellite-based RFI, an increasingly important class of RFI and is an additional contribution. Our approach remains competitive with existing methods in AUROC, AUPRC and F1 scores for the HERA dataset but exhibits difficulty in the LOFAR and Tabascal datasets. Our method maintains this accuracy while completely removing the compute and memory-intense latent sampling step found in NLN. This work demonstrates the viability of SNNs as a promising avenue for machine-learning-based RFI detection in radio telescopes by establishing a minimal performance baseline on traditional and nascent satellite-based RFI sources and is the first work to our knowledge to apply SNNs in astronomy.
Coevolution of Neural Architectures and Features for Stock Market Forecasting: A Multi-objective Decision Perspective
Authors: Faizal Hafiz, Jan Broekaert, Davide La Torre, Akshya Swain
Date: 2023-11-23 00:00:00
Tasks: Evolutionary Algorithms, feature selection

In a multi objective setting, a portfolio manager's highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to identify a set of nondominated neural network models for further selection by the decision maker. A new coevolution approach is proposed to simultaneously select the features and topology of neural networks (collectively referred to as neural architecture), where the features are viewed from a topological perspective as input neurons. Further, the coevolution is posed as a multicriteria problem to evolve sparse and efficacious neural architectures. The well known dominance and decomposition based multiobjective evolutionary algorithms are augmented with a nongeometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the coevolution is augmented to accommodate the data based implications of distinct market behaviors prior to and during the ongoing COVID 19 pandemic. A detailed comparative evaluation is carried out with the conventional sequential approach of feature selection followed by neural topology design, as well as a scalarized coevolution approach. The results on the NASDAQ index in pre and peri COVID time windows convincingly demonstrate that the proposed coevolution approach can evolve a set of nondominated neural forecasting models with better generalization capabilities.
EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia
Authors: Francisco Jesus Martinez-Murcia, Andrés Ortiz, Juan Manuel Górriz, Javier Ramírez, Pedro Javier Lopez-Perez, Miguel López-Zamora, Juan Luis Luque
Date: 2023-11-23 00:00:00
Tasks: Denoising, EEG, Reading Comprehension

The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplitude modulated (AM) noise with slow-rythmic prosodic (0.5-1 Hz), syllabic (4-8 Hz) or the phoneme (12-40 Hz) rates, aimed at detecting differences in perception of oscillatory sampling that could be associated with dyslexia. The purpose of this work is to check whether these differences exist and how they are related to children's performance in different language and cognitive tasks commonly used to detect dyslexia. To this purpose, temporal and spectral inter-channel EEG connectivity was estimated, and a denoising autoencoder (DAE) was trained to learn a low-dimensional representation of the connectivity matrices. This representation was studied via correlation and classification analysis, which revealed ability in detecting dyslexic subjects with an accuracy higher than 0.8, and balanced accuracy around 0.7. Some features of the DAE representation were significantly correlated ($p<0.005$) with children's performance in language and cognitive tasks of the phonological hypothesis category such as phonological awareness and rapid symbolic naming, as well as reading efficiency and reading comprehension. Finally, a deeper analysis of the adjacency matrix revealed a reduced bilateral connection between electrodes of the temporal lobe (roughly the primary auditory cortex) in DD subjects, as well as an increased connectivity of the F7 electrode, placed roughly on Broca's area. These results pave the way for a complementary assessment of dyslexia using more objective methodologies such as EEG.
An Analysis on the Effects of Evolving the Monte Carlo Tree Search Upper Confidence for Trees Selection Policy on Unimodal, Multimodal and Deceptive Landscapes
Authors: Edgar Galvan, Fred Valdez Ameneyro
Date: 2023-11-21 00:00:00

Monte Carlo Tree Search (MCTS) is a best-first sampling method employed in the search for optimal decisions. The effectiveness of MCTS relies on the construction of its statistical tree, with the selection policy playing a crucial role. A selection policy that works particularly well in MCTS is the Upper Confidence Bounds for Trees, referred to as UCT. The research community has also put forth more sophisticated bounds aimed at enhancing MCTS performance on specific problem domains. Thus, while MCTS UCT generally performs well, there may be variants that outperform it. This has led to various efforts to evolve selection policies for use in MCTS. While all of these previous works are inspiring, none have undertaken an in-depth analysis to shed light on the circumstances in which an evolved alternative to MCTS UCT might prove advantageous. Most of these studies have focused on a single type of problem. In sharp contrast, this work explores the use of five functions of different natures, ranging from unimodal to multimodal and deceptive functions. We illustrate how the evolution of MCTS UCT can yield benefits in multimodal and deceptive scenarios, whereas MCTS UCT is robust in all of the functions used in this work.
Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition
Authors: Jesús M. Rodríguez-de-Vera, Pablo Villacorta, Imanol G. Estepa, Marc Bolaños, Ignacio Sarasúa, Bhalaji Nagarajan, Petia Radeva
Date: 2023-10-29 00:00:00
Proceeding: MADiMa Workshop in ACM Multimedia 2023 10
Tasks: Fine-Grained Image Classification, Fine-Grained Image Recognition, Food Recognition, Multi-Task Learning

In the field of fine-grained food recognition, subset learning-based methods offer a strategic approach that groups classes into subsets to guide the training process. Our study introduces a novel approach, referred to as the Dining on Details (DoD), an innovative expert learning framework for food classification. This method ingeniously harnesses the power of large language models to construct subsets of classes within the dataset. The Dining on Details's efficacy is rooted in the robustness of the ImageBind multi-modality embedding space, which can identify meaningful similarities across varied categories. Trained through an end-to-end multi-task learning process, this method enhances performance in the fine-grained food recognition task, showing exceptional prowess with highly similar classes. A key advantage of DoD is its universal compatibility, allowing it to be applied seamlessly to any existing classification architecture. Our comprehensive validation of this method on various food datasets and backbones, both convolutional and transformer-based, reveals competitive results with significant performance gains ranging from 0.5% to 1.61%. Notably, it achieves state-of-the-art results on the Food-101 dataset.
Cross-layer scheme for low latency multiple description video streaming over Vehicular Ad-hoc NETworks (VANETs)
Authors: Mohamed Aymen Labiod, Mohamed Gharbi, Francois-Xavier Coudoux, Patrick Corlay, Noureddine Doghmane
Date: 2023-11-05 00:00:00

There is nowadays a growing demand in vehicular communications for real-time applications requiring video assistance. The new state-of-the-art high-efficiency video coding (HEVC) standard is very promising for real-time video streaming. It offers high coding efficiency, as well as dedicated low delay coding structures. Among these, the all intra (AI) coding structure guarantees minimal coding time at the expense of higher video bitrates, which therefore penalizes transmission performances. In this work, we propose an original cross-layer system in order to enhance received video quality in vehicular communications. The system is low complex and relies on a multiple description coding (MDC) approach. It is based on an adaptive mapping mechanism applied at the IEEE 802.11p standard medium access control (MAC) layer. Simulation results in a realistic vehicular environment demonstrate that for low delay video communications, the proposed method provides significant video quality improvements on the receiver side.
Uncertainties in Robust Planning and Control of Autonomous Tractor-Trailer Vehicles
Authors: Theodor Westny, Björn Olofsson, Erik Frisk
Date: 2023-11-24 00:00:00
Tasks: Motion Planning, Sensitivity

To study the effects of uncertainty in autonomous motion planning and control, an 8-DOF model of a tractor-semitrailer is implemented and analyzed. The implications of uncertainties in the model are then quantified and presented using sensitivity analysis and closed-loop simulations. The analysis reveals that the significance of various model parameters varies depending on the specific scenario under investigation. By using sampling-based closed-loop predictions, uncertainty bounds on state variable trajectories are determined. Our findings suggest the potential for the inclusion of our method within a robust predictive controller or as a driver-assistance system for rollover or lane departure warnings.
Analyzing Cross-Phase Effects of Reactive Power Intervention on Distribution Voltage Control
Authors: Dhaval Dalal, Anamitra Pal, Raja Ayyanar
Date: 2023-11-24 00:00:00

Increasing photovoltaic (PV) penetration in the distribution system can often lead to voltage violations. Mitigation of these violations requires reactive power intervention from PV inverters. However, the unbalanced nature of the distribution system leads to mixed effects on the voltages of nearby nodes for each inverter injecting or absorbing reactive power. In particular, reactive power absorption to reduce over-voltage in one phase can exacerbate over-voltage in a different phase. In this paper, the factors impacting the incremental and decremental voltage effects of reactive power intervention are analyzed in detail. The result of these effects on the distribution system performance is presented to highlight their significance and the need to factor them in for any coordinated voltage control algorithm.
Evaluation of a Non-Coherent Ultra-Wideband Transceiver for Micropower Sensor Nodes
Authors: Jonah Imfeld, Silvano Cortesi, Philipp Mayer, Michele Magno
Date: 2023-11-24 00:00:00

Spatial and contextual awareness has the potential to revolutionize sensor nodes, enabling spatially augmented data collection and location-based services. With its high bandwidth, superior energy efficiency, and precise time-of-flight measurements, ultra-wideband (UWB) technology emerges as an ideal solution for such devices. This paper presents an evaluation and comparison of a non-coherent UWB transceiver within the context of highly energy-constrained wireless sensing nodes and pervasive Internet of Things (IoT) devices. Experimental results highlight the unique properties of UWB transceivers, showcasing efficient data transfer ranging from 2 kbit/s to 7.2 Mbit/s while reaching an energy consumption of 0.29 nJ/bit and 1.39 nJ/bit for transmitting and receiving, respectively. Notably, a ranging accuracy of up to +/-25 cm can be achieved. Moreover, the peak power consumption of the UWB transceiver is with 6.7 mW in TX and 23 mW in RX significantly lower than that of other commercial UWB transceivers.
FAMAC: A Federated Assisted Modified Actor-Critic Framework for Secured Energy Saving in 5G and Beyond Networks
Authors: Attai Ibrahim Abubakar, Michael S. Mollel, Metin Ozturk, Naeem Ramzan
Date: 2023-11-24 00:00:00
Tasks: Federated Learning

The constant surge in the traffic demand on cellular networks has led to continuous expansion in network capacity in order to accommodate existing and new service demands. This has given rise to ultra-dense base station deployment in 5G and beyond networks which leads to increased energy consumption in the network. Hence, these ultra-dense base station deployments must be operated in a way that the energy consumption of the network can be adapted to the spatio-temporal traffic demands on the network in order to minimize the overall energy consumption of the network. To achieve this goal, we leverage two artificial intelligence algorithms, federated learning and actor-critic algorithm, to develop a proactive and intelligent base station switching framework that can learn the operating policy of the small base station in an ultra-dense heterogeneous network (UDHN) that would result in maximum energy saving in the network while respecting the quality of service (QoS) constraints. The performance evaluation reveals that the proposed framework can achieve an energy saving that is about 77% more than that of the state-of-the-art solutions while respecting the QoS constraints of the network.
Towards an Efficient Simulation Approach for Transmission Systems with ICT Infrastructures
Authors: Frédéric Sabot, Pierre-Etienne Labeau, Jean-Michel Dricot, Pierre Henneaux
Date: 2023-11-24 00:00:00

With the transition towards a smart grid, Information and Communications Technology (ICT) infrastructures play a growing role in the operation of transmission systems. Cyber-physical systems are usually studied using co-simulation. The latter allows to leverage existing simulators and models for both power systems and communication networks. A major drawback of co-simulation is however the computation time. Indeed, simulators have to be frequently paused in order to stay synchronised and to exchange information. This is especially true for large systems for which lots of interactions between the two domains occur. We thus propose a self-consistent simulation approach as an alternative to co-simulation. We compare the two approaches on the IEEE 39-bus test system equipped with an all-PMU state estimator. We show that our approach can reach the same accuracy as co-simulation, while using drastically less computer resources.
Investigation on the Impact of Heat Waves on Distribution System Failures
Authors: Andrea Mazza, Gianfranco Chicco, Carmen L. T. Borges
Date: 2023-11-24 00:00:00

This paper discusses some aspects referring to the characterization and modelling of the resilience of distribution systems in the presence of heat waves. The aim is to identify the specific features that can lead to more detailed modelling of the impact of heat waves on the failures that happen in distribution systems. In particular, with heat waves there are differences between the cumulative distribution function of the time to failure in practical cases and in the theoretical reference used for reliability analysis. These differences may be considered to refine the resilience models due to heat waves. Examples taken from real cases are illustrated and commented.
Lead-acid battery lifetime extension in solar home systems under different operating conditions
Authors: Rebecca Perriment, Volkan Kumtepeli, Malcolm McCulloch, David Howey
Date: 2023-11-23 00:00:00

Solar home systems (SHS) provide low-cost electricity access for rural off-grid communities. Batteries are a crucial part of the system, however they are often the first point of failure due to shorter lifetimes. Using field data, this work models the degradation of lead-acid batteries for different SHS use-cases, finding the dominant ageing mechanisms in each case. Corrosion is the dominant ageing mechanisms in all cases apart from the highest use case. This is caused by extended time at high state of charge (SOC) and hence high voltage. A new voltage control scheme is proposed for one of the use cases dominated by corrosion, whereby the number of days between full recharges varies depending on the degradation mechanisms the battery experiences. Simulating the new voltage control scheme yields a 25% increase in battery lifetime whilst ensuring no loss of load to the user.