pwc-archive/papers-with-abstracts huggingface dataset:
A Real Benchmark Swell Noise Dataset for Performing Seismic Data Denoising via Deep Learning
Authors: Pablo M. Barros, Roosevelt de L. Sardinha, Giovanny A. M. Arboleda, Lessandro de S. S. Valente, Isabelle R. V. de Melo, Albino Aveleda, André Bulcão, Sergio L. Netto, Alexandre G. Evsukoff
Date: 2024-10-02 00:00:00
Tasks: Benchmarking, Denoising, Geophysics

The recent development of deep learning (DL) methods for computer vision has been driven by the creation of open benchmark datasets on which new algorithms can be tested and compared with reproducible results. Although DL methods have many applications in geophysics, few real seismic datasets are available for benchmarking DL models, especially for denoising real data, which is one of the main problems in seismic data processing scenarios in the oil and gas industry. This article presents a benchmark dataset composed of synthetic seismic data corrupted with noise extracted from a filtering process implemented on real data. In this work, a comparison between two well-known DL-based denoising models is conducted on this dataset, which is proposed as a benchmark for accelerating the development of new solutions for seismic data denoising. This work also introduces a new evaluation metric that can capture small variations in model results. The results show that DL models are effective at denoising seismic data, but some issues remain to be solved.
A Recurrent Neural Network Approach to the Answering Machine Detection Problem
Authors: Kemal Altwlkany, Sead Delalic, Elmedin Selmanovic, Adis Alihodzic, Ivica Lovric
Date: 2024-10-07 00:00:00
Tasks: Transfer Learning

In the field of telecommunications and cloud communications, accurately and in real-time detecting whether a human or an answering machine has answered an outbound call is of paramount importance. This problem is of particular significance during campaigns as it enhances service quality, efficiency and cost reduction through precise caller identification. Despite the significance of the field, it remains inadequately explored in the existing literature. This paper presents an innovative approach to answering machine detection that leverages transfer learning through the YAMNet model for feature extraction. The YAMNet architecture facilitates the training of a recurrent-based classifier, enabling real-time processing of audio streams, as opposed to fixed-length recordings. The results demonstrate an accuracy of over 96% on the test set. Furthermore, we conduct an in-depth analysis of misclassified samples and reveal that an accuracy exceeding 98% can be achieved with the integration of a silence detection algorithm, such as the one provided by FFmpeg.
NetDiff: Deep Graph Denoising Diffusion for Ad Hoc Network Topology Generation
Authors: Félix Marcoccia, Cédric Adjih, Paul Mühlethaler
Date: 2024-10-09 00:00:00
Tasks: Denoising, Graph Generation

This work introduces NetDiff, an expressive graph denoising diffusion probabilistic architecture that generates wireless ad hoc network link topologies. Such networks, with directional antennas, can achieve unmatched performance when the communication links are designed to provide good geometric properties, notably by reducing interference between these links while respecting diverse physical constraints. How to craft such a link assignment algorithm is yet a real problem. Deep graph generation offers multiple advantages compared to traditional approaches: it allows to relieve the network nodes of the communication burden caused by the search of viable links and to avoid resorting to heavy combinatorial methods to find a good link topology. Denoising diffusion also provides a built-in method to update the network over time. Given that graph neural networks sometimes tend to struggle with global, structural properties, we augment the popular graph transformer with cross-attentive modulation tokens in order to improve global control over the predicted topology. We also incorporate simple node and edge features, as well as additional loss terms, to facilitate the compliance with the network topology physical constraints. A network evolution algorithm based on partial diffusion is also proposed to maintain a stable network topology over time when the nodes move. Our results show that the generated links are realistic, present structural properties similar to the dataset graphs', and require only minor corrections and verification steps to be operational.
Removal of clouds from satellite images using time compositing techniques
Authors: Atma Bharathi Mani, Nagashree TR, Manavalan P, Diwakar PG
Date: 2024-09-26 00:00:00

Clouds in satellite images are a deterrent to qualitative and quantitative study. Time compositing methods compare a series of co-registered images and retrieve only those pixels that have comparatively lesser cloud cover for the resultant image. Two different approaches of time compositing were tested. The first method recoded the clouds to value 0 on all the constituent images and ran a 'max' function. The second method directly ran a 'min' function without recoding on all the images for the resultant image. The 'max' function gave a highly mottled image while the 'min' function gave a superior quality image with smoother texture. Persistent clouds on all constituent images were retained in both methods, but they were readily identifiable and easily extractable in the 'max' function image as they were recoded to 0, while that in the 'min' function appeared with varying DN values. Hence a hybrid technique was created which recodes the clouds to value 255 and runs a 'min' function. This method preserved the quality of the 'min' function and the advantage of retrieving clouds as in the 'max' function image. The models were created using Erdas Imagine Modeler 9.1 and MODIS 250 m resolution images of coastal Karnataka in the months of May, June 2008 were used. A detailed investigation on the different methods is described and scope for automating different techniques is discussed.
Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends
Authors: Xinrui Wang, Wenhai Wan, Chuanxin Geng, ShaoYuan Li, Songcan Chen
Date: 2023-10-06 00:00:00
Proceeding: NeurIPS 2023 11

Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the impressive empirical performance of recent PUL methods, challenges like accumulated errors and increased estimation bias persist due to the absence of negative labels. In this paper, we unveil an intriguing yet long-overlooked observation in PUL: \textit{resampling the positive data in each training iteration to ensure a balanced distribution between positive and unlabeled examples results in strong early-stage performance. Furthermore, predictive trends for positive and negative classes display distinctly different patterns.} Specifically, the scores (output probability) of unlabeled negative examples consistently decrease, while those of unlabeled positive examples show largely chaotic trends. Instead of focusing on classification within individual time frames, we innovatively adopt a holistic approach, interpreting the scores of each example as a temporal point process (TPP). This reformulates the core problem of PUL as recognizing trends in these scores. We then propose a novel TPP-inspired measure for trend detection and prove its asymptotic unbiasedness in predicting changes. Notably, our method accomplishes PUL without requiring additional parameter tuning or prior assumptions, offering an alternative perspective for tackling this problem. Extensive experiments verify the superiority of our method, particularly in a highly imbalanced real-world setting, where it achieves improvements of up to $11.3\%$ in key metrics. The code is available at \href{https://github.com/wxr99/HolisticPU}{https://github.com/wxr99/HolisticPU}.
New technologies and AI: envisioning future directions for UNSCR 1540
Authors: Clara Punzi
Date: 2024-09-25 00:00:00

This paper investigates the emerging challenges posed by the integration of Artificial Intelligence (AI) in the military domain, particularly within the context of United Nations Security Council Resolution 1540 (UNSCR 1540), which seeks to prevent the proliferation of weapons of mass destruction (WMDs). While the resolution initially focused on nuclear, chemical, and biological threats, the rapid advancement of AI introduces new complexities that were previously unanticipated. We critically analyze how AI can both exacerbate existing risks associated with WMDs (e.g., thorough the deployment of kamikaze drones and killer robots) and introduce novel threats (e.g., by exploiting Generative AI potentialities), thereby compromising international peace and security. The paper calls for an expansion of UNSCR 1540 to address the growing influence of AI technologies in the development, dissemination, and potential misuse of WMDs, urging the creation of a governance framework to mitigate these emerging risks.
Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models
Authors: Yang An, Yuhao Tong, Weikai Wang, Steven W. Su
Date: 2024-09-14 00:00:00
Tasks: Brain Computer Interface, Data Augmentation, EEG, Electroencephalogram (EEG)

The present study introduces an innovative approach to the synthesis of Electroencephalogram (EEG) signals by integrating diffusion models with reinforcement learning. This integration addresses key challenges associated with traditional EEG data acquisition, including participant burden, privacy concerns, and the financial costs of obtaining high-fidelity clinical data. Our methodology enhances the generation of EEG signals with detailed temporal and spectral features, enriching the authenticity and diversity of synthetic datasets. The uniqueness of our approach lies in its capacity to concurrently model time-domain characteristics, such as waveform morphology, and frequency-domain features, including rhythmic brainwave patterns, within a cohesive generative framework. This is executed through the reinforcement learning model's autonomous selection of parameter update strategies, which steers the diffusion process to accurately reflect the complex dynamics inherent in EEG signals. We validate the efficacy of our approach using both the BCI Competition IV 2a dataset and a proprietary dataset, each collected under stringent experimental conditions. Our results indicate that the method preserves participant privacy by generating synthetic data that lacks biometric identifiers and concurrently improves the efficiency of model training by minimizing reliance on large annotated datasets. This research offers dual contributions: firstly, it advances EEG research by providing a novel tool for data augmentation and the advancement of machine learning algorithms; secondly, it enhances brain-computer interface technologies by offering a robust solution for training models on diverse and representative EEG datasets. Collectively, this study establishes a foundation for future investigations in neurological care and the development of tailored treatment protocols in neurorehabilitation.
Fast Deep Convolutional Face Detection in the Wild Exploiting Hard Sample Mining
Authors: Danai Triantafyllidou, Paraskevi Nousi, Anastasios Tefas
Date: 2017-01-10 00:00:00
Proceeding: Big Data Research 2017 1
Tasks: Face Detection, POS

Face detection constitutes a key visual information analysis task in MachineLearning. The rise of Big Data has resulted in the accumulation of a massivevolume of visual data which requires proper and fast analysis. Deep Learn-ing methods are powerful approaches towards this task as training with largeamounts of data exhibiting high variability has been shown to significantly en-hance their effectiveness, but often require expensive computations and lead tomodels of high complexity. When the objective is to analyse visual content inmassive datasets the complexity of the model becomes crucial to the success ofthe model. In this paper, a light-weight deep Convolutional Neural Network(CNN) is introduced for the purpose of face detection, designed with a view tominimize training and testing time, and outperforms previously published deepconvolutional networks in this task in terms of both effectiveness and efficiency.The model consists of 76.375 free parameters whereas most of the competitiveones consisted of millions of parameters. To train this lightweight deep networkwithout compromising its efficiency, a new training method of progressive pos-itive and hard negative sample mining is introduced and shown to drasticallyimprove training speed and accuracy. Additionally, a separate deep networkwas trained to detect individual facial features and a model that combines theoutputs of the two networks was created and evaluated. Both methods are ableto detect faces under severe occlusion and unconstrained pose variation andmeet the difficulties and the large variations of large scale real-world, real-timeface detection.
Expert load matters: operating networks at high accuracy and low manual effort
Authors: Sara Sangalli, Ertunc Erdil, Ender Konukoglu
Date: 2023-08-09 00:00:00
Proceeding: NeurIPS 2023 11

In human-AI collaboration systems for critical applications, in order to ensure minimal error, users should set an operating point based on model confidence to determine when the decision should be delegated to human experts. Samples for which model confidence is lower than the operating point would be manually analysed by experts to avoid mistakes. Such systems can become truly useful only if they consider two aspects: models should be confident only for samples for which they are accurate, and the number of samples delegated to experts should be minimized. The latter aspect is especially crucial for applications where available expert time is limited and expensive, such as healthcare. The trade-off between the model accuracy and the number of samples delegated to experts can be represented by a curve that is similar to an ROC curve, which we refer to as confidence operating characteristic (COC) curve. In this paper, we argue that deep neural networks should be trained by taking into account both accuracy and expert load and, to that end, propose a new complementary loss function for classification that maximizes the area under this COC curve. This promotes simultaneously the increase in network accuracy and the reduction in number of samples delegated to humans. We perform experiments on multiple computer vision and medical image datasets for classification. Our results demonstrate that the proposed loss improves classification accuracy and delegates less number of decisions to experts, achieves better out-of-distribution samples detection and on par calibration performance compared to existing loss functions.
Transformer Temperature Management and Voltage Control in Electric Distribution Systems with High Solar PV Penetration
Authors: Amirhossein Ghorbansarvi, Dakota Hamilton, Mads R. Almassalkhi, Hamid R. Ossareh
Date: 2024-10-11 00:00:00
Tasks: Management

The increasing penetration of photovoltaic (PV) systems in distribution grids can lead to overvoltage and transformer overloading issues. While voltage regulation has been extensively studied and some research has addressed transformer temperature control, there is limited work on simultaneously managing both challenges. This paper addresses this gap by proposing an optimization-based strategy that efficiently manages voltage regulation and transformer temperature while minimizing the curtailment of PV generation. In order to make this problem convex, a relaxation is applied to the transformer temperature dynamics constraint. We also provide analysis to determine under which conditions this relaxation remains tight. The proposed approach is validated through simulations, demonstrating its effectiveness in achieving the desired control objectives.
I-SCOUT: Integrated Sensing and Communications to Uncover Moving Targets in NextG Networks
Authors: Utku Demir, Kemal Davaslioglu, Yalin E. Sagduyu, Tugba Erpek, Gustave Anderson, Sastry Kompella
Date: 2024-10-11 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing and Communication (ISAC) represents a transformative approach within 5G and beyond, aiming to merge wireless communication and sensing functionalities into a unified network infrastructure. This integration offers enhanced spectrum efficiency, real-time situational awareness, cost and energy reductions, and improved operational performance. ISAC provides simultaneous communication and sensing capabilities, enhancing the ability to detect, track, and respond to spectrum dynamics and potential threats in complex environments. In this paper, we introduce I-SCOUT, an innovative ISAC solution designed to uncover moving targets in NextG networks. We specifically repurpose the Positioning Reference Signal (PRS) of the 5G waveform, exploiting its distinctive autocorrelation characteristics for environment sensing. The reflected signals from moving targets are processed to estimate both the range and velocity of these targets using the cross ambiguity function (CAF). We conduct an in-depth analysis of the tradeoff between sensing and communication functionalities, focusing on the allocation of PRSs for ISAC purposes. Our study reveals that the number of PRSs dedicated to ISAC has a significant impact on the system's performance, necessitating a careful balance to optimize both sensing accuracy and communication efficiency. Our results demonstrate that I-SCOUT effectively leverages ISAC to accurately determine the range and velocity of moving targets. Moreover, I-SCOUT is capable of distinguishing between multiple targets within a group, showcasing its potential for complex scenarios. These findings underscore the viability of ISAC in enhancing the capabilities of NextG networks, for both commercial and tactical applications where precision and reliability are critical.
Double opponency serves as a basis for color constancy
Authors: Kai-Fu Yang, Yong-Jie Li
Date: 2024-10-11 00:00:00
Tasks: Color Constancy

Color constancy (CC) is one of the important perceptual abilities of the human visual system, which states that despite changes in illumination, the perceived colors of surfaces generally tend to remain constant. Nevertheless, the mechanisms underlying CC have been debated for several decades. A specific type of cell, known as the double opponent cell in the primary visual cortex (V1), is strongly implicated in achieving CC. However, the exact functioning manner of this cell type remains uncertain. In this work, our quantitative analysis of concentric double-opponent cells in V1 revealed their ability to identify gray surfaces within color-biased scenes. These gray surfaces can then be used to estimate the illumination easily. For the first time, this finding offers a clear functional explanation of concentric double-opponent receptive fields of this cell type in the visual system. Building on this insight, we introduced a novel computational theory--gray-anchoring (GA) theory--to explain how CC is achieved in the visual system. Specifically, GA-based CC involves detecting and anchoring gray surfaces within complex scenes. Our new theory serves as a bridge among the retinex theory, anchoring theory, and the neural mechanisms underlying visual CC in color vision.
Accelerated Distributed Stochastic Non-Convex Optimization over Time-Varying Directed Networks
Authors: Yiyue Chen, Abolfazl Hashemi, Haris Vikalo
Date: 2024-10-11 00:00:00
Tasks: Federated Learning, image-classification, Image Classification

Distributed stochastic non-convex optimization problems have recently received attention due to the growing interest of signal processing, computer vision, and natural language processing communities in applications deployed over distributed learning systems (e.g., federated learning). We study the setting where the data is distributed across the nodes of a time-varying directed network, a topology suitable for modeling dynamic networks experiencing communication delays and straggler effects. The network nodes, which can access only their local objectives and query a stochastic first-order oracle to obtain gradient estimates, collaborate to minimize a global objective function by exchanging messages with their neighbors. We propose an algorithm, novel to this setting, that leverages stochastic gradient descent with momentum and gradient tracking to solve distributed non-convex optimization problems over time-varying networks. To analyze the algorithm, we tackle the challenges that arise when analyzing dynamic network systems which communicate gradient acceleration components. We prove that the algorithm's oracle complexity is $\mathcal{O}(1/\epsilon^{1.5})$, and that under Polyak-$\L$ojasiewicz condition the algorithm converges linearly to a steady error state. The proposed scheme is tested on several learning tasks: a non-convex logistic regression experiment on the MNIST dataset, an image classification task on the CIFAR-10 dataset, and an NLP classification test on the IMDB dataset. We further present numerical simulations with an objective that satisfies the PL condition. The results demonstrate superior performance of the proposed framework compared to the existing related methods.
A Comprehensive Review: Impacts of Extreme Temperatures due to Climate Change on Power Grid Infrastructure and Operation
Authors: Kishan Prudhvi Guddanti, Alok Kumar Bharati, Sameer Nekkalapu, Joseph McWheter, Scott Morris
Date: 2024-10-11 00:00:00
Tasks: Articles

The power grid is experiencing a multi-fold transformation while the global climate evolves with record-breaking extreme temperatures during heat domes, polar vortices, and severe ice. Over the decades, these extreme temperature events have increased in frequency, duration, and intensity. The power grid infrastructure is geographically spread over thousands of square miles with millions of small and large components, and the impact of extreme temperature operations on the grid infrastructure needs to be researched further. This paper reviews academic literature, standards, industry articles, and federal reports to identify the impacts of heat domes, polar vortices, and icing on all the T&D grid equipment, including substations (assets owned and operated by the utilities and independent system operators). This paper classifies the equipment into primary and auxiliary equipment and determines its vulnerability to extreme temperatures for a deeper analysis of a more critical and vulnerable set of grid equipment. For each equipment under consideration, its fundamental role in the system, the impact of extreme temperatures on its operation, available monitoring, and mitigation of these impacts are discussed. The paper develops insights on standards readiness and identifies gaps concerning extreme temperature definitions. The paper also develops summary tables to identify the critical failure modes for each type of equipment, failure influence diagrams, and cascading influence diagrams to highlight and aid in translating the equipment vulnerability information into power grid contingency definitions that need to be considered in grid planning and operations.