pwc-archive/papers-with-abstracts huggingface dataset:
A Learning Based Scheme for Fair Timeliness in Sparse Gossip Networks
Authors: Purbesh Mitra, Sennur Ulukus
Date: 2023-10-02 00:00:00
Tasks: Bayesian Optimization, Fairness

We consider a gossip network, consisting of $n$ nodes, which tracks the information at a source. The source updates its information with a Poisson arrival process and also sends updates to the nodes in the network. The nodes themselves can exchange information among themselves to become as timely as possible. However, the network structure is sparse and irregular, i.e., not every node is connected to every other node in the network, rather, the order of connectivity is low, and varies across different nodes. This asymmetry of the network implies that the nodes in the network do not perform equally in terms of timelines. Due to the gossiping nature of the network, some nodes are able to track the source very timely, whereas, some nodes fall behind versions quite often. In this work, we investigate how the rate-constrained source should distribute its update rate across the network to maintain fairness regarding timeliness, i.e., the overall worst case performance of the network can be minimized. Due to the continuous search space for optimum rate allocation, we formulate this problem as a continuum-armed bandit problem and employ Gaussian process based Bayesian optimization to meet a trade-off between exploration and exploitation sequentially.
Prompting for explanations improves Adversarial NLI. Is this true? {Yes} it is {true} because {it weakens superficial cues}
Authors: Pride Kavumba, Ana Brassard, Benjamin Heinzerling, Kentaro Inui
Date: 2023-05-01 00:00:00
Proceeding: EACL 2023 5
Tasks: Adversarial Natural Language Inference, Natural Language Inference

Explanation prompts ask language models to not only assign a particular label to a giveninput, such as true, entailment, or contradiction in the case of natural language inference but also to generate a free-text explanation that supports this label. For example: “This is label because explanation.” While this type of prompt was originally introduced with the aim of improving model interpretability, we showhere that explanation prompts also improve robustness to adversarial perturbations in naturallanguage inference benchmarks. Compared to prompting for labels only, explanation prompting consistently yields stronger performance on adversarial benchmarks, outperforming the state of the art on Adversarial Natural Language Inference, Counterfactually-Augmented Natural Language Inference, and SNLI-Hard datasets. We argue that the increase in robustness is due to the fact that prompting for explanations weakens superficial cues. Specifically, single tokens that are highly predictive of the correct answer in the label-only setting become uninformative when the model also has to generate explanations.
Optimal Control of Grid-Interfacing Inverters With Current Magnitude Limits
Authors: Trager Joswig-Jones, Baosen Zhang
Date: 2023-09-30 00:00:00
Tasks: Model Predictive Control

Grid-interfacing inverters act as the interface between renewable resources and the electric grid, and have the potential to offer fast and programmable controls compared to synchronous generators. With this flexibility there has been significant research efforts into determining the best way to control these inverters. Inverters are limited in their maximum current output in order to protect semiconductor devices, presenting a nonlinear constraint that needs to be accounted for in their control algorithms. Existing approaches either simply saturate a controller that is designed for unconstrained systems, or assume small perturbations and linearize a saturated system. These approaches can lead to stability issues or limiting the control actions to be too conservative. In this paper, we directly focus on a nonlinear system that explicitly accounts for the saturation of the current magnitude. We use a Lyapunov stability approach to determine a stability condition for the system, guaranteeing that a class of controllers would be stabilizing if they satisfy a simple SDP condition. With this condition we fit a linear-feedback controller by sampling the output (offline) model predictive control problems. This learned controller has improved performances with existing designs.
Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance
Authors: Qisen Yang, Shenzhi Wang, Qihang Zhang, Gao Huang, Shiji Song
Date: 2023-09-04 00:00:00
Tasks: Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Offline reinforcement learning (RL) optimizes the policy on a previously collected dataset without any interactions with the environment, yet usually suffers from the distributional shift problem. To mitigate this issue, a typical solution is to impose a policy constraint on a policy improvement objective. However, existing methods generally adopt a ``one-size-fits-all'' practice, i.e., keeping only a single improvement-constraint balance for all the samples in a mini-batch or even the entire offline dataset. In this work, we argue that different samples should be treated with different policy constraint intensities. Based on this idea, a novel plug-in approach named Guided Offline RL (GORL) is proposed. GORL employs a guiding network, along with only a few expert demonstrations, to adaptively determine the relative importance of the policy improvement and policy constraint for every sample. We theoretically prove that the guidance provided by our method is rational and near-optimal. Extensive experiments on various environments suggest that GORL can be easily installed on most offline RL algorithms with statistically significant performance improvements.
GCLIQUE: An Open Source Genetic Algorithm for the Maximum Clique Problem
Authors: Shalin Shah
Date: 2020-05-15 00:00:00
Proceeding: journal 2020 5
Tasks: C++ code

A clique in a graph is a set of vertices that are all connected to each other. A maximum clique is a clique of maximum size. A graph may have more than one maximum cliques. The problem of finding a maximum clique is a strongly hard NP-hard problem. It is not possible to find an approximation algorithm which finds a maximum clique that is a constant factor of the optimum solution. In this work, we present a genetic algorithm for the maximum clique problem that is able to find optimum or close to optimum solutions to most DIMACS graphs. The genetic algorithm uses new crossover mechanisms which are able to find reasonably good cliques which can then be used in other applications downstream. We also provide C++ code for our algorithm. Results show that our algorithm is able to find maximum cliques for most DIMACS instances, and if not, close to optimum solutions for the other instances.
Welding Defect Classification Based on Convolution Neural Network (CNN) and Gaussian Kernel
Authors: Agus Khumaidi1, Eko Mulyanto Yuniarno1, 2, Mauridhi Hery Purnomo1, 2 1 Department of Electrical Engineering, 2 Department of Computer Engineering, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia
Date: 2017-09-30 00:00:00
Proceeding: IEEE 2017 9
Tasks: Classification, image-classification, Image Classification

Visual inspection process for weld defects still
manually operated by human vision, so the result of the test still
highly subjective. In this research, the visual inspection process
will be done through image processing on the image sequence to
make data accuracy more better. CNN as one of the image
processing technique can determine the feature automatically
which is suitable for this problem in order to classify the variation
of each weld defect pattern. Classification using Convolution
Neural Network (CNN) consist of two stages: extraction image
using image convolution and image classification using neural
network. Gaussian kernel used for blurring image, it helps the
extraction of images without losing the main information from the
original image, this filter also minimize the occurrence of
interference or noise. Results of the classification used to get the
category of weld defects with high accuracy as a variable of a weld
inspection process whether the weld is pass the standard or not.
The proposed system has obtained classification with validation
accuracy of 95.83% for four different type of welding defect. The
data input of this research is the result of images captured by a
webcam.
Sparse representation-based over-sampling technique for classification of imbalanced dataset
Authors: Xionggao Zou, Yueping Feng, Huiying Li, Shuyu Jiang
Date: 2017-08-04 00:00:00
Proceeding: journal 2017 8

As one of the most popular research fields in machine learning, the research on imbalanced dataset receives more and more attentions in recent years. The imbalanced problem usually occurs in when minority classes have extremely fewer samples than the others. Traditional classification algorithms have not taken the distribution of dataset into consideration, thus they fail to deal with the problem of class-imbalanced learning, and the performance of classification tends to be dominated by the majority class. SMOTE is one of  the most effective over-sampling methods processing this problem, which changes the  distribution of training sets by increasing the size of minority class. However, SMOTE would  easily result in over-fitting on account of too many repetitive data samples. According to this issue, this paper proposes an improved method based on sparse representation theory and oversampling technique, named SROT (Sparse Representation-based Over-sampling Technique).  The SROT uses a sparse dictionary to create synthetic samples directly for solving the  imbalanced problem. The experiments are performed on 10 UCI datasets using C4.5 as the learning algorithm. The experimental results show that compared our algorithm with Random Over-sampling techniques, SMOTE and other methods, SROT can achieve better performance on AUC value
CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks
Authors: Anwar Saida, Rabeeh Ayaz Abbasi, Onaiza Maqbool, Ali Daud, Naif Radi Aljohani
Date: 2017-11-16 00:00:00
Proceeding: journal 2017 11
Tasks: Recommendation Systems

A community structure is an integral part of a social network. Detecting such communities plays an important role in a wide range of 
applications, including but not limited to cluster analysis, recommendation systems and understanding the behaviour of complex systems. Researchers have derived many algorithms to discover the community structures of networks. Discovering communities is a challenging task, and there is no single algorithm that produces the best results for all networks. Therefore, despite many elegant solutions, discovering communities remains an active area of research. In this paper, we propose a novel algorithm, the Clustering Coefficient-based Genetic Algorithm (CC-GA), for detecting them in social and complex networks. Researchers have used several genetic algorithms to detect communities, but the proposed algorithm is novel in terms of both the generation of the initial population and the mutation method, and these improve its efficiency and accuracy. Experiments on a variety of real-world datasets and a comparison to state-of-the-art genetic and non-genetic-based algorithms show improved results
Variational Autoencoder based Anomaly Detection using Reconstruction Probability
Authors: Jinwon An, Sungzoon Cho
Date: 2015-12-27 00:00:00
Proceeding: Special Lecture on IE, SNU Data Mining Center 2015 12
Tasks: Anomaly Detection

We propose an anomaly detection method using the reconstruction probability from the variational autoencoder. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution of variables. The reconstruction probability has a theoretical background making it a more principled and objective anomaly score than the reconstruction error, which is used by autoencoder and principal components based anomaly detection methods. Experimental results show that the proposed method outperforms autoencoder based and principal components based methods. Utilizing the generative characteristics of the variational autoencoder enables deriving the reconstruction of the data to analyze the underlying cause of the anomaly.
CARLA: Adjusted common average referencing for cortico-cortical evoked potential data
Authors: Harvey Huang, Gabriela Ojeda Valencia, Nicholas M. Gregg, Gamaleldin M. Osman, Morgan N. Montoya, Gregory A. Worrell, Kai J. Miller, Dora Hermes
Date: 2023-09-29 00:00:00
Tasks: EEG, Specificity

Human brain connectivity can be mapped by single pulse electrical stimulation during intracranial EEG measurements. The raw cortico-cortical evoked potentials (CCEP) are often contaminated by noise. Common average referencing (CAR) removes common noise and preserves response shapes but can introduce bias from responsive channels. We address this issue with an adjusted, adaptive CAR algorithm termed "CAR by Least Anticorrelation (CARLA)". CARLA was tested on simulated CCEP data and real CCEP data collected from four human participants. In CARLA, the channels are ordered by increasing mean cross-trial covariance, and iteratively added to the common average until anticorrelation between any single channel and all re-referenced channels reaches a minimum, as a measure of shared noise. We simulated CCEP data with true responses in 0 to 45 of 50 total channels. We quantified CARLA's error and found that it erroneously included 0 (median) truly responsive channels in the common average with less than or equal to 42 responsive channels, and erroneously excluded less than or equal to 2.5 (median) unresponsive channels at all responsiveness levels. On real CCEP data, signal quality was quantified with the mean R-squared between all pairs of channels, which represents inter-channel dependency and is low for well-referenced data. CARLA re-referencing produced significantly lower mean R-squared than standard CAR, CAR using a fixed bottom quartile of channels by covariance, and no re-referencing. CARLA minimizes bias in re-referenced CCEP data by adaptively selecting the optimal subset of non-responsive channels. It showed high specificity and sensitivity on simulated CCEP data and lowered inter-channel dependency compared to CAR on real CCEP data.
Spatiotemporal Image Reconstruction to Enable High-Frame Rate Dynamic Photoacoustic Tomography with Rotating-Gantry Volumetric Imagers
Authors: Refik M. Cam, Chao Wang, Weylan Thompson, Sergey A. Ermilov, Mark A. Anastasio, Umberto Villa
Date: 2023-10-01 00:00:00
Tasks: 4D reconstruction, Image Reconstruction

Significance: Dynamic photoacoustic computed tomography (PACT) is a valuable technique for monitoring physiological processes. However, current dynamic PACT techniques are often limited to 2D spatial imaging. While volumetric PACT imagers are commercially available, these systems typically employ a rotating gantry in which the tomographic data are sequentially acquired. Because the object varies during the data-acquisition process, the sequential data-acquisition poses challenges to image reconstruction associated with data incompleteness. The proposed method is highly significant in that it will address these challenges and enable volumetric dynamic PACT imaging with existing imagers. Aim: The aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy. The proposed method aims to overcome the challenges caused by the limited number of tomographic measurements acquired per frame. Approach: A low-rank matrix estimation-based STIR method (LRME-STIR) is proposed to enable dynamic volumetric PACT. The LRME-STIR method leverages the spatiotemporal redundancies to accurately reconstruct a 4D spatiotemporal image. Results: The numerical studies substantiate the LRME-STIR method's efficacy in reconstructing 4D dynamic images from measurements acquired with a rotating gantry. The experimental study demonstrates the method's ability to faithfully recover the flow of a contrast agent at a frame rate of 0.1 s even when only a single tomographic measurement per frame is available. Conclusions: The LRME-STIR method offers a promising solution to the challenges faced by enabling 4D dynamic imaging using commercially available volumetric imagers. By enabling accurate 4D reconstruction, this method has the potential to advance preclinical research.
GPU acceleration of NL-means, BM3D and VBM3D
Authors: Axel Davy, Thibaud Ehret
Date: 2020-02-07 00:00:00
Proceeding: Journal of Real-Time Image Processing (2021) 18:57–74 2020 2
Tasks: CPU, Denoising, GPU, Video Denoising

Denoising is an essential part of any image- or video-processing pipeline. Unfortunately, due to time-processing constraints, many pipelines do not consider the use of modern denoisers. These algorithms have only CPU implementations or suboptimal GPU implementations. We propose a new efficient GPU implementation of NL-means and BM3D, and, to our knowledge, the first GPU implementation of the video-denoising algorithm VBM3D. The performance of these implementations enable their use in real-time scenarios.
A mobile digital device proficiency performance test for cognitive clinical research
Authors: Alan Cronemberger Andrade, Diógenes de Souza Bido, Ana Carolina Bottura de Barros, Walter Richard Boot, Paulo Henrique Ferreira Bertolucci
Date: 2023-10-03 00:00:00

Mobile device proficiency is increasingly important for everyday living, including to deliver healthcare services. Human-device interactions represent a potential in cognitive neurology and aging research. Although traditional pen-and-paper evaluations serve as valuable tools within public health strategies for population-scale cognitive assessments, digital devices could amplify cognitive assessment. However, even person-centered studies often fail to incorporate measures of mobile device proficiency and research with digital mobile technology frequently neglects these evaluations. Besides that, cognitive screening, a fundamental part of brain health evaluation and a widely accepted strategy to identify high-risk individuals vulnerable to cognitive impairment and dementia, has research using digital devices for older adults in need for standardization. To address this shortfall, the DigiTAU collaborative and interdisciplinary project is creating refined methodological parameters for the investigation of digital biomarkers. With careful consideration of cognitive design elements, here we describe the open-source and performance-based Mobile Device Abilities Test (MDAT), a simple, low-cost, and reproductible open-sourced test framework. This result was achieved with a cross-sectional study population sample of 101 low and middle-income subjects aged 20 to 79 years old. Partial least squares structural equation modeling (PLS-SEM) was used to assess the measurement of the construct. It was possible to achieve a reliable method with internal consistency, good content validity related to digital competences, and that does not have much interference with auto-perceived global functional disability, health self-perception, and motor dexterity. Limitations for this method are discussed and paths to improve and establish better standards are highlighted.
Leveraging Data-Driven Models for Accurate Analysis of Grid-Tied Smart Inverters Dynamics
Authors: Sunil Subedi, Nischal Guruwacharya, Bidur Poudel, Jesus D. Vasquez-Plaza, Fabio Andrade, Robert Fourney, Hossein Moradi Rekabdarkolaee, Timothy M. Hansen, Reinaldo Tonkoski
Date: 2023-10-03 00:00:00
Tasks: Fault Detection, parameter estimation

The integration of power electronic converters (PECs) and distributed energy resources (DERs) in modern power systems has introduced dynamism and complexity. Accurate simulation becomes essential to comprehend the influence of converter domination on the power grid. This study addresses the fast-switching and stochastic behaviors exhibited by inverter-based resources in converter-dominated power systems, highlighting the necessity for precise analytical models. In the realm of modeling real-world systems, multiple methodologies exist. Notably, black-box and data-driven system identification techniques are employed to construct PEC models using experimental data, without relying on a priori knowledge of the internal system physics. This approach entails a systematic process of model class selection, parameter estimation, and model validation. While a range of linear and nonlinear model structures and estimation algorithms are at our disposal, it remains imperative to harness creativity and a profound understanding of the physical system to craft data-driven models that align seamlessly with their intended applications. These applications may encompass simulation, prediction, control, or fault detection. This report offers valuable insights into the collection of datasets from commercial off-the-shelf inverters, along with the presentation of intricate simulation models.
Constructing Image-Text Pair Dataset from Books
Authors: Yamato Okamoto, Haruto Toyonaga, Yoshihisa Ijiri, Hirokatsu Kataoka
Date: 2023-10-03 00:00:00
Tasks: Image-text Retrieval, Optical Character Recognition (OCR), Retrieval, Text Retrieval

Digital archiving is becoming widespread owing to its effectiveness in protecting valuable books and providing knowledge to many people electronically. In this paper, we propose a novel approach to leverage digital archives for machine learning. If we can fully utilize such digitized data, machine learning has the potential to uncover unknown insights and ultimately acquire knowledge autonomously, just like humans read books. As a first step, we design a dataset construction pipeline comprising an optical character reader (OCR), an object detector, and a layout analyzer for the autonomous extraction of image-text pairs. In our experiments, we apply our pipeline on old photo books to construct an image-text pair dataset, showing its effectiveness in image-text retrieval and insight extraction.
Automatic Pair Construction for Contrastive Post-training
Authors: Canwen Xu, Corby Rosset, Ethan C. Chau, Luciano del Corro, Shweti Mahajan, Julian McAuley, Jennifer Neville, Ahmed Hassan Awadallah, Nikhil Rao
Date: 2023-10-03 00:00:00

Alignment serves as an important step to steer large language models (LLMs) towards human preferences. In this paper, we propose an automatic way to construct contrastive data for LLM, using preference pairs from multiple models of varying strengths (e.g., InstructGPT, ChatGPT and GPT-4). We compare the contrastive techniques of SLiC and DPO to SFT baselines and find that DPO provides a step-function improvement even after continuing SFT saturates. We also explore a data curriculum learning scheme for contrastive post-training, which starts by learning from "easier" pairs and transitioning to "harder" ones, which further improves alignment. Finally, we scale up our experiments to train with more data and larger models like Orca. Remarkably, our automatic contrastive post-training further improves the performance of Orca, already a state-of-the-art instruction learning model tuned with GPT-4 outputs, to outperform ChatGPT.