pwc-archive/papers-with-abstracts huggingface dataset:
All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation
Authors: Liyao Tang, Zhe Chen, Shanshan Zhao, Chaoyue Wang, DaCheng Tao
Date: 2023-05-25 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: All, Pseudo Label, Segmentation, Weakly-supervised Learning

Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. Existing methods often rely on empirical label selection strategies, such as confidence thresholding, to generate beneficial pseudo-labels for model training. This approach may, however, hinder the comprehensive exploitation of unlabeled data points. We hypothesize that this selective usage arises from the noise in pseudo-labels generated on unlabeled data. The noise in pseudo-labels may result in significant discrepancies between pseudo-labels and model predictions, thus confusing and affecting the model training greatly. To address this issue, we propose a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions. More specifically, our method introduces an Entropy Regularization loss and a Distribution Alignment loss for weakly supervised learning in 3D segmentation tasks, resulting in an ERDA learning strategy. Interestingly, by using KL distance to formulate the distribution alignment loss, it reduces to a deceptively simple cross-entropy-based loss which optimizes both the pseudo-label generation network and the 3D segmentation network simultaneously. Despite the simplicity, our method promisingly improves the performance. We validate the effectiveness through extensive experiments on various baselines and large-scale datasets. Results show that ERDA effectively enables the effective usage of all unlabeled data points for learning and achieves state-of-the-art performance under different settings. Remarkably, our method can outperform fully-supervised baselines using only 1% of true annotations. Code and model will be made publicly available at https://github.com/LiyaoTang/ERDA.
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
Authors: Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, Roger Zimmermann
Date: 2023-06-14 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Traffic Prediction

Road traffic forecasting plays a critical role in smart city initiatives and has experienced significant advancements thanks to the power of deep learning in capturing non-linear patterns of traffic data. However, the promising results achieved on current public datasets may not be applicable to practical scenarios due to limitations within these datasets. First, the limited sizes of them may not reflect the real-world scale of traffic networks. Second, the temporal coverage of these datasets is typically short, posing hurdles in studying long-term patterns and acquiring sufficient samples for training deep models. Third, these datasets often lack adequate metadata for sensors, which compromises the reliability and interpretability of the data. To mitigate these limitations, we introduce the LargeST benchmark dataset. It encompasses a total number of 8,600 sensors in California with a 5-year time coverage and includes comprehensive metadata. Using LargeST, we perform in-depth data analysis to extract data insights, benchmark well-known baselines in terms of their performance and efficiency, and identify challenges as well as opportunities for future research. We release the datasets and baseline implementations at: https://github.com/liuxu77/LargeST.
SAAL: Sharpness-Aware Active Learning
Authors: Yoon-Yeong Kim, Youngjae Cho, JoonHo Jang, Byeonghu Na, Yeongmin Kim, Kyungwoo Song, Wanmo Kang, Il-Chul Moon
Date: 2023-02-02 00:00:00
Proceeding: Proceedings of the 40th International Conference on Machine Learning 2023 2
Tasks: Active Learning, image-classification, Image Classification, object-detection, Object Detection, Semantic Segmentation

While deep neural networks play significant roles in many research areas, they are also prone to overfitting problems under limited data instances. To overcome overfitting, this paper introduces the first active learning method to incorporate the sharpness of loss space into the acquisition function. Specifically, our proposed method, Sharpness-Aware Active Learning (SAAL), constructs its acquisition function by selecting unlabeled instances whose perturbed loss becomes maximum. Unlike the Sharpness-Aware learning with fully-labeled datasets, we design a pseudolabeling mechanism to anticipate the perturbed loss w.r.t. the ground-truth label, which we provide the theoretical bound for the optimization. We conduct experiments on various benchmark
datasets for vision-based tasks in image classification, object detection, and domain adaptive semantic segmentation. The experimental results confirm that SAAL outperforms the baselines by selecting instances that have the potentially maximal perturbation on the loss. The code is available at https://github.com/YoonyeongKim/SAAL.
No trends in spring and autumn phenology during the global warming hiatus
Authors: Xufeng Wang, Jingfeng Xiao, Xin Li, Guodong Cheng, Mingguo Ma, Gaofeng Zhu, M. Altaf Arain, T. Andrew Black & Rachhpal S. Jassal
Date: 2019-06-03 00:00:00
Proceeding: Nature Communications 2019 6

Phenology plays a fundamental role in regulating photosynthesis, evapotranspiration, and surface energy fluxes and is sensitive to climate change. The global mean surface air temperature data indicate a global warming hiatus between 1998 and 2012, while its impacts on global phenology remains unclear. Here we use long-term satellite and FLUXNET records to examine phenology trends in the northern hemisphere before and during the warming hiatus. Our results based on the satellite record show that the phenology change rate slowed down during the warming hiatus. The analysis of the long-term FLUXNET measurements, mainly within the warming hiatus, shows that there were no widespread advancing (or delaying) trends in spring (or autumn) phenology. The lack of widespread phenology trends partly led to the lack of widespread trends in spring and autumn carbon fluxes. Our findings have significant implications for understanding the responses of phenology to climate change and the climate-carbon feedbacks.
CalBERT - Code-mixed Adaptive Language representations using BERT
Authors: Aditeya Baral, Ansh Sarkar, Aronya Baksy, Deeksha D, Ashwini M Joshi
Date: 2022-04-14 00:00:00
Proceeding: AAAI-MAKE 2022 4
Tasks: Natural Language Understanding, Sentiment Analysis

A code-mixed language is a type of language that involves the combination of two or more language varieties in its script or speech. Analysis of code-text is difficult to tackle because the language present is not consistent and does not work with existing monolingual approaches. We propose a novel approach to improve performance in Transformers by introducing an additional step called "Siamese Pre-Training", which allows pre-trained monolingual Transformers to adapt language representations for code-mixed languages with a few examples of code-mixed data. The proposed architectures beat the state of the art F1-score on the Sentiment Analysis for Indian Languages (SAIL) dataset, with the highest possible improvement being 5.1 points, while also achieving the state-of-the-art accuracy on the IndicGLUE Product Reviews dataset by beating the benchmark by 0.4 points.
Object-Oriented Test Case Generation Using Teaching Learning-Based Optimization (TLBO) Algorithm
Authors: OHOOD AL-MASRI, WEDAD AL-SORORI
Date: 2022-10-14 00:00:00
Proceeding: 2022/10/14 2022 10
Tasks: All, software testing

Researchers are currently seeking effective methods for automated software testing to reduce
time, avoid test case redundancy, and create comprehensive test cases to cover (paths, benches, conditions,
and statements). Generating a minimum number of test cases and covering all code paths is challenging in
automated test case generation. Therefore, the use of optimization algorithms has become a popular trend for
generating test cases to achieve many goals. In this study, we used a teaching-learning-based optimization
algorithm to generate the minimum number of test cases. We compared our results with those of other stateof-the-art methods based on the path coverage for ten Java programs. The motive for using this algorithm
is to optimize the number of test cases that cover all code paths in the unit test. The results emphasize that
the proposed algorithm generates the minimum number of test cases and covers all paths in the code at a
full-coverage rate.
SRMAC -- Smoothed Recursive Moving Average Crossover for Real-Time Systolic Peak Detection in Photoplethysmography
Authors: Cesar Abascal Machado, Victor O. Costa, Cesar Augusto Prior, Cesar Ramos Rodrigues
Date: 2023-12-15 00:00:00
Tasks: Photoplethysmography (PPG)

Purpose. Photoplethysmography (PPG) is a non-invasive technique that measures changes in blood flow volume through optical means. Previous research has established the feasibility of PPG peak detection based on the crossover of moving averages. This paper proposes the Smoothed Recuarsive Moving Average Crossover, which eliminates the need for post-processing and nonlinear pre-processing of previous crossover-based peak detectors. The proposed model is advantageous regarding memory and computational complexity, making it attractive for implementations on embedded devices. Methods. Along with this paper, we make available a novel dataset comprising 66 minutes of PPG recordings. The optimization and assessment of the proposed peak detection model use this dataset. Its optimization is accomplished with the simple random search heuristic, while the leave-subject-out cross-validation method provides the means to assess its performance. The source code for all experiments reported in this research is also available in an online repository. Results. The experimental study examines the performance of the proposed model considering different arrangements of the PPG data. The experiments show that the proposed model performs better than the previous crossover-based approach from the literature regarding the precision and recall metrics. More specifically, our model has an average precision of 0.9937 and an average recall of 0.9968. Conclusion. The contribution of this research to the scientific community and literature is twofold. The dataset we collected is open for any researcher, and we improve upon the leading edge on crossover-based PPG peak detection. This improvement comes in terms of performance metrics and computational cost.
A Case Study of Image Enhancement Algorithms' Effectiveness of Improving Neural Networks' Performance on Adverse Images
Authors: Jonathan Sanderson, Syed Rafay Hasan
Date: 2023-12-15 00:00:00
Tasks: Autonomous Vehicles, Image Enhancement

Neural Networks (NNs) have become indispensable for applications of Computer Vision (CV) and their use has been ever-growing. NNs are commonly trained for long periods of time on datasets like ImageNet and COCO that have been carefully created to represent common "real-world" environments. When deployed in the field, such as applications of autonomous vehicles, NNs can encounter adverse scenarios that degrade performance. Using image enhancements algorithms to enhance images before being inferenced on a NN model poses an intriguing alternative to retraining, however, published literature on the effectiveness of this solution is scarce. To fill this knowledge gap, we provide a case study on two popular image enhancement algorithms, Histogram Equalization (HE) and Retinex (RX). We simulate four types of adverse scenarios an autonomous vehicle could encounter, dark, over exposed, foggy, and dark & rainy weather conditions. We evaluate the effectiveness of HE and RX using several well established models:, Resnet, GoogleLeNet, YOLO, and a Vision Transformer.
A Novel 1D Generative Adversarial Network-based Framework for Atrial Fibrillation Detection using Restored Wrist Photoplethysmography Signals
Authors: Faizul Rakib Sayem, Mosabber Uddin Ahmed, Saadia Binte Alam, Sakib Mahmud, Md. Mamun Sheikh, Abdulrahman Alqahtani, Md Ahasan Atick Faisal, Muhammad E. H. Chowdhury
Date: 2023-11-13 00:00:00
Tasks: Atrial Fibrillation Detection, Generative Adversarial Network, Photoplethysmography (PPG)

Atrial fibrillation (AF) increases the risk of stroke. Electrocardiogram (ECG) is used for AF detection, while photoplethysmography (PPG) is simple to use and appropriate for long-term monitoring. We have developed a novel approach to detect AF from smartwatch-based wrist PPG signals. To the best of our knowledge, this is the first study to employ 1D CycleGAN for the reconstruction of 1D wrist PPG signals, since the quality of wrist PPG signals is very poor due to motion artifacts and acquisition site limitations. Our proposed approach is validated on a dataset of 21,278 10s long wrist PPG segments. The dataset was divided into two halves and two experiments were conducted. One half was used for training and the other half for testing and vice versa. Our classification model (Self-AFNet) with 1D-CycleGAN for restoration achieves an accuracy of 96.41% and 97.09% for both splits, respectively. There is a noteworthy improvement in accuracy (2.94% and 5.08% for test splits, respectively) for restored signals as compared to unrestored PPG. AF detection task was also performed using ECG signals with the matched PPG signals to assess the validity of employing reconstructed PPG-based AF classification. Self-AFNet managed to achieve an accuracy of 98.07% and 98.97%, respectively using two ECG splits which is comparable to the performance of AF detection utilizing reconstructed PPG segments. This study reflects that reconstructed wrist PPG signals acquired from wearable devices can be used for reliable detection of atrial fibrillation, which can significantly help in the reduction of the risk of stroke.
NM-FlowGAN: Modeling sRGB Noise without Paired Images using a Hybrid Approach of Normalizing Flows and GAN
Authors: Young Joo Han, Ha-Jin Yu
Date: 2023-12-15 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, Noise Estimation, Noise Level Prediction

Modeling and synthesizing real sRGB noise is crucial for various low-level vision tasks, such as building datasets for training image denoising systems. The distribution of real sRGB noise is highly complex and affected by a multitude of factors, making its accurate modeling extremely challenging. Therefore, recent studies have proposed methods that employ data-driven generative models, such as Generative Adversarial Networks (GAN) and Normalizing Flows. These studies achieve more accurate modeling of sRGB noise compared to traditional noise modeling methods. However, there are performance limitations due to the inherent characteristics of each generative model. To address this issue, we propose NM-FlowGAN, a hybrid approach that exploits the strengths of both GAN and Normalizing Flows. We combine pixel-wise noise modeling networks based on Normalizing Flows and spatial correlation modeling networks based on GAN. Specifically, the pixel-wise noise modeling network leverages the high training stability of Normalizing Flows to capture noise characteristics that are affected by a multitude of factors, and the spatial correlation networks efficiently model pixel-to-pixel relationships. In particular, unlike recent methods that rely on paired noisy images, our method synthesizes noise using clean images and factors that affect noise characteristics, such as easily obtainable parameters like camera type and ISO settings, making it applicable to various fields where obtaining noisy-clean image pairs is not feasible. In our experiments, our NM-FlowGAN outperforms other baselines in the sRGB noise synthesis task. Moreover, the denoising neural network trained with synthesized image pairs from our model shows superior performance compared to other baselines. Our code is available at: \url{https://github.com/YoungJooHan/NM-FlowGAN}.
Towards 6G Digital Twin Channel Using Radio Environment Knowledge Pool
Authors: Jialin Wang, Jianhua Zhang, Yuxiang Zhang, Yutong Sun, Gaofeng, Nie, Lianzheng Shi, Ping Zhang, Guangyi Liu
Date: 2023-12-16 00:00:00

The digital twin channel (DTC) is crucial for 6G wireless autonomous networks as it replicates the wireless channel fading states in 6G air interface transmissions. It is well known that the physical environment influences channels. A key task for accurately twinning channels in complex 6G scenarios is establishing precise relationships between the environment and the channels. In this article, the radio environment knowledge pool (REKP) is proposed, with its core function being to construct and store as much knowledge between the environment and channels as possible. Firstly, the research progress related to DTC is summarized, and a comparative analysis of these achievements on key indicators in digital twin is conducted, proposing the challenges faced in knowledge construction. Secondly, instructions on how to construct and update REKP are given. Then, a typical case is presented to demonstrate the great potential of REKP in enabling DTC. Finally, how to utilize REKP to address open issues in the 6G wireless communication system is discussed, including enhancing performance, reducing costs, and keeping a trustworthy DTC.
Tender Notice Extraction from E-papers Using Neural Network
Authors: Ashmin Bhattarai, Anuj Sedhai, Devraj Neupane, Manish Khadka, Rama Bastola
Date: 2023-12-16 00:00:00

Tender notices are usually sought by most of the companies at regular intervals as a means for obtaining the contracts of various projects. These notices consist of all the required information like description of the work, period of construction, estimated amount of project, etc. In the context of Nepal, tender notices are usually published in national as well as local newspapers. The interested bidders should search all the related tender notices in newspapers. However, it is very tedious for these companies to manually search tender notices in every newspaper and figure out which bid is best suited for them. This project is built with the purpose of solving this tedious task of manually searching the tender notices. Initially, the newspapers are downloaded in PDF format using the selenium library of python. After downloading the newspapers, the e-papers are scanned and tender notices are automatically extracted using a neural network. For extraction purposes, different architectures of CNN namely ResNet, GoogleNet and Xception are used and a model with highest performance has been implemented. Finally, these extracted notices are then published on the website and are accessible to the users. This project is helpful for construction companies as well as contractors assuring quality and efficiency. This project has great application in the field of competitive bidding as well as managing them in a systematic manner.
Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
Authors: Conghan Yue, Zhengwei Peng, Junlong Ma, Shiyan Du, Pengxu Wei, Dongyu Zhang
Date: 2023-12-16 00:00:00
Tasks: Image Inpainting, Image Restoration, Image Super-Resolution, Rain Removal, Single Image Deraining, Super-Resolution

Diffusion models exhibit powerful generative capabilities enabling noise mapping to data via reverse stochastic differential equations. However, in image restoration, the focus is on the mapping relationship from low-quality to high-quality images. Regarding this issue, we introduce the Generalized Ornstein-Uhlenbeck Bridge (GOUB) model. By leveraging the natural mean-reverting property of the generalized OU process and further eliminating the variance of its steady-state distribution through the Doob's h-transform, we achieve diffusion mappings from point to point enabling the recovery of high-quality images from low-quality ones. Moreover, we unravel the fundamental mathematical essence shared by various bridge models, all of which are special instances of GOUB and empirically demonstrate the optimality of our proposed models. Additionally, we present the corresponding Mean-ODE model adept at capturing both pixel-level details and structural perceptions. Experimental outcomes showcase the state-of-the-art performance achieved by both models across diverse tasks, including inpainting, deraining, and super-resolution. Code is available at \url{https://github.com/Hammour-steak/GOUB}.
SHaRPose: Sparse High-Resolution Representation for Human Pose Estimation
Authors: Xiaoqi An, Lin Zhao, Chen Gong, Nannan Wang, Di Wang, Jian Yang
Date: 2023-12-17 00:00:00
Tasks: Pose Estimation

High-resolution representation is essential for achieving good performance in human pose estimation models. To obtain such features, existing works utilize high-resolution input images or fine-grained image tokens. However, this dense high-resolution representation brings a significant computational burden. In this paper, we address the following question: "Only sparse human keypoint locations are detected for human pose estimation, is it really necessary to describe the whole image in a dense, high-resolution manner?" Based on dynamic transformer models, we propose a framework that only uses Sparse High-resolution Representations for human Pose estimation (SHaRPose). In detail, SHaRPose consists of two stages. At the coarse stage, the relations between image regions and keypoints are dynamically mined while a coarse estimation is generated. Then, a quality predictor is applied to decide whether the coarse estimation results should be refined. At the fine stage, SHaRPose builds sparse high-resolution representations only on the regions related to the keypoints and provides refined high-precision human pose estimations. Extensive experiments demonstrate the outstanding performance of the proposed method. Specifically, compared to the state-of-the-art method ViTPose, our model SHaRPose-Base achieves 77.4 AP (+0.5 AP) on the COCO validation set and 76.7 AP (+0.5 AP) on the COCO test-dev set, and infers at a speed of $1.4\times$ faster than ViTPose-Base.
Combinatorial Multi-Armed Bandit Based Unknown Worker Recruitment in Heterogeneous Crowdsensing
Authors: Guoju Gao, Jie Wu, Mingjun Xiao, Guoliang Chen
Date: 2020-07-06 00:00:00
Proceeding: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications 2020 7

Mobile crowdsensing, through which a requester can coordinate a crowd of workers to complete some sensing tasks, has attracted significant attention recently. In this paper, we focus on the unknown worker recruitment problem in mobile crowdsensing, where workers' sensing qualities are unknown a priori. We consider the scenario of recruiting workers to complete some continuous sensing tasks. The whole process is divided into multiple rounds. In each round, every task may be covered by more than one recruited workers, but its completion quality only depends on these workers' maximum sensing quality. Each recruited worker will incur a cost, and each task is attached a weight to indicate its importance. Our objective is to determine a recruiting strategy to maximize the total weighted completion quality under a limited budget. We model such an unknown worker recruitment process as a novel combinatorial multi-armed bandit problem, and propose an extended UCB based worker recruitment algorithm. Moreover, we extend the problem to the case where the workers' costs are also unknown and design the corresponding algorithm. We analyze the regrets of the two proposed algorithms and demonstrate their performance through extensive simulations on real-world traces.