pwc-archive/papers-with-abstracts huggingface dataset:
Solutions to Elliptic and Parabolic Problems via Finite Difference Based Unsupervised Small Linear Convolutional Neural Networks
Authors: Adrian Celaya, Keegan Kirk, David Fuentes, Beatrice Riviere
Date: 2023-11-01 00:00:00

In recent years, there has been a growing interest in leveraging deep learning and neural networks to address scientific problems, particularly in solving partial differential equations (PDEs). However, many neural network-based methods like PINNs rely on auto differentiation and sampling collocation points, leading to a lack of interpretability and lower accuracy than traditional numerical methods. As a result, we propose a fully unsupervised approach, requiring no training data, to estimate finite difference solutions for PDEs directly via small linear convolutional neural networks. Our proposed approach uses substantially fewer parameters than similar finite difference-based approaches while also demonstrating comparable accuracy to the true solution for several selected elliptic and parabolic problems compared to the finite difference method.
RAUNE-Net: A Residual and Attention-Driven Underwater Image Enhancement Method
Authors: Wangzhen Peng, Chenghao Zhou, Runze Hu, Jingchao Cao, Yutao Liu
Date: 2023-11-01 00:00:00
Tasks: Image Enhancement, UIE

Underwater image enhancement (UIE) poses challenges due to distinctive properties of the underwater environment, including low contrast, high turbidity, visual blurriness, and color distortion. In recent years, the application of deep learning has quietly revolutionized various areas of scientific research, including UIE. However, existing deep learning-based UIE methods generally suffer from issues of weak robustness and limited adaptability. In this paper, inspired by residual and attention mechanisms, we propose a more reliable and reasonable UIE network called RAUNE-Net by employing residual learning of high-level features at the network's bottle-neck and two aspects of attention manipulations in the down-sampling procedure. Furthermore, we collect and create two datasets specifically designed for evaluating UIE methods, which contains different types of underwater distortions and degradations. The experimental validation demonstrates that our method obtains promising objective performance and consistent visual results across various real-world underwater images compared to other eight UIE methods. Our example code and datasets are publicly available at https://github.com/fansuregrin/RAUNE-Net.
1DFormer: a Transformer Architecture Learning 1D Landmark Representations for Facial Landmark Tracking
Authors: Shi Yin, Shijie Huan, Shangfei Wang, Jinshui Hu, Tao Guo, Bing Yin, BaoCai Yin, Cong Liu
Date: 2023-11-01 00:00:00
Tasks: Landmark Tracking

Recently, heatmap regression methods based on 1D landmark representations have shown prominent performance on locating facial landmarks. However, previous methods ignored to make deep explorations on the good potentials of 1D landmark representations for sequential and structural modeling of multiple landmarks to track facial landmarks. To address this limitation, we propose a Transformer architecture, namely 1DFormer, which learns informative 1D landmark representations by capturing the dynamic and the geometric patterns of landmarks via token communications in both temporal and spatial dimensions for facial landmark tracking. For temporal modeling, we propose a recurrent token mixing mechanism, an axis-landmark-positional embedding mechanism, as well as a confidence-enhanced multi-head attention mechanism to adaptively and robustly embed long-term landmark dynamics into their 1D representations; for structure modeling, we design intra-group and inter-group structure modeling mechanisms to encode the component-level as well as global-level facial structure patterns as a refinement for the 1D representations of landmarks through token communications in the spatial dimension via 1D convolutional layers. Experimental results on the 300VW and the TF databases show that 1DFormer successfully models the long-range sequential patterns as well as the inherent facial structures to learn informative 1D representations of landmark sequences, and achieves state-of-the-art performance on facial landmark tracking.
Decodable and Sample Invariant Continuous Object Encoder
Authors: Dehao Yuan, Furong Huang, Cornelia Fermüller, Yiannis Aloimonos
Date: 2023-10-31 00:00:00
Tasks: Object, Surface Normal Estimation

We propose Hyper-Dimensional Function Encoding (HDFE). Given samples of a continuous object (e.g. a function), HDFE produces an explicit vector representation of the given object, invariant to the sample distribution and density. Sample distribution and density invariance enables HDFE to consistently encode continuous objects regardless of their sampling, and therefore allows neural networks to receive continuous objects as inputs for machine learning tasks, such as classification and regression. Besides, HDFE does not require any training and is proved to map the object into an organized embedding space, which facilitates the training of the downstream tasks. In addition, the encoding is decodable, which enables neural networks to regress continuous objects by regressing their encodings. Therefore, HDFE serves as an interface for processing continuous objects. We apply HDFE to function-to-function mapping, where vanilla HDFE achieves competitive performance as the state-of-the-art algorithm. We apply HDFE to point cloud surface normal estimation, where a simple replacement from PointNet to HDFE leads to immediate 12% and 15% error reductions in two benchmarks. In addition, by integrating HDFE into the PointNet-based SOTA network, we improve the SOTA baseline by 2.5% and 1.7% in the same benchmarks.
Image Restoration with Point Spread Function Regularization and Active Learning
Authors: Peng Jia, Jiameng Lv, Runyu Ning, Yu Song, Nan Li, Kaifan Ji, Chenzhou Cui, Shanshan Li
Date: 2023-10-31 00:00:00
Tasks: Active Learning, Image Restoration

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can directly restore images obtained by the telescope, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by LSST, Euclid, and CSST, to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research.
Object-centric Video Representation for Long-term Action Anticipation
Authors: Ce Zhang, Changcheng Fu, Shijie Wang, Nakul Agarwal, Kwonjoon Lee, Chiho Choi, Chen Sun
Date: 2023-10-31 00:00:00
Tasks: Action Anticipation, Human-Object Interaction Detection, Long Term Action Anticipation, Object, Retrieval, Video Recognition

This paper focuses on building object-centric representations for long-term action anticipation in videos. Our key motivation is that objects provide important cues to recognize and predict human-object interactions, especially when the predictions are longer term, as an observed "background" object could be used by the human actor in the future. We observe that existing object-based video recognition frameworks either assume the existence of in-domain supervised object detectors or follow a fully weakly-supervised pipeline to infer object locations from action labels. We propose to build object-centric video representations by leveraging visual-language pretrained models. This is achieved by "object prompts", an approach to extract task-specific object-centric representations from general-purpose pretrained models without finetuning. To recognize and predict human-object interactions, we use a Transformer-based neural architecture which allows the "retrieval" of relevant objects for action anticipation at various time scales. We conduct extensive evaluations on the Ego4D, 50Salads, and EGTEA Gaze+ benchmarks. Both quantitative and qualitative results confirm the effectiveness of our proposed method.
Hierarchical Information-sharing Convolutional Neural Network for the Prediction of Arctic Sea Ice Concentration and Velocity
Authors: Younghyun Koo, Maryam Rahnemoonfar
Date: 2023-10-31 00:00:00
Tasks: Prediction

Forecasting sea ice concentration (SIC) and sea ice velocity (SIV) in the Arctic Ocean is of great significance as the Arctic environment has been changed by the recent warming climate. Given that physical sea ice models require high computational costs with complex parameterization, deep learning techniques can effectively replace the physical model and improve the performance of sea ice prediction. This study proposes a novel multi-task fully conventional network architecture named hierarchical information-sharing U-net (HIS-Unet) to predict daily SIC and SIV. Instead of learning SIC and SIV separately at each branch, we allow the SIC and SIV layers to share their information and assist each other's prediction through the weighting attention modules (WAMs). Consequently, our HIS-Unet outperforms other statistical approaches, sea ice physical models, and neural networks without such information-sharing units. The improvement of HIS-Unet is more significant to when and where SIC changes seasonally, which implies that the information sharing between SIC and SIV through WAMs helps learn the dynamic changes of SIC and SIV. The weight values of the WAMs imply that SIC information plays a more critical role in SIV prediction, compared to that of SIV information in SIC prediction, and information sharing is more active in marginal ice zones (e.g., East Greenland and Hudson/Baffin Bays) than in the central Arctic.
Medi-CAT: Contrastive Adversarial Training for Medical Image Classification
Authors: Pervaiz Iqbal Khan, Andreas Dengel, Sheraz Ahmed
Date: 2023-10-31 00:00:00
Tasks: Contrastive Learning, image-classification, Image Classification, Medical Image Classification

There are not many large medical image datasets available. For these datasets, too small deep learning models can't learn useful features, so they don't work well due to underfitting, and too big models tend to overfit the limited data. As a result, there is a compromise between the two issues. This paper proposes a training strategy Medi-CAT to overcome the underfitting and overfitting phenomena in medical imaging datasets. Specifically, the proposed training methodology employs large pre-trained vision transformers to overcome underfitting and adversarial and contrastive learning techniques to prevent overfitting. The proposed method is trained and evaluated on four medical image classification datasets from the MedMNIST collection. Our experimental results indicate that the proposed approach improves the accuracy up to 2% on three benchmark datasets compared to well-known approaches, whereas it increases the performance up to 4.1% over the baseline methods.
Joint Depth Prediction and Semantic Segmentation with Multi-View SAM
Authors: Mykhailo Shvets, Dongxu Zhao, Marc Niethammer, Roni Sengupta, Alexander C. Berg
Date: 2023-10-31 00:00:00
Tasks: Decoder, Depth Estimation, Depth Prediction, Prediction, Segmentation, Semantic Segmentation

Multi-task approaches to joint depth and segmentation prediction are well-studied for monocular images. Yet, predictions from a single-view are inherently limited, while multiple views are available in many robotics applications. On the other end of the spectrum, video-based and full 3D methods require numerous frames to perform reconstruction and segmentation. With this work we propose a Multi-View Stereo (MVS) technique for depth prediction that benefits from rich semantic features of the Segment Anything Model (SAM). This enhanced depth prediction, in turn, serves as a prompt to our Transformer-based semantic segmentation decoder. We report the mutual benefit that both tasks enjoy in our quantitative and qualitative studies on the ScanNet dataset. Our approach consistently outperforms single-task MVS and segmentation models, along with multi-task monocular methods.
Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection
Authors: Mohammad Azizmalayeri, Reza Abbasi, Amir Hosein Haji Mohammad rezaie, Reihaneh Zohrabi, Mahdi Amiri, Mohammad Taghi Manzuri, Mohammad Hossein Rohban
Date: 2023-10-31 00:00:00
Tasks: Object, object-detection, Object Detection, Open-vocabulary object detection, Open Vocabulary Object Detection

Deep neural networks have exhibited remarkable performance in various domains. However, the reliance of these models on spurious features has raised concerns about their reliability. A promising solution to this problem is last-layer retraining, which involves retraining the linear classifier head on a small subset of data without spurious cues. Nevertheless, selecting this subset requires human supervision, which reduces its scalability. Moreover, spurious cues may still exist in the selected subset. As a solution to this problem, we propose a novel ranking framework that leverages an open vocabulary object detection technique to identify images without spurious cues. More specifically, we use the object detector as a measure to score the presence of the target object in the images. Next, the images are sorted based on this score, and the last-layer of the model is retrained on a subset of the data with the highest scores. Our experiments on the ImageNet-1k dataset demonstrate the effectiveness of this ranking framework in sorting images based on spuriousness and using them for last-layer retraining.
YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes
Authors: Om M. Khare, Shubham Gandhi, Aditya M. Rahalkar, Sunil Mane
Date: 2023-10-31 00:00:00
Tasks: Computational Efficiency, object-detection, Object Detection, Open Vocabulary Object Detection

Effective detection of road hazards plays a pivotal role in road infrastructure maintenance and ensuring road safety. This research paper provides a comprehensive evaluation of YOLOv8, an object detection model, in the context of detecting road hazards such as potholes, Sewer Covers, and Man Holes. A comparative analysis with previous iterations, YOLOv5 and YOLOv7, is conducted, emphasizing the importance of computational efficiency in various applications. The paper delves into the architecture of YOLOv8 and explores image preprocessing techniques aimed at enhancing detection accuracy across diverse conditions, including variations in lighting, road types, hazard sizes, and types. Furthermore, hyperparameter tuning experiments are performed to optimize model performance through adjustments in learning rates, batch sizes, anchor box sizes, and augmentation strategies. Model evaluation is based on Mean Average Precision (mAP), a widely accepted metric for object detection performance. The research assesses the robustness and generalization capabilities of the models through mAP scores calculated across the diverse test scenarios, underlining the significance of YOLOv8 in road hazard detection and infrastructure maintenance.
View Classification and Object Detection in Cardiac Ultrasound to Localize Valves via Deep Learning
Authors: Derya Gol Gungor, Bimba Rao, Cynthia Wolverton, Ismayil Guracar
Date: 2023-10-31 00:00:00
Proceeding: MIDL 2019 7
Tasks: Classification, Image Segmentation, Object, object-detection, Object Detection, Semantic Segmentation

Echocardiography provides an important tool for clinicians to observe the function of the heart in real time, at low cost, and without harmful radiation. Automated localization and classification of heart valves enables automatic extraction of quantities associated with heart mechanical function and related blood flow measurements. We propose a machine learning pipeline that uses deep neural networks for separate classification and localization steps. As the first step in the pipeline, we apply view classification to echocardiograms with ten unique anatomic views of the heart. In the second step, we apply deep learning-based object detection to both localize and identify the valves. Image segmentation based object detection in echocardiography has been shown in many earlier studies but, to the best of our knowledge, this is the first study that predicts the bounding boxes around the valves along with classification from 2D ultrasound images with the help of deep neural networks. Our object detection experiments applied to the Apical views suggest that it is possible to localize and identify multiple valves precisely.
Decision Support Framework for Home Health Caregiver Allocation Using Optimally Tuned Spectral Clustering and Genetic Algorithm
Authors: Seyed Mohammad Ebrahim Sharifnia, Faezeh Bagheri, Rupy Sawhney, John E. Kobza, Enrique Macias De Anda, Mostafa Hajiaghaei-Keshteli, Michael Mirrielees
Date: 2023-11-01 00:00:00
Tasks: Management

Population aging is a global challenge, leading to increased demand for health care and social services for the elderly. Home Health Care (HHC) is a vital solution to serve this segment of the population. Given the increasing demand for HHC, it is essential to coordinate and regulate caregiver allocation efficiently. This is crucial for both budget-optimized planning and ensuring the delivery of high-quality care. This research addresses a fundamental question in home health agencies (HHAs): "How can caregiver allocation be optimized, especially when caregivers prefer flexibility in their visit sequences?". While earlier studies proposed rigid visiting sequences, our study introduces a decision support framework that allocates caregivers through a hybrid method that considers the flexibility in visiting sequences and aims to reduce travel mileage, increase the number of visits per planning period, and maintain the continuity of care; a critical metric for patient satisfaction. Utilizing data from an HHA in Tennessee, United States, our approach led to an impressive reduction in average travel mileage (up to 42%, depending on discipline) without imposing restrictions on caregivers. Furthermore, the proposed framework is used for caregivers' supply analysis to provide valuable insights into caregiver resource management.
Software Repositories and Machine Learning Research in Cyber Security
Authors: Mounika Vanamala, Keith Bryant, Alex Caravella
Date: 2023-11-01 00:00:00

In today's rapidly evolving technological landscape and advanced software development, the rise in cyber security attacks has become a pressing concern. The integration of robust cyber security defenses has become essential across all phases of software development. It holds particular significance in identifying critical cyber security vulnerabilities at the initial stages of the software development life cycle, notably during the requirement phase. Through the utilization of cyber security repositories like The Common Attack Pattern Enumeration and Classification (CAPEC) from MITRE and the Common Vulnerabilities and Exposures (CVE) databases, attempts have been made to leverage topic modeling and machine learning for the detection of these early-stage vulnerabilities in the software requirements process. Past research themes have returned successful outcomes in attempting to automate vulnerability identification for software developers, employing a mixture of unsupervised machine learning methodologies such as LDA and topic modeling. Looking ahead, in our pursuit to improve automation and establish connections between software requirements and vulnerabilities, our strategy entails adopting a variety of supervised machine learning techniques. This array encompasses Support Vector Machines (SVM), Na\"ive Bayes, random forest, neural networking and eventually transitioning into deep learning for our investigation. In the face of the escalating complexity of cyber security, the question of whether machine learning can enhance the identification of vulnerabilities in diverse software development scenarios is a paramount consideration, offering crucial assistance to software developers in developing secure software.
Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation
Authors: Ta-Chung Chi, Ting-Han Fan, Alexander I. Rudnicky
Date: 2023-11-01 00:00:00
Tasks: Code Completion, Language Modeling, Language Modelling, Question Answering, Retrieval

An ideal length-extrapolatable Transformer language model can handle sequences longer than the training length without any fine-tuning. Such long-context utilization capability relies heavily on a flexible positional embedding design. Upon investigating the flexibility of existing large pre-trained Transformer language models, we find that the T5 family deserves a closer look, as its positional embeddings capture rich and flexible attention patterns. However, T5 suffers from the dispersed attention issue: the longer the input sequence, the flatter the attention distribution. To alleviate the issue, we propose two attention alignment strategies via temperature scaling. Our findings show improvement on the long-context utilization capability of T5 on language modeling, retrieval, multi-document question answering, and code completion tasks without any fine-tuning. This suggests that a flexible positional embedding design and attention alignment can go a long way toward Transformer length extrapolation.
Deep Learning-Based Classification of Gamma Photon Interactions in Room-Temperature Semiconductor Radiation Detectors
Authors: Sandeep K. Chaudhuri, Qinyang Li, Krishna C. Mandal, Jianjun Hu
Date: 2023-11-01 00:00:00

Photon counting radiation detectors have become an integral part of medical imaging modalities such as Positron Emission Tomography or Computed Tomography. One of the most promising detectors is the wide bandgap room temperature semiconductor detectors, which depends on the interaction gamma/x-ray photons with the detector material involves Compton scattering which leads to multiple interaction photon events (MIPEs) of a single photon. For semiconductor detectors like CdZnTeSe (CZTS), which have a high overlap of detected energies between Compton and photoelectric events, it is nearly impossible to distinguish between Compton scattered events from photoelectric events using conventional readout electronics or signal processing algorithms. Herein, we report a deep learning classifier CoPhNet that distinguishes between Compton scattering and photoelectric interactions of gamma/x-ray photons with CdZnTeSe (CZTS) semiconductor detectors. Our CoPhNet model was trained using simulated data to resemble actual CZTS detector pulses and validated using both simulated and experimental data. These results demonstrated that our CoPhNet model can achieve high classification accuracy over the simulated test set. It also holds its performance robustness under operating parameter shifts such as Signal-Noise-Ratio (SNR) and incident energy. Our work thus laid solid foundation for developing next-generation high energy gamma-rays detectors for better biomedical imaging.