pwc-archive/papers-with-abstracts huggingface dataset:
RAMBO: Enhancing RAG-based Repository-Level Method Body Completion
Authors: Tuan-Dung Bui, Duc-Thieu Luu-Van, Thanh-Phat Nguyen, Thu-Trang Nguyen, Son Nguyen, Hieu Dinh Vo
Date: 2024-09-23 00:00:00
Tasks: Code Completion, Code Generation, RAG

Code completion is essential in software development, helping developers by predicting code snippets based on context. Among completion tasks, Method Body Completion (MBC) is particularly challenging as it involves generating complete method bodies based on their signatures and context. This task becomes significantly harder in large repositories, where method bodies must integrate repositoryspecific elements such as custom APIs, inter-module dependencies, and project-specific conventions. In this paper, we introduce RAMBO, a novel RAG-based approach for repository-level MBC. Instead of retrieving similar method bodies, RAMBO identifies essential repository-specific elements, such as classes, methods, and variables/fields, and their relevant usages. By incorporating these elements and their relevant usages into the code generation process, RAMBO ensures more accurate and contextually relevant method bodies. Our experimental results with leading code LLMs across 40 Java projects show that RAMBO significantly outperformed the state-of-the-art repository-level MBC approaches, with the improvements of up to 46% in BLEU, 57% in CodeBLEU, 36% in Compilation Rate, and up to 3X in Exact Match. Notably, RAMBO surpassed RepoCoder Oracle method by up to 12% in Exact Match, setting a new benchmark for repository-level MBC.
Fast and Accurate Triangle Counting in Graph Streams Using Predictions
Authors: Cristian Boldrin, Fabio Vandin
Date: 2024-09-23 00:00:00

In this work, we present the first efficient and practical algorithm for estimating the number of triangles in a graph stream using predictions. Our algorithm combines waiting room sampling and reservoir sampling with a predictor for the heaviness of edges, that is, the number of triangles in which an edge is involved. As a result, our algorithm is fast, provides guarantees on the amount of memory used, and exploits the additional information provided by the predictor to produce highly accurate estimates. We also propose a simple and domain-independent predictor, based on the degree of nodes, that can be easily computed with one pass on a stream of edges when the stream is available beforehand. Our analytical results show that, when the predictor provides useful information on the heaviness of edges, it leads to estimates with reduced variance compared to the state-of-the-art, even when the predictions are far from perfect. Our experimental results show that, when analyzing a single graph stream, our algorithm is faster than the state-of-the-art for a given memory budget, while providing significantly more accurate estimates. Even more interestingly, when sequences of hundreds of graph streams are analyzed, our algorithm significantly outperforms the state-of-the-art using our simple degree-based predictor built by analyzing only the first graph of the sequence.
HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning
Authors: Naghmeh Shafiee Roudbari, Ursula Eicker, Charalambos Poullis, Zachary Patterson
Date: 2024-09-23 00:00:00
Tasks: Graph Learning, image-classification, Image Classification

Hydrometric forecasting is crucial for managing water resources, flood prediction, and environmental protection. Water stations are interconnected, and this connectivity influences the measurements at other stations. However, the dynamic and implicit nature of water flow paths makes it challenging to extract a priori knowledge of the connectivity structure. We hypothesize that terrain elevation significantly affects flow and connectivity. To incorporate this, we use LiDAR terrain elevation data encoded through a Vision Transformer (ViT). The ViT, which has demonstrated excellent performance in image classification by directly applying transformers to sequences of image patches, efficiently captures spatial features of terrain elevation. To account for both spatial and temporal features, we employ GRU blocks enhanced with graph convolution, a method widely used in the literature. We propose a hybrid graph learning structure that combines static and dynamic graph learning. A static graph, derived from transformer-encoded LiDAR data, captures terrain elevation relationships, while a dynamic graph adapts to temporal changes, improving the overall graph representation. We apply graph convolution in two layers through these static and dynamic graphs. Our method makes daily predictions up to 12 days ahead. Empirical results from multiple water stations in Quebec demonstrate that our method significantly reduces prediction error by an average of 10\% across all days, with greater improvements for longer forecasting horizons.
Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach
Authors: Wang Wumian, Sajal Saha, Anwar Haque, Greg Sidebottom
Date: 2024-09-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Traffic routing is vital for the proper functioning of the Internet. As users and network traffic increase, researchers try to develop adaptive and intelligent routing algorithms that can fulfill various QoS requirements. Reinforcement Learning (RL) based routing algorithms have shown better performance than traditional approaches. We developed a QoS-aware, reusable RL routing algorithm, RLSR-Routing over SDN. During the learning process, our algorithm ensures loop-free path exploration. While finding the path for one traffic demand (a source destination pair with certain amount of traffic), RLSR-Routing learns the overall network QoS status, which can be used to speed up algorithm convergence when finding the path for other traffic demands. By adapting Segment Routing, our algorithm can achieve flow-based, source packet routing, and reduce communications required between SDN controller and network plane. Our algorithm shows better performance in terms of load balancing than the traditional approaches. It also has faster convergence than the non-reusable RL approach when finding paths for multiple traffic demands.
Machine Learning Toric Duality in Brane Tilings
Authors: Pietro Capuozzo, Tancredi Schettini Gherardini, Benjamin Suzzoni
Date: 2024-09-23 00:00:00

We apply a variety of machine learning methods to the study of Seiberg duality within 4d $\mathcal{N}=1$ quantum field theories arising on the worldvolumes of D3-branes probing toric Calabi-Yau 3-folds. Such theories admit an elegant description in terms of bipartite tessellations of the torus known as brane tilings or dimer models. An intricate network of infrared dualities interconnects the space of such theories and partitions it into universality classes, the prediction and classification of which is a problem that naturally lends itself to a machine learning investigation. In this paper, we address a preliminary set of such enquiries. We begin by training a fully connected neural network to identify classes of Seiberg dual theories realised on $\mathbb{Z}_m\times\mathbb{Z}_n$ orbifolds of the conifold and achieve $R^2=0.988$. Then, we evaluate various notions of robustness of our methods against perturbations of the space of theories under investigation, and discuss these results in terms of the nature of the neural network's learning. Finally, we employ a more sophisticated residual architecture to classify the toric phase space of the $Y^{6,0}$ theories, and to predict the individual gauged linear $\sigma$-model multiplicities in toric diagrams thereof. In spite of the non-trivial nature of this task, we achieve remarkably accurate results; namely, upon fixing a choice of Kasteleyn matrix representative, the regressor achieves a mean absolute error of $0.021$. We also discuss how the performance is affected by relaxing these assumptions.
Intrinsic Single-Image HDR Reconstruction
Authors: Sebastian Dille, Chris Careaga, Yağız Aksoy
Date: 2024-09-20 00:00:00
Tasks: HDR Reconstruction, Single-Image-Based Hdr Reconstruction

The low dynamic range (LDR) of common cameras fails to capture the rich contrast in natural scenes, resulting in loss of color and details in saturated pixels. Reconstructing the high dynamic range (HDR) of luminance present in the scene from single LDR photographs is an important task with many applications in computational photography and realistic display of images. The HDR reconstruction task aims to infer the lost details using the context present in the scene, requiring neural networks to understand high-level geometric and illumination cues. This makes it challenging for data-driven algorithms to generate accurate and high-resolution results. In this work, we introduce a physically-inspired remodeling of the HDR reconstruction problem in the intrinsic domain. The intrinsic model allows us to train separate networks to extend the dynamic range in the shading domain and to recover lost color details in the albedo domain. We show that dividing the problem into two simpler sub-tasks improves performance in a wide variety of photographs.
ViTGuard: Attention-aware Detection against Adversarial Examples for Vision Transformer
Authors: Shihua Sun, Kenechukwu Nwodo, Shridatt Sugrim, Angelos Stavrou, Haining Wang
Date: 2024-09-20 00:00:00
Tasks: image-classification, Image Classification, Image Reconstruction

The use of transformers for vision tasks has challenged the traditional dominant role of convolutional neural networks (CNN) in computer vision (CV). For image classification tasks, Vision Transformer (ViT) effectively establishes spatial relationships between patches within images, directing attention to important areas for accurate predictions. However, similar to CNNs, ViTs are vulnerable to adversarial attacks, which mislead the image classifier into making incorrect decisions on images with carefully designed perturbations. Moreover, adversarial patch attacks, which introduce arbitrary perturbations within a small area, pose a more serious threat to ViTs. Even worse, traditional detection methods, originally designed for CNN models, are impractical or suffer significant performance degradation when applied to ViTs, and they generally overlook patch attacks. In this paper, we propose ViTGuard as a general detection method for defending ViT models against adversarial attacks, including typical attacks where perturbations spread over the entire input and patch attacks. ViTGuard uses a Masked Autoencoder (MAE) model to recover randomly masked patches from the unmasked regions, providing a flexible image reconstruction strategy. Then, threshold-based detectors leverage distinctive ViT features, including attention maps and classification (CLS) token representations, to distinguish between normal and adversarial samples. The MAE model does not involve any adversarial samples during training, ensuring the effectiveness of our detectors against unseen attacks. ViTGuard is compared with seven existing detection methods under nine attacks across three datasets. The evaluation results show the superiority of ViTGuard over existing detectors. Finally, considering the potential detection evasion, we further demonstrate ViTGuard's robustness against adaptive attacks for evasion.
SSE: Multimodal Semantic Data Selection and Enrichment for Industrial-scale Data Assimilation
Authors: Maying Shen, Nadine Chang, Sifei Liu, Jose M. Alvarez
Date: 2024-09-20 00:00:00
Tasks: Autonomous Vehicles, Diversity, Navigate

In recent years, the data collected for artificial intelligence has grown to an unmanageable amount. Particularly within industrial applications, such as autonomous vehicles, model training computation budgets are being exceeded while model performance is saturating -- and yet more data continues to pour in. To navigate the flood of data, we propose a framework to select the most semantically diverse and important dataset portion. Then, we further semantically enrich it by discovering meaningful new data from a massive unlabeled data pool. Importantly, we can provide explainability by leveraging foundation models to generate semantics for every data point. We quantitatively show that our Semantic Selection and Enrichment framework (SSE) can a) successfully maintain model performance with a smaller training dataset and b) improve model performance by enriching the smaller dataset without exceeding the original dataset size. Consequently, we demonstrate that semantic diversity is imperative for optimal data selection and model performance.
Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning
Authors: Yixin Wang, Wei Peng, Yu Zhang, Ehsan Adeli, Qingyu Zhao, Kilian M. Pohl
Date: 2024-09-20 00:00:00
Tasks: Contrastive Learning, Graph Attention

Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing so requires accurate encoding of their multidimensional relationship while accounting for individual variability over time. For this purpose, we propose an unsupervised learning model (called \underline{\textbf{Co}}ntrastive Learning-based \underline{\textbf{Gra}}ph Generalized \underline{\textbf{Ca}}nonical Correlation Analysis (CoGraCa)) that encodes their relationship via Graph Attention Networks and generalized Canonical Correlational Analysis. To create brain-cognition fingerprints reflecting unique neural and cognitive phenotype of each person, the model also relies on individualized and multimodal contrastive learning. We apply CoGraCa to longitudinal dataset of healthy individuals consisting of resting-state functional MRI and cognitive measures acquired at multiple visits for each participant. The generated fingerprints effectively capture significant individual differences and outperform current single-modal and CCA-based multimodal models in identifying sex and age. More importantly, our encoding provides interpretable interactions between those two modalities.
OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping
Authors: Jiale Wei, Junwei Zheng, Ruiping Liu, Jie Hu, Jiaming Zhang, Rainer Stiefelhagen
Date: 2024-09-20 00:00:00
Tasks: Autonomous Driving, Mamba

In the field of autonomous driving, Bird's-Eye-View (BEV) perception has attracted increasing attention in the community since it provides more comprehensive information compared with pinhole front-view images and panoramas. Traditional BEV methods, which rely on multiple narrow-field cameras and complex pose estimations, often face calibration and synchronization issues. To break the wall of the aforementioned challenges, in this work, we introduce OneBEV, a novel BEV semantic mapping approach using merely a single panoramic image as input, simplifying the mapping process and reducing computational complexities. A distortion-aware module termed Mamba View Transformation (MVT) is specifically designed to handle the spatial distortions in panoramas, transforming front-view features into BEV features without leveraging traditional attention mechanisms. Apart from the efficient framework, we contribute two datasets, i.e., nuScenes-360 and DeepAccident-360, tailored for the OneBEV task. Experimental results showcase that OneBEV achieves state-of-the-art performance with 51.1% and 36.1% mIoU on nuScenes-360 and DeepAccident-360, respectively. This work advances BEV semantic mapping in autonomous driving, paving the way for more advanced and reliable autonomous systems.
Data Pruning via Separability, Integrity, and Model Uncertainty-Aware Importance Sampling
Authors: Steven Grosz, Rui Zhao, Rajeev Ranjan, Hongcheng Wang, Manoj Aggarwal, Gerard Medioni, Anil Jain
Date: 2024-09-20 00:00:00
Tasks: Classification, image-classification, Image Classification

This paper improves upon existing data pruning methods for image classification by introducing a novel pruning metric and pruning procedure based on importance sampling. The proposed pruning metric explicitly accounts for data separability, data integrity, and model uncertainty, while the sampling procedure is adaptive to the pruning ratio and considers both intra-class and inter-class separation to further enhance the effectiveness of pruning. Furthermore, the sampling method can readily be applied to other pruning metrics to improve their performance. Overall, the proposed approach scales well to high pruning ratio and generalizes better across different classification models, as demonstrated by experiments on four benchmark datasets, including the fine-grained classification scenario.
Monocular Event-Inertial Odometry with Adaptive decay-based Time Surface and Polarity-aware Tracking
Authors: Kai Tang, Xiaolei Lang, Yukai Ma, Yuehao Huang, Laijian Li, Yong liu, Jiajun Lv
Date: 2024-09-21 00:00:00

Event cameras have garnered considerable attention due to their advantages over traditional cameras in low power consumption, high dynamic range, and no motion blur. This paper proposes a monocular event-inertial odometry incorporating an adaptive decay kernel-based time surface with polarity-aware tracking. We utilize an adaptive decay-based Time Surface to extract texture information from asynchronous events, which adapts to the dynamic characteristics of the event stream and enhances the representation of environmental textures. However, polarity-weighted time surfaces suffer from event polarity shifts during changes in motion direction. To mitigate its adverse effects on feature tracking, we optimize the feature tracking by incorporating an additional polarity-inverted time surface to enhance the robustness. Comparative analysis with visual-inertial and event-inertial odometry methods shows that our approach outperforms state-of-the-art techniques, with competitive results across various datasets.
Improving 3D Semi-supervised Learning by Effectively Utilizing All Unlabelled Data
Authors: Sneha Paul, Zachary Patterson, Nizar Bouguila
Date: 2024-09-21 00:00:00
Proceeding: European Conference on Computer Vision (ECCV), 2024 2024 11
Tasks: 3D Classification, 3D Object Recognition

Semi-supervised learning (SSL) has shown its effectiveness in learning effective 3D representation from a small amount of labelled data while utilizing large unlabelled data. Traditional semi-supervised approaches rely on the fundamental concept of predicting pseudo-labels for unlabelled data and incorporating them into the learning process. However, we identify that the existing methods do not fully utilize all the unlabelled samples and consequently limit their potential performance. To address this issue, we propose AllMatch, a novel SSL-based 3D classification framework that effectively utilizes all the unlabelled samples. AllMatch comprises three modules: (1) an adaptive hard augmentation module that applies relatively hard augmentations to the high-confident unlabelled samples with lower loss values, thereby enhancing the contribution of such samples, (2) an inverse learning module that further improves the utilization of unlabelled data by learning what not to learn, and (3) a contrastive learning module that ensures learning from all the samples in both supervised and unsupervised settings. Comprehensive experiments on two popular 3D datasets demonstrate a performance improvement of up to 11.2% with 1% labelled data, surpassing the SOTA by a significant margin. Furthermore, AllMatch exhibits its efficiency in effectively leveraging all the unlabelled data, demonstrated by the fact that only 10% of labelled data reaches nearly the same performance as fully-supervised learning with all labelled data. The code of our work is available at: https://github.com/snehaputul/AllMatch.
CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise
Authors: Fuyang Yu, Runze Tian, Zhen Wang, Xiaochuan Wang, Xiaohui Liang
Date: 2024-09-21 00:00:00
Tasks: Open Vocabulary Semantic Segmentation, Open-Vocabulary Semantic Segmentation, Segmentation, Semantic Segmentation, Unsupervised Semantic Segmentation

To ease the difficulty of acquiring annotation labels in 3D data, a common method is using unsupervised and open-vocabulary semantic segmentation, which leverage 2D CLIP semantic knowledge. In this paper, unlike previous research that ignores the ``noise'' raised during feature projection from 2D to 3D, we propose a novel distillation learning framework named CUS3D. In our approach, an object-level denosing projection module is designed to screen out the ``noise'' and ensure more accurate 3D feature. Based on the obtained features, a multimodal distillation learning module is designed to align the 3D feature with CLIP semantic feature space with object-centered constrains to achieve advanced unsupervised semantic segmentation. We conduct comprehensive experiments in both unsupervised and open-vocabulary segmentation, and the results consistently showcase the superiority of our model in achieving advanced unsupervised segmentation results and its effectiveness in open-vocabulary segmentation.
Multilateral Cascading Network for Semantic Segmentation of Large-Scale Outdoor Point Clouds
Authors: Haoran Gong, Haodong Wang, Di Wang
Date: 2024-09-21 00:00:00
Tasks: Scene Understanding, Semantic Segmentation

Semantic segmentation of large-scale outdoor point clouds is of significant importance in environment perception and scene understanding. However, this task continues to present a significant research challenge, due to the inherent complexity of outdoor objects and their diverse distributions in real-world environments. In this study, we propose the Multilateral Cascading Network (MCNet) designed to address this challenge. The model comprises two key components: a Multilateral Cascading Attention Enhancement (MCAE) module, which facilitates the learning of complex local features through multilateral cascading operations; and a Point Cross Stage Partial (P-CSP) module, which fuses global and local features, thereby optimizing the integration of valuable feature information across multiple scales. Our proposed method demonstrates superior performance relative to state-of-the-art approaches across two widely recognized benchmark datasets: Toronto3D and SensatUrban. Especially on the city-scale SensatUrban dataset, our results surpassed the current best result by 2.1\% in overall mIoU and yielded an improvement of 15.9\% on average for small-sample object categories comprising less than 2\% of the total samples, in comparison to the baseline method.
Cycle-Consistency Uncertainty Estimation for Visual Prompting based One-Shot Defect Segmentation
Authors: Geonuk Kim
Date: 2024-09-21 00:00:00
Tasks: Defect Detection, Visual Prompting

Industrial defect detection traditionally relies on supervised learning models trained on fixed datasets of known defect types. While effective within a closed set, these models struggle with new, unseen defects, necessitating frequent re-labeling and re-training. Recent advances in visual prompting offer a solution by allowing models to adaptively infer novel categories based on provided visual cues. However, a prevalent issue in these methods is the over-confdence problem, where models can mis-classify unknown objects as known objects with high certainty. To addresssing the fundamental concerns about the adaptability, we propose a solution to estimate uncertainty of the visual prompting process by cycle-consistency. We designed to check whether it can accurately restore the original prompt from its predictions. To quantify this, we measure the mean Intersection over Union (mIoU) between the restored prompt mask and the originally provided prompt mask. Without using complex designs or ensemble methods with multiple networks, our approach achieved a yield rate of 0.9175 in the VISION24 one-shot industrial challenge.