pwc-archive/papers-with-abstracts huggingface dataset:
Cooperative Load Transportation Using Multiple UAVs
Authors: Behzad Shirani, Majdeddin Najafi, Iman Izadi
Date: 2019-08-01 00:00:00
Proceeding: Aerospace Science and Technology 2019 8

The aim of this paper is cooperative task assignment to multiple unmanned aerial vehicles (UAV) for load transportation. The main goal is to transport a slung load safely with minimal swing. To this end, for each UAV, which is regarded as an agent,
a distributed controller is proposed. The proposed controller guarantees a fixed formation, which in turn achieves the main objective. A model of the system is obtained using the Udwadia-Kalaba method for an arbitrary number of UAVs and one slung load with ropes. This method leads to a novel multi-agent system model with interactions between neighbor and non-neighbor agents. The control law is then proposed based on sub-optimal LQR-PID for the extended system. Simulation results are presented to verify the ability of the proposed method to keep the formation of the agents, and to guide the load in the desired direction
Learning Self-prior for Mesh Denoising using Dual Graph Convolutional Networks
Authors: Shota Hattori, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki ;
Date: 2022-01-01 00:00:00
Proceeding: ECCV 2022 1
Tasks: Denoising, Image Restoration

"This study proposes a deep-learning framework for mesh denoising from a single noisy input, where two graph convolutional networks are trained jointly to filter vertex positions and facet normals apart. The prior obtained only from a single input is particularly referred to as a self-prior. The proposed method leverages the framework of the deep image prior (DIP), which obtains the self-prior for image restoration using a convolutional neural network (CNN). Thus, we obtain a denoised mesh without any ground-truth noise-free meshes. Compared to the original DIP that transforms a fixed random code into a noise-free image by the neural network, we reproduce vertex displacement from a fixed random code and reproduce facet normals from feature vectors that summarize local triangle arrangements. After tuning several hyperparameters with a few validation samples, our method achieved significantly higher performance than traditional approaches working with a single noisy input mesh. Moreover, its performance is better than the other methods using deep neural networks trained with a large-scale shape dataset. The independence of our method of either large-scale datasets or ground-truth noise-free mesh will allow us to easily denoise meshes whose shapes are rarely included in the shape datasets."
GeoBi-GNN: Geometry-aware Bi-domain Mesh Denoising via Graph Neural Networks
Authors: Yingkui Zhang, Guibao Shen, Qiong Wang, Yinling Qian, Mingqiang Wei, Jing Qin
Date: 2022-03-01 00:00:00
Proceeding: Computer-Aided Design 2022 3
Tasks: Denoising, Graph Neural Network

Mesh denoising is an essential geometric processing step for raw meshes generated by 3D scanners and depth cameras. It is intended to remove noise while preserving surface intrinsic features of the underlying model. Existing mesh denoising wisdoms of (1) updating mesh vertices directly (referred to as one-step mesh denoising in the spatial domain) or (2) normal filtering followed by vertex updating (referred to as two-step mesh denoising in the normal domain) seldom consider the correlation between vertex updating and normal filtering. This paper proposes a novel end-to-end geometry-aware dual-graph neural network, called GeoBi-GNN, to perform denoising in spatial and normal domains simultaneously. For the first time, we optimize both positions and normals (i.e., dual domains) in a unified framework of GNN, and show the powerful inter-coordination between the dual domains. GeoBi-GNN fully excavates the native dual-graph structure in the mesh, and creates two graph structures for the spatial noise and the normal noise respectively through the adjacency relationship between vertices and surfaces. We design each GNN as a three-layer U-Net architecture to gradually extract multi-scale features from the input graphs. In addition, a specific graph pooling layer with a cascaded weight estimation strategy is designed to improve the robustness and denoising effect. Due to the intuitive relation between the mesh connectivity and the dual graphs, the proposed method is simple to implement. Comprehensive experiments exhibit that the proposed method is significantly superior to the state-of-the-arts of mesh denoising, especially for the large-scale noise and the complex real scans.
A RUpture-Based detection method for the Active mesopeLagIc Zone (RUBALIZ): A crucial step toward rigorous carbon budget assessments
Authors: Robin Fuchs, Chloé M. J. Baumas, Marc Garel, David Nerini, Frédéric A. C. Le Moigne, Christian Tamburini
Date: 2022-10-28 00:00:00
Proceeding: Limnology & Oceanography: Methods 2022 10

Determining mesopelagic organic carbon budgets is essential to characterize the ocean's role as a carbon dioxide sink. This is because the biological processes observed in the mesopelagic zone are crucial for understanding the biological carbon pump. Yet, field assessments of carbon budgets are often unbalanced with the carbon demand exceeding its supply. This underlines either methodological issues in the budget calculations or incomplete knowledge of the mesopelagic carbon cycling with potentially missing sources. Carbon budgets are built by partitioning the ocean into vertical depth zones. Vertical boundaries are conventionally defined between 200 and 1000 m depth or using various thresholds. Such approaches lack consistent methodology preventing robust comparison of mesopelagic carbon budget from region to region. Here, using a statistical rupture detection method applied to conductivity–temperature–depth (CTD)-cast variables (fluorescence, O2 concentration, potential temperature, salinity, and density), we aim to provide independent estimates of mesopelagic boundaries. We demonstrate that the so-determined upper boundary is highly correlated with the knee points of the particulate organic carbon (POC) fluxes estimated by a power law and that over 90% of the POC flux attenuation occurs within our method boundaries. The identified zone therefore corresponds to the most active part of the conventional mesopelagic zone and we name it the “active mesopelagic zone” (AMZ). We find that the depths of the mesopelagic zone depend on the region considered. Our results demonstrate that the mesopelagic carbon budget discrepancy can vary up to four folds depending on the boundaries chosen and hence provide novel grounds to reassess existing and future mesopelagic carbon budgets.
A large-Scale TV Dataset for partial video copy detection
Authors: Van-Hao LE, Mathieu Delalandre, and Donatello Conte
Date: 2022-05-15 00:00:00
Proceeding: ICIAP 2022 5
Tasks: Copy Detection, Partial Video Copy Detection

This paper is interested with the performance evaluation of the partial video copy detection. Several public datasets exist designed from web videos. The detection problem is inherent to the continuous video broadcasting. The alternative is then to process with TV datasets offering a deeper scalability and a control of degradations for a fine performance evaluation. We propose in this paper a TV dataset called STVD. It is designed with a protocol ensuring a scalable capture and robust groundtruthing. STVD is the largest public dataset on the task with a near 83k videos having a total duration of 10, 660 hours. Perfor- mance evaluation results of representative methods on the dataset are reported in the paper for a baseline comparison.
Automatic recognition of flow cytometric phytoplankton functional groups using convolutional neural networks
Authors: Robin Fuchs, Melilotus Thyssen, Véronique Creach, Mathilde Dugenne, Lloyd Izard, Marie Latimier, Arnaud Louchart, Pierre Marrec, Machteld Rijkeboer, Gérald Grégori, Denys Pommeret
Date: 2022-06-02 00:00:00
Proceeding: Limnology and Oceanography: Methods 2022 6
Tasks: Classification

The variability of phytoplankton distribution has been unraveled by high-frequency measurements. Such a resolution can be approached by automated pulse-shape recording flow cytometry (AFCM) operating at hourly sampling resolution. AFCM records morphological and physiological traits as single-cell optical pulse shapes that can be used to classify cells into phytoplankton functional groups (PFGs). However, the associated manual post-processing of the data coupled with the increasing size and number of datasets is time-consuming and error-prone. Machine learning models are increasingly used to run automatic classification. Yet, most of the existing methods either present a long training process, need to manually design features from the raw optical pulse shapes, or are dedicated to images only. In this study, we present a convolutional neural network (CNN) to classify several PFGs using AFCM pulse shapes. The uncertainties of manual classification were first estimated by comparing experts' recognition of six PFGs. Consensual particles from the manual PFG classification were used to train and validate the CNN. The CNN obtained competitive performances compared to other models used in the literature and remained robust across several sampling areas, and instrumental hardware and settings. Finally, we assessed the ability of this classifier to predict phytoplankton counts at a Mediterranean coastal station and from a cruise in the South-West Indian Ocean, providing a comparison with the manual classification over 3-month periods and a 2h frequency. These promising results strengthen the near real-time observation of PFGs, especially required with the increasing use of AFCM in monitoring research programs.
ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
Authors: Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, Matthias Müller
Date: 2023-02-23 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Zero-shot Generalization

This paper tackles the problem of depth estimation from a single image. Existing work either focuses on generalization performance disregarding metric scale, i.e. relative depth estimation, or state-of-the-art results on specific datasets, i.e. metric depth estimation. We propose the first approach that combines both worlds, leading to a model with excellent generalization performance while maintaining metric scale. Our flagship model, ZoeD-M12-NK, is pre-trained on 12 datasets using relative depth and fine-tuned on two datasets using metric depth. We use a lightweight head with a novel bin adjustment design called metric bins module for each domain. During inference, each input image is automatically routed to the appropriate head using a latent classifier. Our framework admits multiple configurations depending on the datasets used for relative depth pre-training and metric fine-tuning. Without pre-training, we can already significantly improve the state of the art (SOTA) on the NYU Depth v2 indoor dataset. Pre-training on twelve datasets and fine-tuning on the NYU Depth v2 indoor dataset, we can further improve SOTA for a total of 21% in terms of relative absolute error (REL). Finally, ZoeD-M12-NK is the first model that can jointly train on multiple datasets (NYU Depth v2 and KITTI) without a significant drop in performance and achieve unprecedented zero-shot generalization performance to eight unseen datasets from both indoor and outdoor domains. The code and pre-trained models are publicly available at https://github.com/isl-org/ZoeDepth .
Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
Authors: Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, Jianfeng Gao
Date: 2023-02-24 00:00:00
Tasks: Informativeness, Open-Domain Question Answering, Question Answering

Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. However, applying LLMs to real-world, mission-critical applications remains challenging mainly due to their tendency to generate hallucinations and their inability to use external knowledge. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules. Our system makes the LLM generate responses grounded in external knowledge, e.g., stored in task-specific databases. It also iteratively revises LLM prompts to improve model responses using feedback generated by utility functions, e.g., the factuality score of a LLM-generated response. The effectiveness of LLM-Augmenter is empirically validated on two types of scenarios, task-oriented dialog and open-domain question answering. LLM-Augmenter significantly reduces ChatGPT's hallucinations without sacrificing the fluency and informativeness of its responses. We make the source code and models publicly available.
HULAT at SemEval-2023 Task 9: Data augmentation for pre-trained transformers applied to Multilingual Tweet Intimacy Analysis
Authors: Isabel Segura-Bedmar
Date: 2023-02-24 00:00:00
Tasks: Data Augmentation, Position

This paper describes our participation in SemEval-2023 Task 9, Intimacy Analysis of Multilingual Tweets. We fine-tune some of the most popular transformer models with the training dataset and synthetic data generated by different data augmentation techniques. During the development phase, our best results were obtained by using XLM-T. Data augmentation techniques provide a very slight improvement in the results. Our system ranked in the 27th position out of the 45 participating systems. Despite its modest results, our system shows promising results in languages such as Portuguese, English, and Dutch. All our code is available in the repository \url{https://github.com/isegura/hulat_intimacy}.
STA: Self-controlled Text Augmentation for Improving Text Classifications
Authors: Congcong Wang, Gonzalo Fiz Pontiveros, Steven Derby, Tri Kurniawan Wijaya
Date: 2023-02-24 00:00:00
Tasks: Benchmarking, Text Augmentation

Despite recent advancements in Machine Learning, many tasks still involve working in low-data regimes which can make solving natural language problems difficult. Recently, a number of text augmentation techniques have emerged in the field of Natural Language Processing (NLP) which can enrich the training data with new examples, though they are not without their caveats. For instance, simple rule-based heuristic methods are effective, but lack variation in semantic content and syntactic structure with respect to the original text. On the other hand, more complex deep learning approaches can cause extreme shifts in the intrinsic meaning of the text and introduce unwanted noise into the training data. To more reliably control the quality of the augmented examples, we introduce a state-of-the-art approach for Self-Controlled Text Augmentation (STA). Our approach tightly controls the generation process by introducing a self-checking procedure to ensure that generated examples retain the semantic content of the original text. Experimental results on multiple benchmarking datasets demonstrate that STA substantially outperforms existing state-of-the-art techniques, whilst qualitative analysis reveals that the generated examples are both lexically diverse and semantically reliable.
Language-Driven Representation Learning for Robotics
Authors: Siddharth Karamcheti, Suraj Nair, Annie S. Chen, Thomas Kollar, Chelsea Finn, Dorsa Sadigh, Percy Liang
Date: 2023-02-24 00:00:00
Tasks: Contrastive Learning, Imitation Learning, Representation Learning, Text Generation

Recent work in visual representation learning for robotics demonstrates the viability of learning from large video datasets of humans performing everyday tasks. Leveraging methods such as masked autoencoding and contrastive learning, these representations exhibit strong transfer to policy learning for visuomotor control. But, robot learning encompasses a diverse set of problems beyond control including grasp affordance prediction, language-conditioned imitation learning, and intent scoring for human-robot collaboration, amongst others. First, we demonstrate that existing representations yield inconsistent results across these tasks: masked autoencoding approaches pick up on low-level spatial features at the cost of high-level semantics, while contrastive learning approaches capture the opposite. We then introduce Voltron, a framework for language-driven representation learning from human videos and associated captions. Voltron trades off language-conditioned visual reconstruction to learn low-level visual patterns, and visually-grounded language generation to encode high-level semantics. We also construct a new evaluation suite spanning five distinct robot learning problems $\unicode{x2013}$ a unified platform for holistically evaluating visual representations for robotics. Through comprehensive, controlled experiments across all five problems, we find that Voltron's language-driven representations outperform the prior state-of-the-art, especially on targeted problems requiring higher-level features.
Modularity-based approach for tracking communities in dynamic social networks
Authors: Michele Mazza, Guglielmo Cola, Maurizio Tesconi
Date: 2023-02-24 00:00:00
Tasks: Community Detection

Community detection is a crucial task to unravel the intricate dynamics of online social networks. The emergence of these networks has dramatically increased the volume and speed of interactions among users, presenting researchers with unprecedented opportunities to explore and analyze the underlying structure of social communities. Despite a growing interest in tracking the evolution of groups of users in real-world social networks, the predominant focus of community detection efforts has been on communities within static networks. In this paper, we introduce a novel framework for tracking communities over time in a dynamic network, where a series of significant events is identified for each community. Our framework adopts a modularity-based strategy and does not require a predefined threshold, leading to a more accurate and robust tracking of dynamic communities. We validated the efficacy of our framework through extensive experiments on synthetic networks featuring embedded events. The results indicate that our framework can outperform the state-of-the-art methods. Furthermore, we utilized the proposed approach on a Twitter network comprising over 60,000 users and 5 million tweets throughout 2020, showcasing its potential in identifying dynamic communities in real-world scenarios. The proposed framework can be applied to different social networks and provides a valuable tool to gain deeper insights into the evolution of communities in dynamic social networks.
A Uniform Quantum Computing Model Based on Virtual Quantum Processors
Authors: George Gesek
Date: 2023-02-24 00:00:00

Quantum Computers, one fully realized, can represent an exponential boost in computing power. However, the computational power of the current quantum computers, referred to as Noisy Internediate Scale Quantum, or NISQ, is severely limited because of environmental and intrinsic noise, as well as the very low connectivity between qubits compared to their total amount. We propose a virtual quantum processor that emulates a generic hybrid quantum machine which can serve as a logical version of quantum computing hardware. This hybrid classical quantum machine powers quantum-logical computations which are substitutable by future native quantum processors.
Hiding Data Helps: On the Benefits of Masking for Sparse Coding
Authors: Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge
Date: 2023-02-24 00:00:00
Tasks: Dictionary Learning, Self-Supervised Learning

Sparse coding, which refers to modeling a signal as sparse linear combinations of the elements of a learned dictionary, has proven to be a successful (and interpretable) approach in applications such as signal processing, computer vision, and medical imaging. While this success has spurred much work on provable guarantees for dictionary recovery when the learned dictionary is the same size as the ground-truth dictionary, work on the setting where the learned dictionary is larger (or over-realized) with respect to the ground truth is comparatively nascent. Existing theoretical results in this setting have been constrained to the case of noise-less data. We show in this work that, in the presence of noise, minimizing the standard dictionary learning objective can fail to recover the elements of the ground-truth dictionary in the over-realized regime, regardless of the magnitude of the signal in the data-generating process. Furthermore, drawing from the growing body of work on self-supervised learning, we propose a novel masking objective for which recovering the ground-truth dictionary is in fact optimal as the signal increases for a large class of data-generating processes. We corroborate our theoretical results with experiments across several parameter regimes showing that our proposed objective also enjoys better empirical performance than the standard reconstruction objective.
Language Models are Few-shot Learners for Prognostic Prediction
Authors: Zekai Chen, Mariann Micsinai Balan, Kevin Brown
Date: 2023-02-24 00:00:00
Tasks: Few-Shot Learning, Prediction

Clinical prediction is an essential task in the healthcare industry. However, the recent success of transformers, on which large language models are built, has not been extended to this domain. In this research, we explore the use of transformers and language models in prognostic prediction for immunotherapy using real-world patients' clinical data and molecular profiles. This paper investigates the potential of transformers to improve clinical prediction compared to conventional machine learning approaches and addresses the challenge of few-shot learning in predicting rare disease areas. The study benchmarks the efficacy of baselines and language models on prognostic prediction across multiple cancer types and investigates the impact of different pretrained language models under few-shot regimes. The results demonstrate significant improvements in accuracy and highlight the potential of NLP in clinical research to improve early detection and intervention for different diseases.
Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues
Authors: Riccardo Albertoni, Sara Colantonio, Piotr Skrzypczyński, Jerzy Stefanowski
Date: 2023-02-24 00:00:00

Reproducibility is one of the core dimensions that concur to deliver Trustworthy Artificial Intelligence. Broadly speaking, reproducibility can be defined as the possibility to reproduce the same or a similar experiment or method, thereby obtaining the same or similar results as the original scientists. It is an essential ingredient of the scientific method and crucial for gaining trust in relevant claims. A reproducibility crisis has been recently acknowledged by scientists and this seems to affect even more Artificial Intelligence and Machine Learning, due to the complexity of the models at the core of their recent successes. Notwithstanding the recent debate on Artificial Intelligence reproducibility, its practical implementation is still insufficient, also because many technical issues are overlooked. In this survey, we critically review the current literature on the topic and highlight the open issues. Our contribution is three-fold. We propose a concise terminological review of the terms coming into play. We collect and systematize existing recommendations for achieving reproducibility, putting forth the means to comply with them. We identify key elements often overlooked in modern Machine Learning and provide novel recommendations for them. We further specialize these for two critical application domains, namely the biomedical and physical artificial intelligence fields.