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arXiv 2022年

作者： Chowdhury, Townim Cheraghian, Ali Ramasinghe, Sameera Ahmadi, Sahar Saberi, Morteza Rahman, Shafin Dept. of Electrical and Computer Engineering North South University Bangladesh School of Engineering Australian National University Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia Australian Institute for Machine Learning University of Adelaide Australia Business School The University of New South Wales Australia School of Computer Science and DSI University of Technology Sydney Australia

Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training. Recent efforts address this problem primarily on 2D images. However, due to the advancement of camera technology, 3D point cloud data has become more available than ever, which warrants considering FSCIL on 3D data. This paper addresses FSCIL in the 3D domain. In addition to well-known issues of catastrophic forgetting of past knowledge and overfitting of few-shot data, 3D FSCIL can bring newer challenges. For example, base classes may contain many synthetic instances in a realistic scenario. In contrast, only a few real-scanned samples (from RGBD sensors) of novel classes are available in incremental steps. Due to the data variation from synthetic to real, FSCIL endures additional challenges, degrading performance in later incremental steps. We attempt to solve this problem using Microshapes (orthogonal basis vectors) by describing any 3D objects using a pre-defined set of rules. It supports incremental training with few-shot examples minimizing synthetic to real data variation. We propose new test protocols for 3D FSCIL using popular synthetic datasets (ModelNet and ShapeNet) and 3D real-scanned datasets (ScanObjectNN and CO3D). By comparing state-of-the-art methods, we establish the effectiveness of our approach in the 3D domain. Code is available at: https://***/townim-faisal/FSCIL-3D. © 2022, CC BY.

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Transition State Search and Geometry Relaxation throughout Chemical Compound Space with Quantum machine learning

arXiv

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arXiv 2022年

作者： Heinen, Stefan Von Rudorff, Guido Falk Von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Vector Institute for Artificial Intelligence ON TorontoM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus ON Toronto Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

We use energies and forces predicted within response operator based quantum machine learning (OQML) to perform geometry optimization and transition state search calculations with legacy optimizers. For randomly sampled initial coordinates of small organic query molecules we report systematic improvement of equilibrium and transition state geometry output as training set sizes increase. Out-of-sample SN2 reactant complexes and transition state geometries have been predicted using the LBFGS and the QST2 algorithm with an RMSD of 0.16 and 0.4 Å - after training on up to 200 reactant complexes relaxations and transition state search trajectories from the QMrxn20 data-set, respectively. For geometry optimizations, we have also considered relaxation paths up to 5'500 constitutional isomers with sum formula C7H10O2from the QM9-database. Using the resulting OQML models with an LBFGS optimizer reproduces the minimum geometry with an RMSD of 0.14 Å. For converged equilibrium and transition state geometries subsequent vibrational normal mode frequency analysis indicates deviation from MP2 reference results by on average 14 and 26 cm-1, respectively. While the numerical cost for OQML predictions is negligible in comparison to DFT or MP2, the number of steps until convergence is typically larger in either case. The success rate for reaching convergence, however, improves systematically with training set size, underscoring OQML's potential for universal applicability. © 2022, CC BY.

关键词： machine learning

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Monitoring medication optimization in patients with Parkinson’s disease

Monitoring medication optimization in patients with Parkinso...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Hamid Moradi Julius Hannink Sabine Stallforth Till Gladow Stefan Ringbauer Martin Mayr Jürgen Winkler Jochen Klucken Bjoern M. Eskofier Department of Artificial Intelligence in Biomedical Engineering Machine Learning and Data Analytics Laboratory (MaD Lab) Friedrich Alexander University Erlangen Nuremberg Erlangen-Nuremberg Germany Portabiles HealthCare Technologies GmbH Erlangen Germany Department of Molecular-Neurology University Hospital Erlangen Erlangen Germany Medical Valley Digital Health Application Center Bamberg Germany NeuroSys GmbH Ulm Germany Luxembourg Institute of Health Centre Hospitalier du Luxembourg University of Luxembourg

Medication optimization is a common component of the treatment strategy in patients with Parkinson’s disease. As the disease progresses, it is essential to compensate for the movement deterioration in patients. Conventionally, examining motor deterioration and prescribing medication requires the patient’s onsite presence in hospitals or practices. Home-monitoring technologies can remotely deliver essential information to physicians and help them devise a treatment decision according to the patient’s need. Additionally, they help to observe the patient’s response to these changes. In this regard, we conducted a longitudinal study to collect gait data of patients with Parkinson’s disease while they received medication changes. Using logistic regression classifier, we could detect the annotated motor deterioration during medication optimization with an accuracy of 92%. Moreover, an in-depth examination of the best features illustrated a decline in gait speed and swing phase duration in the deterioration phases due to suboptimal *** relevance— Our proposed gait analysis method in this study provides objective, detailed, and punctual information to physicians. Revealing clinically relevant time points related to the patient’s need for medical adaption alleviates therapy optimization for physicians and reduces the duration of suboptimal treatment for patients. As the home-monitoring system acts remotely, embedding it in the medical care pathways could improve patients’ quality of life.

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Exploring the Capabilities of a Language Model-Only Approach for Depression Detection in Text data

Exploring the Capabilities of a Language Model-Only Approach...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Misha Sadeghi Bernhard Egger Reza Agahi Robert Richer Klara Capito Lydia Helene Rupp Lena Schindler-Gmelch Matthias Berking Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen- Nürnberg (FAU) Erlangen Germany Department of Computer Science Chair of Visual Computing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Syenah GMBH Eschborn Germany Chair of Clinical Psychology and Psychotherapy Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Depression is a prevalent and debilitating mental health condition that requires accurate and efficient detection for timely and effective treatment. In this study, we utilized the E-DAIC (Extended Distress Analysis Interview Corpus-Wizard-of-Oz) dataset, an extended version of the DAIC-WOZ dataset, which consists of semi-clinical interviews conducted by an animated virtual interviewer called Ellie, controlled by a human interviewer in another room. With 275 participants, the E-DAIC dataset represents a valuable resource for investigating depression detection methods. Our aim is to predict PHQ-8 scores through text analysis. Leveraging state-of-the-art speech processing, LLM-based text summarization, and a specialized depression detection module, we demonstrate the transformative potential of language data analysis in enhancing depression screening. By overcoming the limitations of manual feature extraction methods, our automated techniques provide a more efficient and effective means of evaluating depression. In our evaluation, we achieve robust accuracy on the development set of the E-DAIC dataset, with a Mean Absolute Error (MAE) of 3.65 in estimating PHQ-8 scores from recorded interviews. This remarkable performance highlights the efficacy of our approach in automatically predicting depression severity. Our research contributes to the growing evidence supporting the use of LLMs in mental health assessment, showcasing the role of innovative technologies in advancing patient care for depression.

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Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation

arXiv

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arXiv 2021年

作者： Liao, Zehui Hu, Shishuai Xie, Yutong Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Australian Institute for Machine Learning The University of Adelaide AdelaideSA5000 Australia

Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases. Deep learning models may surpass human performance on a variety of tasks, but they may also mimic or amplify these biases. Although we can have multiple annotators and fuse their annotations to reduce stochastic errors, we cannot use this strategy to handle the bias caused by annotators’ preferences. In this paper, we highlight the issue of annotator-related biases on medical image segmentation tasks, and propose a Preference-involved Annotation Distribution learning (PADL) framework to address it from the perspective of disentangling an annotator’s preference from stochastic errors using distribution learning so as to produce not only a meta segmentation but also the segmentation possibly made by each annotator. Under this framework, a stochastic error modeling (SEM) module estimates the meta segmentation map and average stochastic error map, and a series of human preference modeling (HPM) modules estimate each annotator’s segmentation and the corresponding stochastic error. We evaluated our PADL framework on two medical image benchmarks with different imaging modalities, which have been annotated by multiple medical professionals, and achieved promising performance on all five medical image segmentation tasks. © 2021, CC BY-NC-ND.

关键词： Stochastic systems

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Analysis of the Research Status of Information Technology Education Literature in China and Abroad

Analysis of the Research Status of Information Technology Ed...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yulin Zhao Kai Liu Junke Li School of Computer and Information Qiannan Normal University for Nationalities Duyun China Key Laboratory of Machine learning and Unstructured Data Processing of Qiannan Duyun China School of Information Engineering Suqian University Suqian Jiangsu China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu China

Information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad.

关键词： Statistical analysis databases Education Bibliometrics Educational technology Market research Internet

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Software for dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy

arXiv

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arXiv 2021年

作者： Anders, Christopher J. Neumann, David Samek, Wojciech Müller, Klaus-Robert Lapuschkin, Sebastian Machine Learning Group Department of Electrical Engineering and Computer Science Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany Machine Learning and Communications Group Department of Electrical Engineering and Computer Science Technische Universität Berlin Germany Department of Artificial Intelligence Korea University Seoul Korea Republic of Max Planck Institut für Informatik Saarbrücken Germany

Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood. With recent advances in Explainable Artificial Intelligence (XAI), approaches are available to explore the reasoning behind those complex models’ predictions. Among post-hoc attribution methods, Layer-wise Relevance Propagation (LRP) shows high performance. For deeper quantitative analysis, manual approaches exist, but without the right tools they are unnecessarily labor intensive. In this software paper, we introduce three software packages targeted at scientists to explore model reasoning using attribution approaches and beyond: (1) Zennit – a highly customizable and intuitive attribution framework implementing LRP and related approaches in PyTorch, (2) CoRelAy – a framework to easily and quickly construct quantitative analysis pipelines for dataset-wide analyses of explanations, and (3) ViRelAy – a web-application to interactively explore data, attributions, and analysis results. With this, we provide a standardized implementation solution for XAI, to contribute towards more reproducibility in our field. © 2021, CC BY-SA.

关键词： Deep neural networks

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Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

arXiv

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arXiv 2024年

作者： Li, Haopeng Deng, Andong Liu, Jun Rahmani, Hossein Guo, Yulan Schiele, Bernt Bennamoun, Mohammed Ke, Qiuhong School of Computing and Information Systems University of Melbourne Australia Center for Research in Computer Vision University of Central Florida United States Pillar Singapore University of Technology and Design Singapore School of Computing and Communications Lancaster University United Kingdom School of Electronics and Communication Engineering Sun Yat-sen University China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Germany School of Physics Maths and Computing University of Western Australia Australia Department of Data Science & AI Monash University Australia

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

arXiv

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arXiv 2024年

作者： Stone, Connor Adam, Alexandre Coogan, Adam Yantovski-Barth, M.J. Filipp, Andreas Setiawan, Landung Core, Cordero Legin, Ronan Wilson, Charles Barco, Gabriel Missael Hezaveh, Yashar Perreault-Levasseur, Laurence Ciela Institute Montréal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Department of Physics Université de Montréal MontréalQC Canada Mila Québec Artificial Intelligence Institute MontréalQC Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States eScience Institute Scientific Software Engineering Center 1410 NE Campus Pkwy SeattleWA98195 United States

Gravitational lensing is the deflection of light rays due to the gravity of intervening masses. This phenomenon is observed in a variety of scales and configurations, involving any non-uniform mass such as planets, stars, galaxies, clusters of galaxies, and even the large scale structure of the universe. Strong lensing occurs when the distortions are significant and multiple images of the background source are observed. The lens objects must align on the sky of order ∼ 1 arcsecond for galaxy-galaxy lensing, or 10’s of arcseonds for cluster-galaxy lensing. As the discovery of lens systems has grown to the low thousands, these systems have become pivotal for precision measurements and addressing critical questions in astrophysics. Notably, they facilitate the measurement of the Universe’s expansion rate (e.g. K. C. Wong et al., 2020), dark matter (e.g. Hezaveh et al., 2016;Vegetti & Vogelsberger, 2014), supernovae (e.g. Rodney et al., 2021), quasars (e.g. Peng et al., 2006), and the first stars (e.g. Welch et al., 2022) among other topics. With future surveys expected to discover hundreds of thousands of lensing systems, the modelling and simulation of such systems must occur at orders of magnitude larger scale then ever before. Here we present caustics, a Python package designed to handle the extensive computational demands of modeling such a vast number of lensing systems. © 2024, CC BY.

关键词： Python

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Occam's razor for AI: Coarse-graining Hammett Inspired Product Ansatz in Chemical Space

arXiv

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arXiv 2023年

作者： Bragato, Marco Von Rudorff, Guido Falk Von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 1416 WienAT1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Straße 40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

data-hungry machine learning methods have become a new standard to efficiently navigate chemical compound space for molecular and materials design and discovery. Due to the severe scarcity and cost of high-quality experimental or synthetic simulated training data, however, data-acquisition costs can be considerable. Relying on reasonably accurate approximate baseline labels with low computational complexity represents one of the most effective strategies to curb data-needs, e.g. through Δ-, transfer-, or multi-fidelity learning. We introduce baseline labels in the form of a generic coarse-graining Hammett Inspired Product (HIP) Ansatz which generalizes the empirical Hammett equation towards arbitrary systems and properties. Numerical evidence for the applicability of HIP includes solvation free energies of molecules, formation energies of quaternary elpasolite crystals, carbon adsorption energies on heterogeneous catalytic surfaces, HOMO-LUMO gaps of metallorganic complexes, and activation energies for SN2 reactions. After calibration on the same training sets, HIP yields an effective baseline for improved Δ-quantum machine learning models with superior data-efficiency when compared to previously introduced specialised domain-specific models. © 2023, CC0.

关键词： data acquisition

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