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PC024 / #1011 DEEP LEARNING OF BRAIN SPACETIME TO PREDICT OUTCOME OF VISION RESTORATION THERAPY USING NON-INVASIVE BRAIN STIMULATION

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Neuromodulation: Technology at the Neural Interface 2022年第7期25卷 S20-S20页

作者： Zheng Wu Jiahua Xu Andreas Nürnberger Bernhard A. Sabel Institute of Medical Psychology Medical Faculty Otto-von-guericke University Of Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-guericke University Of Magdeburg Magdeburg Germany

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ARCH-COMP20 category report: Continuous and hybrid systems with nonlinear dynamics 7th

ARCH-COMP20 category report: Continuous and hybrid systems w...

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7th International Workshop on Applied Verification of Continuous and Hybrid Systems, ARCH 2020

作者： Geretti, Luca Dit Sandretto, Julien Alexandre Althoff, Matthias Benet, Luis Chapoutot, Alexandre Chen, Xin Collins, Pieter Forets, Marcelo Freire, Daniel Immler, Fabian Kochdumper, Niklas Sanders, David P. Schilling, Christian Department of Computer Science University of Verona Verona Italy ENSTA Paris Institut Polytechnique de Paris Palaiseau France Technische Universität München Munich Germany Mexico University of Dayton DaytonOH United States Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Universidad de la República Montevideo Uruguay Computer Science Department Carnegie Mellon University United States Mexico City Mexico Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States IST Austria Klosterneuburg Austria

We present the results of a friendly competition for formal verification of continuous and hybrid systems with nonlinear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2020. This year, 6 tools Ariadne, CORA, DynIbex, Flow*, Isabelle/HOL, and JuliaReach (in alphabetic order) participated. These tools are applied to solve reachability analysis problems on six benchmark problems, two of them featuring hybrid dynamics. We do not rank the tools based on the results, but show the current status and discover the potential advantages of different tools. © 2020 EasyChair. All rights reserved.

关键词： Hybrid systems

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Improving answer selection with analogy-preserving sentence embeddings

Improving answer selection with analogy-preserving sentence ...

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2019 Conference on "Learning, knowledge, data, Analytics", LWDA 2019

作者： Diallo, Aïssatou Zopf, Markus Fürnkranz, Johannes Research Training Group AIPHES Department of Computer Science Technische Universität Darmstadt Hochschulstraße 10 Darmstadt64289 Germany Knowledge Engineering Group Department of Computer Science Technische Universität Darmstadt Hochschulstraße 10 Darmstadt64289 Germany

Answer selection aims at identifying the correct answer for a given question from a set of potentially correct answers. Contrary to previous works, which typically focus on the semantic similarity between a question and its answer, our hypothesis is that question-answer pairs are often in analogical relation to each other. We propose a framework for learning dedicated sentence embeddings that preserve analogical properties in the semantic space. We evaluate the proposed method on benchmark datasets for answer selection and demonstrate that our sentence embeddings are competitive with similarity-based techniques. Copyright © 2019 for this paper by its authors.

关键词： Semantics

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End-to-end AI Framework for Interpretable Prediction of Molecular and Crystal Properties

arXiv

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arXiv 2022年

作者： Park, Hyun Zhu, Ruijie Huerta, E.A. Chaudhuri, Santanu Tajkhorshid, Emad Cooper, Donny Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Multiscale Materials and Manufacturing Lab University of Illinois Chicago ChicagoIL60607 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Data Science and AI Department TotalEnergies EP Research & Technology USA LLC HoustonTX77002 United States

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab. Copyright © 2022, The Authors. All rights reserved.

关键词： Organometallics

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Machine Memory Intelligence: Inspired by Human Memory Mechanisms

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engineering 2025年

作者： Qinghua Zheng Huan Liu Xiaoqing Zhang Caixia Yan Xiangyong Cao Tieliang Gong Yong-Jin Liu Bin Shi Zhen Peng Xiaocen Fan Ying Cai Jun Liu School of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an 710049 China Translational Research Institute of Brain and Brain-Like Intelligence Shanghai Fourth People’s Hospital Shanghai 200434 China School of Medicine Tongji University Shanghai 200092 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China Department of Computer Science Iowa State University Ames IA 50011 USA

Large models, exemplified by ChatGPT, have reached the pinnacle of contemporary artificial intelligence (AI). However, they are plagued by three inherent drawbacks: excessive training data and computing power consumption, susceptibility to catastrophic forgetting, and a deficiency in logical reasoning capabilities within black-box models. To address these challenges, we draw insights from human memory mechanisms to introduce “machine memory,” which we define as a storage structure formed by encoding external information into a machine-representable and computable format. Centered on machine memory, we propose the brand-new machine memory intelligence (M 2 I) framework, which encompasses representation, learning, and reasoning modules and loops. We explore the key issues and recent advances in the four core aspects of M 2 I, including neural mechanisms, associative representation, continual learning, and collaborative reasoning within machine memory. M 2 I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models, driving a qualitative leap from weak to strong AI.

关键词： Machine memory intelligence Neural mechanism Associative representation Continual learning Collaborative reasoning

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Neural multi-task learning for teacher question detection in online classrooms

arXiv

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arXiv 2020年

作者： Huang, Gale Yan Chen, Jiahao Liu, Haochen Fu, Weiping Ding, Wenbiao Tang, Jiliang Yang, Songfan Li, Guoliang Liu, Zitao TAL Education Group Beijing China Data Science and Engineering Lab Michigan State University United States Department of Computer Science Tsinghua University Beijing China

Asking questions is one of the most crucial pedagogical techniques used by teachers in class. It not only offers open-ended discussions between teachers and students to exchange ideas but also provokes deeper student thought and critical analysis. Providing teachers with such pedagogical feedback will remarkably help teachers improve their overall teaching quality over time in classrooms. Therefore, in this work, we build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. Compared with traditional methods, our approach not only avoids cumbersome feature engineering, but also adapts to the task of multi-class question detection in real education scenarios. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions. We conducted extensive experiments on the question detection tasks in a real-world online classroom dataset and the results demonstrate the superiority of our model in terms of various evaluation metrics. Copyright © 2020, The Authors. All rights reserved.

关键词： Learning systems

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Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique

Liver Segmentation in Time-resolved C-arm CT Volumes Reconst...

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International Image Processing, Applications and Systems Conference (IPAS)

作者： Soumick Chatterjee Hana Haseljić Robert Frysch Vojtěch Kulvait Vladimir Semshchikov Bennet Hensen Frank Wacker Inga Brüsch Thomas Werncke Oliver Speck Andreas Nürnberger Georg Rose Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Department of Biomedical Magnetic Resonance Faculty of Nature Science Otto von Guericke University Magdeburg Magdeburg Germany Faculty of Electrical Engineering and Information Technology Institute for Medical Engineering Otto von Guericke University Magdeburg Magdeburg Germany Faculty of Electrical Engineering and Information Technology Research Campus STIMULATE Otto von Guericke University Magdeburg Magdeburg Germany Institute of Materials Physics Helmholtz-Zentrum hereon Geesthacht Germany Institute of Diagnostic and Interventional Radiology Hannover Medical School Hannover Germany Research Campus STIMULATE Otto von Guericke University Magdeburg Magdeburg Germany Institute for Laboratory Animal Science Hannover Medical School Hannover Germany Department of Biomedical Magnetic Resonance Faculty of Nature Science Otto von Guericke University Magdeburg German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany

ISBN: (纸本)9781665462204

Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours. The time separation technique (TST) has been successfully used for modelling C-arm cone-beam computed tomography (CBCT) perfusion data. The reconstruction can be accompanied by the segmentation of the liver - for better visualisation and for generating comprehensive perfusion maps. Recently introduced Turbolift learning has been seen to perform well while working with TST reconstructions, but has not been explored for the time-resolved volumes (TRV) estimated out of TST reconstructions. The segmentation of the TRVs can be useful for tracking the movement of the liver over time. This research explores this possibility by training the multi-scale attention UNet of Turbolift learning at its third stage on the TRVs and shows the robustness of Turbolift learning since it can even work efficiently with the TRVs, resulting in a Dice score of $0.864\pm 0.004$ .

关键词： Training Image segmentation Computed tomography Computational modeling Motion segmentation Liver Imaging

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Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows

arXiv

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arXiv 2021年

作者： Huang, Qiangqiang Pu, Can Khosoussi, Kasra Rosen, David M. Fourie, Dehann How, Jonathan P. Leonard, John J. MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA02139 United States MIT Department of Nuclear Science and Engineering CambridgeMA02139 United States Robotics and Autonomous Systems Group DATA61 CSIRO BrisbaneQLD4069 Australia Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States NavAbility BostonMA02110 United States MIT Department of Aeronautical and Astronautical Engineering CambridgeMA02139 United States

This paper presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to model and sample the full posterior. By leveraging the Bayes tree, NF-iSAM enables efficient incremental updates similar to iSAM2, albeit in the more challenging non-Gaussian setting. We demonstrate the advantages of NF-iSAM over state-of-the-art point and distribution estimation algorithms using range-only SLAM problems with data association ambiguity. NF-iSAM presents superior accuracy in describing the posterior beliefs of continuous variables (e.g., position) and discrete variables (e.g., data association). Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Detection of Pulsar Candidates using Bagging Method

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Procedia computer science 2020年 170卷 1096-1101页

作者： Mourad Azhari Abdallah Abarda Altaf Alaoui Badia Ettaki Jamal Zerouaoui Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and knowledge Engineering School of Information Sciences Rabat Morocco

The pulsar classification represents a major issue in the astrophysical area. The Bagging Algorithm is an ensemble method widely used to improve the performance of classification algorithms, especially in the case of pulsar search. In this way, our paper tries to prove how the Bagging Method can improve the performance of pulsar candidate detection in connection with four basic classifiers: Core Vector Machines (CVM), the K-Nearest-Neighbors (KNN), the Artificial Neural Network (ANN), and Cart Decision Tree (CDT). The Error Rate, Area Under the Curve (AUC), and Computation Time (CT) are measured to compare the performance of different classifiers. The High Time Resolution Universe (HTRU2) dataset, collected from the UCI Machine Learning Repository, is used in the experimentation phase.

关键词： Bagging CVM ANN KNN CDT AUC Error Rate Computation Time(CT)

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Improving Automatic Speech Recognition Utilizing Audio-codecs for data Augmentation

Improving Automatic Speech Recognition Utilizing Audio-codec...

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IEEE Workshop on Multimedia Signal Processing

作者： Nirayo Hailu Ingo Siegert Andreas Nürnberger Faculty of Computer Science Data and Knowledge Engineering Group Otto-von-Guericke University Magdeburg Magdeburg Germany Faculty of Electrical Engineering and Information Technology Mobile Dialog Systems Otto-von-Guericke University Magdeburg Magdeburg Germany

ISBN: (数字)9781728193205

ISBN: (纸本)9781728193236

To train end-to-end automatic speech recognition models, it requires a large amount of labeled speech data. This goal is challenging for languages with fewer resources. In contrast to the commonly used feature level data augmentation, we propose to expand the training set by using different audio codecs at the data level. The augmentation method consists of using different audio codecs with changed bit rate, sampling rate, and bit depth. The change reassures variation in the input data without drastically affecting the audio quality. Besides, we can ensure that humans still perceive the audio, and any feature extraction is possible later. To demonstrate the general applicability of the proposed augmentation technique, we evaluated it in an end-to-end automatic speech recognition architecture in four languages. After applying the method, on the Amharic, Dutch, Slovenian, and Turkish datasets, we achieved a 1.57 average improvement in the character error rates (CER) without integrating language models. The result is comparable to the baseline result, showing CER improvement of 2.78, 1.25, 1.21, and 1.05 for each language. On the Amharic dataset, we reached a syllable error rate reduction of 6.12 compared to the baseline result.

关键词： Training Codecs Speech coding Error analysis Bit rate Feature extraction Automatic speech recognition

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