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  1. Buch ; Online: Graph-ToolFormer

    Zhang, Jiawei

    To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT

    2023  

    Abstract: In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extensions of which have also been ... ...

    Abstract In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extensions of which have also been applied to study the vision tasks with multi-modal data. However, when it comes to the graph learning tasks, existing LLMs present very serious flaws due to their several inherited weaknesses in performing {multi-step logic reasoning}, {precise mathematical calculation} and {perception about the spatial and temporal factors}. To address such challenges, in this paper, we will investigate the principles, methodologies and algorithms to empower existing LLMs with graph reasoning ability, which will have tremendous impacts on the current research of both LLMs and graph learning. Inspired by the latest ChatGPT and Toolformer models, we propose the Graph-ToolFormer (Graph Reasoning oriented Toolformer) framework to teach LLMs themselves with prompts augmented by ChatGPT to use external graph reasoning API tools. Specifically, we will investigate to teach Graph-ToolFormer to handle various graph data reasoning tasks in this paper, including both (1) very basic graph data loading and graph property reasoning tasks, ranging from simple graph order and size to the graph diameter and periphery, and (2) more advanced reasoning tasks on real-world graph data, such as bibliographic networks, protein molecules, sequential recommender systems, social networks and knowledge graphs.

    Comment: 34 pages, 3 figures, 8 tables
    Schlagwörter Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2023-04-10
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    Dokumenttyp Buch ; Online
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  2. Buch ; Online: Robot Kinematics

    Zhang, Jiawei

    Motion, Kinematics and Dynamics

    2022  

    Abstract: This is a follow-up tutorial article of our previous article entitled "Robot Basics: Representation, Rotation and Velocity". For better understanding of the topics covered in this articles, we recommend the readers to first read our previous tutorial ... ...

    Abstract This is a follow-up tutorial article of our previous article entitled "Robot Basics: Representation, Rotation and Velocity". For better understanding of the topics covered in this articles, we recommend the readers to first read our previous tutorial article on robot basics. Specifically, in this article, we will cover some more advanced topics on robot kinematics, including robot motion, forward kinematics, inverse kinematics, and robot dynamics. For the topics, terminologies and notations introduced in the previous article, we will use them directly without re-introducing them again in this article. Also similar to the previous article, math and formulas will also be heavily used in this article as well (hope the readers are well prepared for the upcoming math bomb). After reading this article, readers should be able to have a deeper understanding about how robot motion, kinematics and dynamics. As to some more advanced topics about robot control, we will introduce them in the following tutorial articles for readers instead.

    Comment: 56 pages, 18 figures
    Schlagwörter Computer Science - Robotics ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 629
    Erscheinungsdatum 2022-11-28
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    Dokumenttyp Buch ; Online
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  3. Buch ; Online: Robot Basics

    Zhang, Jiawei

    Representation, Rotation and Velocity

    2022  

    Abstract: In this article, we plan to provide an introduction about some basics about robots for readers. Several key topics of classic robotics will be introduced, including robot representation, robot rotational motion, coordinates transformation and velocity ... ...

    Abstract In this article, we plan to provide an introduction about some basics about robots for readers. Several key topics of classic robotics will be introduced, including robot representation, robot rotational motion, coordinates transformation and velocity transformation. By now, classic rigid-body robot analysis is still the main-stream approach in robot controlling and motion planning. In this article, no data-driven or machine learning based methods will be introduced. Most of the materials covered in this article are based on the rigid-body kinematics that the readers probably have learned from the physics course at high-school or college. Meanwhile, these classic robot kinematics analyses will serve as the foundation for the latest intelligent robot control algorithms in modern robotics studies.

    Comment: 29 Pages, 11 Figures
    Schlagwörter Computer Science - Robotics ; Computer Science - Artificial Intelligence
    Erscheinungsdatum 2022-11-04
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Buch ; Online: Iso-CapsNet

    Zhang, Jiawei

    Isomorphic Capsule Network for Brain Graph Representation Learning

    2022  

    Abstract: Brain graph representation learning serves as the fundamental technique for brain diseases diagnosis. Great efforts from both the academic and industrial communities have been devoted to brain graph representation learning in recent years. The isomorphic ...

    Abstract Brain graph representation learning serves as the fundamental technique for brain diseases diagnosis. Great efforts from both the academic and industrial communities have been devoted to brain graph representation learning in recent years. The isomorphic neural network (IsoNN) introduced recently can automatically learn the existence of sub-graph patterns in brain graphs, which is also the state-of-the-art brain graph representation learning method by this context so far. However, IsoNN fails to capture the orientations of sub-graph patterns, which may render the learned representations to be useless for many cases. In this paper, we propose a new Iso-CapsNet (Isomorphic Capsule Net) model by introducing the graph isomorphic capsules for effective brain graph representation learning. Based on the capsule dynamic routing, besides the subgraph pattern existence confidence scores, Iso-CapsNet can also learn other sub-graph rich properties, including position, size and orientation, for calculating the class-wise digit capsules. We have compared Iso-CapsNet with both classic and state-of-the-art brain graph representation approaches with extensive experiments on four brain graph benchmark datasets. The experimental results also demonstrate the effectiveness of Iso-CapsNet, which can out-perform the baseline methods with significant improvements.

    Comment: 11 pages, 3 figures, 2 tables. arXiv admin note: text overlap with arXiv:1908.00187
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Quantitative Biology - Neurons and Cognition
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-06-27
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Artikel ; Online: NeoMUST: an accurate and efficient multi-task learning model for neoantigen presentation.

    Ma, Wang / Zhang, Jiawei / Yao, Hui

    Life science alliance

    2024  Band 7, Heft 4

    Abstract: Accurate identification of neoantigens is important for advancing cancer immunotherapies. This study introduces Neoantigen MUlti-taSk Tower (NeoMUST), a model employing multi-task learning to effectively capture task-specific information across related ... ...

    Abstract Accurate identification of neoantigens is important for advancing cancer immunotherapies. This study introduces Neoantigen MUlti-taSk Tower (NeoMUST), a model employing multi-task learning to effectively capture task-specific information across related tasks. Our results show that NeoMUST rivals existing algorithms in predicting the presentation of neoantigens via MHC-I molecules, while demonstrating a significantly shorter training time for enhanced computational efficiency. The use of multi-task learning enables NeoMUST to leverage shared knowledge and task dependencies, leading to improved performance metrics and a significant reduction in the training time. NeoMUST, implemented in Python, is freely accessible at the GitHub repository. Our model will facilitate neoantigen prediction and empower the development of effective cancer immunotherapeutic approaches.
    Mesh-Begriff(e) Humans ; Antigens, Neoplasm ; Neoplasms/therapy ; Algorithms
    Chemische Substanzen Antigens, Neoplasm
    Sprache Englisch
    Erscheinungsdatum 2024-01-30
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2575-1077
    ISSN (online) 2575-1077
    DOI 10.26508/lsa.202302255
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel ; Online: Tetherless and Batteryless Soft Navigators and Grippers.

    Han, Zhen / Li, Yang / Wu, Xinjun / Zhang, Jiawei

    ACS applied materials & interfaces

    2024  Band 16, Heft 11, Seite(n) 14345–14356

    Abstract: Remotely controllable soft actuators have promising potential applications in many fields including soft robotics, exploration, and invasion medical treatment. Shape memory polymers could store and release energy, resulting in shape deformation, and have ...

    Abstract Remotely controllable soft actuators have promising potential applications in many fields including soft robotics, exploration, and invasion medical treatment. Shape memory polymers could store and release energy, resulting in shape deformation, and have been regarded as promising candidates to fabricate untethered soft robots. Herein, an untethered and battery-free soft navigator and gripper based on a shape memory hydrogel is presented. The shape memory hydrogel is obtained through hydrogen bonding between gelatin and tannic acid, and the hydrogel displays excellent shape memory properties on the basis of hydrogen bonding and the coil-triple helix transition of gelatin. Moreover, Fe
    Sprache Englisch
    Erscheinungsdatum 2024-03-05
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 1944-8252
    ISSN (online) 1944-8252
    DOI 10.1021/acsami.4c00354
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Artikel ; Online: Intelligent Construction Technology Adoption Driving Strategy in China: A Tripartite Evolutionary Game Analysis.

    Zhang, Jiawei / Li, Lihong

    Journal of environmental and public health

    2022  Band 2022, Seite(n) 9372443

    Abstract: The adoption of intelligent construction technology (ICT) is regarded as one of the important strategies for the transformation and upgrading of the Chinese construction industry and the achievement of high-quality development. In the ICT adoption ... ...

    Abstract The adoption of intelligent construction technology (ICT) is regarded as one of the important strategies for the transformation and upgrading of the Chinese construction industry and the achievement of high-quality development. In the ICT adoption process, the government is the driving subject, the owner is an important subject, and ICT is applied in practice by the general contractor. This study first analyses the evolutionary process and the impact of participants' strategy choices on the system equilibrium by establishing a tripartite evolutionary game framework which includes the government, the owner, and the general contractor as the main stakeholders; then tests the feasibility and rationality of the model by analysing the ESS corresponding to the three phases of ICT adoption. The results show that the conditions for each ESS to be established mainly depend on the relationship between the costs and benefits of each stakeholder, and that owners are more sensitive to government subsidies and penalties than general contractors, so the government should establish a dynamic reward and punishment mechanism based on the results of the model. High adoption costs are a key barrier to ICT adoption for both owners and general contractors. This paper provides a new framework for research related to ICT adoption and a reference for the strategic adjustment of stakeholders in ICT adoption.
    Mesh-Begriff(e) Humans ; Construction Industry ; Technology ; China
    Sprache Englisch
    Erscheinungsdatum 2022-10-11
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2526611-1
    ISSN 1687-9813 ; 1687-9813
    ISSN (online) 1687-9813
    ISSN 1687-9813
    DOI 10.1155/2022/9372443
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel: Effects of non-invasive neurostimulation on autism spectrum disorder: A systematic review.

    Zhang, Jiawei / Zhang, Hao

    Frontiers in psychiatry

    2022  Band 13, Seite(n) 989905

    Abstract: Non-invasive neurostimulation techniques (NIBS) have shown benefits in psychiatric conditions. While in ASD patients, no guideline has so-far been recommended on these techniques due to a lack of high-quality synthetic evidence. Here, a comprehensive ... ...

    Abstract Non-invasive neurostimulation techniques (NIBS) have shown benefits in psychiatric conditions. While in ASD patients, no guideline has so-far been recommended on these techniques due to a lack of high-quality synthetic evidence. Here, a comprehensive search from database inception onward was conducted in PubMed, EMBASE, and Cochrane library. Sham-controlled studies assessing the effects of NIBS in ASD patients were identified. After screening, twenty-two studies were included. A total of 552 patients were involved, and the sample size ranged from 5 to 78 patients. Although an iteration from exploratory attempts to more strictly designed trials has been seen to evaluate the efficacy of NIBS on ASD, further trials should also be needed to enable the clinicians and researchers to reach any consensus.
    Systematic review registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021292434], identifier [CRD42021292434].
    Sprache Englisch
    Erscheinungsdatum 2022-11-02
    Erscheinungsland Switzerland
    Dokumenttyp Systematic Review
    ZDB-ID 2564218-2
    ISSN 1664-0640
    ISSN 1664-0640
    DOI 10.3389/fpsyt.2022.989905
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Artikel: Gut microbiota and autoimmune neurologic disorders: a two-sample bidirectional Mendelian randomization study.

    Zhang, Mengyuan / Fang, Jie / Zheng, Chamou / Lin, Qing / Zhang, Jiawei

    Frontiers in microbiology

    2024  Band 15, Seite(n) 1337632

    Abstract: Background: Increasing evidence has suggested that alterations in the gut microbiome are correlated with autoimmune neurologic disorders, yet the causal relationship between them has yet to be established.: Methods: From the published genome-wide ... ...

    Abstract Background: Increasing evidence has suggested that alterations in the gut microbiome are correlated with autoimmune neurologic disorders, yet the causal relationship between them has yet to be established.
    Methods: From the published genome-wide association study (GWAS) summary statistics, we obtained data on the gut microbiota and three autoimmune neurologic disorders (Multiple Sclerosis, Guillain-Barré Syndrome, and Myasthenia Gravis). We then implemented a two-sample Mendelian Randomization (MR) to determine the causal relationship between the gut microbiota and the diseases. To validate the results, we conducted a series of sensitivity analyses. Finally, to verify the direction of causality, a reverse-causality analysis was done.
    Results: We discovered that a higher relative abundance of the genus
    Conclusion: Our findings demonstrate a causal relationship between the gut microbiota and three autoimmune neurologic disorders, providing novel insights into the mechanisms of these autoimmune neurologic disorders that are mediated by gut microbiota.
    Sprache Englisch
    Erscheinungsdatum 2024-04-24
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2024.1337632
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  10. Buch ; Online: G5

    Zhang, Jiawei

    A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning

    2020  

    Abstract: The recent GRAPH-BERT model introduces a new approach to learning graph representations merely based on the attention mechanism. GRAPH-BERT provides an opportunity for transferring pre-trained models and learned graph representations across different ... ...

    Abstract The recent GRAPH-BERT model introduces a new approach to learning graph representations merely based on the attention mechanism. GRAPH-BERT provides an opportunity for transferring pre-trained models and learned graph representations across different tasks within the same graph dataset. In this paper, we will further investigate the graph-to-graph transfer of a universal GRAPH-BERT for graph representation learning across different graph datasets, and our proposed model is also referred to as the G5 for simplicity. Many challenges exist in learning G5 to adapt the distinct input and output configurations for each graph data source, as well as the information distributions differences. G5 introduces a pluggable model architecture: (a) each data source will be pre-processed with a unique input representation learning component; (b) each output application task will also have a specific functional component; and (c) all such diverse input and output components will all be conjuncted with a universal GRAPH-BERT core component via an input size unification layer and an output representation fusion layer, respectively. The G5 model removes the last obstacle for cross-graph representation learning and transfer. For the graph sources with very sparse training data, the G5 model pre-trained on other graphs can still be utilized for representation learning with necessary fine-tuning. What's more, the architecture of G5 also allows us to learn a supervised functional classifier for data sources without any training data at all. Such a problem is also named as the Apocalypse Learning task in this paper. Two different label reasoning strategies, i.e., Cross-Source Classification Consistency Maximization (CCCM) and Cross-Source Dynamic Routing (CDR), are introduced in this paper to address the problem.

    Comment: Keywords: Graph-Bert; Representation Learning; Apocalypse Learning; Transfer Learning; Graph Mining; Data Mining
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Neural and Evolutionary Computing ; Computer Science - Social and Information Networks ; Statistics - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2020-06-11
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    Dokumenttyp Buch ; Online
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