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TechRxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

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Community Detection with the Map Equation and Infomap: Theory and Applications

arXiv

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arXiv 2023年

作者： Smiljanić, Jelena Blöcker, Christopher Holmgren, Anton Edler, Daniel Neuman, Magnus Rosvall, Martin Integrated Science Lab Department of Physics Umeå University UmeåSE-901 87 Sweden Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Pregrevica 118 Belgrade11080 Serbia Center for Artificial Intelligence and Data Science University of Würzburg WürzburgDE-97070 Germany Data Analytics Group Department of Informatics University of Zürich ZürichCH-8006 Switzerland Gothenburg Global Biodiversity Centre University of Gothenburg Gothenburg Sweden

Real-world networks have a complex topology comprising many elements often structured into communities. Revealing these communities helps researchers uncover the organizational and functional structure of the system that the network represents. However, detecting community structures in complex networks requires selecting a community detection method among a multitude of alternatives with different network representations, community interpretations, and underlying mechanisms. This review and tutorial focuses on a popular community detection method called the map equation and its search algorithm Infomap. The map equation framework for community detection describes communities by analyzing dynamic processes on the network. Thanks to its flexibility, the map equation provides extensions that can incorporate various assumptions about network structure and dynamics. To help decide if the map equation is a suitable community detection method for a given complex system and problem at hand - and which variant to choose - we review the map equation's theoretical framework and guide users in applying the map equation to various research problems. © 2023, CC BY-NC-ND.

关键词： Information theory

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Ensuring the robustness and reliability of data-driven knowledge discovery models in production and manufacturing

arXiv

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arXiv 2020年

作者： Tripathi, Shailesh Muhr, David Manuel, Brunner Emmert-Streib, Frank Jodlbauer, Herbert Dehmer, Matthias Production and Operations Management University of Applied Sciences Upper Austria Austria Predictive Society and Data Analytics Lab Faculty of Information Technolgy and Communication Sciences Tampere University TampereFI-33101 Finland Institute of Biosciences and Medical Technology TampereFI-33101 Finland Department of Biomedical Computer Science and Mechatronics UMIT-The Health and Life Science University Tyrol6060 Hall Austria College of Artificial Intelligence Nankai University Tianjin300350 China

The implementation of robust, stable, and user-centered data analytics and machine learning models is confronted by numerous challenges in production and manufacturing. Therefore, a systematic approach is required to develop, evaluate, and deploy such models. The data-driven knowledge discovery framework provides an orderly partition of the data-mining processes to ensure the practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data– and model-development–related issues. These issues should be carefully addressed by allowing a flexible, customized, and industry-specific knowledge discovery framework;in our case, this takes the form of the cross-industry standard process for data mining (CRISP-DM). This framework is designed to ensure active cooperation between different phases to adequately address data- and model-related issues. In this paper, we review several extensions of CRISP-DM models and various data-robustness– and model-robustness–related problems in machine learning, which currently lacks proper cooperation between data experts and business experts because of the limitations of data-driven knowledge discovery models. Copyright © 2020, The Authors. All rights reserved.

关键词： Industry 4.0

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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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Towards Unified Token Learning for Vision-Language Tracking

arXiv

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arXiv 2023年

作者： Zheng, Yaozong Zhong, Bineng Liang, Qihua Li, Guorong Ji, Rongrong Li, Xianxian The Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China The Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China The School of Computer Science and Technology Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100049 China Media Analytics and Computing Lab Department of Artificial Intelligence School of Informatics Xiamen University 361005 China

In this paper, we present a simple, flexible and effective vision-language (VL) tracking pipeline, termed MMTrack, which casts VL tracking as a token generation task. Traditional paradigms address VL tracking task indirectly with sophisticated prior designs, making them over-specialize on the features of specific architectures or mechanisms. In contrast, our proposed framework serializes language description and bounding box into a sequence of discrete tokens. In this new design paradigm, all token queries are required to perceive the desired target and directly predict spatial coordinates of the target in an auto-regressive manner. The design without other prior modules avoids multiple sub-tasks learning and hand-designed loss functions, significantly reducing the complexity of VL tracking modeling and allowing our tracker to use a simple cross-entropy loss as unified optimization objective for VL tracking task. Extensive experiments on TNL2K, LaSOT, LaSOText and OTB99-Lang benchmarks show that our approach achieves promising results, compared to other state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

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FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures

arXiv

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arXiv 2023年

作者： Getahun, Melaku N. Rogov, Oleg Y. Dylov, Dmitry V. Somov, Andrey Bouridane, Ahmed Hamoudi, Rifat Moscow Russia Center for Data Analytics and Cybersecurity University of Sharjah Sharjah United Arab Emirates Moscow Russia Research Institute for Medical and Health Sciences College of Medicine University of Sharjah Sharjah United Arab Emirates Division of Surgery and Interventional Science Faculty of Medical Science University College London London United Kingdom BIMAI-Lab Biomedically Informed Artificial Intelligence Laboratory University of Sharjah Sharjah United Arab Emirates Bian Que AI Hong Kong

Retinal vascular segmentation, a widely researched topic in biomedical image processing, aims to reduce the workload of ophthalmologists in treating and detecting retinal disorders. Segmenting retinal vessels presents unique challenges;previous techniques often failed to effectively segment branches and microvascular structures. Recent neural network approaches struggle to balance local and global properties and frequently miss tiny end vessels, hindering the achievement of desired results. To address these issues in retinal vessel segmentation, we propose a comprehensive micro-vessel extraction mechanism based on an encoder-decoder neural network architecture. This network includes residual, encoder booster, bottleneck enhancement, squeeze, and excitation building blocks. These components synergistically enhance feature extraction and improve the prediction accuracy of the segmentation map. Our solution has been evaluated using the DRIVE, CHASE-DB1, and STARE datasets, yielding competitive results compared to previous studies. The AUC and accuracy on the DRIVE dataset are 0.9884 and 0.9702, respectively. For the CHASE-DB1 dataset, these scores are 0.9903 and 0.9755, respectively, and for the STARE dataset, they are 0.9916 and 0.9750. Given its accurate and robust performance, the proposed approach is a solid candidate for being implemented in real-life diagnostic centers and aiding ophthalmologists. © 2023, CC BY-NC-ND.

关键词： Medical image processing

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Stock Price Prediction and Portfolio Optimization Using Hidden Markov Model and Quadratic Programming

Stock Price Prediction and Portfolio Optimization Using Hidd...

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Integrated intelligence and Communication Systems (ICIICS), International Conference on

作者： August C. Thio-Ac Daniel D. Cabrales Drexanne E. Calibara Maries V. Madrazo Agustine Christian C. Matawaran Genesis E. Micaller Edmon O. Fernandez Timothy M. Amado Romeo L. Jorda Lean Karlo S. Tolentino Lejan Alfred C. Enriquez Department of Electronics Engineering Technological University of the Philippines Manila Philippines Integrated Research and Training Center Technological University of the Philippines Manila Philippines Intelligent Systems Applications and Circuits Laboratory (ISAAC) Integrated Research and Training Center Technological University of the Philippines Manila Philippines Data Analytics Visualization Information Driven (DAVID) Research Lab Integrated Research and Training Center Technological University of the Philippines Manila Philippines Center for Artificial Intelligence and Nanoelectronics (CAIN) Integrated Research and Training Center Technological University of the Philippines Manila Philippines

This study introduces a decision support system for optimizing stock portfolios. It employs a Hidden Markov Model for 95% accurate stock price forecasts over a five-day period. The system utilizes Quadratic Programming to refine allocation, focusing on up to 15 blue-chip companies. Key indicators like MACD, RSI, and Bollinger Bands offer clear buy, sell, or hold recommendations. It actively monitors the Philippine Stock Exchange, notifying users of significant deviations. Developed in Python and C#, both models were trained on a consistent 5-year dataset for reliable performance. Results show high prediction accuracy, averaging 98.923% (high price) and 98.741% (low price), benefiting investment strategies. The study compared SPOT-allocated portfolios to random ones, revealing a notable difference in profit/loss allocation. This underscores its value for informed and profitable decision-making in stock trading.

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A case-control study of reaction time deficits in a 3D virtual reality in patients with Post-COVID syndrome

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Scientific Reports 2024年第1期14卷 1-12页

作者： Güttes, Moritz Lucio, Marianna Skornia, Adam Rühl, Eva Steußloff, Fritz Zott, Julia Mardin, Christian Mehringer, Wolfgang Ganslmayer, Marion Michelson, Georg Hohberger, Bettina Department of Ophthalmology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen- Nürnberg Erlangen Germany Research Unit Analytical BioGeoChemistry Helmholtz Zentrum München Neuherberg Germany Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Internal Medicine 1 Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

Following the Coronavirus disease 2019 (COVID-19) pandemic, a large number of people continue to report Post-COVID symptoms (PCS). A wide variety of symptoms are described, including fatigue, post-exertional malaise and cognitive impairment. However, adequate objective diagnostic tests for PCS are not yet available. Since the neurotropism of SARS-CoV-2 could be a possible factor for cognitive impairment, the aim of this study was to clarify whether visual reaction time (RT) in a stereoscopic setting can be a marker in PCS diagnostics. The Virtual-Reality-Oculomotor-Test-System (VR-OTS) was used testing binocular vision in 9 gaze directions via stereoscopic stimuli displayed in a virtual reality (VR)-environment (disparity: 275″, 550″, 1100″) in 179 individuals: 130 patients with PCS and 49 healthy controls. The results from the generalized linear models indicated that both group membership (PCS vs. control) and covariates (age and sex) yielded statistically significant different RT across the models. Accounting for the effect of covariates a statistically significant difference of RT was observed between patients with PCS and controls (disparity 275″ p-value = 0.001;550″ p-value = 0.001;1100″ p-value = 0.003). Patients with PCS performed worse in RT in all gaze directions, respectively. Adjusting for the influence of covariates, correct responses (CR) differed significantly between patients with PCS and controls (disparity 275″ p-value < 0.001;550″ p-value = 0.003;1100″ p-value = 0.019). Statistically significant effects of covariates on RT were observed for sex (disparity 275″ p-value = 0.047;550″ p-value = 0.012;1100″ p-value = 0.005) and age (disparity 275″ p-value < 0.001;550″ p-value < 0.001;1100″ p-value < 0.001). However, regarding covariates, no significant effects were found for CR, except for age at disparity 275″ (p-value = 0.035). The present data suggested that the mentioned variables uniquely contributed to explain the variation of the response variabl

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Order symmetry breaking and broad distribution of events in spiking neural networks with continuous membrane potential

arXiv

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arXiv 2021年

作者： Stucchi, Marco Pittorino, Fabrizio di Volo, Matteo Vezzani, Alessandro Burioni, Raffaella Department of Biology University of Konstanz Universitätsstrasse 10 Konstanz78457 Germany Artificial Intelligence Lab Institute for Data Science and Analytics Bocconi University Milano Italy Laboratoire de Physique Théorique et Modelisation Université de Cergy-Pontoise Cergy-Pontoise Cedex95302 France IMEM-CNR Parco Area delle Scienze 37/A Parma43124 Italy Dipartimento di Scienze Matematiche Fisiche e Informatiche Università di Parma Parco Area delle Scienze 7/A Parma43124 Italy INFN Gruppo Collegato di Parma Parco Area delle Scienze 7/A Parma43124 Italy

We introduce an exactly integrable version of the well-known leaky integrate-and-fire (LIF) model, with continuous membrane potential at the spiking event, the c-LIF. We investigate the dynamical regimes of a fully connected network of excitatory c-LIF neurons in the presence of short-term synaptic plasticity. By varying the coupling strength among neurons, we show that a complex chaotic dynamics arises, characterized by scale free avalanches. The origin of this phenomenon in the c-LIF can be related to the order symmetry breaking in neurons spike-times, which corresponds to the onset of a broad activity distribution. Our analysis uncovers a general mechanism through which networks of simple neurons can be attracted to a complex basin in the phase space. Copyright © 2021, The Authors. All rights reserved.

关键词： Neurons

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