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Artificial Intelligence in Obsessive-Compulsive Disorder: A systematic Review

Current treatment options in psychiatry 2025年第1期12卷 23页

作者： Jiyeong Kim Juan Pablo Gonzalez Pacheco Ashleigh Golden Elias Aboujaoude Peter van Roessel Aayushi Gandhi Pavithra Mukunda Tatevik Avanesyan Haopeng Xue Ehsan Adeli Jane Paik Kim Manish Saggar Shannon Wiltsey Stirman Eric Kuhn Kaustubh Supekar Kilian M Pohl Carolyn I Rodriguez Stanford Center for Digital Health Stanford School of Medicine Stanford CA USA. Department of Psychiatry and Behavioral Sciences Stanford School of Medicine Stanford CA USA. Program in Internet Health and Society Cedars-Sinai Medical Center Los Angeles CA USA. Artificial Intelligence for Mental Health Initiative (AI4MH) Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA. Department of Computer Science Stanford University Stanford CA USA. Department of Biomedical Data Science Stanford School of Medicine Stanford CA USA. Dissemination and Training Division National Center for PTSD VA Palo Alto Health Care System Palo Alto CA USA. Veterans Affairs Palo Alto Health Care System Palo Alto CA USA. Department of Electrical Engineering Stanford University Stanford CA USA.

Purpose of Review:Obsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there is an average delay of 17 years from symptom onset to diagnosis and treatment, resulting in poorer outcomes. This systematic review aims to synthesize current findings on the application of AI in OCD, highlighting opportunities for early symptom detection, scalable therapy training, clinical decision support, novel therapeutics, computer vision-based approaches, and multimodal biomarker discovery. Recent Findings:While previous reviews focused on biomarker-based OCD detection and treatment using machine learning (ML), the findings of the current review add information about novel applications of deep learning technology, specifically generative artificial intelligence (GenAI) and natural language processing (NLP). Among the included 13 articles, most studies (84.6%) utilized secondary data analyses, primarily through GenAI/NLP. Nearly 77% of these studies were published in the past two years, with high quality of evidence. The primary focus areas were enhancing treatment and management, and timely OCD detection (both 38.5%); followed by AI tool development for broader mental health applications. Summary:AI technologies offer transformative potential for improvements related to OCD if diagnosis occurs earlier after onset; thereby lessening the consequential economic burden. Prioritizing investment in ethically sound AI research could significantly improve OCD outcomes in mental health care. Supplementary Information:The online version contains supplementary material available at 10.1007/s40501-025-00359-8.

关键词： AI Diagnosis Mental health OCD Obsessive-compulsive disorder Treatment

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Leveraging Landsat and Google Earth Engine for long-term chlorophyll-a monitoring: A case study of Lake Balaton's water quality

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Ecological Informatics 2025年 90卷

作者： Li, Huan Somogyi, Boglárka Chen, Xiaona Luo, Zengliang Blix, Katalin Wu, Sirui Duan, Zheng Tóth, Viktor R. HUN-REN Balaton Limnological Research Institute Tihany Hungary National Laboratory for Water Science and Water Security HUN-REN Balaton Limnological Research Institute Tihany Hungary State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China Hubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information Engineering China University of Geosciences Wuhan 430074 China NORCE Norwegian Research Centre Norway School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Physical Geography and Ecosystem Science Lund University Sweden ESGreen LLC New York United States

Chlorophyll-a (Chl-a) is one of the critical water quality indicators that shows the eutrophication status of aquatic ecosystems. As the largest lake and a well-known attraction in middle Europe, Lake Balaton contributes 70 % or more of local economy through tourism, while also maintaining a unique biodiversity. Therefore, long-term monitoring of water quality is essential for its effective management. With the longest global environmental record and a preferable spatial resolution, the satellite constellation Landsat is used for retrieving Chl-a in this study. However, the common low-frequent in-situ samplings and ∼ 16-day revisit of Landsat have limited both the quality and applicability of Landsat to Chl-a retrieval. Initially, we trained both linear and several machine learning models using matchups between in-situ measurements and satellite data from Landsat 4–9 missions during 1984 and 2023. To address the imbalanced data problem, which lacks high concentration samples due to the rare blooming events, we extend the time tolerance, incorporate temporal information, which connotes the phenology information, and apply an oversampling technique during the training process. Validated on Lake Balaton, which has a spatiotemporal amplitude of Chl-a concentration ranging from 5 to 260 μg/L since 1980s, Random Forest model has the best accuracy, which shows an R-square 0.86 and RMSE 8.16 μg/L. The oversampling technique improves the accuracy by 9.5 % than the non-oversampled. Leveraging all strategies improves overall accuracy by 21 %. The result also shows a reasonable trade-off via increasing the number of matchups 3 to 8 times by extending the time tolerance from the same day to 3 days regardless of the high variability of Chl-a due to the sinking and floating movement of algae. The enhancement framework can be applied to other lakes, especially for lakes with coarse samplings and wide Chl-a fluctuations. We present an open-source online tool for historical and real-

关键词： Applicability of Landsat Integrating temporal information Machine learning Oversampling imbalanced dataset Remote sensing Water quality

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Improved Model Predictive Control for Mitigating False Data Injection on Cascaded H-Bridge Inverters

Improved Model Predictive Control for Mitigating False Data ...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Rao Rao Zhaoxi Liu Lingfeng Wang Shiying Hou Yingqiao He The State Key Laboratory of Power Transmission Equipment & System Security and New Technology College of Electrical Engineering Chongqing University China Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee Milwaukee USA

The cybersecurity of the smart grid is becoming a critical issue to the power systems. Mitigating potential cyberattacks on the control of the power electronic devices is an important concern in the controller design to enhance the resilience of the overall power grid. In this paper, a novel model predictive control (MPC) is proposed for the multilevel cascaded H-bridge inverters considering the false data injection (FDI) attacks. The FDI attack is a vicious attack scenario when the attack signals are injected into the sensors in the cyberphysical systems (CPS). It is shown that FDI signals can effectively penetrate the commonly used Kalman Filter (KF) based state estimation and jeopardize the control of the inverters. The k-Nearest Neighbors (KNN) algorithm is employed in the proposed scheme to mitigate the FDI attacks. The proposed KNN-based MPC scheme was tested with various types of attack signals. The results show that the proposed scheme exhibits a robust performance in the face of the malicious FDI attacks.

关键词： Inverters Control systems Predictive control Sensors Kalman filters Power system stability Mathematical model

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Integrated Response to Boost Distribution system Resilience Against Windstorms

Integrated Response to Boost Distribution System Resilience ...

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Sustainable Power and Energy Conference (iSPEC)

作者： Gang Wang Zhaoxi Liu Lingfeng Wang Bo Hu Kaigui Xie Jiahao Yan State Key Laboratory of Power Transmission Equipment & System Security College of Electrical Engineering Chongqing University 400044 China Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee Milwaukee WI USA

Improving distribution system resilience is one of the urgent tasks for the modern society. This paper proposes a novel integrated preventive and emergency response model for system operators to boost the distribution system resilience considering all possible component damage statuses. Topology reconfiguration and generation redispatch are allowed in both the preventive response (PR) and emergency response (ER), while load curtailment is only deployed in the ER. A two-stage stochastic mixed-integer optimization model is developed for the integrated optimization problem, which is solved by the progressive hedging (PH) algorithm. Case studies on the IEEE 33-bus system and a 69-bus distribution system validate the correctness and effectiveness of the proposed model. Sensitivity analysis is also carried out to evaluate the influence of the weather severity and generator availability. The key conclusions of this paper include the following: 1) integrated response (IR) improves distribution system resilience with the minimum expected cost; and 2) integrated response is preferable comparing with the individual PR or ER.

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Experimental Evaluation of a Novel Personal Assistant in Greek Language for Ambient Assisted Living Environments employing home robots

Experimental Evaluation of a Novel Personal Assistant in Gre...

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2018 South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA_CECNSM 2018

作者： Spournias, Alexandros Christopoulos, Konstantinos Antonopoulos, Konstantinos Panagiotou, Christos Antonopoulos, Christos Orfanoudakis, Theofanis Voros, Nikolaos Computer and Informatics Engineering Department Embedded System Design and Application Laboratory TEI of Western Greece Antirio Greece Embedded System Design and Application Laboratory School of Science and Technology Digital Systems and Media Computing Laboratory TEI of Western Greece Hellenic Open University Patras Greece Embedded System Design and Application Laboratory TEI of Western Greece Antirio Greece School of Science and Technology Digital Systems and Media Computing Laboratory Hellenic Open University Patras Greece

ISBN: (纸本)9786188331419

A personal assistance, is a software component offering advanced Human Machine Interface (HMI) features by initiating and making a "conversations" between humans and machines. Its main objective is to identify the sentences and the context from human's question and respectively give an adequate response to the question posed. The proper response is given after analyzing the words of the question with those registered in its database. This type of application is called a personal assistant - chatbot. In this work, such an application is presented where it can be installed in any smart home as well as assisted living environment. Through the application, all automations, as well as the home robot also, are controlled by human voice in Greek language. Also, a number of auxiliary applications can be performed in the direction of better human service. © 2018 Tei of Western Macedonia.

关键词： Assisted living

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Block-based interpolation filter in video coding

Block-based interpolation filter in video coding

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International Workshop on Advanced Image Technology (IWAIT)

作者： MyungJun Kim Yung-Lyul Lee Department of Computer Engineering Sejong University Digital Media System Laboratory Seoul Republic of Korea

ISBN: (纸本)9781538626160

In high-efficiency video coding (HEVC), fractional pixel motion compensation uses an 8-point filter and a 7-point filter, based on the discrete cosine transform (DCT), for the 1/2-pixel and 1/4-pixel interpolations, respectively. In this paper, discrete sine transform (DST)-based interpolation filters (DST-IFs) and DCT-based interpolation filters (DCT-IFs) are proposed for fractional pixel motion compensation. The DCT-IFs and DST-IFs are applied to the block, coding tree unit (CTU), to decide between DCT-IFs and DST-IFs in terms of rate-distortion optimization (RDO) process. The block-based interpolation filter method showed average Bjøntegaard Delta (BD)-rate reductions of 0.8% and 0.7% in the random access (RA) and low delay B (LDB) configurations, respectively, in HEVC, whereas BD-rate is increased 0.6% in the low delay P (LDP) configuration.

关键词： Interpolation Encoding High efficiency video coding Motion compensation Discrete cosine transforms Bit rate

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Large-scale video compression： recent advances and challenges

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Frontiers of computer Science 2018年第5期12卷 825-839页

作者： Tao TIAN Hanli WANG Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Shanghai Engineering Research Center of Industrial Vision Perception & Intelligent Computing Shanghai 200092 China

The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression （LSVC） is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate video coding approaches. Finally, the challenges and fu- ture research trends for LSVC are discussed.

关键词： large-scale video compression fast video coding compression efficiency

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Rule-guided compositional representation learning on knowledge graphs

arXiv

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arXiv 2019年

作者： Niu, Guanglin Zhang, Yongfei Li, Bo Cui, Peng Liu, Si Li, Jingyang Zhang, Xiaowei Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University Department of Computer Science and Technology Tsinghua University College of Computer Science and Technology Qingdao University

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Moreover, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning. Copyright © 2019, The Authors. All rights reserved.

关键词： Knowledge graph

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Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification

arXiv

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arXiv 2020年

作者： Qu, Ying Baghbaderani, Razieh Kaviani Li, Wei Gao, Lianru Zhang, Yuxiang Qi, Hairong The Advanced Imaging and Collaborative Information Processing Group Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Hyperspectral image (HSI) classification is one of the most active research topics and has achieved promising results boosted by the recent development of deep learning. However, most state-of-the-art approaches tend to perform poorly when the training and testing images are on different domains, e.g., source domain and target domain, respectively, due to the spectral variability caused by different acquisition conditions. Transfer learning-based methods address this problem by pre-training in the source domain and fine-tuning on the target domain. Nonetheless, a considerable amount of data on the target domain has to be labeled and non-negligible computational resources are required to retrain the whole network. In this paper, we propose a new transfer learning scheme to bridge the gap between the source and target domains by projecting the HSI data from the source and target domains into a shared abundance space based on their own physical characteristics. In this way, the domain discrepancy would be largely reduced such that the model trained on the source domain could be applied on the target domain without extra efforts for data labeling or network retraining. The proposed method is referred to as physically-constrained transfer learning through shared abundance space (PCTL-SAS). Extensive experimental results demonstrate the superiority of the proposed method as compared to the state-of-the-art. The success of this endeavor would largely facilitate the deployment of HSI classification for real-world sensing scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Power Efficient User Cooperative Computation to Maximize Completed Tasks in MEC Networks

Power Efficient User Cooperative Computation to Maximize Com...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yijin Pan Cunhua Pan Kezhi Wang Huiling Zhu Jiangzhou Wang National Mobile Communications Research Laboratory Southeast University China School of Engineering and Digital Arts University of Kent UK School of Electronic Engineering and Computer Science Queen Mary University of London UK Department of Computer and Information Sciences Northumbria University UK

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the non-cooperative scheme.

关键词： Task analysis Servers Power demand Device-to-device communication Mobile handsets Computational modeling Edge computing

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