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Bioimaging of Dissolvable Microneedle Arrays: Challenges and Opportunities

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Research 2022年第4期2022卷 633-655页

作者： Yanni Wang Gehua Ma Guangzhi Gao Ji Tao Wenzhao Cao Haohao Sun Fengsen Ma Yilong Zhang Yen Wei Mei Tian Laboratory of Biologics and Biomaterials Collge of PharmacyZhejiang University of TechnologyHangzhou 310014China Cllge of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Human Phenome Institute Fudan UniversityShanghai 201203China College of Information Engineering Zhejiang University of TechnologyHangzhou 310023China Life Science Research Center Frontier Crossing InstituteZhejiang University of TechnologyHangzhou 310023China Engineering Research Center of Intelligent Sensing and System Ministry of EducationHangzhou 310023China College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China Department of Chemistry and the Tsinghua Center for Frontier Polymer Research Tsinghua UniversityBeijing 100084China

The emergence of microneedle arrays(MNAs)as a novel,simple,and minimally invasive administration approach largely addresses the challenges of traditional drug *** particular,the dissolvable MNAs act as a promising,multifarious,and well-controlled platform for micro-nanotransport in medical research and cosmetic formulation *** effective delivery mostly depends on the behavior of the MNAs penetrated into the body,and accurate assessment is urgently *** imaging technologies offer high sensitivity and resolution visualization of cross-scale,multidimensional,and multiparameter information,which can be used as an important aid for the evaluation and development of new *** combination of MNA technology and imaging can generate considerable new knowledge in a cost-effective manner with regards to the pharmacokinetics and bioavailability of active substances for the treatment of various *** addition,noninvasive imaging techniques allow rapid,receptive assessment of transdermal penetration and drug deposition in various tissues,which could greatly facilitate the translation of experimental MNAs into clinical *** on the recent promising development of bioimaging,this review is aimed at summarizing the current status,challenges,and future perspective on in vivo assessment of MNA drug delivery by various imaging technologies.

关键词： needle mostly formulation

From Role-Play to Drama-interaction: An LLM Solution

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arXiv 2024年

作者： Wu, Weiqi Wu, Hongqiu Jiang, Lai Liu, Xingyuan Hong, Jiale Zhao, Hai Zhang, Min Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Harbin Institute of Technology Shenzhen China

Drama is a form of storytelling inspired by human creativity, proceeding with a predefined storyline, carrying emotions and thoughts. This paper introduces LLM-based interactive drama, which endows traditional drama with an unprecedented immersion, where a person is allowed to walk into it and interact with the characters and scenes. We define this new artistic genre by 6 essential elements-plot, character, thought, diction, spectacle and interaction-and study the entire pipeline to forge a backbone drama LLM to drive the playing process, which is challenged by limited drama resources, uncontrollable narrative development, and complicated instruction following. We propose Narrative Chain to offer finer control over the narrative progression during interaction with players;Auto-Drama to synthesize drama scripts given arbitrary stories;Sparse Instruction Tuning to allow the model to follow sophisticated instructions. We manually craft 3 scripts, Detective Conan, Harry Potter, Romeo and Juliet, and design a 5-dimension principle to evaluate the drama LLM comprehensively. Copyright © 2024, The Authors. All rights reserved.

关键词：

Efficiently Fusing Sparse Lidar for Enhanced Self-Supervised Monocular Depth Estimation 48

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Efficiently Fusing Sparse Lidar for Enhanced Self-Supervised...

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Yue Gong, Mingrong Xia, Lei Zhang, Qieshi Cheng, Jun Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China University College London Department of Computer Science Gower Street WC1E 6BT United Kingdom

ISBN: (纸本)9781728163277

Monocular self-supervised depth estimation with a low-cost sensor is the mainstream solution to gathering dense depth maps for robots and autonomous driving. In this paper, based on the philosophy "less is more"(i.e., focusing only on valid pixels in sparse LiDAR), we propose a novel framework, Efficient Sparse Depth (EffisDepth), for predicting dense depth. The Sparse Feature Extractor (SFE) embedded in the proposed framework effectively handles sparse LiDAR by forming sparse tensors. The Slender Group Block (SGB) is the main building block in SFE, which extracts features from sparse tensors via a structure of two branches. Extensive experiments show that our method achieves state-of-the-art performance on the KITTI benchmark, demonstrating the effectiveness of each proposed component and the self-supervised learning framework. © 2023 IEEE.

关键词： Self-supervised depth estimation Sparse LiDAR Sparse tensor

A Passive Variable Impedance Control Strategy with Viscoelastic Parameters Estimation of Soft Tissues for Safe Ultrasonography

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arXiv 2023年

作者： Beber, Luca Lamon, Edoardo Nardi, Davide Fontanelli, Daniele Saveriano, Matteo Palopoli, Luigi Department of Industrial Engineering Università di Trento Trento Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

In the context of telehealth, robotic approaches have proven a valuable solution to in-person visits in remote areas, with decreased costs for patients and infection risks. In particular, in ultrasonography, robots have the potential to reproduce the skills required to acquire high-quality images while reducing the sonographer’s physical efforts. In this paper, we address the control of the interaction of the probe with the patient’s body, a critical aspect of ensuring safe and effective ultrasonography. We introduce a novel approach based on variable impedance control, allowing the real-time optimisation of compliant controller parameters during ultrasound procedures. This optimisation is formulated as a quadratic programming problem and incorporates physical constraints derived from viscoelastic parameter estimations. Safety and passivity constraints, including an energy tank, are also integrated to minimise potential risks during human-robot interaction. The proposed method’s efficacy is demonstrated through experiments on a patient’s dummy torso, highlighting its potential for achieving safe behaviour and accurate force control during ultrasound procedures, even in cases of contact loss. Copyright © 2023, The Authors. All rights reserved.

关键词： Parameter estimation

GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment

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arXiv 2024年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

The burgeoning size of Large Language Models (LLMs) has led to enhanced capabilities in generating responses, albeit at the expense of increased inference times and elevated resource demands. Existing methods of acceleration, predominantly hinged on knowledge distillation, generally necessitate fine-tuning of considerably large models, posing a challenge for average users. Furthermore, present techniques for expediting inference and reducing costs operate independently. To address these issues, we introduce a novel and intuitive Guidance-based Knowledge Transfer (GKT) framework. This approach leverages a larger LLM as a "teacher" to create guidance prompts, paired with a smaller "student" model to finalize responses. Remarkably, GKT requires no fine-tuning and doesn’t necessitate the teacher and student models to have the same vocabulary, allowing for extensive batch generation to accelerate the process while ensuring user customization. GKT can be seamlessly integrated into cloud-edge collaboration architectures, and is versatile enough for plug-and-play application across various models. When utilizing ChatGPT as teacher model and Llama2-70B as the student model, we can achieve 95.00% of ChatGPT’s performance at 52% of the cost. The results highlight substantial enhancements in accuracy and processing speed on the GSM8K and CSQA datasets, surpassing the performance of using either the student or teacher models in isolation. Our code is publicly available at https://***/Zoeyyao27/GKT © 2024, CC BY.

关键词： Distillation

Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models

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arXiv 2024年

作者： Zhang, Shenglin Zhu, Pengtian Ma, Minghua Wang, Jiagang Sun, Yongqian Li, Dongwen Wang, Jingyu Guo, Qianying Hua, Xiaolei Zhu, Lin Pei, Dan Nankai University China Microsoft United States Tsinghua University China China Mobile Research Institute China Haihe Laboratory of Information Technology Application Innovation China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Beijing National Research Center for Information Science and Technology China

Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge. Commercial companies face the dual challenges of privacy protection and resource constraints when involving LLMs for fine-tuning. This paper propose a novel framework, Self-Evolution, designed to address these issues by leveraging lightweight open-source LLMs through multiple iterative fine-tuning rounds. To enhance the efficiency of iterative fine-tuning, Self-Evolution employ a strategy that filters and reinforces the knowledge with higher value during the iterative process. We employed Self-Evolution on Qwen1.5-7B-Chat using 4,000 documents containing rich domain knowledge from China Mobile, achieving a performance score 174% higher on domain-specific question-answering evaluations than Qwen1.5-7B-Chat and even 22% higher than Qwen1.5-72B-Chat. Self-Evolution has been deployed in China Mobile's daily operation and maintenance for 117 days, and it improves the efficiency of locating alarms, fixing problems, and finding related reports, with an average efficiency improvement of over 18.6%. In addition, we release Self-Evolution framework code in https://***/Zero-Pointer/Self-Evolution. Copyright © 2024, The Authors. All rights reserved.

关键词： Data mining

Head-wise Shareable Attention for Large Language Models

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arXiv 2024年

作者： Cao, Zouying Yang, Yifei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

Large Language Models (LLMs) suffer from huge number of parameters, which restricts their deployment on edge devices. Weight sharing is one promising solution that encourages weight reuse, effectively reducing memory usage with less performance drop. However, current weight sharing techniques primarily focus on small-scale models like BERT and employ coarse-grained sharing rules, e.g., layer-wise. This becomes limiting given the prevalence of LLMs and sharing an entire layer or block obviously diminishes the flexibility of weight sharing. In this paper, we present a perspective on head-wise shareable attention for large language models. We further propose two memory-efficient methods that share parameters across attention heads, with a specific focus on LLMs. Both of them use the same dynamic strategy to select the shared weight matrices. The first method directly reuses the pre-trained weights without retraining, denoted as DirectShare. The second method first post-trains with constraint on weight matrix similarity and then shares, denoted as PostShare. Experimental results reveal our head-wise shared models still maintain satisfactory capabilities, demonstrating the feasibility of fine-grained weight sharing applied to LLMs1 Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

Structural and functional neural correlates of sensorimotor deficits in progression of hepatic encephalopathy

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Magnetic Resonance Letters 2025年第2期5卷

作者： Sha, Guanchen Liu, Yuefan Cao, Yutong Zhang, Qi Zhang, Yining Chen, Yuanyuan Fan, Qiuyun Cheng, Yue Department of Biomedical Engineering College of Precision Instruments and Optoelectronics Engineering Tianjin University Tianjin300072 China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin300072 China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Department of Medical Imaging Tianjin First Central Hospital Tianjin300192 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin300392 China State Key Laboratory of Advanced Medical Materials and Devices Tianjin300072 China

Hepatic encephalopathy (HE) is a neurological condition that occurs as a complication of liver dysfunction that involves sensorimotor symptoms in addition to cognitive and behavioral changes, particularly in cases of severe liver disease or cirrhosis. Previous studies have reported spatially distributed structural and functional abnormalities related to HE, but the exact relationship between the structural and functional alterations with respect to disease progression remains unclear. In this study, we performed surface-based cortical thickness comparisons and functional connectivity (FC) analyses between three cross-sectional groups: healthy controls (HC, N = 51), patients with minimal hepatic encephalopathy (MHE, N = 50), patients with overt hepatic encephalopathy (OHE, N = 51). In addition to the distributed cortical thinning that is extensively thought to be associated with cognitive decline in HE, we found significant cortical thickening in the left parahippocampal gyrus cortex in the OHE group (p © 2025 The Authors

关键词： Diseases

CauSkelNet: Causal Representation Learning for human Behaviour Analysis

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arXiv 2024年

作者： Gu, Xingrui Jiang, Chuyi Wang, Erte Wu, Zekun Cui, Qiang Tian, Leimin Wu, Lianlong Song, Siyang Yu, Chuang EECS University of California Berkeley BerkeleyCA United States Department of Electrical Engineering Columbia University New YorkNY United States Department of Computer Science University College London London United Kingdom Holistic AI University College London London United Kingdom The Future Laboratory Tsinghua University Beijing China School of Electrical and Computer Systems Engineering Monash University Melbourne Australia Department of Computer Science Oxford University Oxford United Kingdom Department of Computer Science University of Exeter Exeter United Kingdom UCL Interaction Centre University College London London United Kingdom

Constrained by the lack of model interpretability and a deep understanding of human movement in traditional movement recognition machine learning methods, this study introduces a novel representation learning method based on causal inference to better understand human joint dynamics and complex behaviors. We propose a two-stage framework that combines the Peter-Clark (PC) algorithm and Kullback-Leibler (KL) divergence to identify and quantify causal relationships between joints. Our method effectively captures interactions and produces interpretable, robust representations. Experiments on the EmoPain dataset show that our causal GCN outperforms traditional GCNs in accuracy, F1 score, and recall, especially in detecting protective behaviors. The model is also highly invariant to data scale changes, enhancing its reliability in practical applications. Our approach advances human motion analysis and paves the way for more adaptive intelligent healthcare solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning