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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Songling Zhang Zhaohui Yang Mingzhe Chen Danpu Liu Kai-Kit Wong H. Vincent Poor Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Zhejiang University Hangzhou China Zhejiang Lab Hangzhou China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Electronic and Electrical Engineering University College London London UK Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (纸本)9781665435413

In this paper, the problem of maximizing the sum rate of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 13.3 % gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS.

关键词： Manifolds Multicast algorithms Array signal processing Surface waves Simulation Performance gain Matrices

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An Evaluation of Augmentative and Alternative Communication Research for ASD Children in Developing Countries: Benefits and Barriers 22nd

An Evaluation of Augmentative and Alternative Communication ...

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22nd International Conference on Human-computer Interaction, HCI International 2020

作者： Farzana, Walia Sarker, Farhana Hossain, Quazi Delwar Chau, Tom Mamun, Khondaker A. Advanced Intelligent Multidisciplinary Systems Lab Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Electrical & Electronic Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Institute of Biomaterials and Biomedical Engineering University of Toronto Toronto Canada

ISBN: (纸本)9783030607029

Augmentative and Alternative Communication (AAC) technology research not only enhance communication but also communal skills in verbally challenged children with Autism Spectrum Disorder (ASD). However, the research on AAC technology is mainly concentrated in developed countries and less explored in developing countries. In this study, we utilized evaluation method to analyze the prospects, existing practices and future possibilities, benefits and barriers of AAC research in developing countries. It is found that Speech Generating Devices (SGD) are mostly preferred by children and in future artificial intelligence (AI) based mobile application will augment communication skills among verbally challenged children. We conclude with general recommendation on succeeding research, collaborative approach and implementation with funding opportunities for substantial growth of AAC technology research in developing countries. This study will facilitate directions for initiating AAC research in developing countries and will accommodate researchers, developers and stakeholders to acknowledge opportunities, barriers, probable and current state of AAC research. © 2020, Springer Nature Switzerland AG.

关键词： Developing countries

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Retrieve, reason, and refine: generating accurate and faithful patient instructions 22

Retrieve, reason, and refine: generating accurate and faithf...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Fenglin Liu Bang Yang Chenyu You Xian Wu Shen Ge Zhangdaihong Liu Xu Sun Yang Yang David A. Clifton Department of Engineering Science University of Oxford School of ECE Peking University Department of Electrical Engineering Yale University Tencent JARVIS Lab China Department of Engineering Science University of Oxford and Oxford-Suzhou Centre for Advanced Research China MOE Key Lab of Computational Linguistics School of Computer Science Peking University School of Public Health Shanghai Jiao Tong University School of Medicine China

ISBN: (纸本)9781713871088

The "Patient Instruction" (PI), which contains critical instructional information provided both to carers and to the patient at the time of discharge, is essential for the patient to manage their condition outside hospital. An accurate and easy-to-follow PI can improve the self-management of patients which can in turn reduce hospital readmission rates. However, writing an appropriate PI can be extremely time-consuming for physicians, and is subject to being incomplete or error-prone for (potentially overworked) physicians. Therefore, we propose a new task that can provide an objective means of avoiding incompleteness, while reducing clinical workload: the automatic generation of the PI, which is imagined as being a document that the clinician can review, modify, and approve as necessary (rather than taking the human "out of the loop"). We build a benchmark clinical dataset and propose the Re3Writer, which imitates the working patterns of physicians to first retrieve related working experience from historical PIs written by physicians, then reason related medical knowledge. Finally, it refines the retrieved working experience and reasoned medical knowledge to extract useful information, which is used to generate the PI for previously-unseen patient according to their health records during hospitalization. Our experiments show that, using our method, the performance of five different models can be substantially boosted across all metrics, with up to 20%, 11% and 19% relative improvements in BLEU-4, ROUGE-L and METEOR, respectively. Meanwhile, we show results from human evaluations to measure the effectiveness in terms of its usefulness for clinical practice.

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PipePar: A Pipelined Hybrid Parallel Approach for Accelerating Distributed DNN Training

PipePar: A Pipelined Hybrid Parallel Approach for Accelerati...

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International Conference on computer Supported Cooperative Work in Design

作者： Jiange Li Yuchen Wang Jinghui Zhang Jiahui Jin Fang Dong Lei Qian School of Computer Science and Engineering Southeast University Nanjing P.R. China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi P.R. China

Large scale DNN training tasks are exceedingly compute-intensive and time-consuming, which are usually executed on highly-parallel platforms. Data and model parallelization is a common way to speed up the training progress across devices. However, they tend to achieve sub-optimal performance due to the communication overheads and unbalanced load among servers. Recent emerging pipelining solutions mitigate the above issues, incorporating the advantages of data and model parallelism. In this paper, we make a step further towards optimizing the execution of pipelining. We introduce PipePar, a pipeline-parallel DNN training method that provides optimized execution strategies of layer-stacked DNNs. PipePar considers the entire tensor partition space of pipelining and explores potential hybrid parallel configurations of each stage in the pipeline. Additionally, we notice the network heterogeneity between different GPU servers and it is inevitable to transfer tensors with different bandwidths and latency. So, taking into account both computation and communication capacity of different GPU servers, PipePar is intended to find a elastic load distribution strategy at different levels. We evaluate PipePar with a set of real-world DNNs on 4 GPU servers. Our experimental results show that PipePar is able to find an efficient strategy that are up to 2.16× faster than state-of-the-art hybrid parallelization approaches.

关键词： Training Performance evaluation Tensors Computational modeling Graphics processing units Data models Servers

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ToPoFM: Topology-Guided Pathology Foundation Model for High-Resolution Pathology Image Synthesis with Cellular-Level Control

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Jingxiong Zhu, Chenglu Zheng, Sunyi Chen, Pingyi Sun, Yuxuan Li, Honglin Yang, Lin Zhejiang University College of Computer Science and Technology Hangzhou310027 China Westlake University School of Engineering Hangzhou310024 China Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center for Cancer Tianjin's Clinical Research Center for Cancer State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine Tianjin Key Laboratory of Digestive Cancer Key Laboratory of Cancer Prevention and Therapy Department of Radiology Tianjin China Westlake University Artificial Intelligence and Biomedical Image Analysis Lab School of Engineering Hangzhou310024 China Westlake Institute for Advanced Study Institute of Advanced Technology Hangzhou310024 China Muyuan Laboratory Center for Interdisciplinary Research and Innovation Henan Zhengzhou450016 China

Synthetic data generation emerges as a strategy to mitigate data scarcity in digital pathology, where complicated tissue and cellular features are correlated with cancer diagnosis. The synthesis of such visuals, however, suffers from limited inter class diversity and scarcity of cellular annotations. Current methodologies struggle with capturing the broad spectrum of pathology features, causing unpredictable objects and defected fidelity. Moreover, discrepancies in image resolution across developmental and operational phases can amplify the distribution shifts, undermining the precision of diagnosis. To address these challenges, we introduce TOpology guided PathOlogy Foundation Model (ToPoFM), a visual foundation model designed for the synthesis of high-resolution pathology images with cellular-level control. Our approach integrates a topology-informed cell arrangement generator to steer large language models for crafting synthetic cell arrangements. We correlate cell arrangement guidance with diffusion model for pathology content generation, then further implement a random sliding inference strategy, merging discrete low-resolution samplings into single high-resolution representation. Our model requires only small patches for training. The efficacy of ToPoFM is demonstrated through extensive experiments, complemented by expert validations, showing high fidelity on data synthesis. Additionally, we underscore the utility of our generated imagery as an augmentation tool, enhancing the performance of downstream tasks, including cancer subtype classification and segmentation. © 2025 IEEE.

关键词： Image segmentation

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Machine learning aided parameter analysis in Perovskite X-ray Detector

arXiv

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arXiv 2024年

作者： Zhang, Bobo Huang, Endai Du, Xinyi Ma, Xiaokang Zhang, Lu You, Jiaxue Jen, Alex K.Y. Liu, Shengzhong Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology Institute for Advanced Energy Materials School of Materials Science and Engineering Shaanxi Normal University Xi’an710119 China Research Institute of Medical and Biological Engineering Ningbo University Zhejiang315211 China Department of Computer Science and Engineering The Chinese University of Hong Kong 999077 Hong Kong Department of Chemical and Biomolecular Engineering National University of Singapore Singapore Singapore National University of Singapore Singapore Singapore State Key Laboratory of Solidification Processing Northwestern Polytechnical University Shaanxi Xi’an710072 China Department of Materials Science and Engineering Hong Kong Institute for Clean Energy City University of Hong Kong 999077 Hong Kong Dalian National Laboratory for Clean Energy iChEM Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian116023 China University of the Chinese Academy of Sciences Beijing100039 China

Many factors in perovskite X-ray detectors, such as crystal lattice and carrier dynamics, determine the final device performance (e.g., sensitivity and detection limit). However, the relationship between these factors remains unknown due to the complexity of the material. In this study, we employ machine learning to reveal the relationship between 15 intrinsic properties of halide perovskite materials and their device performance. We construct a database of X-ray detectors for the training of machine learning. The results show that the band gap is mainly influenced by the atomic number of the B-site metal, and the lattice length parameter b has the greatest impact on the carrier mobility-lifetime product (μτ). An X-ray detector (m-F-PEA)2PbI4 were generated in the experiment and it further verified the accuracy of our ML models. We suggest further study on random forest regression for X-ray detector applications. © 2024, CC BY.

关键词： Machine learning

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Communication Support Utilizing AAC for Verbally Challenged Children in Developing Countries During COVID-19 Pandemic 22nd

Communication Support Utilizing AAC for Verbally Challenged ...

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22nd International Conference on Human-computer Interaction, HCI International 2020

作者： Farzana, Walia Sarker, Farhana Vaidyanathan, Ravi Chau, Tom Mamun, Khondaker A. Advanced Intelligent Multidisciplinary Systems Lab Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Mechanical Engineering Imperial College London London United Kingdom Institute of Biomaterials and Biomedical Engineering University of Toronto Toronto Canada

ISBN: (纸本)9783030607029

Functional communication is indispensable for child development at all times but during this COVID-19, non-verbal children become more anxious about social distancing and self-quarantine due to sudden aberration on daily designed practices and professional support. These verbally challenged children require the support of Augmentative and Alternative Communication (AAC) for intercommunication. Therefore, during COVID-19, assistance must be provided remotely to these users by a AAC team involving caregivers, teachers, Speech Language Therapist (SLT) to ensure collaborative learning and development of non-verbal child communication skills. However, most of the advanced AAC, such as Speech Generating Devices (SGD), Picture Exchange Communication System (PECS) based mobile applications (Android & iOS) are designed considering the scenario of developed countries and less accessible in developing countries. Therefore, in this study, we are focusing on representing feasible short term strategies, prospective challenges and as long term strategy, a cloud based framework entitled as "Bolte Chai+", which is an intelligent integrated collaborative learning platform for non-verbal children, parents, caregivers, teachers and SLT. The intelligent analytics within the platform monitors child overall progress by tracking child activity in mobile application and conversely support parents and AAC team to concentrate on individual child ubiquitous abilities. We believe, the proposed framework and strategies will empower non-verbal children and assist researchers, policy makers to acknowledge a definitive solution to implement AAC as communication support in developing countries during COVID-19 pandemic. © 2020, Springer Nature Switzerland AG.

关键词： Intelligent systems

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Neuromorphic model-based neural decoders for brain-computer interfaces: a comparative study

Neuromorphic model-based neural decoders for brain-computer ...

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IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Yijie Weng Yu Qi Yueming Wang Gang Pan College of Biomedical Engineering & Instrument Science Zhejiang University Hangzhou China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

Brain-computer interfaces that convert neural signals into control commands for external devices have demonstrated great potential in neural rehabilitation. Most recently, the growing demand for developing large-scale brain signal recording devices has highlighted the need for power-efficient neural signal processing. Neuromorphic computing models, implemented with chips, can provide ultra-low-cost computation of neural signals, facilitating brain-implantable devices. In our work, we evaluated the neural decoding ability of neuromorphic models in comparison with traditional neural decoding approaches. We found that neuromorphic model-based neural decoders showed high accuracy comparable to existing decoders while with significantly lower computational costs. Our results suggest the potential of using neuromorphic models for highly efficient computing with large-scale brain data.

关键词： Neuromorphic engineering Computational modeling Biological system modeling Brain modeling Brain-computer interfaces Data models Decoding Recording Computational efficiency Integrated circuit modeling

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GeoAI-based epidemic control with geo-social data sharing on blockchain 22

GeoAI-based epidemic control with geo-social data sharing on...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Peng, Shaoliang Bai, Liang Xiong, Li Qu, Qiang Xie, Xiaolan Wang, Shulin College of Computer Science and Electronic Engineering Hunan University China National Supercomputing Centre in Changsha Hunan University China Peng Cheng Lab Shenzhen China The Second Xiangya Hospital of Central South University China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Guilin University of Technology China

ISBN: (纸本)9781728162676

Epidemics especially those caused by major contagious diseases have entailed huge losses in human history. The fights have thus never stopped to prevent pandemics. Due to its acute outbreak, is generally susceptible to the population regardless of ages, so strict quarantine of the infections becomes the most effective means for the epidemic control, which has been proved in the prevention of other contagious diseases such as SARS and H1N1. The key strategy widely used to find infected and suspected patients is still the epidemiological tracking of confirmed cases. However, this may fail to identify infections especially when patients do not show any symptoms. Therefore, the approach to rapid, effective, and simple infection identification is essential to prevent the spread of a contagious disease. This paper proposes to leverage a social apps and Geospatial artificial intelligence (GeoAI) with Blockchain to effectively identify infections with privacy concern. Since people widely use social apps, a large scale of social data with geospatial information could be easily collected and kept on Blockchain with privacy preservation, which thus provides a framework of decentralized, tamper-proof, and privacy-preserved information sharing. With the support of GeoAI, which analyzes the spatial distribution of diseases from the shared data, we could study the influence factors based on spatial propagation of contagious diseases for infection identification. Since WeChat is widely used in China, we take COVID-19 as an example to use the experiments on real-life datasets demonstrate the effectiveness of our method, and provide insight into epidemic control in terms of geo-social data sharing. © 2021 IEEE.

关键词： COVID-19

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Anhydride-terminated Solid-State Carbon Dots with Bright Orange Emission Induced by Weak Excitonic ElectronicCoupling

Anhydride-terminated Solid-State Carbon Dots with Bright Ora...

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2021全国太阳能电池材料器件与技术大会

作者： Fan Liu Shuhong Xu Pengfei Xia Hongyu Yang Ziting Qian Yuan Jiang Zhuyuan Wang Dayan Ban Chunlei Wang Advanced Photonics Center School of Electronic Science and Engineering Southeast University Lab for Nanoelectronics and NanoDevices Lab Department of Electronics Information Hangzhou Dianzi University Waterloo Institute for Nanotechnology and Department of Electrical and Computer Engineering University of Waterloo

Though great achievements have been made in fluorescent carbon dots(CDs) research, fabricating solid-state CDs with bright emission still remains very challenging. One common solution is to embed CDs into host matrix. However, it often suffers from low photoluminescence quantum yields(PLQY) as well as limited emission color of blue to green. In this work, we successfully synthesized a new type of anhydride-terminated CDs with strong orange fluorescence in powder form. The experimental results revealed that anhydride groups can emit orange fluorescence very efficiently at around 580 nm in wavelength. Due to the negligible hydrogen bonding interaction between the anhydride groups, the electronic coupling of excitons between neighboring anhydride groups is weak, leading to high PLQY of 28%. In addition, the hydrophobicity of the anhydride groups greatly facilitates the purification of the CDs, in which water can be used as an anti-solvent in precipitation process. Anhydride-terminated CDs can be partly converted into carboxyl-terminated CDs after dispersion in ethanol. However, strong electronic coupling of excitons quenches the blue emission of the carboxyl-terminated CDs in solid state form. A useful blue emission for solid state CDs is from low sp2 hybridized carbon atoms, which is realized by adjusting the feed ratio of reactants in the synthesis process of anhydride-terminated CDs. As a result, single component anhydride-terminated CDs powder with tunable emission color from orange to white light can be achieved. As-preparedanhydride-terminated CDs can be used for fabricating light emitting diodes(LEDs) and white LEDs.

关键词： carbon dots fluorescent solid-state anhydride electronic coupling white light emitting diodes

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