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X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20220101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231207
DTEND;VALUE=DATE:20231208
DTSTAMP:20260513T064931
CREATED:20231127T062651Z
LAST-MODIFIED:20250114T080012Z
UID:17865-1701907200-1701993599@ece.hku.hk
SUMMARY:Application of Functional Nanomaterials in Tumor Therapy
DESCRIPTION:Cancer is one of the most serious diseases that threaten human health. It is very urgent to develop advanced technology for cancer diagnosis and therapy. The traditional cancer therapy methods include operation\, chemotherapy and radiotherapy etc\, which suffer from long treatment period\, drug resistance\, big side effects. The newly developed minimally invasive treatment processes such as photothermal (PTT)/photodynamic (PDT)/sonodynamic (SDT) therapy and immunotherapy show great promise for cancer therapy\, with advantages of good time-space selection\, safe\, low toxicity and minor side effects. \nIn this lecture\, nanomaterials based photothermal and photodynamic therapy\, sonodynamic therapy and immunotherapy will be discussed. For NIR (laser)-triggered PTT and PDT\, we will present metal/semiconductor heterojunction structure to increase photothermal conversion efficacy\, Pd/Cu single-atom nano-enzyme to realize mild temperature PTT and double photosensitisers to improve ROS yield and increase the therapy efficacy in PDT. For sonodynamic therapy (SDT)\, we will discuss the design and optimization strategy of sonosensitizers\, including defect engineering strategy and heterojunctions route. For immunotherapy\, we will present how nanomaterials function in potentiating immune response\, including immunogenic cell death (ICD)\, construction of tumor nanovaccines and pyroptosis adjuvants. \nFinally\, the future challenges for the application of nanomaterials in tumor therapy will be forecasted. \nBiography of the speaker: \nDr. Jun Lin\, Professor\, and the group leader of luminescent and biomedical materials in Changchun Institute of Applied Chemistry\, Chinese Academy of Sciences. His research mainly focuses on luminescent materials for various applications\, including photoluminescence and spectral properties of rare earth ions and perovskite QDs for applications in displays and lightening (especially for white LED)\, as well as multifunctional materials as theragnostic agents for biomedical applications. He won Grade -1 Award for Science and Technology Progress of Jilin Province three times and was selected as a highly cited researcher in materials science and cross-field in 2014-2023. So far\, he has published more than 900 peer-reviewed journal articles with total citations over 70000 and H index of 140; He also gave more than 100 invited lectures in various kinds of conferences.
URL:https://ece.hku.hk/events/application-of-functional-nanomaterials-in-tumor-therapy/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/02/Seminar-s-banner.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231209
DTEND;VALUE=DATE:20231210
DTSTAMP:20260513T064931
CREATED:20231114T070036Z
LAST-MODIFIED:20250114T080439Z
UID:17847-1702080000-1702166399@ece.hku.hk
SUMMARY:Bruce Lee-Inspired Fluid Antenna System for Extreme Massive Connectivity
DESCRIPTION:“Be formless … shapeless\, like water!”\, which were the words used by Bruce Lee\, as he was revealing the philosophy of Jeet Kune Do\, the martial arts system Lee founded in 1967. Many parallels can be drawn in wireless communications technologies where engineers have been seeking greater flexibility in using the spectral and energy resources for improving network performance. In this talk\, I will speak on some new ideas for improving wireless communications performance\, using a novel antenna technology\, referred to as fluid antenna and its great potential to achieve extreme massive connectivity that is not possible by other technologies we know so far. \nBiography of the speaker: \n(Kit) Kai-Kit Wong received the BEng\, the MPhil\, and the PhD degrees\, all in Electrical and Electronic Engineering\, from the Hong Kong University of Science and Technology\, Hong Kong\, in 1996\, 1998\, and 2001\, respectively. He is Chair Professor of Wireless Communications at the Department of Electronic and Electrical Engineering\, University College London. His current research centers around 6G mobile communications. He is one of the early researchers who proposed multiuser MIMO. His first paper on multiuser MIMO was published in WCNC 2000 which appeared to be the first ever research paper on this topic. He is Fellow of IEEE and IET. He currently serves as the Editor-in-Chief for IEEE Wireless Communications Letters.
URL:https://ece.hku.hk/events/bruce-lee-inspired-fluid-antenna-system-for-extreme-massive-connectivity/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/02/Seminar-s-banner.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231212
DTEND;VALUE=DATE:20231213
DTSTAMP:20260513T064931
CREATED:20231204T090338Z
LAST-MODIFIED:20250114T075949Z
UID:17868-1702339200-1702425599@ece.hku.hk
SUMMARY:Visualization of interfacial electrochemistry using a glass pipette
DESCRIPTION:Electrocatalysis has never been more important than today\, with electrochemistry finding renewed interest in the future of renewable energy. Conventional evaluation of nanoparticular catalysts are based on macroscopic measurement of the composite electrodes\, where conductive agents and binders are integrated. Structural and compositional heterogeneity further obscures the definitive understanding of structure-activity relation. Therefore\, investigation of electrochemical process with correlative imaging and at nanoscale become urgent. In this talk\, we are going to introduce our study of single particle electrochemistry using the scanning electrochemical cell microscopy\, where a small pipette is utilized to confine reaction medium. Particles from individual nanocrystals to superlattices and gas bubbles associated with gas evolution reaction at electrode surface will be covered. \nBiography of the speaker: \nDr. Chen obtained his bachelor degree in Sichuan University (2008) and PhD in the Chinese University of Hong Kong (2013). Then\, he had his postdoctoral with Professor Henry White in University of Utah\, and with Professor Allen Bard in the University of Texas at Austin. He joined Donghua University in 2017\, starting his independent work in the area of nanoelectrochemistry and electrochemical mapping. He has published over 40 articles in international journals including PNAS\, JACS\, Angew. Chem. Int. Ed\, Anal. Chem. He has received Second Prize of Natural Science Award in Chinese Society of Particuology (2022)\, National Ten Thousand Talent Program for Young Top-Notch Talent (2023).
URL:https://ece.hku.hk/events/visualization-of-interfacial-electrochemistry-using-a-glass-pipette/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/02/Seminar-s-banner.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231213
DTEND;VALUE=DATE:20231214
DTSTAMP:20260513T064931
CREATED:20231206T082433Z
LAST-MODIFIED:20250114T081120Z
UID:17871-1702425600-1702511999@ece.hku.hk
SUMMARY:RPG Seminar – Unsupervised Domain Adaptation for 3D Object Detection from Point Cloud
DESCRIPTION:3D object detection aims to categorize and localize objects from 3D sensor data  with many applications in autonomous driving\, robotics\, virtual reality\, etc. Recently\, this field has obtained remarkable advancements driven by deep neural networks and large-scale human-annotated datasets. However\, 3D detectors developed on one specific domain (i.e. source domain) might not generalize well to novel testing domains (i.e. target domains) due to unavoidable domain-shifts arising from different types of 3D sensors\, weather conditions and geographical locations\, etc. Though collecting more training data from different domains could alleviate this problem\, it unfortunately might be infeasible given various real-world scenarios and enormous costs for 3D annotation. Therefore\, approaches to effectively adapting 3D detector trained on labeled source domain to a new unlabeled target domain is highly demanded in practical applications. This task is also known as unsupervised domain adaptation (UDA) for 3D object detection. We propose a self-training pipeline ST3D to tackle this problem. \nZoom Link :\nhttps://hku.zoom.us/j/9122109333\nMeeting ID: 912 210 9333 \nBiography of the speaker:\n\nJihan Yang is a PhD candidate at EEE\, The University of Hong Kong\, advised by Dr. Xiaojuan Qi. Before that\, he obtained Bachelor’s degree from Sun Yat-sen University\, supervised by Prof. Liang Lin and Prof. Guanbin Li. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-unsupervised-domain-adaptation-for-3d-object-detection-from-point-cloud/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231213
DTEND;VALUE=DATE:20231214
DTSTAMP:20260513T064931
CREATED:20231206T083214Z
LAST-MODIFIED:20250114T075814Z
UID:17872-1702425600-1702511999@ece.hku.hk
SUMMARY:RPG Seminar – Language-driven Open-vocabulary 3D Scene Understanding
DESCRIPTION:Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space. The recent breakthrough of 2D open-vocabulary perception is largely driven by Internet-scale paired image-text data with rich vocabulary concepts. However\, this success cannot be directly transferred to 3D scenarios. In this seminar\, we will introduce the challenges faced in open-vocabulary 3D scene understanding and presents our proposed solution that harness powerful vision-language models to tackle this challenge. \nZoom Link :\nhttps://hku.zoom.us/j/3533656068\nMeeting ID: 353 365 6068 \nBiography of the speaker:\n\nRunyu Ding received her B.Eng. degree at Tsinghua University. Currently\, she is pursuing Ph.D. degree at the University of Hong Kong. Her research interests focus on 3D vision and embodied intelligence. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-language-driven-open-vocabulary-3d-scene-understanding/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231220
DTEND;VALUE=DATE:20231221
DTSTAMP:20260513T064931
CREATED:20231206T081937Z
LAST-MODIFIED:20250114T075922Z
UID:17870-1703030400-1703116799@ece.hku.hk
SUMMARY:RPG Seminar – Direct Data-Driven Control Methods: From Linear Systems to Nonlinear Systems
DESCRIPTION:Data-driven control (DDC) is a control strategy that develops controllers from data for unknown systems\, and it is increasingly popular in various research fields. DDC can be divided into indirect and direct approaches. The former uses data to identify system models and subsequently designs controllers based on those models\, while the latter simplifies the process by designing controllers directly from data\, skipping the system identification step. In this seminar\, we will explore different types of direct DDC methods for linear and nonlinear systems. Initially\, we will focus on linear systems\, particularly those with unmeasurable states. In this situation\, an output feedback controller will be designed solely using input-output data. Next\, the discussion will shift towards linear systems impacted by disturbances\, where a data-driven H-infinity controller will be developed. Furthermore\, we will extend these methods to nonlinear systems\, with a critical aspect involving the introduction of the piecewise affine system as a connecting element. \nZoom Link :\nhttps://hku.zoom.us/j/96557319827?pwd=TUlSRnA0ZFI3UC9Fc0Z2VlZzWEV1Zz09\nMeeting ID: 965 5731 9827\nPassword: 477912 \nBiography of the speaker:\n\nMr. Kaijian Hu received his B.Eng. degree in automation from Liaoning University of Science and Technology and his M.Eng. in control theory and control engineering from Dalian University of Technology. Currently\, he is pursuing his Ph.D. in the Department of Electrical and Electronic Engineering at the University of Hong Kong. His primary research interests are data-driven control and unmanned aerial vehicle (UAV) control. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-direct-data-driven-control-methods-from-linear-systems-to-nonlinear-systems/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231220T103000
DTEND;TZID=Asia/Hong_Kong:20231220T113000
DTSTAMP:20260513T064931
CREATED:20231213T063716Z
LAST-MODIFIED:20250114T075611Z
UID:17877-1703068200-1703071800@ece.hku.hk
SUMMARY:RPG Seminar – Dynamic Sparse Dataflow Architecture for Event-based Vision Inference
DESCRIPTION:Event-based vision represents a paradigm shift in how vision information is captured and processed. By only responding to dynamic intensity changes in the scene\, event-based sensing produces far less data than conventional frame-based cameras\, promising to springboard a new generation of high-speed\, low-power machines for edge intelligence. However\, processing such dynamically sparse input originated from event cameras efficiently in real time\, particularly with complex deep neural networks (DNN)\, remains a formidable challenge. Existing solutions that employ GPUs and other frame-based DNN accelerators often struggle to efficiently process the dynamically sparse event data\, missing the opportunities to improve processing efficiency with sparse data. To address this\, we propose ESDA\, a composable dynamic sparse dataflow architecture that allows customized DNN accelerators to be constructed rapidly on FPGAs for event-based vision tasks. ESDA is a modular system that is composed of a set of parametrizable modules for each network layer type. These modules share a uniform sparse token-feature interface and can be connected easily to compose an all-on-chip dataflow accelerator on FPGA for each network model. ESDA achieves substantial speedup and improvement in energy efficiency across different applications\, and it allows much wider design space for real-world deployments. \nZoom Link :\nhttps://hku.zoom.us/j/96114813885 \nBiography of the speaker:\n\nYizhao Gao received his B.Eng. degree at the University of Chinese Academy of Sciences\, he is pursuing Ph.D. degree at the University of Hong Kong. His research interests focus on reconfigurable computing and event-based vision processing. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-dynamic-sparse-dataflow-architecture-for-event-based-vision-inference/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231220T150000
DTEND;TZID=Asia/Hong_Kong:20231220T160000
DTSTAMP:20260513T064931
CREATED:20231213T084541Z
LAST-MODIFIED:20250114T075349Z
UID:17880-1703084400-1703088000@ece.hku.hk
SUMMARY:RPG Seminar – Parallel imaging reconstruction using spatial nulling maps
DESCRIPTION:Parallel imaging is widely used in clinical MRI to accelerate data acquisition or correct artifacts through the use of multiple receiving coils where each coil exhibits a unique spatial coil sensitivity map. Parallel reconstruction using null operations (PRUNO) is a k-space reconstruction method where a k-space nulling system is derived using null-subspace bases of the calibration matrix. ESPIRiT reconstruction extends the PRUNO subspace concept by exploiting the linear relationship between signal-subspace bases and spatial coil sensitivity characteristics\, yielding a hybrid-domain approach. Yet it requires empirical eigenvalue thresholding to mask the coil sensitivity information and is sensitive to signal- and null-subspace division. In this study\, we combine the concepts of null-subspace PRUNO and hybrid-domain ESPIRiT to provide a more robust reconstruction method that extracts null-subspace bases of calibration matrix to calculate image-domain spatial nulling maps (SNMs). The proposed reconstruction method eliminates the need for coil sensitivity masking and is relatively insensitive to subspace separation\, presenting a robust parallel imaging reconstruction procedure in practice. \nZoom Link :\nhttps://hku.zoom.us/j/91980394611\nMeeting ID: 919 8039 4611 \nBiography of the speaker:\n\nJiahao Hu received his B.E. degree from the Southern University of Science and Technology in 2020. He is currently pursuing a Ph.D. in EEE department at the University of Hong Kong. His research interests include analytical reconstruction algorithms\, data-driven and model-based deep learning methods for improving biomedical imaging quality and efficiency. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-parallel-imaging-reconstruction-using-spatial-nulling-maps/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231220T160000
DTEND;TZID=Asia/Hong_Kong:20231220T170000
DTSTAMP:20260513T064931
CREATED:20231213T084213Z
LAST-MODIFIED:20250114T075441Z
UID:17879-1703088000-1703091600@ece.hku.hk
SUMMARY:RPG Seminar – Pushing the limits of ultra-low-field MRI by dual-acquisition super-resolution
DESCRIPTION:Recent development of ultra-low-field (ULF) MRI presents opportunities for low-power\, EMI shielding-free\, and portable clinical applications of MRI. However\, the imaging performance of these emerging ULF MRI scanners remains limited due to its three orders of magnitude weaker main magnetic field\, resulting in the poor signal-to-noise ratio. Advancements in deep learning have opened new frontiers for improving ULF MRI image quality. In this seminar\, we present a novel dual-acquisition deep learning method for enhancing spatial resolution and suppressing noise/artifacts of 3D ULF brain MRI images acquired at our custom-built 0.055T brain MRI scanner.. \nZoom Link :\nhttps://hku.zoom.us/j/98172884162?pwd=VWVmeE5DWjhvcGJIeUZGdTBtRWdzQT09 \nBiography of the speaker:\n\nMan Hin Lau (Vick) obtained his MEng degree in Biomedical Engineering from Imperial College London in 2019. After a year working as a research assistant at HKU\, he is now pursuing a PhD degree with Prof Ed X Wu in the Department of Electrical and Electronic Engineering. His research focuses on the application of deep learning techniques to MRI image processing and reconstruction. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-pushing-the-limits-of-ultra-low-field-mri-by-dual-acquisition-super-resolution/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231220T170000
DTEND;TZID=Asia/Hong_Kong:20231220T180000
DTSTAMP:20260513T064931
CREATED:20231213T084857Z
LAST-MODIFIED:20250114T075316Z
UID:17881-1703091600-1703095200@ece.hku.hk
SUMMARY:RPG Seminar – Deep learning enabled fast 3D brain MRI at 0.055 tesla
DESCRIPTION:In recent years\, there has been an intensive development of portable ultralow-field magnetic resonance imaging (MRI) for low-cost\, shielding-free\, and point-of-care applications. However\, its quality is poor and scan time is long. We propose a fast acquisition and deep learning reconstruction framework to accelerate brain MRI at 0.055 tesla. The acquisition consists of a single average three-dimensional (3D) encoding with 2D partial Fourier sampling\, reducing the scan time of T1- and T2-weighted imaging protocols to 2.5 and 3.2 minutes\, respectively. The 3D deep learning leverages the homogeneous brain anatomy available in high-field human brain data to enhance image quality\, reduce artifacts and noise\, and improve spatial resolution to synthetic 1.5-mm isotropic resolution. Our method overcomes low-signal barrier\, reconstructing fine anatomical structures that are reproducible within subjects and consistent across two protocols. It enables fast and quality whole-brain MRI at 0.055 tesla\, with potential for widespread biomedical applications. \nZoom Link :\nhttps://hku.zoom.us/j/93224346406\nMeeting ID: 932 2434 6406 \nBiography of the speaker:\n\nChristopher Man received his bachelor degree in the University of Hong Kong and is currently pursuing PhD in the University of Hong Kong\, under the supervision of Prof. Ed X. Wu. His research interests include MRI image reconstruction and deep learning. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-deep-learning-enabled-fast-3d-brain-mri-at-0-055-tesla/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231221T110000
DTEND;TZID=Asia/Hong_Kong:20231221T120000
DTSTAMP:20260513T064931
CREATED:20231213T085231Z
LAST-MODIFIED:20250114T075234Z
UID:17882-1703156400-1703160000@ece.hku.hk
SUMMARY:RPG Seminar – Modelling AC Loss in Superconductors via Integral Method
DESCRIPTION:Superconductors can potentially be used in electrical machines in future electric aircraft\, since superconductors can increase the power density of electrical machines. When superconductors are carrying ac or are subject to an alternating magnetic field\, they experience ac loss. AC loss affects the efficiency of the machines and the cooling power needed\, which has implications for the mass of the overall system of the electric aircraft. The integral method can model superconductors that are carrying arbitrary ac and under an arbitrary external magnetic field. This talk will review the integral method in the literature\, and explain how the integral method can be used to model superconductors in an electrical machine. In addition\, it will also explain how the integral method can be used to model in 2D cables made of superconducting tapes that are coupled (electrically connected at the ends of the cable or along the whole length of the tapes). \nZoom Link :\nhttps://hku.zoom.us/j/99033734395\nMeeting ID: 990 3373 4395 \nBiography of the speaker:\n\nChung Tin Calvin Chow received the B.A. and M.Eng. degrees in engineering from the University of Cambridge\, Cambridge\, U.K.\, both in 2020\, with specialization in areas including control and information engineering. He is currently pursuing a Ph.D. degree in electrical and electronic engineering with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong\, SAR\, China. He was a visitor at Karlsruhe Institute of Technology\, Germany\, for around 5 months in 2022-2023 and at the University of Strathclyde\, UK\, for around 6 months in 2023. His research interests include superconducting machines and drives\, superconductor modelling and experimentation. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-modelling-ac-loss-in-superconductors-via-integral-method/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231221T140000
DTEND;TZID=Asia/Hong_Kong:20231221T150000
DTSTAMP:20260513T064931
CREATED:20231214T090442Z
LAST-MODIFIED:20250114T075128Z
UID:17883-1703167200-1703170800@ece.hku.hk
SUMMARY:RPG Seminar – Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
DESCRIPTION:Federated learning (FL) inevitably confronts the challenge of system heterogeneity in practical scenarios. To enhance the capabilities of most model-homogeneous FL methods in handling system heterogeneity\, we propose a training scheme that can extend their capabilities to cope with this challenge. In this seminar\, we commence our study with a detailed exploration of homogeneous and heterogeneous FL settings and discover three key observations: (1) a positive correlation between client performance and layer similarities\, (2) higher similarities in the shallow layers in contrast to the deep layers\, and (3) the smoother gradient distributions indicate the higher layer similarities. Building upon these observations\, we introduce InCo Aggregation that leverages internal cross-layer gradients\, a mixture of gradients from shallow and deep layers within a server model\, to augment the similarity in the deep layers without requiring additional communication between clients. Furthermore\, our methods can be tailored to accommodate model-homogeneous FL methods such as FedAvg\, FedProx\, FedNova\, Scaffold\, and MOON\, to expand their capabilities to handle the system heterogeneity. Copious experimental results validate the effectiveness of InCo Aggregation\, spotlighting internal cross-layer gradients as a promising avenue to enhance the performance in heterogeneous FL. \nZoom Link :\nhttps://hku.zoom.us/j/93707944044?pwd=VEtnNkVzYnNlY2IrUWR0UjhwVGV5UT09 \nBiography of the speaker:\n\nYun-Hin Chan received his B.Eng. of Software Engineering from Sun Yat-sen University. Currently\, he is pursuing his Ph.D. degree at the University of Hong Kong. His work is focused on how to solve practical challenges in federated learning\, such as communication efficiency and system heterogeneity. His research interests include deep learning\, distributed optimization\, federated learning\, knowledge distillation\, and transfer learning. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-internal-cross-layer-gradients-for-extending-homogeneity-to-heterogeneity-in-federated-learning/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231222T100000
DTEND;TZID=Asia/Hong_Kong:20231222T110000
DTSTAMP:20260513T064931
CREATED:20231213T064055Z
LAST-MODIFIED:20250114T075508Z
UID:17878-1703239200-1703242800@ece.hku.hk
SUMMARY:RPG Seminar – CL-NeRF: Continual Learning of Neural Radiance Fields for Evolving Scene Representation
DESCRIPTION:Existing methods for adapting Neural Radiance Fields (NeRFs) to scene changes require extensive data capture and model retraining\, which is both time-consuming and labor-intensive. In this paper\, we tackle the challenge of efficiently adapting NeRFs to real-world scene changes over time using a few new images while retaining the memory of unaltered areas\, focusing on the continual learning aspect of NeRFs. To this end\, we propose CL-NeRF\, which consists of two key components: a lightweight expert adaptor for adapting to new changes and evolving scene representations and a conflict-aware knowledge distillation learning objective for memorizing unchanged parts. We also present a new benchmark for evaluating Continual Learning of NeRFs with comprehensive metrics. Our extensive experiments demonstrate that CL-NeRF can synthesize high-quality novel views of both changed and unchanged regions with high training efficiency\, surpassing existing methods in terms of reducing forgetting and adapting to changes. \nZoom Link :\nhttps://hku.zoom.us/j/3164938755 \nBiography of the speaker:\n\nXiuzhe Wu received her B.Eng. and M.Eng degrees at Tongji University. Currently\, she is pursuing Ph.D. degree at the University of Hong Kong\, advised by Dr. Xiaojuan Qi. Her research interests focus on 3D vision. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-cl-nerf-continual-learning-of-neural-radiance-fields-for-evolving-scene-representation/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231222T100000
DTEND;TZID=Asia/Hong_Kong:20231222T110000
DTSTAMP:20260513T064931
CREATED:20231218T080324Z
LAST-MODIFIED:20250114T074936Z
UID:17888-1703239200-1703242800@ece.hku.hk
SUMMARY:RPG Seminar – Can 3D Vision-Language Models Truly Understand Natural Language?
DESCRIPTION:Rapid advancements in 3D vision-language (3D-VL) tasks\, such as 3D Visual Question Answering (3D-VQA) and 3D Visual Grounding (3D-VG)\, have opened up new avenues for human interaction with embodied agents or robots using natural language. Despite this progress\, we find a notable limitation: existing 3D-VL models exhibit heightened sensitivity to language input\, struggling to handle sentences with minor stylistic changes. This observation raises the critical question: “Can 3D vision-language models truly understand natural language?” We first propose a language robustness task for systematically assessing 3D-VL models across various tasks\, benchmarking their performance when presented with different language style variants. We propose the first 3D Language Robustness Dataset\, designed based on the characteristics of human language\, to facilitate the systematic study of robustness. Our comprehensive evaluation uncovers a significant drop in the performance of all existing models across various 3D-VL tasks. Even the state-of-the-art 3D-LLM fails on some variances. Further in-depth analysis suggests that the existing model fails to align the feature space\, which also stems from the low diversity of the existing dataset. We also propose a plug-and-play training free pre-alignment module driven by LLM\, which can improve language robustness. The data will be available to facilitate further research. \nZoom Link :\nhttps://hku.zoom.us/j/96527514816\nMeeting ID: 965 2751 4816 \nBiography of the speaker:\n\nWeipeng DENG received his B.Eng. of Computer Science from South China University of Technology. Currently\, he is pursuing his Ph.D. degree at the University of Hong Kong. His work is focused on how to leverage language to assist vision-language system. His research interests include deep learning\, large language model\, prompt\, vision-language model. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-can-3d-vision-language-models-truly-understand-natural-language/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231222T110000
DTEND;TZID=Asia/Hong_Kong:20231222T120000
DTSTAMP:20260513T064931
CREATED:20231213T063326Z
LAST-MODIFIED:20250114T075645Z
UID:17876-1703242800-1703246400@ece.hku.hk
SUMMARY:RPG Seminar – Video Demoireing with Relation-Based Temporal Consistency
DESCRIPTION:Moire patterns\, appearing as color distortions\, severely degrade the image and video qualities when filming a screen with digital cameras. Considering the increasing demands for capturing videos\, we study how to remove such undesirable moire patterns in videos\, namely video demoireing. To this end\, we introduce the first hand-held video demoireing dataset with a dedicated data collection pipeline to ensure spatial and temporal alignments of captured data. Further\, a baseline video demoireing model with implicit feature space alignment and selective feature aggregation is developed to leverage complementary information from nearby frames to improve frame-level video demoireing. More importantly\, we propose a relation-based temporal consistency loss to encourage the model to learn temporal consistency priors directly from ground-truth reference videos\, which facilitates producing temporally consistent predictions and effectively maintains frame-level qualities. Extensive experiments manifest the superiority of our model. \nZoom Link :\nhttps://hku.zoom.us/j/95468941119?pwd=UVpSUGowM3F2OXlwUzN6ajhvWHVQUT09\nMeeting ID: 954 6894 1119\nPassword: 336484 \nBiography of the speaker:\n\nPeng Dai is a fourth-year Ph.D. candidate at CVMI Lab\, Department of Electrical and Electronic Engineering\, The University of Hong Kong (HKU). His supervisor is Dr. Xiaojuan Qi. His research interest includes computer vision and computer graphics. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-video-demoireing-with-relation-based-temporal-consistency/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231222T110000
DTEND;TZID=Asia/Hong_Kong:20231222T120000
DTSTAMP:20260513T064931
CREATED:20231214T090837Z
LAST-MODIFIED:20250114T075053Z
UID:17884-1703242800-1703246400@ece.hku.hk
SUMMARY:RPG Seminar – Single-pulse optogenetic perturbation of thalamo-cortical networks reveals rsfMRI architecture
DESCRIPTION:Resting-state fMRI (rsfMRI) has emerged as the most valuable\, non-invasive imaging technique to map long-range\, brain-wide functional connectivity networks. Recently\, dynamic rsfMRI network segregation and integration have been shown to facilitate and modulate diverse cognitive functions. Such dynamics are structural-functional hierarchically organized and have been observed across a range of temporal scales\, spanning from days or hours\, to minutes. Converging studies postulate that critical transient states and their transition processes subserve such functional architecture of rsfMRI networks. \nHowever\, previous studies using either rsfMRI measurements only (i.e.\, during awake/sleep state) or task-based fMRI with conventional stimulation paradigms (i.e.\, block-designed/pulse trains) fail to clearly dissect transient/seconds-level reorganization of rsfMRI architecture upon stimulations. Here\, we propose to implement a single-pulse stimulation design to exert minimum influence on spontaneous activities (e.g.\, occurrence/intensity of spontaneous neural events) while modulating rsfMRI transient states and transition processes. \nZoom Link :\nhttps://hku.zoom.us/j/7179232708?omn=92069441167 \nBiography of the speaker:\n\nLinshan Xie obtained her B.Eng degree in Electronic Science and Technology from the University of Electronic Science And Technology Of China in 2020. She is now pursuing PhD in the Department of Electrical and Electronic Engineering at the University of Hong Kong under the supervision of Prof. Ed X Wu and Dr. Alex T.L. Leong. Her research interests focus on fMRI application in neuroscience\, including task-based (optogenetic) and resting-state fMRI acquisition and analysis. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-single-pulse-optogenetic-perturbation-of-thalamo-cortical-networks-reveals-rsfmri-architecture/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231222T140000
DTEND;TZID=Asia/Hong_Kong:20231222T150000
DTSTAMP:20260513T064931
CREATED:20231218T080705Z
LAST-MODIFIED:20250114T074904Z
UID:17889-1703253600-1703257200@ece.hku.hk
SUMMARY:RPG Seminar – Neural computing in random resistive memory
DESCRIPTION:Arrays\, graphs\, and point sets are fundamental data organization forms in mathematics\, with significant applications in scientific computing and engineering problems like signal processing\, molecular discovery\, and 3D vision. However\, the progress of digital circuitry\, limited by Moore’s Law\, has slowed down\, impeding the advancement of these problems. The increasing data and computational demands have amplified the von Neumann bottleneck in traditional computing systems\, necessitating urgent improvements in energy efficiency\, latency\, and computational capacity. Novel memory-based computing paradigms\, such as resistive memories\, have made progress in addressing these challenges. However\, resistive memories suffer from high power consumption and delay during write operations\, due to the need for high voltages to form conductive filaments. Randomness in filament shape and size also introduces inaccuracies. To overcome these issues\, we propose a co-design approach that minimizes programming/write operations in machine learning systems handling arrays\, graphs\, and point sets. This method improves system efficiency and guarantees performance. Validation on tasks involving arrays\, graphs\, and point sets demonstrates superior task performance and significant energy reduction compared to traditional computing systems. These research opens doors to future in-memory computing acceleration system design. \nZoom Link :\nhttps://hku.zoom.us/j/98133426720 \nBiography of the speaker:\n\nShaocong Wang is a fourth-year Ph.D. candidate at IMSC lab\, Department of Electrical and Electronic Engineering\, The University of Hong Kong (HKU). His supervisor is  Dr. Zhongrui Wang. His research interest includes computing-in-memory and analogue computing. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-neural-computing-in-random-resistive-memory/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231222T150000
DTEND;TZID=Asia/Hong_Kong:20231222T160000
DTSTAMP:20260513T064931
CREATED:20231218T075938Z
LAST-MODIFIED:20250114T075012Z
UID:17887-1703257200-1703260800@ece.hku.hk
SUMMARY:RPG Seminar – Large-scale single-cell morphological profiling on a spinning arrayed optofluidic platform
DESCRIPTION:Morphological profiling in imaging microscopy can reveal biological cells characteristics. However\, current image-based assay relays heavily on costly and extensive amount of fluorescent antibodies and is limited by the trade-off between the image resolution and scalability of the experiment. Therefore\, here we presented a spinning arrayed optofluidic platform to achieve live cell\, label free\, large-scale and high-resolution imaging. We demonstrated the stability of the system can achieve long-term imaging monitoring for at least 30 mins and further validated that the platform shows minimal effects on cells health based of a panel of cell-health assays. The application of the platform and morphological profiling was demonstrated through a drug screening analysis on two ling cancer cell lines with four drugs having five different concentrations\, which we observed that drug-concentration applied to lung-cancer cells showed obvious morphological profile shifts. Our profiling technique also showed the potential on understanding the viral entry mechanisms and impact on label-free cell morphology from quantitative phase imaging on CRISPR-based cell-cell fusion assay involving human ACE2 receptor cells and SARS-CoV-2 spike protein expression cells. The results show new and valuable insights on drug treatment and disease- and gene-related phenotypes. \nZoom Link :\nhttps://hku.zoom.us/j/95494140198?pwd=WEtVcEtFRjdzdGdacmFReFZLWkhmUT09\nMeeting ID: 954 9414 0198\nPassword: 217678 \nBiography of the speaker:\n\nVictor Wong received the BEng in medical engineering in 2019 and is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong (HKU). His research interests focus on ultrafast imaging\, single cell analysis\, and morphological profiling. He was also awarded the Research Postgraduate Student Innovation Award in 2022/23 established by Graduate School and the Technology Transfer Office. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-large-scale-single-cell-morphological-profiling-on-a-spinning-arrayed-optofluidic-platform/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20231222T160000
DTEND;TZID=Asia/Hong_Kong:20231222T170000
DTSTAMP:20260513T064931
CREATED:20231218T081037Z
LAST-MODIFIED:20250114T074745Z
UID:17890-1703260800-1703264400@ece.hku.hk
SUMMARY:RPG Seminar – High Speed Quantitative Phase and Polarization Microscopy for Zebrafish In Vivo Blood Cell Imaging
DESCRIPTION:Quantitative phase and polarization microscope (QPPI) is a useful label-free microscopic tool in revealing the quantitative biophysical information of cells\, such as cell shape\, dry mass and their subcellular distribution. However\, the current existing QPPI system could only offer a low imaging speed which is not sufficient to observe the dynamic activities of the live animal samples\, which is very important in understanding the disease developments and drug treatment effects. One example would be on the zebrafish leukaemia model. Fast imaging speed with high content images could be able to help monitor the in vivo blood flowing cells\, which provide the dynamic information of the cancer situations and drug effects after the different treatments. To boost the imaging speed of the QPPI system\, one possible solution is to combine the QPPI imaging technique with the existing developed ultrafast imaging system which is the Free-space Angular-chirp-enhanced Delay (FACED). This could help boost the imaging speed up to kHz which is sufficient to observe any zebrafish blood cell movements and reveal more information on the leukaemia as well as the effect of drugs. \nZoom Link :\nhttps://hku.zoom.us/j/5701095284?pwd=SUJYUFg1Q2pybUZhNE5WbnBqUXBSdz09 \nBiography of the speaker:\n\nRicky Hui received the BEng in Biomedical Engineering (BME) in 2022 in The University of Hong Kong (HKU) and is currently pursuing the MPhil Degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong (HKU). His research interests focus on ultrafast imaging\, volumetric imaging\, and quantitative label-free imaging techniques. \nAll are welcome.
URL:https://ece.hku.hk/events/rpg-seminar-high-speed-quantitative-phase-and-polarization-microscopy-for-zebrafish-in-vivo-blood-cell-imaging/
CATEGORIES:Seminar
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END:VCALENDAR