Materials featuring three-dimensional microarchitectures exhibit various extreme functional properties, including negative thermal expansion, high-efficiency electromechanical conversion, ultrahigh stiffness and damage tolerance. Their remarkable properties are dominated by both the parent material and their microarchitecture and thus, they are commonly referred to as architected metamaterials. The rapid progress in 3D printing techniques has enabled the creation of architected metamaterials and unfolded many potential applications. However, the characterization and applicability of these metamaterials are significantly limited by the manufacturing scalability. Additionally, most currently available 3D printing methods only handle single structural materials, and it remains a challenge to 3D print multifunctional architected metamaterials. This presentation will focus on 3D printing techniques that address these key challenges, the investigation of elusive metamaterial properties, as well as the design and manufacturing of multifunctional architected metamaterials. The talk will first introduce a large-area projection stereolithography system capable of manufacturing submeter scale objects with micro-scale architectures, which enables the investigation of size effect in high-temperature ceramics and fracture toughness of mechanical metamaterials. This talk will then demonstrate the design and multi-material additive manufacturing of a series of robotic metamaterials that seamlessly integrate piezo-active, structural and conducting architectures. These robotic metamaterials can directly serve as micro-robots and achieve multi-degree-of-freedom motion, proprioception as well as responses to remote stimuli. All these works contribute to the understanding of the process-structure-property relationship of architected metamaterials as well as the creation of future intelligent materials and devices.

DNA double strand breaks (DSBs) are highly toxiclesions that can lead to genomic instability. Preciseregulation of DSBs repair is crucial to preserve genomeintegrity, as failure to repair DSBs properly is known todrive tumorigenesis and several human geneticsyndromes. In this seminar, Dr. Liang will describe newhigh throughput genomics and animal modelingapproaches to study the DSBs occurrence and repair.He has developed neural blastocyst complementation(NBC) system, which allows the introduction of geneticalterations into embryonic stem (ES) cells that used togenerate a complete mouse forebrain. He has alsoexploitedhighthroughputgenomicanewtranslocation sequencing method (HTGTS-JoinT-seq)to capture DNA ends arising from a solitary DSBs athigh resolution. Finally, Dr. Liang has applied thesenew methods to elucidate the implications of DSBs andunderlying mechanisms of genomic instability.

我们处理机器人的感知，我们的使命和努力的方向是创建智能系统，同时使用多种感知方式，让它们可以在一个复杂和多样的环境中自主运行。我们专注于那些最适合在大规模和动态的环境、不同的地形中活动的新机器人概念。我们还更热衷于给他们提供在具有挑战性的环境中自主导航的智能。这包括用于人机交互、感知、认知、知识抽象、映射、学习、表示、规划和执行的新方法和工具。在这次演讲中，我将介绍从自动驾驶到交付机器人的感知系统的最新发展。

目前大部分芯片厂商都感觉到遵循摩尔定律之途愈来愈难以为继时，业界一直努力在芯片中以实现更大更复杂的系统来满足市场需求。然而，将如此巨大规模的系统在单一的半导体芯片上实现是非常困难的，整个半导体产业目前也仍在为这种必须跨越工具、制程、设计端并加以整合的技术类别思考适合的解决方案。其中3D IC和异构集成技术能够将来自不同制造者的部件构建在一个芯片系统中，从而解决上述问题，成为了相关产业寻求持续发展的出路之一。为此，香港科技大学（HKUST）于2022年4月26日至28日在线举办为期3天的《3D IC与异构集成国际研讨会》（International Symposium on 3D IC and Heterogeneous Integration），研讨会云集顶尖大学和世界领先企业的科技精英，通过12场讲座和2场小组讨论，分享他们对异构技术最新趋势、机遇和挑战的看法，以期加强国际间的学术交流。

Today's transportation system is rapidly growingin scale and complexity, in terms of both theinfrastructure and transportation participants. Thisrenders safety and efficiency more challenging toachieve than ever before, and thereby posesunprecedented urgency on the intelligence ofthe overall transportation system. Fortunately.the increasingsensingdeploymentofcommunications and computing devices at thetransportation infrastructure,vehicles,andpassengers provide an abundance of data thatrecord the spatial, temporal, environmental andemotional status of the transportationevensystem and its participants. Such data possess thepower of fueling the intelligence in autonomousdriving,systemmodelingtransportationfactorsintransportation economics,humantransportation,and traffic flow modelingandcontrol

Come join this Information Webinar to learn more about:the benefits youwill gain from studying research postgraduate programs;why choose HKUST to pursue your MPhil or PhD studies;how HKUST research postgraduate programs will put you onto a promising path;the latest development of HKUST(Guangzhou) and admission to Guangzhou Pilot Scheme; and the first-hand experience from our International research postgraduate students.

China Intelligent Manufacturing 2025 is a vital national strategy for developing China as a world super power in manufacturing, which can make everything with high quality and efficiency. In this strategy, the critical but most difficult objective is to build smart machine tools. Smart machine tools can efficiently cut workpieces into qualified parts without manual operation. On these machines, cutters can be automatically measured for wear with a sensor (an on-machine tool setter) during machining, and then the following toolpath is compensated with the tool wear. Besides, the workpiece is measured with a sensor (a touch probe) right after each geometric feature is cut, and re-machining of the out-of-tolerance area is automatically planned. However, smart machine tools have not been built and applied in the manufacturing industry. It is urgent and important for Chinese researchers to develop the kernel techniques of smart machine tools. Prof. Chen is leading an international research team to conduct advanced research on smart machine tools in the following topics: (a) automatic multi-axis tool path generation and compensation, (b) automatic, in-process and on-machine cutter inspection, and (c) automatic, in-process and on-machine workpiece measurement. His team has successfully addressed many technical challenges and developed several new products for smart machine tools.

We learn and remember multiple new experiencesthroughout the day. The neural principles enablingcontinuous rapid learning and formation of distinctrepresentationsofnumeroussequentiaexperiences without major interference are notunderstood. To understand this process, weinterrogated ensembles of hippocampal place cellsas rats explored 15 novel linear environmentsinterleaved with sleep sessions over continuous16-hour periods. Remarkably, we found that apopulation of place cells were selective toenvironment orientation and topology.Thisorientation selectivityproperty biasedthenetwork-level discrimination and re/mappingbetweenmultipleenvironments.Novelenvironmental representations emerged rapidly asmore generic, but repeated experience within theenvironments subsequently enhanced theirdiscriminability. Generalization of prior experiencewithdifferentenvironmentsconsequentlyimproved network predictability of future novelenvironmental representations via strengthenedgenerative predictive codes. These coding schemesreveal a high-capacity, high-efficiency neuronalframework for rapid representation of numeroussequentialwithexperiencesoptimaldiscrimination-generalization balance and reducedinterference.

Robots are being increasingly deployed in the real world for applications such as warehouse automation, search and rescue, surveillance and reconnaissance, and environmental monitoring. However, when performing long-term tasks, robots need to deal with uncertain, changing environmental conditions. Robots may fail or if they are operating in adversarial environments, they may get attacked.I will describe resilient coordination algorithms to cope with adversarial attacks and applications to information gathering and target tracking with aerial robots. In addition, I will show how to use ideas from stock portfolio optimization to build risk-aware robot teams that can balance the trade-off between risk and reward. Further, I will describe how to employ graph neural networks to enable large-scale, decentralized multi-robot coordination and planning. In the end, I will discuss some future work on securing robot teams against adversarial attacks and uncertainties when the robots use machine learning techniques for perceptions and communications.

Car-following is the mostcommontask. lt refers to a process where the followingvehicle (FV) tries to keep a safe distance betweenitself and the lead vehicle (LV) by adjusting itsacceleration in response to the actions of thevehicle ahead. The corresponding car-followingmodels are functions that determine FV's futureaccelerationsbased on current (and historicaldriving situations. Car-following models are thecornerstone for microscopic traffic simulation andintelligent vehicle development. One majormotivation of car-following models is to replicatehuman drivers'longitudinal driving trajectories. Inthis seminar, will first talk about how to calibrate
classical
evaluate,andcross-comparecar-following models using large-scale real-worldnaturalistic driving data. To model the long-termdependency of future actions on historical drivingsituations, will also introduce a long-sequencecar-following trajectory prediction model based onthe attention-based Transformer model.