Resource:Seminar - Revision history

Zhiwei at 02:37, 10 April 2026

2026-04-10T02:37:15Z

← Older revision		Revision as of 02:37, 10 April 2026
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	{{SemNote		{{SemNote
	\|time='''2026-03-20 10:30'''		\|time='''2026-04-10 10:30'''
	\|addr=4th Research Building A518		\|addr=4th Research Building A518
	\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].		\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].

Qinyong at 13:58, 9 April 2026

2026-04-09T13:58:43Z

← Older revision		Revision as of 13:58, 9 April 2026
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	{{Latest_seminar		{{Latest_seminar
	\|abstract = ~~Satellite-based quantum networks are emerging~~ as a promising solution for the development of a global quantum Internet in the near future. The ability to leverage the advantageous lower attenuation of optical signals from satellites to ground presents an exciting opportunity to establish a robust and secure quantum communication infrastructure on a global scale. By utilizing a constellation of satellites, it becomes feasible to continuously distribute high-fidelity quantum entanglements among ground stations over long distances, ~~overcoming the limitations of traditional terrestrial-based quantum communication systems. In~~ this ~~article~~, ~~we first provide~~ a ~~brief survey of existing solutions for satellite-based entanglement distribution, highlighting the various approaches and technologies that have been employed in this rapidly evolving field~~. ~~We then delve into a formulated optimal entanglement distribution problem, aiming~~ to ~~optimize the distribution of quantum entanglement resources across the satellite network~~ to ~~maximize efficiency~~ and ~~reliability~~. This ~~problem~~ is ~~addressed~~ through a ~~detailed exploration of several different methodologies and algorithms~~, ~~each tailored to specific operational settings and constraints. Our experimental results confirm~~ the ~~efficiency~~ of ~~these approaches and provide valuable insights into their practical implementation and performance. Finally~~, ~~we identify several key directions for further study~~ and ~~development in the realm of satellite-based quantum networks.~~		\|abstract = To effectively utilize heterogeneous specialized hardware units in modern GPUs, such as TensorCores and Tensor Memory Accelerators, this paper introduces PipeThreader, a new DNN compiler. PipeThreader proposes shifting scheduling functionality from hardware to software so as to enable more efficient and sophisticated computation pipelining with minimal manual effort. This is achieved through sTask-graph, a new DNN computation abstraction, a hierarchical hardware abstraction that captures the capabilities of specialized units, and new scheduling primitives. As a result, PipeThreader can discover efficient pipeline scheduling for well-studied DNN architectures like FlashAttention, achieving comparable or even superior performance. Additionally, it can uncover novel pipeline schemes for emerging models like Mamba2, delivering significantly better performance compared to state-of-the-art hand-crafted implementations. The code is open-sourced at https://github.com/tile-ai/tilelang.
	~~\|confname =IEEE Network'25~~		\|confname =OSDI'25
	~~\|link = https://ieeexplore.ieee.org/document/10526298~~		\|link = https://www.usenix.org/conference/osdi25/presentation/cheng
	~~\|title= Optimal Entanglement Distribution Problem in Satellite-Based Quantum Networks~~		\|title= PipeThreader: Software-defined pipelining for efficient DNN execution
	~~\|speaker=Yaliang~~		\|speaker=Junzhe
	~~\|date=2026-3-20~~		\|date=2026-4-9
	}}
	~~{{Latest_seminar~~
	\|abstract =Rapid advances in low Earth orbit (LEO) satellite technology and satellite edge computing (SEC) have facilitated a key role for LEO satellites in enhanced Earth observation missions (EOM). These missions (e.g.~~, remote object detection) typically require multi-satellite cooperative observations of~~ a ~~large region of interest (RoI) area~~, as well ~~as the observation image routing and computation processing, enabling accurate and real~~-~~time responsiveness. However~~, ~~optimizing the resources of LEO satellite networks is nontrivial in the presence of its dynamic and heterogeneous properties~~. ~~To this end~~, ~~we propose SECO~~, ~~a SEC~~-~~enabled framework that jointly optimizes multi~~-~~satellite observation scheduling, routing and computation node selection for enhanced EOM. Specifically, in~~ the ~~observation phase, we leverage the orbital motion and the rotatable onboard cameras of satellites, and propose a distributed game~~-~~based scheduling strategy to minimize the overall size of captured images while ensuring full (observation) coverage~~. In the sequent routing and computation phase, we first adopt image splitting technology to achieve parallel transmission and computation. Then, we propose an efficient iterative algorithm to jointly optimize image splitting, routing and computation node selection for each captured image. On this basis, we propose a theoretically guaranteed systemwide greedy-~~based strategy to reduce the total time cost (i~~.~~e., transmission, computation and queuing delay) over simultaneous processing for multiple images. Extensive experiments based on real~~-~~world datasets demonstrate that SECO can achieve up to a 60.7% reduction in overall time cost compared to baselines~~.
	\|confname =~~INFOCOM~~'24
	\|link = https://~~ieeexplore~~.~~ieee~~.org/~~document~~/~~10621270~~
	\|title= ~~SECO~~: ~~Multi~~-~~Satellite Edge Computing Enabled Wide-Area and Real-Time Earth Observation Missions~~
	\|speaker=~~LinQi~~
	\|date=2026-3-20
	}}		}}

	{{Resource:Previous_Seminars}}		{{Resource:Previous_Seminars}}

Qinyong at 17:27, 19 March 2026

2026-03-19T17:27:15Z

← Older revision		Revision as of 17:27, 19 March 2026
Line 1:		Line 1:
	{{SemNote		{{SemNote
	\|time='''2026-01-30 10:30'''		\|time='''2026-03-20 10:30'''
	\|addr=4th Research Building A518		\|addr=4th Research Building A518
	\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].		\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].
Line 8:		Line 8:

	{{Latest_seminar		{{Latest_seminar
	\|abstract = ~~The recent proliferation~~ of ~~spacecraft in Earth's orbits has ushered~~ in the ~~rise of large-scale satellite constellations~~. ~~However, this unprecedented growth of constellations has introduced a previously unforeseen challenge: ground track congestion. Specifically,~~ the ~~increasing density~~ of ~~orbital slots forces~~ satellites to ~~share similar orbit planes, causing their nadir-point projections~~ on ~~Earth's surface (i.e~~., ~~ground tracks)~~ to ~~overlap or remain in close proximity within short time intervals. Such orbit~~-~~endowed~~ ground ~~track congestion can degrade constellation performance in remote sensing operations~~, ~~specified by limited constellation coverage, redundant satellite count, and delayed data delivery~~. ~~To address~~ this ~~issue~~, we ~~propose SpaceSched,~~ a ~~hierarchical scheduling framework designed to resolve ground track congestion in~~ satellite ~~constellations. SpaceSched consists of two key components: an on~~-~~ground constellation scheduling pipeline~~, ~~comprising a coverage distributor~~ and ~~a satellite selector, and an~~ in~~-space downlink scheduling pipeline, featuring a queue regulator~~. The coverage distributor assigns attitude profiles over time to satellites, ensuring non-overlapping imagery capture regions. The satellite selector optimizes the constellation by strategically selecting a ~~subset of satellites while maintaining coverage efficiency. During in-space downlink scheduling~~, ~~the queue regulator manages the downlink traffic queue~~ to ~~minimize~~ the ~~delay~~ of ~~high-value data transmission. We evaluate SpaceSched on two operational modes (i.e., stripmap and spotlight)~~ across ~~three well-established~~ satellite ~~constellation systems: SKYSAT, LEMUR,~~ and ~~FLOCK, with 17, 50~~, and ~~126 evaluated satellites, respectively~~. ~~Experimental~~ results ~~demonstrate that SpaceSched improves coverage by up to 1~~.~~84×, reduces satellite counts by up to 2.38×~~, and ~~decreases downlink queue load by up to 36.46×, compared to~~ the ~~plain~~ satellite ~~constellation systems. Furthermore, our case study highlights SpaceSched's potential to meet diverse task demands~~.		\|abstract = Satellite-based quantum networks are emerging as a promising solution for the development of a global quantum Internet in the near future. The ability to leverage the advantageous lower attenuation of optical signals from satellites to ground presents an exciting opportunity to establish a robust and secure quantum communication infrastructure on a global scale. By utilizing a constellation of satellites, it becomes feasible to continuously distribute high-fidelity quantum entanglements among ground stations over long distances, overcoming the limitations of traditional terrestrial-based quantum communication systems. In this article, we first provide a brief survey of existing solutions for satellite-based entanglement distribution, highlighting the various approaches and technologies that have been employed in this rapidly evolving field. We then delve into a formulated optimal entanglement distribution problem, aiming to optimize the distribution of quantum entanglement resources across the satellite network to maximize efficiency and reliability. This problem is addressed through a detailed exploration of several different methodologies and algorithms, each tailored to specific operational settings and constraints. Our experimental results confirm the efficiency of these approaches and provide valuable insights into their practical implementation and performance. Finally, we identify several key directions for further study and development in the realm of satellite-based quantum networks.
	\|confname =~~Mobicom~~'25		\|confname =IEEE Network'25
	\|link = https://dl.~~acm~~.org/~~doi/10.1145~~/~~3680207.3765249~~		\|link = https://ieeexplore.ieee.org/document/10526298
	\|title= ~~SpaceSched: A Constellation~~-~~Wide Scheduling System for Resolving Ground Track Congestion in Remote Sensing~~		\|title= Optimal Entanglement Distribution Problem in Satellite-Based Quantum Networks
	\|speaker=~~Yifei~~		\|speaker=Yaliang
	\|date=2026-3-13		\|date=2026-3-20
	}}		}}
	{{Latest_seminar		{{Latest_seminar
	\|abstract =~~Offering high-quality immersive content is the ultimate goal of volumetric video streaming. Although point clouds~~ and ~~meshes are dominant volumetric representations, their limitations~~ in ~~depicting photo-realistic content often undermine user experience~~. ~~The recent advent of neural radiance fields~~ (~~NeRF) offers a promising alternative content representation with superior photo-realism~~. ~~However~~, ~~streaming NeRF~~-~~based volumetric videos over wireless networks to mobile headsets faces significant challenges, including substantial bandwidth usage because~~ of ~~the~~ large ~~frame size~~, ~~degraded visual quality due to even a low packet loss rate~~, and ~~content artifacts caused by performance optimizations (e.g~~., ~~remote rendering at~~ the ~~network edge)~~. To ~~address these challenges, in~~ this ~~paper~~, we ~~introduce NeVo~~, a ~~next~~-~~generation volumetric video streaming system~~ for ~~efficient delivery of neural content such as NeRF~~. ~~NeVo incorporates~~ the ~~following innovations into~~ a ~~holistic system:~~ (1) ~~a novel method~~ to ~~model visibility of implicitly encoded neural content~~, ~~thereby avoiding non-essential transmission~~ to ~~drastically reduce network data usage~~, ~~(2)~~ a ~~lightweight, learning~~-based ~~model for real-~~time ~~content reconstruction after packet loss with carefully chosen data~~, and (3) ~~judicious identification and selective delivery of intermediate data in edge-based NeRF rendering to effectively mitigate artifacts~~. ~~Our extensive~~ experiments ~~indicate~~ that ~~compared with the state-of-the-art, NeVo saves~~ up to 68.3% ~~of bandwidth usage, maintains high visual quality despite packet loss, and enhances user experience by reducing artifacts~~.		\|abstract =Rapid advances in low Earth orbit (LEO) satellite technology and satellite edge computing (SEC) have facilitated a key role for LEO satellites in enhanced Earth observation missions (EOM). These missions (e.g., remote object detection) typically require multi-satellite cooperative observations of a large region of interest (RoI) area, as well as the observation image routing and computation processing, enabling accurate and real-time responsiveness. However, optimizing the resources of LEO satellite networks is nontrivial in the presence of its dynamic and heterogeneous properties. To this end, we propose SECO, a SEC-enabled framework that jointly optimizes multi-satellite observation scheduling, routing and computation node selection for enhanced EOM. Specifically, in the observation phase, we leverage the orbital motion and the rotatable onboard cameras of satellites, and propose a distributed game-based scheduling strategy to minimize the overall size of captured images while ensuring full (observation) coverage. In the sequent routing and computation phase, we first adopt image splitting technology to achieve parallel transmission and computation. Then, we propose an efficient iterative algorithm to jointly optimize image splitting, routing and computation node selection for each captured image. On this basis, we propose a theoretically guaranteed systemwide greedy-based strategy to reduce the total time cost (i.e., transmission, computation and queuing delay) over simultaneous processing for multiple images. Extensive experiments based on real-world datasets demonstrate that SECO can achieve up to a 60.7% reduction in overall time cost compared to baselines.
	\|confname =~~Mobicom~~'25		\|confname =INFOCOM'24
	\|link = https://dl.~~acm~~.org/~~doi/10.1145~~/~~3680207.3723473~~		\|link = https://ieeexplore.ieee.org/document/10621270
	\|title= ~~NeVo~~: ~~Advancing Volumetric Video Streaming with Neural Content Representation~~		\|title= SECO: Multi-Satellite Edge Computing Enabled Wide-Area and Real-Time Earth Observation Missions
	\|speaker=~~Mengfan~~		\|speaker=LinQi
	\|date=2026-3-13		\|date=2026-3-20
	}}		}}
	{{Resource:Previous_Seminars}}		{{Resource:Previous_Seminars}}

Qinyong at 17:47, 12 March 2026

2026-03-12T17:47:53Z

← Older revision		Revision as of 17:47, 12 March 2026
Line 8:		Line 8:

	{{Latest_seminar		{{Latest_seminar
	\|abstract = ~~LoRa technology promises to enable Internet~~ of ~~Things applications over~~ large ~~geographical areas~~. However, ~~its performance is often hampered by poor channel quality in urban environments~~, ~~where blockage and multipath effects are prevalent~~. ~~Our study uncovers that a slight shift~~ in ~~the position or attitude of the receiving antenna can substantially improve the received signal quality~~. ~~This phenomenon~~ can ~~be attributed to the rich multipath characteristics of wireless signal propagation~~ in ~~urban environments~~, ~~wherein even small antenna movement can alter the dominant signal path or reduce the polarization angular difference between transceivers~~. ~~Leveraging these key observations~~, we propose ~~and implement MoLoRa~~, ~~an intelligent mobile antenna system~~ designed to ~~enhance LoRa packet reception~~. ~~At its core~~, ~~MoLoRa represents the position~~ and ~~attitude of an antenna as~~ a ~~state~~ and ~~employs~~ a ~~statistical optimization method~~ to ~~search for states that offer optimal signal quality efficiently~~. ~~Through extensive evaluation, we demonstrate that MoLoRa achieves~~ a ~~maximum Signal~~-to-~~Noise Ratio~~ (~~SNR~~) ~~gain of 13 dB in a few attempts~~, ~~enabling formerly problematic blind spots~~ to ~~reconnect~~ and ~~strengthening links for other nodes~~.		\|abstract = The recent proliferation of spacecraft in Earth's orbits has ushered in the rise of large-scale satellite constellations. However, this unprecedented growth of constellations has introduced a previously unforeseen challenge: ground track congestion. Specifically, the increasing density of orbital slots forces satellites to share similar orbit planes, causing their nadir-point projections on Earth's surface (i.e., ground tracks) to overlap or remain in close proximity within short time intervals. Such orbit-endowed ground track congestion can degrade constellation performance in remote sensing operations, specified by limited constellation coverage, redundant satellite count, and delayed data delivery. To address this issue, we propose SpaceSched, a hierarchical scheduling framework designed to resolve ground track congestion in satellite constellations. SpaceSched consists of two key components: an on-ground constellation scheduling pipeline, comprising a coverage distributor and a satellite selector, and an in-space downlink scheduling pipeline, featuring a queue regulator. The coverage distributor assigns attitude profiles over time to satellites, ensuring non-overlapping imagery capture regions. The satellite selector optimizes the constellation by strategically selecting a subset of satellites while maintaining coverage efficiency. During in-space downlink scheduling, the queue regulator manages the downlink traffic queue to minimize the delay of high-value data transmission. We evaluate SpaceSched on two operational modes (i.e., stripmap and spotlight) across three well-established satellite constellation systems: SKYSAT, LEMUR, and FLOCK, with 17, 50, and 126 evaluated satellites, respectively. Experimental results demonstrate that SpaceSched improves coverage by up to 1.84×, reduces satellite counts by up to 2.38×, and decreases downlink queue load by up to 36.46×, compared to the plain satellite constellation systems. Furthermore, our case study highlights SpaceSched's potential to meet diverse task demands.
	\|confname =~~SenSys~~'25		\|confname =Mobicom'25
	\|link = https://dl.acm.org/doi/10.1145/~~3715014~~.~~3722075~~		\|link = https://dl.acm.org/doi/10.1145/3680207.3765249
	\|title= ~~MoLoRa~~: ~~Intelligent Mobile Antenna~~ System for ~~Enhanced LoRa Reception~~ in ~~Urban Environments~~		\|title= SpaceSched: A Constellation-Wide Scheduling System for Resolving Ground Track Congestion in Remote Sensing
	\|speaker=~~Kai Chen~~		\|speaker=Yifei
	\|date=2026-1-30		\|date=2026-3-13
	}}		}}
	{{Latest_seminar		{{Latest_seminar
	\|abstract =~~Large language models~~ (~~LLMs~~) ~~achieve~~ superior ~~performance in generative tasks~~. However, ~~due~~ to the ~~natural gap between language model generation and structured information extraction in three dimensions: task type~~, ~~output format~~, and ~~modeling granularity~~, ~~they often fall short in structured information extraction, a crucial capability for effective data utilization on~~ the ~~web~~. In this paper, we ~~define the~~ generation ~~process~~ of ~~the language model~~ as the ~~controllable state transition, aligning the generation and extraction processes~~ to ~~ensure the integrity~~ of ~~the output structure and adapt to the goals of the information extraction task. Furthermore~~, ~~we propose the Structure2Text decider~~ to ~~help the language model understand the fine-grained extraction information~~, ~~which converts the structured output into natural language and makes state decisions~~, ~~thereby focusing on the task~~-~~specific information kernels~~, and ~~alleviating language model hallucinations~~ and ~~incorrect content generation~~. ~~We conduct~~ extensive experiments ~~and detailed analyses on myriad information extraction tasks~~, ~~including named entity recognition~~, ~~relation extraction~~, and ~~event argument extraction. Our method not only achieves significant performance improvements but also considerably~~ enhances ~~the model's capability to generate precise and relevant content, making the extracted content easy to parse~~.		\|abstract =Offering high-quality immersive content is the ultimate goal of volumetric video streaming. Although point clouds and meshes are dominant volumetric representations, their limitations in depicting photo-realistic content often undermine user experience. The recent advent of neural radiance fields (NeRF) offers a promising alternative content representation with superior photo-realism. However, streaming NeRF-based volumetric videos over wireless networks to mobile headsets faces significant challenges, including substantial bandwidth usage because of the large frame size, degraded visual quality due to even a low packet loss rate, and content artifacts caused by performance optimizations (e.g., remote rendering at the network edge). To address these challenges, in this paper, we introduce NeVo, a next-generation volumetric video streaming system for efficient delivery of neural content such as NeRF. NeVo incorporates the following innovations into a holistic system: (1) a novel method to model visibility of implicitly encoded neural content, thereby avoiding non-essential transmission to drastically reduce network data usage, (2) a lightweight, learning-based model for real-time content reconstruction after packet loss with carefully chosen data, and (3) judicious identification and selective delivery of intermediate data in edge-based NeRF rendering to effectively mitigate artifacts. Our extensive experiments indicate that compared with the state-of-the-art, NeVo saves up to 68.3% of bandwidth usage, maintains high visual quality despite packet loss, and enhances user experience by reducing artifacts.
	\|confname =~~WWW~~'25		\|confname =Mobicom'25
	\|link = https://dl.acm.org/doi~~/abs~~/10.1145/~~3696410~~.~~3714571~~		\|link = https://dl.acm.org/doi/10.1145/3680207.3723473
	\|title= ~~Bridging the Gap~~: ~~Aligning Language Model Generation~~ with ~~Structured Information Extraction via Controllable State Transition~~		\|title= NeVo: Advancing Volumetric Video Streaming with Neural Content Representation
	\|speaker=~~Daobin~~		\|speaker=Mengfan
	\|date=2026-1-30		\|date=2026-3-13
	}}		}}
	{{Resource:Previous_Seminars}}		{{Resource:Previous_Seminars}}

Zhiwei at 02:51, 30 January 2026

2026-01-30T02:51:20Z

← Older revision		Revision as of 02:51, 30 January 2026
Line 20:		Line 20:
	\|link = https://dl.acm.org/doi/abs/10.1145/3696410.3714571		\|link = https://dl.acm.org/doi/abs/10.1145/3696410.3714571
	\|title= Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition		\|title= Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition
	\|speaker=~~Daobing~~		\|speaker=Daobin
	\|date=2026-1-30		\|date=2026-1-30
	}}		}}
	{{Resource:Previous_Seminars}}		{{Resource:Previous_Seminars}}

Qinyong at 17:00, 29 January 2026

2026-01-29T17:00:59Z

← Older revision		Revision as of 17:00, 29 January 2026
Line 1:		Line 1:
	{{SemNote		{{SemNote
	\|time='''2026-01-23 10:30'''		\|time='''2026-01-30 10:30'''
	\|addr=4th Research Building A518		\|addr=4th Research Building A518
	\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].		\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].

Qinyong at 17:00, 29 January 2026

2026-01-29T17:00:45Z

← Older revision		Revision as of 17:00, 29 January 2026
Line 8:		Line 8:

	{{Latest_seminar		{{Latest_seminar
	\|abstract = ~~Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing~~ applications. However, ~~object detection algorithms are usually heavy~~ in ~~computation~~, ~~hindering their deployments on resource-constrained edge devices. Traditional edge-cloud collaboration schemes, like deep neural network (DNN) partitioning across edge~~ and ~~cloud,~~ are ~~unfit for object detection due to~~ the ~~significant communication costs incurred by~~ the ~~large size of intermediate results~~. ~~To this end, we propose a Difficult-Case based Small-Big model (DCSB) framework. It employs a difficult-case discriminator on the edge device~~ to ~~control data transfer between~~ the small ~~model on~~ the ~~edge and~~ the ~~large model in the cloud~~. ~~We also adopt regional sampling~~ to ~~further reduce~~ the ~~bandwidth consumption~~ and ~~create~~ a ~~discriminator zoo~~ to ~~accommodate the varying networking conditions~~. ~~Additionally~~, we ~~extend DCSB to video tasks by developing an adaptive sampling rate update algorithm, aiming to minimize computational demands without sacrificing detection accuracy. Extensive experiments show~~ that ~~DCSB can detect 97.26%~~-~~97.96% objects while saving 74.37%~~-~~82.23% network bandwidth~~, ~~compared~~ to cloud-only methods. Furthermore, DCSB significantly outperforms the latest DNN partitioning methods, reducing inference time by 92.60%-95.10% given an 8Mbps transmission bandwidth. In video tasks, DCSB matches the detection accuracy of leading video analysis methods while cutting the computational overhead by 40%.		\|abstract = LoRa technology promises to enable Internet of Things applications over large geographical areas. However, its performance is often hampered by poor channel quality in urban environments, where blockage and multipath effects are prevalent. Our study uncovers that a slight shift in the position or attitude of the receiving antenna can substantially improve the received signal quality. This phenomenon can be attributed to the rich multipath characteristics of wireless signal propagation in urban environments, wherein even small antenna movement can alter the dominant signal path or reduce the polarization angular difference between transceivers. Leveraging these key observations, we propose and implement MoLoRa, an intelligent mobile antenna system designed to enhance LoRa packet reception. At its core, MoLoRa represents the position and attitude of an antenna as a state and employs a statistical optimization method to search for states that offer optimal signal quality efficiently. Through extensive evaluation, we demonstrate that MoLoRa achieves a maximum Signal-to-Noise Ratio (SNR) gain of 13 dB in a few attempts, enabling formerly problematic blind spots to reconnect and strengthening links for other nodes.
	\|confname =~~TMC~~'25		\|confname =SenSys'25
	\|link = https://~~ieeexplore~~.~~ieee~~.org/~~document~~/~~10705683~~		\|link = https://dl.acm.org/doi/10.1145/3715014.3722075
	\|title= ~~Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator~~		\|title= MoLoRa: Intelligent Mobile Antenna System for Enhanced LoRa Reception in Urban Environments
	\|speaker=~~Menghao Liu~~		\|speaker=Kai Chen
	\|date=2026-1-23		\|date=2026-1-30
	}}		}}
	{{Latest_seminar		{{Latest_seminar
	\|abstract =~~Video conferencing systems suffer from poor user experience when network conditions deteriorate because current video codecs simply cannot operate at extremely low bitrates~~. ~~Recently~~, ~~several neural alternatives have been proposed that reconstruct talking head videos at very low bitrates using sparse representations of each frame such as facial landmark~~ information~~. However~~, ~~these approaches produce poor reconstructions in scenarios with major movement or occlusions over the course of a call~~, and ~~do not scale to higher resolutions. We design Gemino~~, a ~~new neural compression system~~ for ~~video conferencing based~~ on ~~a novel high-frequency-conditional super-resolution pipeline. Gemino upsamples a very low-resolution version of each target frame while enhancing high-frequency details (e.g~~., ~~skin texture, hair, etc.) based on information extracted from a single high-resolution reference image. We use a multi-scale architecture that runs different components~~ of the model ~~at different resolutions~~, ~~allowing it~~ to ~~scale~~ to ~~resolutions comparable~~ to ~~720p~~, and ~~we personalize~~ the ~~model to learn~~ specific ~~details of each person~~, ~~achieving much better fidelity at low bitrates~~. We ~~implement Gemino atop aiortc~~, ~~an open-source Python implementation of WebRTC~~, ~~and show that it operates on 1024x1024 videos in real-time on a Titan X GPU~~, and achieves ~~2.2–5x lower bitrate than traditional video codecs for~~ the ~~same perceptual quality~~.		\|abstract =Large language models (LLMs) achieve superior performance in generative tasks. However, due to the natural gap between language model generation and structured information extraction in three dimensions: task type, output format, and modeling granularity, they often fall short in structured information extraction, a crucial capability for effective data utilization on the web. In this paper, we define the generation process of the language model as the controllable state transition, aligning the generation and extraction processes to ensure the integrity of the output structure and adapt to the goals of the information extraction task. Furthermore, we propose the Structure2Text decider to help the language model understand the fine-grained extraction information, which converts the structured output into natural language and makes state decisions, thereby focusing on the task-specific information kernels, and alleviating language model hallucinations and incorrect content generation. We conduct extensive experiments and detailed analyses on myriad information extraction tasks, including named entity recognition, relation extraction, and event argument extraction. Our method not only achieves significant performance improvements but also considerably enhances the model's capability to generate precise and relevant content, making the extracted content easy to parse.
	\|confname =~~NSDI~~'24		\|confname =WWW'25
	\|link = https://~~www~~.~~usenix~~.org/~~conference~~/~~nsdi24~~/~~presentation~~/~~sivaraman~~		\|link = https://dl.acm.org/doi/abs/10.1145/3696410.3714571
	\|title= ~~Gemino~~: ~~Practical and Robust Neural Compression for Video Conferencing~~		\|title= Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition
	\|speaker=~~Xinyan~~		\|speaker=Daobing
	\|date=2026-1-23		\|date=2026-1-30
	}}		}}
	{{Resource:Previous_Seminars}}		{{Resource:Previous_Seminars}}

Qinyong at 16:51, 22 January 2026

2026-01-22T16:51:15Z

← Older revision		Revision as of 16:51, 22 January 2026
Line 1:		Line 1:
	{{SemNote		{{SemNote
	\|time='''2026-01-09 10:30'''		\|time='''2026-01-23 10:30'''
	\|addr=4th Research Building A518		\|addr=4th Research Building A518
	\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].		\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].
Line 8:		Line 8:

	{{Latest_seminar		{{Latest_seminar
	\|abstract = ~~DistServe improves the performance of large language models~~ (~~LLMs~~) ~~serving by disaggregating the prefill~~ and ~~decoding computation. Existing LLM serving systems colocate~~ the ~~two phases and batch~~ the ~~computation~~ of ~~prefill and decoding across all users and requests~~. ~~We find that~~ this ~~strategy not only leads to strong prefill~~-~~decoding interferences but also couples the resource allocation and parallelism plans for both phases. LLM applications often emphasize individual latency for each phase: time to first token~~ (~~TTFT~~) ~~for the prefill phase and time per output token (TPOT) of each request for the decoding phase~~. In the ~~presence of stringent latency requirements, existing systems have~~ to ~~prioritize one latency over~~ the ~~other, or over-provision compute resources to meet both. DistServe assigns prefill~~ and ~~decoding computation to different GPUs, hence eliminating prefill-decoding interferences. Given~~ the ~~application's TTFT and TPOT requirements, DistServe co-optimizes~~ the ~~resource allocation and parallelism strategy tailored for each phase~~. ~~DistServe~~ also ~~places the two phases according~~ to the ~~serving cluster's~~ bandwidth to ~~minimize~~ the ~~communication caused by disaggregation~~. ~~As a result~~, ~~DistServe significantly improves LLM serving performance in terms of the maximum~~ rate ~~that can be served within both TTFT and TPOT constraints on each GPU~~. ~~Our evaluations~~ show that ~~on various popular LLMs, applications, and latency requirements, DistServe~~ can ~~serve 7~~.~~4× more requests or 12~~.~~6× tighter SLO~~, compared to ~~state-of~~-the-~~art systems~~, while ~~staying within latency constraints for > 90~~% ~~of requests~~.		\|abstract = Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices. Traditional edge-cloud collaboration schemes, like deep neural network (DNN) partitioning across edge and cloud, are unfit for object detection due to the significant communication costs incurred by the large size of intermediate results. To this end, we propose a Difficult-Case based Small-Big model (DCSB) framework. It employs a difficult-case discriminator on the edge device to control data transfer between the small model on the edge and the large model in the cloud. We also adopt regional sampling to further reduce the bandwidth consumption and create a discriminator zoo to accommodate the varying networking conditions. Additionally, we extend DCSB to video tasks by developing an adaptive sampling rate update algorithm, aiming to minimize computational demands without sacrificing detection accuracy. Extensive experiments show that DCSB can detect 97.26%-97.96% objects while saving 74.37%-82.23% network bandwidth, compared to cloud-only methods. Furthermore, DCSB significantly outperforms the latest DNN partitioning methods, reducing inference time by 92.60%-95.10% given an 8Mbps transmission bandwidth. In video tasks, DCSB matches the detection accuracy of leading video analysis methods while cutting the computational overhead by 40%.
	\|confname =~~OSDI~~'24		\|confname =TMC'25
	\|link = https://~~www~~.~~usenix~~.org/~~conference~~/~~osdi24/presentation/zhong-yinmin~~		\|link = https://ieeexplore.ieee.org/document/10705683
	\|title= ~~DistServe: Disaggregating Prefill and Decoding for Goodput~~-~~optimized Large Language Model Serving~~		\|title= Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator
	\|speaker=~~Ruizheng~~		\|speaker=Menghao Liu
	\|date=2026-1-09		\|date=2026-1-23
	}}		}}
	{{Latest_seminar		{{Latest_seminar
	\|abstract =~~In virtual machine (VM) allocation~~ systems~~, caching repetitive and similar VM allocation requests and associated resolution rules is crucial for reducing computational costs and meeting strict latency requirements~~. ~~While modern allocation systems distribute requests among multiple allocator agents and use caching to improve performance~~, ~~current schedulers often neglect the cache state and latency considerations when assigning~~ each ~~new request to an agent~~. ~~Due to the high variance~~ in ~~costs of cache hits and misses and~~ the ~~associated processing overheads~~ of ~~updating the caches~~, ~~simple load-balancing~~ and ~~cache-aware mechanisms result in high latencies~~. We ~~introduce Kamino~~, a high-~~performance, latency~~-~~driven and cache~~-~~aware request scheduling system aimed at minimizing end~~-to-~~end latencies~~. ~~Kamino employs~~ a ~~novel scheduling algorithm grounded in theory which uses partial indicators from~~ the ~~cache state~~ to ~~assign each new request~~ to the ~~agent with the lowest estimated latency. Evaluation~~ of ~~Kamino using a high-~~fidelity ~~simulator on large~~-~~scale production workloads shows a 42% reduction in average request latencies. Our deployment~~ of ~~Kamino~~ in ~~the control plane of~~ a ~~large public cloud confirms these improvements~~, ~~with a 33% decrease in cache miss rates~~ and ~~17% reduction in memory usage~~.		\|abstract =Video conferencing systems suffer from poor user experience when network conditions deteriorate because current video codecs simply cannot operate at extremely low bitrates. Recently, several neural alternatives have been proposed that reconstruct talking head videos at very low bitrates using sparse representations of each frame such as facial landmark information. However, these approaches produce poor reconstructions in scenarios with major movement or occlusions over the course of a call, and do not scale to higher resolutions. We design Gemino, a new neural compression system for video conferencing based on a novel high-frequency-conditional super-resolution pipeline. Gemino upsamples a very low-resolution version of each target frame while enhancing high-frequency details (e.g., skin texture, hair, etc.) based on information extracted from a single high-resolution reference image. We use a multi-scale architecture that runs different components of the model at different resolutions, allowing it to scale to resolutions comparable to 720p, and we personalize the model to learn specific details of each person, achieving much better fidelity at low bitrates. We implement Gemino atop aiortc, an open-source Python implementation of WebRTC, and show that it operates on 1024x1024 videos in real-time on a Titan X GPU, and achieves 2.2–5x lower bitrate than traditional video codecs for the same perceptual quality.
	\|confname =~~OSDI~~'25		\|confname =NSDI'24
	\|link = https://www.usenix.org/conference/~~osdi25~~/presentation/~~domingo~~		\|link = https://www.usenix.org/conference/nsdi24/presentation/sivaraman
	\|title= ~~Kamino~~: ~~Efficient VM Allocation at Scale with Latency-Driven Cache-Aware Scheduling~~		\|title= Gemino: Practical and Robust Neural Compression for Video Conferencing
	\|speaker=~~Chenli~~		\|speaker=Xinyan
	\|date=2026-1-09		\|date=2026-1-23
	}}		}}
	{{Resource:Previous_Seminars}}		{{Resource:Previous_Seminars}}

Qinyong at 16:25, 8 January 2026

2026-01-08T16:25:27Z

← Older revision		Revision as of 16:25, 8 January 2026
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	{{SemNote		{{SemNote
	\|time='''~~2025~~-12-26 10:30'''		\|time='''2026-01-09 10:30'''
	\|addr=4th Research Building A518		\|addr=4th Research Building A518
	\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].		\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].
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	{{Latest_seminar		{{Latest_seminar
	\|abstract = ~~Machine learning~~ (ML) ~~clusters stack multiple network interface cards (NICs) within each server to improve inter-server GPU communication bandwidth~~. ~~However, existing~~ systems ~~fall short in fully utilizing NICs because~~ of ~~static GPU-NIC bindings~~. ~~This~~ leads to ~~bottlenecks at hot~~-~~spot NICs when handling imbalanced communication in ML tasks~~. ~~For example, large language model serving instances may have different communication demands across NICs; expert-parallel training tasks have imbalanced all-~~to~~-all traffic;~~ and the ~~embedding transmission volumes during recommendation model training vary across GPUs~~. ~~To fully utilize all NICs~~, ~~we propose FuseLink~~ to ~~enable efficient GPU communication~~ over ~~multiple NICs~~. ~~FuseLink extends inter~~-~~server network by integrating high~~-~~speed intra-server connections,~~ and ~~leverages GPUs~~ to ~~efficiently relay traffic~~ to ~~idle NICs~~. ~~We implement FuseLink and integrate it into NCCL~~, so that ~~ML applications~~ can ~~benefit from FuseLink seamlessly without code modifications~~. ~~Compared to NCCL~~, ~~we demonstrate that FuseLink achieves up to 212GBps bandwidth between two inter-server GPUs~~ and ~~accelerates ML tasks with dynamic traffic patterns. Specifically~~, ~~it reduces the latencies of first-token generation in LLM model servings by 1~~.~~04-2~~.~~73×~~, ~~improves the training throughput~~ of ~~mixture~~-of-~~experts model by up to 1.3×~~, ~~and accelerates deep learning recommendation model training by up to 1.2×~~.		\|abstract = DistServe improves the performance of large language models (LLMs) serving by disaggregating the prefill and decoding computation. Existing LLM serving systems colocate the two phases and batch the computation of prefill and decoding across all users and requests. We find that this strategy not only leads to strong prefill-decoding interferences but also couples the resource allocation and parallelism plans for both phases. LLM applications often emphasize individual latency for each phase: time to first token (TTFT) for the prefill phase and time per output token (TPOT) of each request for the decoding phase. In the presence of stringent latency requirements, existing systems have to prioritize one latency over the other, or over-provision compute resources to meet both. DistServe assigns prefill and decoding computation to different GPUs, hence eliminating prefill-decoding interferences. Given the application's TTFT and TPOT requirements, DistServe co-optimizes the resource allocation and parallelism strategy tailored for each phase. DistServe also places the two phases according to the serving cluster's bandwidth to minimize the communication caused by disaggregation. As a result, DistServe significantly improves LLM serving performance in terms of the maximum rate that can be served within both TTFT and TPOT constraints on each GPU. Our evaluations show that on various popular LLMs, applications, and latency requirements, DistServe can serve 7.4× more requests or 12.6× tighter SLO, compared to state-of-the-art systems, while staying within latency constraints for > 90% of requests.
	\|confname =OSDI'25		\|confname =OSDI'24
	\|link = https://www.usenix.org/conference/~~osdi25~~/presentation/~~ren~~		\|link = https://www.usenix.org/conference/osdi24/presentation/zhong-yinmin
	\|title= ~~Enabling Efficient GPU Communication over Multiple NICs with FuseLink~~		\|title= DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving
	\|speaker=~~Jiahao~~		\|speaker=Ruizheng
	\|date=~~2025~~-12-26		\|date=2026-1-09
	}}		}}
	{{Latest_seminar		{{Latest_seminar
	\|abstract =~~Operating a quantum network incurs high capital~~ and ~~operational expenditures~~, ~~which are expected~~ to ~~be compensated by~~ the high ~~value~~ of ~~enabled quantum applications. However~~, ~~existing~~ mechanisms ~~mainly focus on maximizing the entanglement distribution rate and neglect the cost incurred on users~~. ~~This paper aims to address how to utilize quantum network resources in~~ a ~~cost~~-~~efficient manner while sustaining high~~-~~quantity~~ and ~~high~~-~~quality entanglement distribution. We first consider how~~ to ~~establish a steady stream of entanglements between remote nodes with the minimum cost~~. ~~Utilizing a recent flow-based abstraction and~~ a novel ~~graph representation, we design an optimal~~ algorithm ~~for min-cost remote entanglement distribution. Next, we consider distributing entanglements~~ with the ~~highest fidelity subject to~~ a ~~cost bound and prove its NP-hardness. To explore the cost~~-fidelity ~~trade~~-~~off due to swapping and purification, we propose an approximation scheme for maximizing fidelity while satisfying an arbitrary cost bound~~. Our ~~algorithms provide rigorous tools for supporting high-performance quantum network applications~~ with ~~financial consideration~~ and ~~offer strong theoretical guarantees. Extensive simulation results validate the advantageous performance~~ in ~~cost efficiency and/or fidelity compared to existing solutions and heuristics~~.		\|abstract =In virtual machine (VM) allocation systems, caching repetitive and similar VM allocation requests and associated resolution rules is crucial for reducing computational costs and meeting strict latency requirements. While modern allocation systems distribute requests among multiple allocator agents and use caching to improve performance, current schedulers often neglect the cache state and latency considerations when assigning each new request to an agent. Due to the high variance in costs of cache hits and misses and the associated processing overheads of updating the caches, simple load-balancing and cache-aware mechanisms result in high latencies. We introduce Kamino, a high-performance, latency-driven and cache-aware request scheduling system aimed at minimizing end-to-end latencies. Kamino employs a novel scheduling algorithm grounded in theory which uses partial indicators from the cache state to assign each new request to the agent with the lowest estimated latency. Evaluation of Kamino using a high-fidelity simulator on large-scale production workloads shows a 42% reduction in average request latencies. Our deployment of Kamino in the control plane of a large public cloud confirms these improvements, with a 33% decrease in cache miss rates and 17% reduction in memory usage.
	\|confname =~~ToN~~'25		\|confname =OSDI'25
	\|link = https://~~ieeexplore~~.~~ieee~~.org/~~document~~/~~11153500~~		\|link = https://www.usenix.org/conference/osdi25/presentation/domingo
	\|title= ~~Cost~~-Aware ~~High-Fidelity Entanglement Distribution and Purification in the Quantum Internet~~		\|title= Kamino: Efficient VM Allocation at Scale with Latency-Driven Cache-Aware Scheduling
	\|speaker=~~Bangguo~~		\|speaker=Chenli
	\|date=~~2025~~-12-26		\|date=2026-1-09
	}}		}}
	{{Resource:Previous_Seminars}}		{{Resource:Previous_Seminars}}

Qinyong at 12:24, 25 December 2025

2025-12-25T12:24:29Z

← Older revision		Revision as of 12:24, 25 December 2025
Line 1:		Line 1:
	{{SemNote		{{SemNote
	\|time='''2025-12-19 10:30'''		\|time='''2025-12-26 10:30'''
	\|addr=4th Research Building A518		\|addr=4th Research Building A518
	\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].		\|note=Useful links: [[Resource:Reading_List\|📚 Readling list]]; [[Resource:Seminar_schedules\|📆 Schedules]]; [[Resource:Previous_Seminars\|🧐 Previous seminars]].
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	{{Latest_seminar		{{Latest_seminar
	\|abstract = ~~Low Earth Orbit~~ (~~LEO~~) ~~satellite networks are expected~~ to ~~enable global connectivity for next~~-~~generation communications~~. ~~To provide space-centric solutions~~, ~~the limited coverage time and limited resources~~ of ~~LEO satellites pose challenges~~ to ~~maintaining service continuity and ensuring low latency for users~~. ~~Furthermore~~, ~~LEO satellites rely on solar panels~~ to ~~obtain energy, so a balance needs to be struck between energy consumption~~ and ~~service provision for satellite mobile edge computing~~. ~~In this paper~~, we ~~aim~~ to ~~achieve space~~-~~centric computational task offloading in LEO satellite networks. The goal is to minimize end~~-to-~~end task offloading latency while considering the constraints posed by the limited onboard computing~~, ~~storage~~, ~~and energy resources in constantly moving LEO satellites~~. ~~To achieve this~~, we ~~formulate a joint problem of service migration and power control in energy~~-~~harvesting LEO satellite networks. The problem is then converted into a Markov decision process (MDP)~~ and ~~solved~~ with ~~SpaceEdge~~, ~~a novel algorithm based on Deep Reinforcement Learning (DRL)~~. ~~SpaceEdge offers supports for both centralized learning and multi~~-~~agent learning~~. ~~Simulation results show that SpaceEdge~~, ~~particularly~~ the ~~multi~~-~~agent~~ model, ~~outperforms existing solutions, demonstrating its effectiveness in deploying space-centric task offloading services in LEO satellite networks~~.		\|abstract = Machine learning (ML) clusters stack multiple network interface cards (NICs) within each server to improve inter-server GPU communication bandwidth. However, existing systems fall short in fully utilizing NICs because of static GPU-NIC bindings. This leads to bottlenecks at hot-spot NICs when handling imbalanced communication in ML tasks. For example, large language model serving instances may have different communication demands across NICs; expert-parallel training tasks have imbalanced all-to-all traffic; and the embedding transmission volumes during recommendation model training vary across GPUs. To fully utilize all NICs, we propose FuseLink to enable efficient GPU communication over multiple NICs. FuseLink extends inter-server network by integrating high-speed intra-server connections, and leverages GPUs to efficiently relay traffic to idle NICs. We implement FuseLink and integrate it into NCCL, so that ML applications can benefit from FuseLink seamlessly without code modifications. Compared to NCCL, we demonstrate that FuseLink achieves up to 212GBps bandwidth between two inter-server GPUs and accelerates ML tasks with dynamic traffic patterns. Specifically, it reduces the latencies of first-token generation in LLM model servings by 1.04-2.73×, improves the training throughput of mixture-of-experts model by up to 1.3×, and accelerates deep learning recommendation model training by up to 1.2×.
	\|confname =~~TWC~~'24		\|confname =OSDI'25
	\|link = https://~~ieeexplore~~.~~ieee~~.org/~~abstract~~/~~document~~/~~10623400~~		\|link = https://www.usenix.org/conference/osdi25/presentation/ren
	\|title= ~~SpaceEdge: Optimizing Service Latency and Sustainability for Space-Centric Task Offloading in LEO Satellite Networks~~		\|title= Enabling Efficient GPU Communication over Multiple NICs with FuseLink
	\|speaker=~~Haifeng~~		\|speaker=Jiahao
	\|date=2025-12-19		\|date=2025-12-26
	}}		}}
	{{Latest_seminar		{{Latest_seminar
	\|abstract =~~For highly immersive mobile volumetric video streaming~~, ~~it is essential~~ to ~~deliver photo-realistic full-scene content with smooth playback. Unlike traditional representations such as point clouds, 3D Gaussian Splatting (3DGS) has gained attention for its ability to represent~~ high~~-quality full-scene 3D content~~. However, ~~our preliminary experiments show that~~ existing ~~methods for 3DGS-based videos fail to achieve smooth playback~~ on ~~mobile devices~~. ~~In this~~ paper~~, we propose Vega,~~ a ~~3DGS~~-~~based photo~~-~~realistic full~~-~~scene volumetric video streaming system that ensures real-time playback on mobile devices~~. ~~The core idea behind Vega's real-time rendering is object-level selective computation, which allocates computational resources~~ to ~~visually important objects to meet strict rendering deadlines~~. ~~To enable mobile streaming~~ based ~~on the selective computation~~, ~~Vega addresses two challenges: (1) designing~~ an ~~encoding scheme that optimizes the data size of videos while being compatible with object~~-~~level prioritization~~, and ~~(2) developing a rendering pipeline that efficiently operates on resource~~-~~constrained mobile devices~~. ~~We implemented an end~~-to~~-end Vega system~~, ~~consisting of a streaming server and~~ an ~~Android application~~. ~~Experimental results on commodity smartphones show that Vega achieves 30 frames per second (FPS)~~ for ~~full~~-~~scene volumetric video streaming while maintaining competitive data size~~ and ~~visual quality~~ compared to existing ~~baselines~~.		\|abstract =Operating a quantum network incurs high capital and operational expenditures, which are expected to be compensated by the high value of enabled quantum applications. However, existing mechanisms mainly focus on maximizing the entanglement distribution rate and neglect the cost incurred on users. This paper aims to address how to utilize quantum network resources in a cost-efficient manner while sustaining high-quantity and high-quality entanglement distribution. We first consider how to establish a steady stream of entanglements between remote nodes with the minimum cost. Utilizing a recent flow-based abstraction and a novel graph representation, we design an optimal algorithm for min-cost remote entanglement distribution. Next, we consider distributing entanglements with the highest fidelity subject to a cost bound and prove its NP-hardness. To explore the cost-fidelity trade-off due to swapping and purification, we propose an approximation scheme for maximizing fidelity while satisfying an arbitrary cost bound. Our algorithms provide rigorous tools for supporting high-performance quantum network applications with financial consideration and offer strong theoretical guarantees. Extensive simulation results validate the advantageous performance in cost efficiency and/or fidelity compared to existing solutions and heuristics.
	\|confname =~~Mobicom~~'25		\|confname =ToN'25
	\|link = https://dl.~~acm~~.org/~~doi/10.1145~~/~~3680207.3765267~~		\|link = https://ieeexplore.ieee.org/document/11153500
	\|title= ~~Vega: Fully Immersive Mobile Volumetric Video Streaming with 3D Gaussian Splatting~~		\|title= Cost-Aware High-Fidelity Entanglement Distribution and Purification in the Quantum Internet
	\|speaker=~~Jiyi~~		\|speaker=Bangguo
	\|date=2025-12-19		\|date=2025-12-26
	}}		}}
	{{Resource:Previous_Seminars}}		{{Resource:Previous_Seminars}}