Bo Han

George Mason University

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Feng Qian

University of Minnesota

Songqing Chen

George Mason University

Ruizhi Cheng

George Mason University

Vijay Gopalakrishnan

AT&T (United States)

Nan Wu

George Mason University

Pan Hui

Hong Kong University of Science and Technology

Wenxiao Zhang

East China Normal University

Matteo Varvello

Nokia (United States)

Parth H. Pathak

George Mason University

Puqi Zhou

George Mason University

Cooperative Institutions

Chinese Academy of Sciences

Peking University

George Mason University

Tsinghua University

AT&T (United States)

University of Michigan–Ann Arbor

University of Chinese Academy of Sciences

Zhejiang University

University of Minnesota

University of Science and Technology of China

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When should we surf the mobile web using both wifi and cellular?

Bo Han Feng Qian Lusheng Ji

In this paper, we investigate when to browse the web using WiFi and cellular simultaneously on mobile devices. Our observation, based on empirical measurements, is that mobile web may not always benefit from multipath, which motivates a cost-benefit analysis. However, it is challenging to analyze the benefits (i.e., improved user experience) and costs (e.g., energy consumption) of web browsing, due to HTTP's resource fetching model. We propose to use server push, a standard feature in HTTP/2, to provide an ideal framework for the cost-benefit analysis. We then design a practical system that reduces resource footprint for mobile web over multipath by providing adaptive multipath support.

10.1145/2980055.2980060

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Citations (12)

An Asynchronous Transmission for Inter-Stage Communication in Spark Via Time-Varying Network Bandwidth Utilization

Changpeng Zhu Xi Wang Bo Han Tian Zhou

SPARK (programming language)

10.2139/ssrn.4965188

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Seismic damage simulation for urban buildings based on high-performance GPU computing

Ziran zaihai xuebao (2012)

Bo Han Xinzheng Lu Zhen Xu Yi Li

To meet the continuously increased needs for seismic damage prediction,refined models have become an important development trend in urban regional seismic damage prediction.However,the application of refined models is limited by the high cost of computation if it is implemented on traditional CPU platform.In recent years,a technology with powerful parallel computing capability and lower cost,GPU technology,has been developed and applied rapidly.In this paper,a simulating method for urban regional seismic damage that can significantly reduce the computation time,was developed based on GPU-CPU cooperative computing technology.The proposed method was then applied to the seismic damage simulation of a medium-sized city to illustrate the unique advantages of GPU technology in large-scale regional seismic damage simulation.

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Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization

arXiv (Cornell University) (2022)

Yongqiang Chen Kaiwen Zhou Yatao Bian Binghui Xie Kaili Ma

Recently, there has been a growing surge of interest in enabling machine learning systems to generalize well to Out-of-Distribution (OOD) data. Most efforts are devoted to advancing optimization objectives that regularize models to capture the underlying invariance; however, there often are compromises in the optimization process of these OOD objectives: i) Many OOD objectives have to be relaxed as penalty terms of Empirical Risk Minimization (ERM) for the ease of optimization, while the relaxed forms can weaken the robustness of the original objective; ii) The penalty terms also require careful tuning of the penalty weights due to the intrinsic conflicts between ERM and OOD objectives. Consequently, these compromises could easily lead to suboptimal performance of either the ERM or OOD objective. To address these issues, we introduce a multi-objective optimization (MOO) perspective to understand the OOD optimization process, and propose a new optimization scheme called PAreto Invariant Risk Minimization (PAIR). PAIR improves the robustness of OOD objectives by cooperatively optimizing with other OOD objectives, thereby bridging the gaps caused by the relaxations. Then PAIR approaches a Pareto optimal solution that trades off the ERM and OOD objectives properly. Extensive experiments on challenging benchmarks, WILDS, show that PAIR alleviates the compromises and yields top OOD performances.

Minification

Robustness

Robust Optimization

10.48550/arxiv.2206.07766

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Citations (7)

Will Metaverse be NextG Internet? Vision, Hype, and Reality

arXiv (Cornell University) (2022)

Ruizhi Cheng Nan Wu Songqing Chen Bo Han

Metaverse, with the combination of the prefix "meta" (meaning transcending) and the word "universe", has been deemed as the next-generation (NextG) Internet. It aims to create a shared virtual space that connects all virtual worlds via the Internet, where users, represented as digital avatars, can communicate and collaborate as if they are in the physical world. Nevertheless, there is still no unified definition of the Metaverse. This article first presents our vision of what the key requirements of Metaverse should be and reviews what has been heavily advocated by the industry and the positions of various high-tech companies. It then briefly introduces existing social virtual reality (VR) platforms that can be viewed as early prototypes of Metaverse and conducts a reality check by diving into the network operation and performance of two representative platforms, Workrooms from Meta and AltspaceVR from Microsoft. Finally, it concludes by discussing several opportunities and future directions for further innovation.

Metaverse

Possible world

Social worlds

10.48550/arxiv.2201.12894

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Citations (12)

$\mathbb{Z}_2$ topologically ordered phases on a simple hyperbolic lattice

arXiv (Cornell University) (2022)

Hiromi Ebisu Bo Han

In this work, we consider 2D $\mathbb{Z}_2$ topologically ordered phases ($\mathbb{Z}_2$ toric code and the modified surface code) on a simple hyperbolic lattice. Introducing a 2D lattice consisting of the product of a 1D Cayley tree and a 1D conventional lattice, we investigate two topological quantities of the $\mathbb{Z}_2$ topologically ordered phases on this lattice: the ground state degeneracy on a closed surface and the topological entanglement entropy. We find that both quantities depend on the number of branches and the generation of the Cayley tree. We attribute these results to a huge number of superselection sectors of anyons.

Toric code

Lattice (music)

10.48550/arxiv.2206.05762

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$\mathbb{Z}_2$ topologically ordered phases on a simple hyperbolic lattice

arXiv (Cornell University) (2022)

Hiromi Ebisu Bo Han

Toric code

Lattice (music)

10.1103/physrevresearch.4.04309

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A First Look at Immersive Telepresence on Apple Vision Pro

Ruizhi Cheng Nan Wu Matteo Varvello Eugene Chai Songqing Chen

10.1145/3646547.3689006

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Performance Improvement using Dynamic Contention Window Adjustment for Initial Ranging in IEEE 802.16 P2MP Networks

IEEE Wireless Communications and Networking Conference (2007)

Lidong Lin Weijia Jia Bo Han Lizhuo Zhang

In IEEE 802.16 networks, initial ranging is a primary and important procedure of connection setup between subscriber stations and base station. The mandatory method defined in the standard of contention resolution is based on a truncated binary exponential backoff, with a fixed initial contention window size. However, the original algorithm neglects the possibility that the number of actively contending stations may change dynamically over time, leading to dynamically changing contention intensity. The major contribution of this paper is twofold: 1) we propose an accurate analytical model to analyze the performance of initial ranging requests in IEEE 802.16 networks. Two metrics, connection probability and average connection delay, are investigated to evaluate the network performance; 2) based on the above analysis, we propose an efficient performance improvement method by using dynamic window adjustment for initial ranging. Unlike the standard algorithm, this algorithm automatically adjusts the initial contention window to an optimal trade-off point between connection probability and connection delay. The performance revels that improving the service capacity and buffer size of base station can optimize the connection probability and the average connection delay. The numerical results also show that the optimal contention window adjustment outperforms the algorithm in the standard.

Ranging

IEEE 802

Performance Improvement

Base (topology)

Network Performance

10.1109/wcnc.2007.352

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Citations (20)

FRAS: Federated Reinforcement Learning empowered Adaptive Point Cloud Video Streaming

arXiv (Cornell University) (2022)

Yu Gao Pengyuan Zhou Zhi Liu Bo Han Pan Hui

Point cloud video transmission is challenging due to high encoding/decoding complexity, high video bitrate, and low latency requirement. Consequently, conventional adaptive streaming methodologies often find themselves unsatisfactory to meet the requirements in threefold: 1) current algorithms reuse existing quality of experience (QoE) definitions while overlooking the unique features of point cloud video thus failing to provide optimal user experience, 2) most deep learning approaches require long-span data collections to learn sufficiently varied network conditions and result in long training periods and capacity occupation, 3) cloud training approaches pose privacy risks caused by leakage of user reported service usage and networking conditions. To overcome the limitations, we present FRAS, the first federated reinforcement learning framework, to the best of our knowledge, for adaptive point cloud video streaming. We define a new QoE model which takes the unique features of point cloud video into account. Each client uses reinforcement learning (RL) to train video quality selection with the objective of optimizing the user's QoE under multiple constraints. Then, a federated learning framework is integrated with the RL algorithm to enhance training performance with privacy preservation. Extensive simulations using real point cloud videos and network traces reveal the superiority of the proposed scheme over baseline schemes. We also implement a prototype that demonstrates the performance of FRAS via real-world tests.

10.48550/arxiv.2207.07394

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Citations (8)