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Hong Xu

Hong Xu appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Networking and Internet ArchitectureArtificial IntelligenceDistributed, Parallel, and Cluster ComputingComputer VisionMachine LearningPerformancecond-mat.mtrl-sciphysics.ins-detcond-mat.softhep-exnucl-exphysics.app-phSystems and Control

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Published work

23 published item(s)

preprint2026arXiv

GameGen-Verifier: Parallel Keypoint-Based Verification for LLM-Generated Games via Runtime State Injection

LLM-based game generation promises to turn natural-language specifications into executable games, but progress is limited by the lack of reliable automated verification. Unlike conventional code generation, game correctness is defined over long-horizon interaction: a game may appear correct while violating core mechanics such as state updates, interaction rules, and phase transitions. Existing Agent-as-a-Verifier approaches collapse verification into open-ended gameplay, making verdicts reachability-bound, time-consuming, coverage-limited, and sensitive to the agent's gameplay ability. We present GameGen-Verifier, an automated verification paradigm for LLM-generated games that decomposes a specification into verifiable keypoints and grounds them into independent verification units. Each unit patches the game runtime into a concrete target state, executes a bounded interaction, and judges the outcome against the keypoint assertion. We implement GGV-Harness, a scalable agentic harness providing concurrency management, runtime isolation, and fault recovery. On VeriGame, our dataset of 100 games across seven genres, GameGen-Verifier achieves up to 92.2% accuracy against human judgments versus 58.8% for the coverage-enforced Agent-as-a-Verifier baseline, while reducing wall-clock time by up to 16.6x.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Image2SSM: Reimagining Statistical Shape Models from Images with Radial Basis Functions

Statistical shape modeling (SSM) is an essential tool for analyzing variations in anatomical morphology. In a typical SSM pipeline, 3D anatomical images, gone through segmentation and rigid registration, are represented using lower-dimensional shape features, on which statistical analysis can be performed. Various methods for constructing compact shape representations have been proposed, but they involve laborious and costly steps. We propose Image2SSM, a novel deep-learning-based approach for SSM that leverages image-segmentation pairs to learn a radial-basis-function (RBF)-based representation of shapes directly from images. This RBF-based shape representation offers a rich self-supervised signal for the network to estimate a continuous, yet compact representation of the underlying surface that can adapt to complex geometries in a data-driven manner. Image2SSM can characterize populations of biological structures of interest by constructing statistical landmark-based shape models of ensembles of anatomical shapes while requiring minimal parameter tuning and no user assistance. Once trained, Image2SSM can be used to infer low-dimensional shape representations from new unsegmented images, paving the way toward scalable approaches for SSM, especially when dealing with large cohorts. Experiments on synthetic and real datasets show the efficacy of the proposed method compared to the state-of-art correspondence-based method for SSM.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Particle-Based Shape Modeling for Arbitrary Regions-of-Interest

Statistical Shape Modeling (SSM) is a quantitative method for analyzing morphological variations in anatomical structures. These analyses often necessitate building models on targeted anatomical regions of interest to focus on specific morphological features. We propose an extension to \particle-based shape modeling (PSM), a widely used SSM framework, to allow shape modeling to arbitrary regions of interest. Existing methods to define regions of interest are computationally expensive and have topological limitations. To address these shortcomings, we use mesh fields to define free-form constraints, which allow for delimiting arbitrary regions of interest on shape surfaces. Furthermore, we add a quadratic penalty method to the model optimization to enable computationally efficient enforcement of any combination of cutting-plane and free-form constraints. We demonstrate the effectiveness of this method on a challenging synthetic dataset and two medical datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening

Cryogenic electron microscopy (cryo-EM) has become an enabling technology in drug discovery and in understanding molecular bases of disease by producing near-atomic resolution (less than 0.4 nm) 3D reconstructions of biological macromolecules. The imaging process required for 3D reconstructions involves a highly iterative and empirical screening process, starting with the acquisition of low magnification images of the cryo-EM grids. These images are inspected for squares that are likely to contain useful molecular signals. Potentially useful squares within the grid are then imaged at progressively higher magnifications, with the goal of identifying sub-micron areas within circular holes (bounded by the squares) for imaging at high magnification. This arduous, multi-step data acquisition process represents a bottleneck for obtaining a high throughput data collection. Here, we focus on automating the early decision making for the microscope operator, scoring low magnification images of squares, and proposing the first deep learning framework, XCryoNet, for automated cryo-EM grid screening. XCryoNet is a semi-supervised, attention-guided deep learning approach that provides explainable scoring of automatically extracted square images using limited amounts of labeled data. Results show up to 8% and 37% improvements over a fully supervised and a no-attention solution, respectively, when labeled data is scarce.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Joint Switch-Controller Association and Control Devolution for SDN Systems: An Integration of Online Control and Online Learning

In software-defined networking (SDN) systems, it is a common practice to adopt a multi-controller design and control devolution techniques to improve the performance of the control plane. However, in such systems, the decision-making for joint switch-controller association and control devolution often involves various uncertainties, e.g., the temporal variations of controller accessibility, and computation and communication costs of switches. In practice, statistics of such uncertainties are unattainable and need to be learned in an online fashion, calling for an integrated design of learning and control. In this paper, we formulate a stochastic network optimization problem that aims to minimize time-average system costs and ensure queue stability. By transforming the problem into a combinatorial multi-armed bandit problem with long-term stability constraints, we adopt bandit learning methods and optimal control techniques to handle the exploration-exploitation tradeoff and long-term stability constraints, respectively. Through an integrated design of online learning and online control, we propose an effective Learning-Aided Switch-Controller Association and Control Devolution (LASAC) scheme. Our theoretical analysis and simulation results show that LASAC achieves a tunable tradeoff between queue stability and system cost reduction with a sublinear time-averaged regret bound over a finite time horizon.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Predictive Switch-Controller Association and Control Devolution for SDN Systems

For software-defined networking (SDN) systems, to enhance the scalability and reliability of control plane, existing solutions adopt either multi-controller design with static switch-controller associations, or static control devolution by delegating certain request processing back to switches. Such solutions can fall short in face of temporal variations of request traffics, incurring considerable local computation costs on switches and their communication costs to controllers. So far, it still remains an open problem to develop a joint online scheme that conducts dynamic switch-controller association and dynamic control devolution. In addition, the fundamental benefits of predictive scheduling to SDN systems still remain unexplored. In this paper, we identify the non-trivial trade-off in such a joint design and formulate a stochastic network optimization problem that aims to minimize time-averaged total system costs and ensure long-term queue stability. By exploiting the unique problem structure, we devise a predictive online switch-controller association and control devolution (POSCAD) scheme, which solves the problem through a series of online distributed decision making. Theoretical analysis shows that without prediction, POSCAD can achieve near-optimal total system costs with a tunable trade-off for queue stability. With prediction, POSCAD can achieve even better performance with shorter latencies. We conduct extensive simulations to evaluate POSCAD. Notably, with mild-value of future information, POSCAD incurs a significant reduction in request latencies, even when faced with prediction errors.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Simulating Performance of ML Systems with Offline Profiling

We advocate that simulation based on offline profiling is a promising approach to better understand and improve the complex ML systems. Our approach uses operation-level profiling and dataflow based simulation to ensure it offers a unified and automated solution for all frameworks and ML models, and is also accurate by considering the various parallelization strategies in a real system.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Structure and Properties of Thermoresponsive Diblock Copolymers Embedded with Metal Oxide Nanoparticles

Nanostructured polymer-metal oxide composites are a current research area of great importance due to its highlight applications in sensors, optics, catalysts and drug delivery. Particularly the use of thermoresponsive polymers gives more flexibilities and possibilities in the design and construction of polymer templates. In the present investigation, the structure and magnetic properties of hybrid metal oxide/DBC films composed of two kinds of polystyrene-block-poly (N-isopropylacrylamide)(PS-b-PNIPAM) diblock copolymers (DBCs) with PS and PNIPAM as the major polymer domains respectively, and iron oxide were investigated. The thermoresponsive PNIPAM has a lower critical solution temperature (LCST) in aqueous solution at 32°C, which enables the controllable volume ratio of PS and PNIPAM in the structure of PS-b-PNIPAM diblock copolymers (DBCs). Thus, a temperature and humidity controlling cell was designed and built for precisely tuning the block structure of PS-b-PNIPAM DBCs, which was investigated by in-situ small-angle X-ray scattering (SAXS) and grazing-incidence small-angle X-ray scattering (GISAXS) measurements. The superparamagnetic behavior of the heat-treated hybrid iron oxide/PS-b-PNIPAM DBC films was investigated using a superconducting quantum interference device (SQUID) magnetometer.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

An Alternating Direction Method Approach to Cloud Traffic Management

In this paper, we introduce a unified framework for studying various cloud traffic management problems, ranging from geographical load balancing to backbone traffic engineering. We first abstract these real-world problems as a multi-facility resource allocation problem, and then present two distributed optimization algorithms by exploiting the special structure of the problem. Our algorithms are inspired by Alternating Direction Method of Multipliers (ADMM), enjoying a number of unique features. Compared to dual decomposition, they converge with non-strictly convex objective functions; compared to other ADMM-type algorithms, they not only achieve faster convergence under weaker assumptions, but also have lower computational complexity and lower message-passing overhead. The simulation results not only confirm these desirable features of our algorithms, but also highlight several additional advantages, such as scalability and fault-tolerance.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Benchmarking NFV Software Dataplanes

A key enabling technology of NFV is software dataplane, which has attracted much attention in both academia and industry recently. Yet, till now there is little understanding about its performance in practice. In this paper, we make a benchmark measurement study of NFV software dataplanes in terms of packet processing capability, one of the most fundamental and critical performance metrics. Specifically, we compare two state-of-the-art open-source NFV dataplanes, SoftNIC and ClickOS, using commodity 10GbE NICs under various typical workloads. Our key observations are that (1) both dataplanes have performance issues processing small (<=128B) packets; (2) it is not always best to put all VMs of a service chain on one server due to NUMA effect. We propose resource allocation strategies to remedy the problems, including carefully adding CPU cores and vNICs to VMs, and spreading VMs of a service chain to separate servers. To fundamentally address these problems and scale their performance, SoftNIC and ClickOS could improve the support for NIC queues and multiple cores.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

RepNet: Cutting Tail Latency in Data Center Networks with Flow Replication

Data center networks need to provide low latency, especially at the tail, as demanded by many interactive applications. To improve tail latency, existing approaches require modifications to switch hardware and/or end-host operating systems, making them difficult to be deployed. We present the design, implementation, and evaluation of RepNet, an application layer transport that can be deployed today. RepNet exploits the fact that only a few paths among many are congested at any moment in the network, and applies simple flow replication to mice flows to opportunistically use the less congested path. RepNet has two designs for flow replication: (1) RepSYN, which only replicates SYN packets and uses the first connection that finishes TCP handshaking for data transmission, and (2) RepFlow which replicates the entire mice flow. We implement RepNet on {\tt node.js}, one of the most commonly used platforms for networked interactive applications. {\tt node}'s single threaded event-loop and non-blocking I/O make flow replication highly efficient. Performance evaluation on a real network testbed and in Mininet reveals that RepNet is able to reduce the tail latency of mice flows, as well as application completion times, by more than 50\%.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Low Latency Datacenter Networking: A Short Survey

Datacenters are the cornerstone of the big data infrastructure supporting numerous online services. The demand for interactivity, which significantly impacts user experience and provider revenue, is translated into stringent timing requirements for flows in datacenter networks. Thus low latency networking is becoming a major concern of both industry and academia. We provide a short survey of recent progress made by the networking community for low latency datacenter networks. We propose a taxonomy to categorize existing work based on four main techniques, reducing queue length, accelerating retransmissions, prioritizing mice flows, and exploiting multi-path. Then we review select papers, highlight the principal ideas, and discuss their pros and cons. We also present our perspectives of the research challenges and opportunities, hoping to aspire more future work in this space.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

A Belief-Function Based Decision Support System

In this paper, we present a decision support system based on belief functions and the pignistic transformation. The system is an integration of an evidential system for belief function propagation and a valuation-based system for Bayesian decision analysis. The two subsystems are connected through the pignistic transformation. The system takes as inputs the user's "gut feelings" about a situation and suggests what, if any, are to be tested and in what order, and it does so with a user friendly interface.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

A Decision Calculus for Belief Functions in Valuation-Based Systems

Valuation-based system (VBS) provides a general framework for representing knowledge and drawing inferences under uncertainty. Recent studies have shown that the semantics of VBS can represent and solve Bayesian decision problems (Shenoy, 1991a). The purpose of this paper is to propose a decision calculus for Dempster-Shafer (D-S) theory in the framework of VBS. The proposed calculus uses a weighting factor whose role is similar to the probabilistic interpretation of an assumption that disambiguates decision problems represented with belief functions (Strat 1990). It will be shown that with the presented calculus, if the decision problems are represented in the valuation network properly, we can solve the problems by using fusion algorithm (Shenoy 1991a). It will also be shown the presented decision calculus can be reduced to the calculus for Bayesian probability theory when probabilities, instead of belief functions, are given.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

An Efficient Implementation of Belief Function Propagation

The local computation technique (Shafer et al. 1987, Shafer and Shenoy 1988, Shenoy and Shafer 1986) is used for propagating belief functions in so called a Markov Tree. In this paper, we describe an efficient implementation of belief function propagation on the basis of the local computation technique. The presented method avoids all the redundant computations in the propagation process, and so makes the computational complexity decrease with respect to other existing implementations (Hsia and Shenoy 1989, Zarley et al. 1988). We also give a combined algorithm for both propagation and re-propagation which makes the re-propagation process more efficient when one or more of the prior belief functions is changed.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Evidential Reasoning with Conditional Belief Functions

In the existing evidential networks with belief functions, the relations among the variables are always represented by joint belief functions on the product space of the involved variables. In this paper, we use conditional belief functions to represent such relations in the network and show some relations of these two kinds of representations. We also present a propagation algorithm for such networks. By analyzing the properties of some special evidential networks with conditional belief functions, we show that the reasoning process can be simplified in such kinds of networks.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Experimental studies of THGEM in different Ar/CO2 mixtures

In this paper, the performances of a type of the domestic THGEM (THick Gaseous Electron Multiplier) working in the Ar/CO2 mixtures are reported in details. This kind of single THGEM can provide the gain range from 100 to 1000, which is very suitable for the application in the neutron detection. In order to study its basic characteristics as the references for the development of THGEM based neutron detector, the counting rate plateau, the energy resolution and the gain of the THGEM have been measured in the different Ar/CO2 mixtures with the change of the electrical fields. For the Ar/CO2(90%/10%) gas mixture, a wide counting rate plateau is got from 720V to 770V with the plateau slope of 2.4% / 100 V and the excellent energy resolution about 22% is obtained at the 5.9keV full energy peak of the 55Fe X-ray source.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Generating Explanations for Evidential Reasoning

In this paper, we present two methods to provide explanations for reasoning with belief functions in the valuation-based systems. One approach, inspired by Strat's method, is based on sensitivity analysis, but its computation is simpler thus easier to implement than Strat's. The other one is to examine the impact of evidence on the conclusion based on the measure of the information content in the evidence. We show the property of additivity for the pieces of evidence that are conditional independent within the context of the valuation-based systems. We will give an example to show how these approaches are applied in an evidential network.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Reducing Electricity Demand Charge for Data Centers with Partial Execution

Data centers consume a large amount of energy and incur substantial electricity cost. In this paper, we study the familiar problem of reducing data center energy cost with two new perspectives. First, we find, through an empirical study of contracts from electric utilities powering Google data centers, that demand charge per kW for the maximum power used is a major component of the total cost. Second, many services such as Web search tolerate partial execution of the requests because the response quality is a concave function of processing time. Data from Microsoft Bing search engine confirms this observation. We propose a simple idea of using partial execution to reduce the peak power demand and energy cost of data centers. We systematically study the problem of scheduling partial execution with stringent SLAs on response quality. For a single data center, we derive an optimal algorithm to solve the workload scheduling problem. In the case of multiple geo-distributed data centers, the demand of each data center is controlled by the request routing algorithm, which makes the problem much more involved. We decouple the two aspects, and develop a distributed optimization algorithm to solve the large-scale request routing problem. Trace-driven simulations show that partial execution reduces cost by $3\%--10.5\%$ for one data center, and by $15.5\%$ for geo-distributed data centers together with request routing.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

RepFlow: Minimizing Flow Completion Times with Replicated Flows in Data Centers

Short TCP flows that are critical for many interactive applications in data centers are plagued by large flows and head-of-line blocking in switches. Hash-based load balancing schemes such as ECMP aggravate the matter and result in long-tailed flow completion times (FCT). Previous work on reducing FCT usually requires custom switch hardware and/or protocol changes. We propose RepFlow, a simple yet practically effective approach that replicates each short flow to reduce the completion times, without any change to switches or host kernels. With ECMP the original and replicated flows traverse distinct paths with different congestion levels, thereby reducing the probability of having long queueing delay. We develop a simple analytical model to demonstrate the potential improvement of RepFlow. Extensive NS-3 simulations and Mininet implementation show that RepFlow provides 50%--70% speedup in both mean and 99-th percentile FCT for all loads, and offers near-optimal FCT when used with DCTCP.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Spot Transit: Cheaper Internet Transit for Elastic Traffic

We advocate to create a \emph{spot} Internet transit market, where transit is sold using the under-utilized backbone capacity at a lower price. The providers can improve profit by capitalizing the perishable capacity, and customers can buy transit on-demand without a minimum commitment level for elastic traffic, and as a result improve its surplus (i.e. utility gains). We conduct a systematic study of the economical benefits of spot transit both theoretically and empirically. We propose a simple analytical framework with a general demand function, and solve the pricing problem of maximizing the expected profit, taking into account the revenue loss of regular transit when spot transit traffic hikes. We rigorously prove the price advantage of spot transit, as well as profit and surplus improvements for tier-1 ISPs and customers, respectively. Using real-world price data and traffic statistics of 6 IXPs with more than 1000 ISPs, we quantitatively evaluate spot transit and show that significant financial benefits can be achieved in both absolute and relative terms, robust to parameter values.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Developments of a 2D Position Sensitive Neutron Detector

Chinese Spallation Neutron Source (CSNS), one project of the 12th five-year-plan scheme of China, is under construction in Guangdong province. Three neutron spectrometers will be installed at the first phase of the project, where two-dimensional position sensitive thermal neutron detectors are required. Before the construction of the neutron detector, a prototype of two-dimensional 200 mmx200 mm Multi-wire Proportional Chamber (MWPC) with the flowing gas of Ar/CO2 (90/10) has been constructed and tested with the 55Fe X-Ray using part of the electronics in 2009, which showed a good performance. Following the test in 2009, the neutron detector has been constructed with the complete electronics and filled with the 6atm.3He + 2.5atm.C3H8 gas mixture in 2010. The neutron detector has been primarily tested with an Am/Be source. In this paper, some new developments of the neutron detector including the design of the high pressure chamber, the optimization of the gas purifying system and the gas filling process will be reported. The results and discussion are also presented in this paper.

0 citations0 reviewsNo code linkedOpen access