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Researcher profile

Yiying Zhang

Yiying Zhang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Operating SystemsArtificial Intelligencecond-mat.supr-conCryptography and SecurityDistributed, Parallel, and Cluster ComputingNetworking and Internet Architectureq-fin.RM

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Published work

6 published item(s)

preprint2026arXiv

Optimal design of reinsurance contracts with a continuum of risk assessments

In this article, we employ a principal-agent model to analyze optimal contract design in a monopolistic reinsurance market under adverse selection with a continuum of insurer types. Instead of using the classical expected utility framework, we model each insurer's risk preference through their VaR at their chosen risk tolerance level. Under informational asymmetry, the reinsurer (principal) seeks to maximize expected profit by offering an optimal menu of reinsurance contracts to a continuum of insurers (agents) with hidden characteristics. To avoid the complexity of the traditional duality approach, which yields indirect multivariate utility functions, we introduce a change of variables that reduces the problem to a univariate one. We show that the optimal indirect utility for both stop-loss and quota-share reinsurance is in stop-loss form, implying that the reinsurer will classify agents into two risk groups-high and low-even in the continuum setting. Utilizing this new class of indirect utility functions, we fully solve the problem for three common reinsurance structures: stop-loss, quota-share, and change-loss. Numerical examples are also provided for illustrating the main findings.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

TClone: Low-Latency Forking of Live GUI Environments for Computer-Use Agents

Computer-use agents increasingly operate inside live personal workspaces, where their actions can modify files, applications, GUI state, credentials, and authenticated sessions. This creates a tension between safety and quality: agents need isolation and rollback to avoid damaging user state, but also need fast branching to support speculative execution and parallel search. Existing VMs, containers, and checkpoint/restore systems can isolate or recover workloads, but they do not provide low-latency versioning of a full interactive workspace. We present TClone, a forkable personal workspace system for computer-use agents. TClone enables a live GUI workspace to be snapshotted, forked into isolated branches, rolled back, and selectively committed or merged. Its design separates fast branch creation from durable checkpointing, using sibling containers, copy-on-write memory sharing, filesystem versioning, GUI-local execution, and asynchronous checkpointing. In our end-to-end agent-loop measurement, TClone reduces total task latency by 1.9x and 1.5x over KVM and CRIU. By making workspace versioning a first-class systems primitive, TClone supports safer and higher-quality agent execution over real personal computing environments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Clio: A Hardware-Software Co-Designed Disaggregated Memory System

Memory disaggregation has attracted great attention recently because of its benefits in efficient memory utilization and ease of management. So far, memory disaggregation research has all taken one of two approaches: building/emulating memory nodes using regular servers or building them using raw memory devices with no processing power. The former incurs higher monetary cost and faces tail latency and scalability limitations, while the latter introduces performance, security, and management problems. Server-based memory nodes and memory nodes with no processing power are two extreme approaches. We seek a sweet spot in the middle by proposing a hardware-based memory disaggregation solution that has the right amount of processing power at memory nodes. Furthermore, we take a clean-slate approach by starting from the requirements of memory disaggregation and designing a memory-disaggregation-native system. We built Clio, a disaggregated memory system that virtualizes, protects, and manages disaggregated memory at hardware-based memory nodes. The Clio hardware includes a new virtual memory system, a customized network system, and a framework for computation offloading. In building Clio, we not only co-design OS functionalities, hardware architecture, and the network system, but also co-design compute nodes and memory nodes. Our FPGA prototype of Clio demonstrates that each memory node can achieve 100 Gbps throughput and an end-to-end latency of 2.5 us at median and 3.2us at the 99th percentile. Clio also scales much better and has orders of magnitude lower tail latency than RDMA. It has 1.1x to 3.4x energy saving compared to CPU-based and SmartNIC-based disaggregated memory systems and is 2.7x faster than software-based SmartNIC solutions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Exploiting Network Loss for Distributed Approximate Computing with NetApprox

Many data center applications such as machine learning and big data analytics can complete their analysis without processing the complete set of data. While extensive approximate-aware optimizations have been proposed at hardware, programming language, and application levels. However, to date, the approximate computing optimizations have ignored the network layer. We propose NetApprox, which to the best of our knowledge, is the first approximate-aware network layer comprising transport-layer protocol, network resource allocation schemes, and scheduling/priority-assignment policies. Building on the observation that approximate applications can tolerate loss, NetApprox's main insights are to aggressively send approximate traffic (which improves the performance of approximate applications) and to minimize the network resources allocated to approximate traffic (which simultaneously limits the impact of aggressive approximate traffic while freeing up resources that, in turn, improve non-approximate applications' performance). We ported Flink, Kafka, Spark, and PyTorch to NetApprox and evaluated NetApprox with both large-scale simulation and real implementation. Our evaluation results show that NetApprox improves job completion times by up to 80% compared to network-oblivious approximation solutions, and improves the performance of co-running non-approximate workloads by 79%.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Protecting File Activities via Deception for ARM TrustZone

A TrustZone TEE often invokes an external filesystem. While filedata can be encrypted, the revealed file activities can leak secrets. To hide the file activities from the filesystem and its OS, we propose Enigma, a deception-based defense injecting sybil file activities as the cover of the actual file activities. Enigma contributes three new designs. (1) To make the deception credible, the TEE generates sybil calls by replaying file calls from the TEE code under protection. (2) To make sybil activities cheap, the TEE requests the OS to run K filesystem images simultaneously. Concealing the disk, the TEE backs only one image with the actual disk while backing other images by only storing their metadata. (3) To protect filesystem image identities, the TEE shuffles the images frequently, preventing the OS from observing any image for long. Enigma works with unmodified filesystems shipped withLinux. On a low-cost Arm SoC with EXT4 and F2FS, our system can concurrently run as many as 50 filesystem images with 1% of disk overhead per additional image. Compared to common obfuscation for hiding addresses in a flat space, Enigma hides file activities with richer semantics. Its cost is lower by one order of magnitude while achieving the same level of probabilistic security guarantees.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Tunable Majorana corner modes by orbital-dependent exchange interaction in a two-dimensional topological superconductor

We theoretically study the effect of orbital-dependent exchange field in the formation of second order topological superconductors. We demonstrate that changing the orbital difference can induce topological transition and the Majorana corner modes therein can be manipulated. We further propose to detect the corner modes via a normal probe terminal. The conductance quantization is found to be robust to changes of the relevant system parameters.

0 citations0 reviewsNo code linkedOpen access