arXiv subjects

Yuchen Qian

Publications and source records attributed to Yuchen Qian.

4 recordsLinked to original sources

arXiv2026

Tyche: Composable Isolation as a Foundation to Manage Trust in the Cloud

Cloud workloads combine software components from different parties to process sensitive data. Each component has its own trust model - it must protect its assets from the rest of the system, yet share sensitive data with components it cannot trust to keep confidential. This tension requires composing isolation boundaries for confidentiality and encapsulation. Unfortunately, the cloud offers no direct way to compose such boundaries, forcing tenants to assemble, deploy, and maintain their own solutions. This paper shifts that burden back to the infrastructure by making composable, attestable isolation a first-class systems abstraction. We present Tyche, a security monitor that centers isolation around a unified composable abstraction: security domains (SDs). An SD is an execution environment whose access to machine resources - memory, cores, devices - is controlled through explicit capabilities. A small set of capability operations enables SDs to partition, share, and reclaim resources; by nesting recursively, SDs compose attestable trust boundaries for confidentiality and encapsulation. Tyche attests these compositions, providing end-to-end security guarantees for workloads made of mutually distrustful components. As a first-class cloud primitive, this single abstraction subsumes enclaves, sandboxes, CVMs, and their compositions. Tyche provides composable isolation without sacrificing compatibility with existing hardware and software stacks. It runs on commodity x86 64 hardware without security extensions, and a RISC-V prototype demonstrates portability across platforms. Our SDK composes isolation for unmodified workloads within SDs with minimal overhead. In a confidential LLM inference scenario with mutually distrustful users, model owners, and cloud providers, the slowdown is just 2% compared to bare-metal Linux.

cs.CR↗

arXiv2022

Automatic Recognition and Classification of Future Work Sentences from Academic Articles in a Specific Domain

Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts.

cs.CL↗

arXiv2021

Optimizing Coverage and Capacity in Cellular Networks using Machine Learning

Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes. Many operational parameters affect reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise-ratio (SINR), and, ultimately, throughput. In this paper, we develop and compare two approaches for maximizing coverage and minimizing interference by jointly optimizing the transmit power and downtilt (elevation tilt) settings across sectors. To evaluate different parameter configurations offline, we construct a realistic simulation model that captures geographic correlations. Using this model, we evaluate two optimization methods: deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm, and multi-objective Bayesian optimization (BO). Our simulations show that both approaches significantly outperform random search and converge to comparable Pareto frontiers, but that BO converges with two orders of magnitude fewer evaluations than DDPG. Our results suggest that data-driven techniques can effectively self-optimize coverage and capacity in cellular networks.

eess.SP↗

arXiv2018

Damping Effect on PageRank Distribution

This work extends the personalized PageRank model invented by Brin and Page to a family of PageRank models with various damping schemes. The goal with increased model variety is to capture or recognize a larger number of types of network activities, phenomena and propagation patterns. The response in PageRank distribution to variation in damping mechanism is then characterized analytically, and further estimated quantitatively on 6 large real-world link graphs. The study leads to new observation and empirical findings. It is found that the difference in the pattern of PageRank vector responding to parameter variation by each model among the 6 graphs is relatively smaller than the difference among 3 particular models used in the study on each of the graphs. This suggests the utility of model variety for differentiating network activities and propagation patterns. The quantitative analysis of the damping mechanisms over multiple damping models and parameters is facilitated by a highly efficient algorithm, which calculates all PageRank vectors at once via a commonly shared, spectrally invariant subspace. The spectral space is found to be of low dimension for each of the real-world graphs.

cs.SI↗