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Yingqi Zhang

Publications and source records attributed to Yingqi Zhang.

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Exact Online Rank Recycling in Floyd's Uniform Subset Sampler

A uniformly random $m$-subset of $[n]=\{0,\ldots,n-1\}$ has entropy $\log_2\binom{n}{m}$. Standard without-replacement procedures often expose an additional ordering coordinate that is absent from the returned set. We show that Floyd's subset sampler admits an exact round-local factorization of this coordinate. In round $r$, let $S$ be an $(r-1)$-subset of $[j]$, let $T\sim\operatorname{Unif}([j+1])$, and let $S'$ be the result of Floyd's transition. If $D$ is the zero-based rank of the original draw $T$ in $S'$, then $(S,T)\leftrightarrow(S',D)$ is a bijection between $\binom{[j]}{r-1}\times[j+1]$ and $\binom{[j+1]}{r}\times[r]$. Consequently, $S'$ and $D$ are independent and uniform on their respective spaces. The digit $D$ can therefore be merged immediately into a residual uniform random state; an induction shows that the partial subset remains independent of that state after every round. For $k=\min(m,n-m)$, the sampling phase uses $O(k\log k)$ time and $O(k)$ auxiliary space with an order-statistic tree; explicitly materializing a complement incurs the unavoidable output cost. The combinatorial layer avoids binomial-coefficient arithmetic and recovers the complete $k!$ state-space factor exactly. We also give a finite counterexample showing that analogous immediate rank recycling in a partial Fisher-Yates array is invalid because the unselected suffix retains a correlated ordering. A 64-bit Rust implementation is checked by exhaustive state-space enumeration for all $n\leq 8$ and by an entropy-accounting trace for choosing $20{,}000$ of $30{,}000$ items. We make no claim of runtime superiority over existing subset samplers.

cs.DS

Agent libOS: A Runtime Substrate for Capability-Controlled Self-Evolving LLM Agents

Large language model (LLM) agents can persist across tasks, acquire memory, activate Skills, synthesize tools, fork child processes, attach remote resources, and commit checkpoints as reusable images. These mechanisms expand the action surface after deployment and create authority-escalation and data-exfiltration risks when visibility is mistaken for permission. We present Agent libOS, an agent-native library OS substrate that separates three planes. Operation admission combines process identity, Task Authority ceilings, typed Capabilities, policy or Human approval, budgets, and concrete primitives. Information-flow admission propagates labels and immutable source references, resolves Host-registered Sinks, and requires an exact one-shot Human release for conditional high-sensitivity egress. Durable causal evidence records intent, outcomes, accounting, and causal links but never grants authority. Thus, the model-visible action surface may evolve without implicitly expanding resource authority or permitted information flows. The implementation provides persistent processes, Object Memory, Skills, syscall-mediated JIT Tools, images and checkpoints, typed providers, Human queues, budgets, and durable recovery. Provider-backed effects use a prepare-dispatch-settle protocol that exposes ambiguity and prevents blind replay. In source-bound evaluation, 33/33 deterministic full-runtime tasks pass both task and safety oracles. Across 12 canonical real-model runs, observed safety and strict utility are 12/12. In a paired 30-run Skill projection study, the observable-state oracle passes in all runs, with 13/15 fully correct runs in each arm. These results describe the evaluated model/provider configuration. Agent libOS does not prevent prompt injection, provide kernel-grade sandboxing, or roll back irreversible external effects.

cs.OS

Student Competency Assessment and Presentation Methods Based on Algorithm Courses

This full research paper describes the assessment and presentation of student competencies in algorithm courses, grounded in the CC2020 competency model. With the growing emphasis on bridging the gap between academic training and industry demands, competency-based education, which integrates knowledge, skills, and dispositions, has become pivotal in computer science education. To bridge the gap, we need to develop a comprehensive framework to evaluate competencies (knowledge, skills, and dispositions) in computer science education. The research aims to analyze learning behavior patterns, design methods for competency assessment in algorithm courses, and evaluate the difficulty of course experiments to inform curriculum design. We collected programming experiment and written assignment data from 169 students, adapting it to the xAPI specification for unified analysis. In this work, Markov process modeling was employed to analyze behavioral sequences, revealing cognitive patterns during programming tasks. Multiple methods were applied to quantify competencies (knowledge, skills, dispositions) and identify distinct student clusters. Course difficulty was quantified using proactiveness metrics derived from submission timeliness. This work contributes a scalable framework for competency assessment in algorithm courses and offers actionable insights for personalized teaching and curriculum optimization. Practically, it enables instructors to tailor interventions based on student clusters and optimize task difficulty. Future work will integrate more students' performance to validate competency models and extend the framework to broader computer science curricula.

cs.CY

Room-temperature magnetic p-n junctions for charge-and-spin diodes

Non-magnetic p-n junctions have been fundamental components in the silicon era, serving as the backbone for nearly all Si-based semiconductor devices, including transistors. To tackle challenges such as scaling limitations, excessive latency, and high-power consumption in Si-based electronics, we develop magnetic p-n junctions composed of a p-type amorphous magnetic semiconductor (p-AMS) and n-type Si. These charge-and-spin junctions exhibit typical diode characteristics for charge current, along with distinctive spin diode features. By manipulating spin-polarized space charges, we observed a giant magnetic enhancement of approximately 24.36% at a breakdown current of 5 mA, and an impressive 29-fold increase in magnetic moments for p-AMS. The observed spin behavior is attributed to space charge effects or carrier depletion in the p-AMS with extended hole states.

cond-mat.mtrl-sci

Dynamic Risk Assessment for Geologic CO2 Sequestration

At a geologic CO2 sequestration (GCS) site, geologic uncertainty usually leads to large uncertainty in the predictions of properties that influence metrics for leakage risk assessment, such as CO2 saturations and pressures in potentially leaky wellbores, CO2/brine leakage rates, and leakage consequences such as changes in drinking water quality in groundwater aquifers. The large uncertainty in these risk-related system properties and risk metrics can lead to over-conservative risk management decisions to ensure safe operations of GCS sites. The objective of this work is to develop a novel approach based on dynamic risk assessment to effectively reduce the uncertainty in the predicted risk-related system properties and risk metrics. We demonstrate our framework for dynamic risk assessment on two case studies: a 3D synthetic example and a synthetic field example based on the Rock Springs Uplift (RSU) storage site in Wyoming, USA. Results show that the NRAP-Open-IAM risk assessment tool coupled with a conformance evaluation can be used to effectively quantify and reduce the uncertainty in the predictions of risk-related system properties and risk metrics in GCS.

cs.IT