arXiv subjects

Zheng Xiao

Publications and source records attributed to Zheng Xiao.

13 recordsLinked to original sources

arXiv2026

State-Dependent Diffusion and Spectra of Strongly Driven Thermal Atoms

We propose a state-dependent diffusion model for strongly driven thermal-atom spectra. Starting from the trajectory-dependent internal-state evolution of individual atoms, we derive a closed spatial equation for the local density-matrix field using a velocity-moment expansion. Measurements of an $^{85}$Rb atomic-filter transmission spectrum agree well with the model up to a maximum Gaussian peak intensity of $1.27\times10^{3}$ W/cm$^2$, approaching six orders of magnitude above the $^{85}$Rb D2-line saturation intensity. Counterintuitively, the model reveals an anomalous optical-pumping pathway in which intense light transfers atoms from nominally dark states into bright states. Hyperfine Paschen--Back splitting selectively enhances this anomalous pathway while suppressing conventional optical pumping, allowing the filter to maintain approximately 97$\%$ transmission at the highest intensity studied. This work provides a framework for controlling strongly driven atomic ensembles and designing saturation-resistant atomic optical devices.

physics.atom-ph↗

arXiv2026

Dual-Faraday-laser-pumped cesium beam clock with $7.7\times 10^{-13}/\sqrtτ$ frequency stability

Compact cesium beam clocks are major frequency references for deployable timing systems. However, further improvement of their short-term frequency stability is limited by the clock signal-to-noise ratio (SNR). Although two-laser optical pumping can increase the effective atomic utilization, the achievable clock SNR has long been limited by laser-induced frequency-to-amplitude noise conversion. Here, we demonstrate a compact dual-Faraday-laser-pumped (DFP) Cs beam clock enabled by a low-frequency-noise atom-referenced laser architecture. The intracavity Faraday anomalous dispersion optical filter provides inherent alignment to the Cs D$_2$ resonances, while modulation transfer spectroscopy offers suppressed frequency noise and drift. The resulting laser system supports robust turnkey operation with a Lorentzian linewidth of 2.12 kHz. The DFP Cs clock achieves a clock SNR of 46,365 in a 1-Hz bandwidth and a fractional Allan deviation of $7.7\times 10^{-13}/\sqrtτ$ , with Hadamard deviation reaching $7.7\times 10^{-15}$ at 10,000 s. This work pushes the fractional frequency stability of a compact Cs beam clock into the $10^{-13}/\sqrtτ$ regime, providing a pathway toward high-performance Cs frequency references for field-deployable precision timing, navigation, and synchronization.

physics.atom-ph↗

arXiv2026

CLPO: Curriculum Learning meets Policy Optimization for LLM Reasoning

Online reinforcement learning with verifiable rewards (RLVR) has become an effective paradigm for improving the reasoning abilities of large language models, but most methods still optimize reasoning trajectories over the static problem set, wasting rollout budget on solved or overly difficult problems. We propose \textbf{CLPO (Curriculum Learning meets Policy Optimization)}, a self-evolving curriculum framework that uses on-policy rollout accuracy to identify solved, medium-difficulty, and hard problems, then restructures selected tasks according to the model's current capability. Hard problems are simplified to become learnable, while medium-difficulty problems are diversified to provide useful training variation. This allows the learning curriculum to co-evolve with the policy rather than remaining fixed as the model's capability boundary shifts. Rather than treating these rewrites as static data augmentation, CLPO optimizes restructuring trajectories with credit assigned by the downstream accuracy gain of the rewritten problem, requiring no additional human annotations beyond the original verifiable answers. Experiments across mathematical reasoning and out-of-domain general reasoning benchmarks show that CLPO substantially outperforms GRPO and DAPO on Qwen3-8B by 10.21 and 7.75 average points, respectively. Ablation studies on math and code domains further show that both the restructuring mode and the rewriting loss contribute to the final gains, demonstrating that CLPO provides a scalable and robust pathway for eliciting stronger reasoning capabilities through a self-evolving curriculum.

cs.AI↗

arXiv2026

Families of Unit Equations and Exponential Diophantine Problems via Integral Points

This paper investigates the distribution of integral points on projective varieties via two distinct methods: the Ru-Vojta theorem and our higher-dimensional generalization of the Huang-Levin-Xiao inequalities. These approaches operate under distinct geometric conditions, specifically the transverse and proper intersections of boundary divisors. Applying this framework, we prove degeneracy results for the solution sets of two classes of one-parameter families of unit equations, differentiated by the degrees of their polynomial coefficients. Finally, we extend previous greatest common divisor (GCD) estimates to derive new results for specific exponential Diophantine equations and the distribution of digits in $q$-adic representations.

math.NT↗

arXiv2026

Hyperbolicity and GCD for n+1 divisors with non-empty intersection

We study hyperbolicity for quasi-projective varieties where the boundary divisor consists of n+1 numerically parallel effective divisors on a complex projective variety of dimension n, allowing non-empty intersection. Under explicit local conditions on beta constants or intersection multiplicities, we prove that all entire curves are algebraically degenerate. Our approach extends the method of Levin-Huang-Xiao to higher dimensions, establishing a second main theorem for regular sequences of closed subschemes. This also yields a GCD-type estimate in the same geometric setting.

math.CV↗

arXiv2025

Velocity-comb modulation transfer spectroscopy

Sub-Doppler laser spectroscopy is a crucial technique for laser frequency stabilization, playing a significant role in atomic physics, precision measurement, and quantum communication. However, recent efforts to improve frequency stability appear to have reached a bottleneck, as they primarily focus on external technical approaches while neglecting the fundamental issue of low atomic utilization (< 1%), caused by only near-zero transverse velocity atoms involved in the transition. Here, we propose a velocity-comb modulation transfer spectroscopy (MTS) solution that takes advantage of the velocity-selective resonance effect of multi-frequency comb lasers to enhance the utilization of non-zero-velocity atoms. In the probe-pump configuration, each pair of counter-propagating lasers interacts with atoms from different transverse velocity-comb groups, independently contributing to the spectral amplitude and signal-to-noise ratio. Preliminary proof-of-principle results show that the frequency stability of the triple-frequency laser is optimized by nearly a factor of \sqrt{3} compared to the single-frequency laser, consistent with theoretical expectations. With more frequency comb components, MTS-stabilized lasers are expected to achieve order-of-magnitude breakthroughs in frequency stability, taking an important step toward next-generation compact optical clocks. This unique method can also be widely applied to any quantum system with a wide velocity distribution, inspiring innovative advances in numerous fields with a fresh perspective.

physics.atom-ph↗

arXiv2024

Achieving Interference-Free Degrees of Freedom in Cellular Networks via RIS

It's widely perceived that Reconfigurable Intelligent Surfaces (RIS) cannot increase Degrees of Freedom (DoF) due to their relay nature. A notable exception is Jiang \& Yu's work. They demonstrate via simulation that in an ideal $K$-user interference channel, passive RIS can achieve the interference-free DoF. In this paper, we investigate the DoF gain of RIS in more realistic systems, namely cellular networks, and more challenging scenarios with direct links. We prove that RIS can boost the DoF per cell to that of the interference-free scenario even \textit{ with direct-links}. Furthermore, we \textit{theoretically} quantify the number of RIS elements required to achieve that goal, i.e. $max\left\{ {2L, (\sqrt L + c)η+L } \right\}$ (where $L=GM(GM-1)$, $c$ is a constant and $η$ denotes the ratio of channel strength) for the $G$-cells with more single-antenna users $K$ than base station antennas $M$ per cell. The main challenge lies in addressing the feasibility of a system of algebraic equations, which is difficult by itself in algebraic geometry. We tackle this problem in a probabilistic way, by exploiting the randomness of the involved coefficients and addressing the problem from the perspective of extreme value statistics and convex geometry. Moreover, numerical results confirm the tightness of our theoretical results.

cs.IT↗

arXiv2024

A New Diophantine Approximation Inequality on Surfaces and Its Applications

We prove a Diophantine approximation inequality for closed subschemes on surfaces which can be viewed as a joint generalization of recent inequalities of Ru-Vojta and Heier-Levin in this context. As applications, we study various Diophantine problems on affine surfaces given as the complement of three numerically parallel ample projective curves: inequalities involving greatest common divisors, degeneracy of integral points, and related Diophantine equations including families of S-unit equations. We state analogous results in the complex analytic setting, where our main result is an inequality of Second Main Theorem type for surfaces, with applications to the study and value distribution theory of holomorphic curves in surfaces.

math.NT↗

arXiv2024

Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning

Domain adaptive semantic segmentation enables robust pixel-wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods, making it especially relevant in the context of intelligent vehicles. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. The source code is publicly available at https://github.com/yihong-97/Source-free-IAPC.

cs.CV↗

arXiv2023

Greatest common divisors for polynomials in almost units and applications to linear recurrence sequences

We bound the greatest common divisor of two coprime multivariable polynomials evaluated at algebraic numbers, generalizing work of Levin, and going towards conjectured inequalities of Silverman and Vojta. As an application, we prove results on greatest common divisors of terms from two linear recurrence sequences, extending the results of Levin, who considered the case where the linear recurrences are simple, and improving recent results of Grieve and Wang. The proofs rely on Schmidt's Subspace Theorem.

math.NT↗

arXiv2022

EHAP-ORAM: Efficient Hardware-Assisted Persistent ORAM System for Non-volatile Memory

Oblivious RAM (ORAM) is a provable secure primitive to prevent access pattern leakage on the memory bus. It serves as the intermediate layer between the trusted on-chip components and the untrusted external memory systems to modulate the original memory access patterns into indistinguishable memory sequences. By randomly remapping the data blocks and accessing redundant blocks, ORAM prevents access pattern leakage through obfuscation. While there is much prior work focusing on improving ORAM's performance on the conventional DRAM-based memory system, when the memory technology shifts to use non-volatile memory (NVM), new challenges come up as to how to efficiently support crash consistency for ORAM. In this work, we propose EHAP-ORAM, which studies how to persist ORAM construction with an NVM-based memory system. We first analyze the design requirements for a persistent ORAM system and discuss the need to preserve crash consistency and atomicity for both data and ORAM metadata. Next, we discuss some of the challenges in the design of a persistent ORAM system and propose some solutions to those challenges. Then, we propose the modified on-chip ORAM controller architecture. Based on the improved hardware architecture of the ORAM controller on-chip, we propose different persistency protocols to ensure the crash consistency of the ORAM system and satisfy that the metadata in PosMap is safe when it is persisted to NVM in trusted/untrusted off-chip. The proposed architecture and persistency protocol steps minimize the overhead and leakage during the write-back process. Finally, we compared our persistent ORAM with the system without crash consistency support, show that in non-recursive and recursive cases, EHAP-ORAM only incurs 3.36% and 3.65% performance overhead. The results show that the EHAP-ORAM can support efficient crash consistency with minimal performance and hardware overhead.

cs.AR↗

arXiv2020

A Bilateral Game Approach for Task Outsourcing in Multi-access Edge Computing

Multi-access edge computing (MEC) is a promising architecture to provide low-latency applications for future Internet of Things (IoT)-based network systems. Together with the increasing scholarly attention on task offloading, the problem of edge servers' resource allocation has been widely studied. Most of previous works focus on a single edge server (ES) serving multiple terminal entities (TEs), which restricts their access to sufficient resources. In this paper, we consider a MEC resource transaction market with multiple ESs and multiple TEs, which are interdependent and mutually influence each other. However, this many-to-many interaction requires resolving several problems, including task allocation, TEs' selection on ESs and conflicting interests of both parties. Game theory can be used as an effective tool to realize the interests of two or more conflicting individuals in the trading market. Therefore, we propose a bilateral game framework among multiple ESs and multiple TEs by modeling the task outsourcing problem as two noncooperative games: the supplier and customer side games. In the first game, the supply function bidding mechanism is employed to model the ESs' profit maximization problem. The ESs submit their bids to the scheduler, where the computing service price is computed and sent to the TEs. While in the second game, TEs determine the optimal demand profiles according to ESs' bids to maximize their payoff. The existence and uniqueness of the Nash equilibrium in the aforementioned games are proved. A distributed task outsourcing algorithm (DTOA) is designed to determine the equilibrium. Simulation results have demonstrated the superior performance of DTOA in increasing the ESs' profit and TEs' payoff, as well as flattening the peak and off-peak load.

cs.DC↗

arXiv2014

DualTable: A Hybrid Storage Model for Update Optimization in Hive

Hive is the most mature and prevalent data warehouse tool providing SQL-like interface in the Hadoop ecosystem. It is successfully used in many Internet companies and shows its value for big data processing in traditional industries. However, enterprise big data processing systems as in Smart Grid applications usually require complicated business logics and involve many data manipulation operations like updates and deletes. Hive cannot offer sufficient support for these while preserving high query performance. Hive using the Hadoop Distributed File System (HDFS) for storage cannot implement data manipulation efficiently and Hive on HBase suffers from poor query performance even though it can support faster data manipulation.There is a project based on Hive issue Hive-5317 to support update operations, but it has not been finished in Hive's latest version. Since this ACID compliant extension adopts same data storage format on HDFS, the update performance problem is not solved. In this paper, we propose a hybrid storage model called DualTable, which combines the efficient streaming reads of HDFS and the random write capability of HBase. Hive on DualTable provides better data manipulation support and preserves query performance at the same time. Experiments on a TPC-H data set and on a real smart grid data set show that Hive on DualTable is up to 10 times faster than Hive when executing update and delete operations.

cs.DB↗