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Bin Zhou

Publications and source records attributed to Bin Zhou.

At least 37 records · Page 2Linked to original sources
arXiv2026

Shielded but Lightweight: Building Practical Confidential Containers with ARM CCA

The rapid advancement of cloud-native technologies has created an urgent need for security. Currently, confidential containers are increasingly deployed in multi-tenant environments. Existing confidential container designs mainly adopt a microVM-based architecture. Although this approach improves inter-container isolation, its complex software stack leads to high startup latency and significant resource overhead, making it unsuitable for short-lived container workloads. In this paper, we propose Fasco, a lightweight confidential container runtime based on the ARM Confidential Compute Architecture (CCA). Fasco directly instantiates each container as an independent Container Realm, leveraging CCA's hardware-enforced isolation to ensure the confidentiality and integrity of application data inside the container. In addition, Fasco introduces a dedicated System Realm to provide system services and resource management for container realms. Through exception forwarding and shared buffers, Fasco ensures isolation among different container realms. We have implemented a prototype of Fasco and evaluated its performance on ARMv8 hardware. Experimental results show that Fasco reduces the startup latency and performance overhead of existing confidential container architectures while maintaining a small TCB.

cs.CR↗
arXiv2026

Anisotropic non-Hermitian skin effect in a two-dimensional Lieb photonic crystal

In this contribution paper, we construct a two-dimensional non-Hermitian (NH) photonic crystal (PhC) to prototype its anisotropic non-Hermitian skin effect (NHSE) for experimental proposal. Based on the tight-binding model for Lieb lattice with NH coupling, a nontrivial spectral winding number is pinpointed for certain eigenstates, which translates to geometry-dependent skin modes with tilt boundaries. For ease of implementation, complex refractive indices are employed for the Lieb unit cell of PhC to emulate the NH coupling. Validated by full wave simulation, our work underscores the boundary dependence of skin effect, and provides a concrete prototype design of NHSE implementable by state-of-the-art of topological metamaterial platforms.

physics.optics↗
arXiv2026

Tunable cornerlike states in topological type-II hyperbolic lattices

Type-II hyperbolic lattices constitute a new class of hyperbolic structures that are projected onto the Poincaré ring and possess both an inner and an outer boundary. In this work, we reveal the higher-order topological phases in type-II hyperbolic lattices, characterized by the generalized quadrupole moment. Unlike the type-I hyperbolic lattices where zero-energy cornerlike states exist on a single boundary, the higher-order topological phases in type-II hyperbolic lattices possess zero-energy cornerlike states localized on both the inner and outer boundaries. These findings are verified within both the modified Bernevig-Hughes-Zhang model and the Benalcazar-Bernevig-Hughes model. Furthermore, we demonstrate that the higher-order topological phase remains robust against weak disorder in type-II hyperbolic lattices. Our work provides a route for realizing and controlling higher-order topological states in type-II hyperbolic lattices.

cond-mat.mes-hall↗
arXiv2026

Uniform estimates and Brezis-Merle type inequalities for the $k$-Hessian equation

In this paper, we prove a Brezis-Merle type inequality for $k$-convex functions vanishing on the boundary. As an application, we establish an Alexandrov-Bakelman-Pucci type estimate for the intermediate Hessian equation. Furthermore, we establish a concentration-compactness principle for the blow-up behavior of solutions to the mean field type $k$-Hessian equation.

math.AP↗
arXiv2026

AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units

To meet the ever-increasing demand for computational efficiency, Neural Processing Units (NPUs) have become critical in modern AI infrastructure. However, unlocking their full potential requires developing high-performance compute kernels using vendor-specific Domain-Specific Languages (DSLs), a task that demands deep hardware expertise and is labor-intensive. While Large Language Models (LLMs) have shown promise in general code generation, they struggle with the strict constraints and scarcity of training data in the NPU domain. Our preliminary study reveals that state-of-the-art general-purpose LLMs fail to generate functional complex kernels for Ascend NPUs, yielding a near-zero success rate. To address these challenges, we propose AscendKernelGen, a generation-evaluation integrated framework for NPU kernel development. We introduce Ascend-CoT, a high-quality dataset incorporating chain-of-thought reasoning derived from real-world kernel implementations, and KernelGen-LM, a domain-adaptive model trained via supervised fine-tuning and reinforcement learning with execution feedback. Furthermore, we design NPUKernelBench, a comprehensive benchmark for assessing compilation, correctness, and performance across varying complexity levels. Experimental results demonstrate that our approach significantly bridges the gap between general LLMs and hardware-specific coding. Specifically, the compilation success rate on complex Level-2 kernels improves from 0% to 95.5% (Pass@10), while functional correctness achieves 64.3% compared to the baseline's complete failure. These results highlight the critical role of domain-specific reasoning and rigorous evaluation in automating accelerator-aware code generation. AscendKernGen is available at https://huggingface.co/AscendKernelGen and https://github.com/weich97/NPUKernelBench.

cs.AI↗
arXiv2026

GrandGuard: Taxonomy, Benchmark, and Safeguards for Elderly-Chatbot Interaction Safety

As older adults increasingly use LLM-based chatbots for companionship and assistance, a safety gap is emerging. Older adults may face vulnerabilities from social isolation, limited digital literacy, and cognitive decline, yet existing safety benchmarks largely target general harms and overlook elderly-specific risks. For example, a prompt such as "how to repair a ceiling light alone in the dark" may be benign for most users but poses a serious fall risk for older adults with mobility limitations. We introduce GrandGuard, the first comprehensive framework for assessing and mitigating elderly-specific contextual risks in LLM interactions. We develop a three-level taxonomy with 50 fine-grained risk types across mental well-being, financial, medical, toxicity, and privacy domains, grounded in real-world incidents, community discussions, and analysis of stakeholder studies. Using this taxonomy, we construct a benchmark of 10,404 labeled prompts and responses, showing that several leading LLMs mishandle elderly-specific contextual risks in over 50% of cases. We mitigate these failures with two safeguards: a fine-tuned Llama-Guard-3 and a policy-enhanced gpt-oss-safeguard-20b, achieving up to 96.2% and 90.9% unsafe-prompt detection accuracy, respectively. GrandGuard lays the groundwork for AI systems that move beyond general safety to support aging populations.

cs.HC↗
arXiv2026

Determination of Fragmentation Functions from Charge Asymmetries in Hadron Production

We propose a novel method for extracting non-singlet (NS) fragmentation functions (FFs) of light charged hadrons from charge asymmetries measured in hadron fragmentation, using data from both single-inclusive electron-positron annihilation and semi-inclusive deep-inelastic scattering processes. We determine the NS FFs for pions and kaons at next-to-next-to-leading order in Quantum Chromodynamics, including a comprehensive uncertainty analysis. The extracted FFs reveal a scaling index of about 0.7 at large momentum fractions and low energy scales, a strangeness suppression factor of about 0.5, and universality in fragmentation of light mesons. Our findings provide a valuable benchmark for testing non-perturbative QCD models and Monte Carlo event generators, and serve as crucial input for future electron-ion colliders.

hep-ph↗
arXiv2026

Quantum Tunneling Enables High-Flux Transport in Ion Channels

Classical molecular dynamics and electro-diffusion theories have achieved profound success in elucidating ion selectivity and gating mechanisms. However, reconciling strict selectivity with high flux permeation in Angstrom-scaled biological ion channels poses a universal challenge in nanoscale physics, as classical models consistently underestimate single-channel conductance. Using a non perturbative quantum transport framework, we calculate the ion permeation dynamics through the selectivity filter within a transfer matrix formalism. We demonstrate that quantum tunneling allows ions to bypass classical Arrhenius suppression, quantitatively recovering the experimental conductance of Na+ and K+ channels. Crucially, our findings reveal that the exploitation of quantum mechanics is a fundamental prerequisite for achieving macroscopic physiological efficiency. By reframing ion channels as mesoscopic quantum conductors, this work establishes a transformative paradigm in quantum biology and predicts distinct transport resonances in the terahertz regime.

physics.optics↗
arXiv2026

RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models

Legal Judgment Prediction (LJP) is a pivotal task in legal AI. Existing semantic-enhanced LJP models integrate judicial precedents and legal knowledge for high performance. But they neglect legal reasoning logic, a critical component of legal judgments requiring rigorous logical analysis. Although some approaches utilize legal reasoning logic for high-quality predictions, their logic rigidity hinders adaptation to case-specific logical frameworks, particularly in complex cases that are lengthy and detailed. This paper proposes a rule-enhanced legal judgment prediction framework based on first-order logic (FOL) formalism and comparative learning (CL) to develop an adaptive adjustment mechanism for legal judgment logic and further enhance performance in LJP. Inspired by the process of human exam preparation, our method follows a three-stage approach: first, we initialize judgment rules using the FOL formalism to capture complex reasoning logic accurately; next, we propose a Confusion-aware Contrastive Learning (CACL) to dynamically optimize the judgment rules through a quiz consisting of confusable cases; finally, we utilize the optimized judgment rules to predict legal judgments. Experimental results on two public datasets show superior performance across all metrics. The code is publicly available{https://anonymous.4open.science/r/RLJP-FDF1}.

cs.AI↗
arXiv2026

Evaluation of real-space second Chern number using the kernel polynomial method

We evaluate the real-space second Chern number of four-dimensional Chern insulators using the kernel polynomial method. Our calculations are performed on a four-dimensional system with $30^4$ sites, and the numerical results agree well with theoretical expectations. Moreover, we show that the method is capable of capturing the disorder effects. This is evidenced by the phase diagram obtained for disordered systems, which agrees well with predictions from the self-consistent Born approximation. Furthermore, we extend the method to six dimensions and perform an exploratory real-space calculation of the third Chern number. Although finite-size effects prevent full quantization, the numerical results show qualitative agreement with theoretical expectations. The study represents a step forward in the real-space characterization of higher-dimensional topological phases.

cond-mat.mes-hall↗
arXiv2026

PCL-Reasoner-V1.5: Advancing Math Reasoning with Offline Reinforcement Learning

We present PCL-Reasoner-V1.5, a 32-billion-parameter large language model (LLM) for mathematical reasoning. The model is built upon Qwen2.5-32B and refined via supervised fine-tuning (SFT) followed by reinforcement learning (RL). A central innovation is our proposed offline RL method, which provides superior training stability and efficiency over standard online RL methods such as GRPO. Our model achieves state-of-the-art performance among models post-trained on Qwen2.5-32B, attaining average accuracies of 90.9% on AIME 2024 and 85.6% on AIME 2025. Our work demonstrates offline RL as a stable and efficient paradigm for advancing reasoning in LLMs. All experiments were conducted on Huawei Ascend 910C NPUs.

cs.LG↗
arXiv2026

Topological phase transition induced by modulating unit cells in photonic Lieb lattice

Topological photonics was embarked from realizing the first-order chiral state in gyromagnetic media, but its higher-order states were mostly studied in dielectric lattice instead. In this paper we theoretically unveil a hierarchy of topological phases under broken time-reversal symmetry, which include the first-order Chern, and the second-order dipole, quadrupole phases. Concretely, by relaxing a certain spatial symmetry of unit cell, versatile topological phases including both edge and corner states can be established to transit around, with bandgap closures marking the phase boundaries. Our results on gyromagnetic photonic crystals may broaden the scope of sublattice engineering design for topological phase manipulation, potentially enabling multifunctional disorder-resistant waveguides and integrated photonic circuits for information communication.

physics.optics↗
arXiv2025

U(1) Gauge Potentials on de Sitter Spacetime

The smooth 1-form Verma module of $\mathfrak{so}(1,4)$ is acquired, which can be regarded as the U(1) gauge potential on de Sitter spacetime. It is shown that electromagnetic fields could not be source free on de Sitter background.

hep-th↗
arXiv2025

The Solar Close Observations and Proximity Experiments (SCOPE) mission

The Solar Close Observations and Proximity Experiments (SCOPE) mission will send a spacecraft into the solar atmosphere at a low altitude of just 5 R_sun from the solar center. It aims to elucidate the mechanisms behind solar eruptions and coronal heating, and to directly measure the coronal magnetic field. The mission will perform in situ measurements of the current sheet between coronal mass ejections and their associated solar flares, and energetic particles produced by either reconnection or fast-mode shocks driven by coronal mass ejections. This will help to resolve the nature of reconnections in current sheets, and energetic particle acceleration regions. To investigate coronal heating, the mission will observe nano-flares on scales smaller than 70 km in the solar corona and regions smaller than 40 km in the photosphere, where magnetohydrodynamic waves originate. To study solar wind acceleration mechanisms, the mission will also track the process of ion charge-state freezing in the solar wind. A key achievement will be the observation of the coronal magnetic field at unprecedented proximity to the solar photosphere. The polar regions will also be observed at close range, and the inner edge of the solar system dust disk may be identified for the first time. This work presents the detailed background, science, and mission concept of SCOPE and discusses how we aim to address the questions mentioned above.

astro-ph.SR↗
arXiv2025

The Güler-type acceleration for proximal gradient, linearized augmented Lagrangian and linearized alternating direction method of multipliers

In this paper, we introduce the Güler-type acceleration technique and utilize it to propose three acceleration algorithms: the Güler-type accelerated proximal gradient method (GPGM), the Güler-type accelerated linearized augmented Lagrangian method (GLALM) and the Güler-type accelerated linearized alternating direction method of multipliers (GLADMM). The key idea behind these algorithms is to fully leverage the information of negative term \bm{$-\|x^k-\hat{x}^{k-1}\|^2$} in order to design the extrapolation step. This concept of using negative terms to improve acceleration can be extended to other algorithms as well. Moreover, the proposed GLALM and GLADMM enable simultaneous acceleration of both primal and dual variables. Additionally, GPGM and GLALM achieve the same convergence rate of $O(\frac{1}{k^2})$ with some existing results. Although GLADMM achieves the same total convergence rate of $O(\frac{1}{N})$ as in existing results, the partial convergence rate is improved from $O(\frac{1}{N^{3/2}})$ to $O(\frac{1}{N^2})$. To validate the effectiveness of our algorithms, we conduct numerical experiments on various problem instances, including the $\ell_1$ regularized logistic regression, quadratic programming, and compressive sensing. The experimental results indicate that our algorithms outperform existing methods in terms of efficiency. This also demonstrates the potential of the stochastic algorithmic versions of these algorithms in application areas such as statistics, machine learning, and data mining. Finally, it is worth noting that this paper aims to introduce how Güler's acceleration technique can be applied to gradient-based algorithms and to provide a unified and concise framework for their construction.

math.OC↗
arXiv2025

Local potential and Hölder estimates for the linearized Monge-Ampère equation

In this paper, we establish local potential estimates and Hölder estimates for solutions of linearized Monge-Ampère equations with the right-hand side being a signed measure, under suitable assumptions on the data. In particular, the interior Hölder estimate holds for an inhomogeneous linearized Monge-Ampère equation with right-hand side being the nonnegative divergence of a bounded vector field in all dimensions. As an application, we give a new approach for the interior estimate of the singular Abreu equation.

math.AP↗
arXiv2025

NNLO QCD calculation of hadron multiplicities in light-quark jets at lepton colliders

We present the calculation of next-to-next-to-leading-order (NNLO) QCD corrections to hadron multiplicities in light-quark jets at lepton colliders, employing the ``projection-to-Born" (P2B) method implemented in the FMNLO program. Taking the next-to-leading-order result as an example, we rigorously establish the validity of our P2B-based calculation. We then present NNLO predictions for the normalized asymmetry $D_{K^{-}}$ between hadron and antihadron production in light-quark jets and compare them with SLD data. We find that a suppression of these SLD measurements relative to NPC23 predictions for $D_{K^{-}}$ emerges in the intermediate $z_h$ domain ($0.2 \lesssim z_h \lesssim 0.7$). We expect that incorporating these SLD data into global QCD fits will enable improved determination of fragmentation functions.

hep-ph↗