arXiv subjects

Jian He

Publications and source records attributed to Jian He.

At least 19 recordsLinked to original sources

arXiv2026

A pre-$(n+2)$-angulated category which is not $(n+2)$-angulated

We construct an explicit pre-$9$-angulated category which is not $9$-angulated, thereby giving a genuinely higher counterexample to the implication from pre-$(n+2)$-angulated to $(n+2)$-angulated. The underlying additive category is the category of finitely generated projective right modules over the preprojective algebra $\Pi(A_5)$ over $\mathbb F_2$. The construction is obtained by taking an odd power of the twisted complete comparison used by Chen-Liu-Lu-Zhang in their pre-triangulated counterexample and by showing that the resulting pre-$9$-angulation fails the higher mapping-cone axiom.

math.RT↗

arXiv2026

Auslander-Reiten (n+2)-angles and local finiteness

Let $\mathcal C$ be an $(n+2)$-angulated category. Zhou proved that, when $n$ is odd, if the Auslander-Reiten $(n+2)$-angles generate the relations for the Grothendieck group of $\mathcal C$, then $\mathcal C$ is locally finite. Whether the corresponding statement remains valid for even $n$ is still open. In this paper, we give a partial affirmative answer to this problem by establishing a sufficient condition under which the same implication holds for even $n$. We further show that our sufficient condition is satisfied by a broad class of examples, thereby demonstrating that the result extends well beyond isolated cases.

math.RT↗

arXiv2026

Kleisli convolution representations of power monoids

We show that power semigroups of groups, and more generally reduced finitary power monoids, arise naturally as convolution monoids in Kleisli categories of powerset monads: (1) for the non-empty powerset monad, the Kleisli Hom-space $\mathrm{Hom}_{\mathbf{Kl}(\mathscr P_+)}(1,G)$ is isomorphic to the power monoid $\mathcal P_+(G)$; (2) for the reduced finite powerset monad on pointed sets, the Kleisli Hom-space $\mathrm{Hom}_{\mathbf{Kl}(\mathscr P_{\mathrm{fin}})}$ $(\mathbb Z/2\mathbb Z,H)$ is isomorphic to the reduced finitary power monoid $\mathcal P_{\mathrm{fin},1}(H)$. This unifies several constructions in power semigroup theory: Kleisli convolution representations of semigroups, base change along surjective group homomorphisms, and rigidity of automorphism groups. As an application, we prove that for every proper numerical monoid $S$, the Kleisli Hom-monoid $\mathrm{Hom}_{\mathbf{Kl}(\mathscr P_{\mathrm{fin}})}(\mathbb Z/2\mathbb Z,S)$ is rigid. This gives a proof of the Tringali--Yan conjecture in the language of Kleisli categories. It should be mentioned that this conjecture was already proved by Bhowmik and Tringali in a preprint posted on arXiv on July 25, 2026.

math.GR↗

arXiv2026

Oxygen K-edge X-ray Absorption Spectroscopy Database for NMC811 Layered Cathode Materials

X-ray absorption spectroscopy (XAS) probes the local chemical environment of the absorbing atom and is one of the most powerful characterization techniques for battery materials. Here we present a database of simulated oxygen K-edge XAS spectra for the layered cathode material LiNi0.8Mn0.1Co0.1O2 (NMC811), built on the atomic structures of our recent work[1]. All spectra were obtained using the excited electron and core-hole (XCH) method with the R2SCAN meta-GGA functional, as implemented in the Vienna Ab initio Simulation Package (VASP). The database covers benchmark binary oxides (TiO, Ti2O3, TiO2, Mn3O4, Mn2O3, MnO2) together with a realistic NMC811 supercell containing 60 transition metal sites at three states of charge. Because each spectrum is resolved at the level of individual oxygen sites, the database links O K-edge spectral features to specific oxygen environments defined by their local coordination and transition metal neighbors. All data are freely available and can serve as a reference for spectral fingerprinting, for direct comparison with experiments, and as training data for machine learning models.

cond-mat.mtrl-sci↗

arXiv2026

Heaps of modules: homological aspects

The definitions of projective objects and Gorenstein projective objects in the category of heaps of $T$-modules are posed, where $T$ is a truss. It is shown that a heap of $T$-modules $P$ is projective if and only if $\mathcal{G}_{e_p}(P)$ is a projective $R(T)$-module for all $e_p\in P$ and a heap of $T$-modules $M$ is BP Gorenstein projective if and only if $\mathcal{G}_{e_m}(M)$ is a Gorenstein projective $R(T)$-module for all $e_m\in M$. Moreover, we give a functorial description of the BP Gorenstein projective dimension. Finally, it is also proven that a unital truss $T$ is a Gorenstein truss if and only if $R(T)$ is an Iwanaga-Gorenstein ring.

math.RT↗

arXiv2026

Scalable LLM Agent Tool Access in the Cloud

LLM agents increasingly rely on tool calling to act on external systems, and the Model Context Protocol (MCP) has quickly become its de facto interface. Operating MCP at cloud scale, however, becomes difficult. On the tool provider side, legacy services are not directly callable through MCP; the rapid protocol development also creates ongoing compatibility cost. On the agent side, the number of accessible tool is limited by the LLM context window and inference overhead; mounting a large tool set increases token usage and inference latency and can reduce task success rate. Moreover, for stateful MCP backends with multiple replicas, preserving session affinity increases client-side complexity. We present a cloud-scale gateway system for MCP service. It breaks the direct-connect model on the data plane and offloads legacy service integration, consolidating incompatible MCP variants, access control, tool recommendation, and session-aware routing to the gateway. Hybrid retrieval sustains 98% Top-15 recall; it scales agent tool access to 3,000+ with high tool selection accuracy, and reduces tool selection time by $8.9\times$ and token usage by $23.8\times$, with low per-call overhead, stable under scale-out. Finally, we share the lessons learned from deploying the gateway system in production.

cs.DC↗

arXiv2026

Direct Simulation of LiNi0.8Mn0.1Co0.1O2 Transport Properties Using an Efficient and Accurate Machine Learning Potential

The rate capability of layered lithium nickel manganese cobalt oxide (NMC) cathode materials plays a decisive role in high-power applications such as fast charging, necessitating a detailed understanding of lithium-ion diffusion. However, the mechanisms governing lithium-ion transport in NMC remain insufficiently understood, both experimentally and computationally. In this study, we employ an advanced and efficient machine learning potential (MLP) to simulate lithium self-diffusion in LiNi0.8Mn0.1Co0.1O2 (NMC811), enabling direct large-scale molecular dynamics (MD) simulations. The workflow integrates a fine-tuned MACE (Message Passing Atomic Cluster Expansion) foundation model as a structural generator and leverages an active learning strategy applied to a near-ground-state dataset. This approach enables the construction of a reliable MLP for NMC811 in a data-efficient manner using a limited number of density functional theory (DFT) reference calculations. Based on this potential, we performed MD simulations to predict lithium diffusion coefficients. The MLP-based simulations preserve the accuracy of DFT while overcoming its time and length scale limitations, thereby allowing direct simulation of lithium self-diffusion in NMC811.

cond-mat.mtrl-sci↗

arXiv2026

Bridging the Programming Language Gap: Constructing a Multilingual Shared Semantic Space through AST Unification and Graph Matching

The lexical and syntactic disparities among different programming languages (e.g., Java and Python) pose significant challenges for multi-language software engineering tasks such as cross-language code clone detection and code retrieval, since queries or code snippets written in one programming language often fail to match equivalent artifacts in another. To bridge this gap between different programming languages, we proposed a novel approach to construct a multi-language shared semantic space, in which functionally equivalent source code written in different programming languages are close to each other. In this approach, we first map the Abstract Syntax Tree (AST) node labels of the code snippets written in different programming languages into a unified label set, thus compressing high-dimensional language-specific tokens into a common embedding space. Then, we employ a Graph Matching Network (GMN) to encode the paired AST graphs into "semantic vectors" that capture functional equivalence between programming languages in a unified code vector space. In such a way, we can eliminate the differences in syntax between different programming languages. To validate the effectiveness of this approach, we apply it to two downstream tasks, including cross-language clone detection and cross-language code retrieval. Experiments demonstrate that our approach substantially outperforms the state-of-the-art baselines in cross-language clone detection, improving Precision from 95.62% to 99.94%, Recall from 97.72% to 99.92%, and F1 score from 96.94% to 99.93%. In terms of cross-language code retrieval, our approach raises the average Mean Reciprocal Rank (MRR) from 0.4909 to 0.5547, showing an absolute gain of 0.0638 (13% relative improvement), which demonstrates its superior ability to rank correct code snippets high across multiple programming languages.

cs.SE↗

arXiv2026

Weighted Sum-Rate Maximization for RIS-UAV-assisted Space-Air-Ground Integrated Network with RSMA

In this paper, a rate-splitting multiple access (RSMA) based joint optimization framework for the space-air-ground integrated network (SAGIN) is proposed, where the satellite and base stations employ uniform planar array (UPA) antennas for signal transmission, and unmanned aerial vehicles (UAVs) relay the satellite signals. Earth stations (ESs) and user equipments (UEs) receive signals from satellite and base stations (BSs), respectively, resulting in mutual interference. We first model the channels and signals in this scenario and analyse the interference at BSs and UEs. Then, We formulate a joint optimization problem aimed at maximizing the weighted sum-rate, involving beamforming, RIS-UAV deployment and phase shifts, and rate splitting. However, this problem is highly non-convex. To tackle this challenge, we apply a block coordinate descent (BCD) approach to decompose the problem and employ the weighted minimum mean square error (WMMSE) method to transform the non-convex objective function. For the rate-splitting sub-problem, a greedy algorithm is proposed and a successive convex approximation (SCA) algorithm is used for beamforming. Besides, the alternating direction method of multipliers (ADMM) algorithm is employed for the RIS phase-shift problem with unit-modulus constraints, and an exhaustive search method is adopted for the complex UAV positioning and orientation. Simulation results validate that the proposed algorithm achieves superior performance in terms of user weighted sum-rate.

eess.SP↗

arXiv2026

100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Models

Several data warehouse and database providers have recently introduced extensions to SQL called AI Queries, enabling users to specify functions and conditions in SQL that are evaluated by LLMs, thereby broadening significantly the kinds of queries one can express over the combination of structured and unstructured data. LLMs offer remarkable semantic reasoning capabilities, making them an essential tool for complex and nuanced queries that blend structured and unstructured data. While extremely powerful, these AI queries can become prohibitively costly when invoked thousands of times. This paper provides an extensive evaluation of a recent AI query approximation approach that enables low cost analytics and database applications to benefit from AI queries. The approach delivers >100x cost and latency reduction for the semantic filter operator and also important gains for semantic ranking. The cost and performance gains come from utilizing cheap and accurate proxy models over embedding vectors. We show that despite the massive gains in latency and cost, these proxy models preserve accuracy and occasionally improve accuracy across various benchmark datasets, including the extended Amazon reviews benchmark that has 10M rows. We present an OLAP-friendly architecture within Google BigQuery for this approach for purely online (ad hoc) queries, and a low-latency HTAP database-friendly architecture in AlloyDB that could further improve the latency by moving the proxy model training offline. We present techniques that accelerate the proxy model training.

cs.DB↗

arXiv2026

TextBFGS: A Case-Based Reasoning Approach to Code Optimization via Error-Operator Retrieval

Iterative code generation with Large Language Models (LLMs) can be viewed as an optimization process guided by textual feedback. However, existing LLM self-correction methods predominantly operate in a stateless, trial-and-error manner akin to first-order search, failing to leverage past problem-solving experiences. To bridge this gap, we introduce TextBFGS, a Case-Based Reasoning (CBR) framework inspired by the Quasi-Newton optimization method. Instead of retrieving raw, unstructured textual instances, TextBFGS maintains a dynamic Case Base of historical "Error-to-Operator" correction trajectories to approximate the semantic curvature (inverse Hessian matrix) of the task. Specifically, given a textual error feedback (the target problem), TextBFGS retrieves analogous historical correction patterns (Retrieve) and applies these abstract operators to refine the current code (Reuse/Revise). Furthermore, successful adaptations are continuously retained back into the Case Base (Retain), enabling a self-evolving system. Empirical evaluations on Python code optimization tasks (HumanEval, MBPP) demonstrate that TextBFGS significantly outperforms stateless baselines. It achieves superior pass rates with fewer model calls, establishing an efficient, experience-driven paradigm for LLM-based code optimization.

cs.LG↗

arXiv2026

Slip viscosity and strain-rate viscosity in Taylor-Couette laminar flows: Experimental falsification and end-wall effects

The viscous force should be shear force, the difference between the strain-rate viscosity and the slip viscosity is that the former has conjugate shear force, while the latter does not. The study in this paper verifies the physical authenticity of two viscosity models through Taylor Couette laminar flow experiments with inner and outer cylinders rotating at the same angular velocity, and numerically investigate the influence of relative cylinder spacing and rotational speed on the circumferential velocity under the slip model. The experimental results of LDV measurement with a relative cylinder spacing of 0.3 indicate that the maximum deviation from rigid-body rotation is about 0.86%, which is consistent with the theoretical prediction of slip viscosity model. The numerical simulations show that the end-walls have no effect under the strain-rate viscosity model; but when the slip viscosity model is introduced, the end-walls inevitably bring about the circumferential velocity profile changing along the axial direction, and result in a three-dimensional (3D) spiral streamline pattern influenced by the relative cylinder spacing and angular speed of cylinders.

physics.flu-dyn↗

arXiv2025

Applicability of Electrical Conductivity Ratio Method to Complicated Band Structure and the Carrier Scattering Mechanisms of SnSe

The electrical conductivity ratio (ECR) method can be used to analyze carrier scattering mechanism (CSM) without the need of magenetic transport measurements. In this work, the applicability of the ECR method in the analysis of complex energy band structures is discussed. Combined with the thermoelectric properties of SnSe, the feasibility using ECR method of ideal single band transport model to study the CSM of semiconductor materials with complicated band structure is studied. The results indicate that ECR method is not only applicable to idea band structure as reported before but also to the complicated band structure. The analysis results of the CSM of single crystal SnSe by ECR method using ideal single-band model agree with the carrier mobility temperature dependence in the nonphase transition temperature range. The CSM along three direction of single crystal SnSe are different because of its anisotropic crystal structure. The difference between dislocation scattering (DS) and charged impurity scattering (CIS) is that DS is always accompanied by polar optical phonon scattering (POP), such as the CSM of SnSe along b axis direction. The difference between CIS and DS can be more easily distinguished by the ECR method than the carrier mobility temperature dependent method. DS and POP might be one of the approaches to improve thermoelectric property because the scattering factor for DS or POP is larger than that of acoustic phonon scattering (APS) and alloy scattering (AS). For polycrystalline SnSe, the carrier scattering mechanisms varies with the crystal structure and the temperature. This indicates that the ECR method can better reflect the variation of carrier scattering mechanisms with temperature compared to the carrier mobility temperature dependence method.

cond-mat.mtrl-sci↗

arXiv2025

Chameleon: Adaptive Fault Tolerance for Distributed Training via Real-time Policy Selection

Training large language models faces frequent interruptions due to various faults, demanding robust fault-tolerance. Existing backup-free methods, such as redundant computation, dynamic parallelism, and data rerouting, each incur performance penalties, whether from ongoing overhead, lengthy reconfigurations, or post-recovery inefficiencies. We propose Chameleon, an adaptive fault-tolerant system that intelligently selects optimal recovery strategies when a failure occurs. Chameleon achieves this through a unified performance model, expedient execution plan search, accurate performance estimation, and efficient communication optimizations. Experiments on a 32-card cluster show that Chameleon maintains a performance gap of within 11.00% between post-recovery and failure-free training, while preserving model convergence and efficient memory usage. Compared to state-of-the-art methods, Chameleon achieves up to 1.229x and 1.355x higher average throughput than Oobleck and Recycle, respectively.

cs.DC↗

arXiv2025

Pricing Options on Forwards in Function-Valued Affine Stochastic Volatility Models

We study the pricing of European-style options written on forward contracts within function-valued infinite-dimensional affine stochastic volatility models. The dynamics of the underlying forward price curves are modeled within the Heath-Jarrow-Morton-Musiela framework as solution to a stochastic partial differential equation modulated by a stochastic volatility process. We analyze two classes of affine stochastic volatility models: (i) a Gaussian model governed by a finite-rank Wishart process, and (ii) a pure-jump affine model extending the Barndorff--Nielsen--Shephard framework with state-dependent jumps in the covariance component. For both models, we derive conditions for the existence of exponential moments and develop semi-closed Fourier-based pricing formulas for vanilla call and put options written on forward price curves. Our approach allows for tractable pricing in models with infinitely many risk factors, thereby capturing maturity-specific and term structure risk essential in forward markets.

q-fin.MF↗

arXiv2025

Connecting Feedback to Choice: Understanding Educator Preferences in GenAI vs. Human-Created Lesson Plans in K-12 Education -- A Comparative Analysis

As generative AI (GenAI) models are increasingly explored for educational applications, understanding educator preferences for AI-generated lesson plans is critical for their effective integration into K-12 instruction. This exploratory study compares lesson plans authored by human curriculum designers, a fine-tuned LLaMA-2-13b model trained on K-12 content, and a customized GPT-4 model to evaluate their pedagogical quality across multiple instructional measures: warm-up activities, main tasks, cool-down activities, and overall quality. Using a large-scale preference study with K-12 math educators, we examine how preferences vary across grade levels and instructional components. We employ both qualitative and quantitative analyses. The raw preference results indicate that human-authored lesson plans are generally favored, particularly for elementary education, where educators emphasize student engagement, scaffolding, and collaborative learning. However, AI-generated models demonstrate increasing competitiveness in cool-down tasks and structured learning activities, particularly in high school settings. Beyond quantitative results, we conduct thematic analysis using LDA and manual coding to identify key factors influencing educator preferences. Educators value human-authored plans for their nuanced differentiation, real-world contextualization, and student discourse facilitation. Meanwhile, AI-generated lesson plans are often praised for their structure and adaptability for specific instructional tasks. Findings suggest a human-AI collaborative approach to lesson planning, where GenAI can serve as an assistive tool rather than a replacement for educator expertise in lesson planning. This study contributes to the growing discourse on responsible AI integration in education, highlighting both opportunities and challenges in leveraging GenAI for curriculum development.

cs.CY↗

arXiv2025

Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network

Early and accurate diagnosis of pulmonary hypertension (PH) is essential for optimal patient management. Differentiating between pre-capillary and post-capillary PH is critical for guiding treatment decisions. This study develops and validates a deep learning-based diagnostic model for PH, designed to classify patients as non-PH, pre-capillary PH, or post-capillary PH. This retrospective study analyzed data from 204 patients (112 with pre-capillary PH, 32 with post-capillary PH, and 60 non-PH controls) at the First Affiliated Hospital of Nanjing Medical University. Diagnoses were confirmed through right heart catheterization. We selected 6 samples from each category for the test set (18 samples, 10%), with the remaining 186 samples used for the training set. This process was repeated 35 times for testing. This paper proposes a deep learning model that combines Graph convolutional networks (GCN), Convolutional neural networks (CNN), and Transformers. The model was developed to process multimodal data, including short-axis (SAX) sequences, four-chamber (4CH) sequences, and clinical parameters. Our model achieved a performance of Area under the receiver operating characteristic curve (AUC) = 0.81 +- 0.06(standard deviation) and Accuracy (ACC) = 0.73 +- 0.06 on the test set. The discriminative abilities were as follows: non-PH subjects (AUC = 0.74 +- 0.11), pre-capillary PH (AUC = 0.86 +- 0.06), and post-capillary PH (AUC = 0.83 +- 0.10). It has the potential to support clinical decision-making by effectively integrating multimodal data to assist physicians in making accurate and timely diagnoses.

eess.IV↗

arXiv2025

UB-Mesh: a Hierarchically Localized nD-FullMesh Datacenter Network Architecture

As the Large-scale Language Models (LLMs) continue to scale, the requisite computational power and bandwidth escalate. To address this, we introduce UB-Mesh, a novel AI datacenter network architecture designed to enhance scalability, performance, cost-efficiency and availability. Unlike traditional datacenters that provide symmetrical node-to-node bandwidth, UB-Mesh employs a hierarchically localized nD-FullMesh network topology. This design fully leverages the data locality of LLM training, prioritizing short-range, direct interconnects to minimize data movement distance and reduce switch usage. Although UB-Mesh's nD-FullMesh topology offers several theoretical advantages, its concrete architecture design, physical implementation and networking system optimization present new challenges. For the actual construction of UB-Mesh, we first design the UB-Mesh-Pod architecture, which is based on a 4D-FullMesh topology. UB-Mesh-Pod is implemented via a suite of hardware components that serve as the foundational building blocks, including specifically-designed NPU, CPU, Low-Radix-Switch (LRS), High-Radix-Switch (HRS), NICs and others. These components are interconnected via a novel Unified Bus (UB) technique, which enables flexible IO bandwidth allocation and hardware resource pooling. For networking system optimization, we propose advanced routing mechanism named All-Path-Routing (APR) to efficiently manage data traffic. These optimizations, combined with topology-aware performance enhancements and robust reliability measures like 64+1 backup design, result in 2.04x higher cost-efficiency, 7.2% higher network availability compared to traditional Clos architecture and 95%+ linearity in various LLM training tasks.

cs.AR↗