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arXiv 最近完成扫描:2026-11-09 04:05 · 全站最近入库:2026-11-08 06:02各信源进度 →

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新论文 · v1arXiv:2611.08259

单电机失效下四旋翼紧急着陆的故障几何感知控制分配

Fault-Geometry-Aware Control Allocation for Emergency Quadrotor Landing under Single-Motor Loss of Effectiveness

Suhas S P, Samridhi Salwan, Arshan Ali Khan, Ujwal Pai Jakarbet, Savithramma R M, Ashwini B P

AI 速读 基于摘要

在Crazyflie软件在环仿真中比较三种控制分配器,并按已知失效电机编号选择分配器,以提升单电机部分失效下的紧急着陆安全性。

cs.RO · eess.SY

查看原文摘要

Partial single-motor loss of effectiveness (LoE) changes the remaining control-authority geometry of a quadrotor and can change which control allocator performs best during emergency landing[cite: 11]. We compare three allocators in Crazyflie software-in-the-loop simulation: bounded weighted least squares (reported as PINV), a finite-candidate residual allocator (QP-lite), and the same residual allocator with scoring parameters tuned offline by the Cross-Entropy Method (CEM)[cite: 11]. For this study, fault geometry is represented by the known failed-motor index[cite: 11]. At $η=0.496$ remaining effectiveness, matched development trials select CEM for M1, M3, and M4 and PINV for M2; the M3 selection is a one-trial margin (29/30 versus 28/30)[cite: 11]. After freezing this routing map, the policy achieves 117/120 safe first-contact touchdowns on fresh seeds and 115/120 in a separate randomized-order integrated-supervisor evaluation[cite: 11]. For the critical M2/PINV branch, Firth regression gives a nominal $η_{50}\approx0.4956$ with a within-model 95% bootstrap interval of approximately [0.4955, 0.4956][cite: 11]. One-factor-at-a-time $\pm10\%$ plant perturbations shift $η_{50}$ by up to 0.05495 for thrust coefficient and 0.04974 for mass; the most frequent violated first-contact criterion also changes from vertical speed in many conditions to angular rate for mass -10% and thrust-coefficient +10%[cite: 11]. At fixed $η=0.496$, PINV has a higher M2 safe count than frozen CEM in 9/11 tested plant states and ties in 2/11; the two ties are both 0/30[cite: 11]. The experiments therefore distinguish motor-conditioned allocator selection from sensitivity to plant-model mismatch[cite: 11].

版本更新 · v3arXiv:2607.00516

When A Beats B Needs Evidence: CLAIMSTAB-QC for Auditing Comparative Claims in Quantum Software

Boshuai Ye, Peng Liang, Maryam Tavassoli Sabzevari, Arif Ali Khan

原文摘要

Empirical quantum-software papers often claim that one compiler produces smaller circuits, one optimizer is more reliable, or one ansatz achieves better solution quality. Such comparisons depend on benchmark scope, circuit construction, compilation, sampling, backends, noise models, optimizer choices, and resource budgets. Existing testing, benchmarking, and reproducibility methods help assess programs and experiments but do not directly audit whether the reported comparison is supported by the evidence available in the paper and artifact. We present CLAIMSTAB-QC, a source-bounded framework that records baselines, metric, relation, and admissible evidence; locks the audit design before outcomes are computed; and reports a scoped relation outcome or an explicit evidence boundary. Strict scalar-directional comparisons are classified as Sustained, Unresolved, or Not preserved within the locked audit scope. We examine 455 comparative claims from 119 quantum-software papers. A materialization gap emerges: 175 claims can be represented for audit planning, 79 become scalar-directional planning records, and 53 yield lockable audit or diagnostic designs, but only 8 expose enough matched evidence to audit the original comparison without proxy reconstruction. Among these 8, 2 are Sustained, 4 Unresolved, and 2 Not preserved. Controlled diagnostics over 24 benchmark-relevant quantum-workload comparisons further show that simpler checks can preserve apparent directions despite weaker support under locked designs. These results indicate that performance orderings should be reported with matched evidence sufficient to audit their stated scope.

cs.SE

版本更新 · v2arXiv:2608.28602

Integrating Triaxial IMU Sensors and Ensemble Learning for Effective Parkinson Disease Severity Classification

Rehan Khan, Muhammad Ashraf, Sikandar Ali, Asad Ali Khan, Muhammad Junaid Asif, Nabeel Ahmad

原文摘要

Parkinson disease PD is a progressive neurodegenerative disease that can have a significant impact on motor performance resulting in the appearance of symptoms such as tremors rigidity postural instabilities and bradykinesia. Timely clinical treatment disease management and quality life of the patients are closely linked to early and appropriate identification of PD. Over the past few years the growth of wearable sensor technology and artificial intelligence AI have made it possible to create noninvasive and data driven disease detection methods. This paper proposes a comparative system using artificial intelligence to detect Parkinsons disease by analyzing the motion and tremor data captured by an inertial measurement unit IMU. The data comprises the signals of the acceleration and gyroscope sensors measuring movement in three directions X Y and Z. The signs and symptoms provide helpful information about subtle motor deficits associated with PD. Several classification models like Support Vector Machine SVM Logistic Regression LR KNearest Neighbors KNN Decision Tree DT Extreme Gradient Boosting XGBoost and Light Gradient Boosting Machine LightGBM were used to compare their effectiveness. The Logistic Regression model had a performance around 75 percent in all evaluation metrics and KNearest Neighbours KNN around 90 percent. The support vector machine SVM performed almost 94 percent whereas the performance of classifiers such as Decision Tree and XGBoost was close to 96 percent and overall classification efficacy respectively. LightGBM model performs consistently at the best rank among all of the evaluated methods having Accuracy, Precision, Recall and F1score of around 97 percent. The results show that the proposed machine learning approach offers an accurate and effective predictive capability in the classification of PD severity.

cs.AI · cs.CV

新论文 · v1arXiv:2611.04993

Linking as Evaluation: Correct Separate Compilation via First-Class Environments

Jam Kabeer Ali Khan, Jinhao Tan, Bruno C. d. S. Oliveira

原文摘要

Separate compilation is central to modular software: components are compiled independently and linked into an executable. Separate compilation is correct when a linked program behaves like the program formed by merging its sources. This property is less routine to prove than type soundness because it requires a formal account of linking. We present $λ_{\mathsf{SCE}}$, a simply typed calculus in which linking, modules, and environments are all first-class. Programmers control module linking through a link operator, which a type-directed elaboration translates into ordinary terms of a core calculus of first-class environments. The core calculus's existing evaluation rules then perform the linking: linking as evaluation. Linking independently compiled modules yields the same core term as elaborating their source link. Therefore, compilation and linking commute by construction. As a result, separate compilation correctness follows directly from the semantics preservation for both static and dynamic linking. All meta-theory is mechanized in Lean 4, and we implement a compiler to WebAssembly in OCaml.

cs.PL

新论文 · v1arXiv:2610.38924

TreasuryQA Bench: Can AI Agents Work an Archive of Scanned Financial Records?

Ritiz Tambi, Sahar Shayegan, Joshua Meyer, Sun Kim, Ali Khan, Victor Shih, Mehdi Jamei, Andi Partovi

原文摘要

Organisations keep decades of financial records as scanned PDFs on shared drives, and AI agents can now reach those drives through Model Context Protocol (MCP) servers. Existing benchmarks do not mirror this production setting because they simplify the task. In some, the relevant documents are handed to the agent, so it never has to search. In others, the agent searches through documents with pre-extracted text, so it never has to read scanned images. We introduce TreasuryQA Bench, which requires both: 70 questions over 1,435 scanned U.S. Treasury publications, held in Veris's high-fidelity Dropbox twin. In each task the agent searches for the relevant file through Dropbox's MCP server, reads the pages it needs with Reducto's hosted OCR server or its own tools, and replies with one exact figure. A Python grader checks each answer against an accepted range around the correct value. In this paper we present results from ten agents. The best, Claude Opus 5.5 in Claude Code, solves 64.8% of attempts and the worst, GPT-5.6 Terra in Codex, solves 16.7%, so the benchmark is far from saturated. We also test how scan quality affects performance: every agent solves fewer tasks on the blurriest scans. Most failed attempts reply with a wrong number rather than no number, and the most common cause is a digit misread from the scan. Three worked examples are included with their answer keys.

cs.AI

新论文 · v1arXiv:2610.36798

Polymorphic First-Class Environments

Jinhao Tan, Jam Kabeer Ali Khan, Bruno C. d. S. Oliveira

原文摘要

First-class environments expose runtime environments as ordinary values and support a substitution-free, environment-based operational semantics. They also support applications such as first-class modules, dynamic composition of environment fragments, and sandboxing via local environments. Existing environment-based calculi, however, are mostly studied in a simply typed setting and stop short of System F and polymorphism. We present $F_E$, a polymorphic calculus with first-class environments that conservatively extends System F with a small-step environment semantics. $F_E$ eliminates all forms of substitution in System F. Term and type abstractions evaluate to closures, boxes run code under local environments, and box types expose the local typing assumptions of both. Manifest types, together with a two-sided type equivalence, play a dual role: they model type-level instantiation, and enable transparent type components in environments and modules. We prove type soundness, normalization, decidability, and standard properties of type equivalence, and conservativity over System F. The metatheory of $F_E$ is based on instantiation lemmas rather than substitution lemmas. This foundation supports polymorphic first-class modules with dynamic composition and sandboxing. We study $F_E$ in two forms. Our main presentation is an explicit, type-passing calculus that follows conventional System F. We also develop an implicitly-typed variant aligned with the semantics of polymorphic functional languages. All metatheory is mechanized in the Rocq theorem prover.

cs.PL

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.75ACL Anthology · ACL 2026

FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use

Suparno Roy Chowdhury, Tejas Anvekar Arizona State University, Manan Roy Choudhury Arizona State University, Muhammad Ali Khan Mayo Clinic, Kaneez Zahra Rubab Khakwani Mayo Clinic, M Bassam Sonbol Mayo Clinic, Irbaz Bin Riaz Mayo Clinic, Vivek Gupta Arizona State University

原文摘要

Clinicians exploring oncology trial repositories often need ad-hoc, multi-constraint queries over biomarkers, endpoints, interventions, and time, yet writing SQL requires schema expertise. We demo FD-NL2SQL, a feedback-driven clinical NL2SQL assistant for SQLite-based oncology databases. Given a natural-language question, a schema-aware LLM decomposes it into predicate-level sub-questions, retrieves semantically similar expert-verified NL2SQL exemplars via sentence embeddings, and synthesizes executable SQL conditioned on the decomposition, retrieved exemplars, and schema, with post-processing validity checks. To improve with use, FD-NL2SQL incorporates two update signals: (i) clinician edits of generated SQL are approved and added to the exemplar bank; and (ii) lightweight logic-based SQL augmentation applies a single atomic mutation (e.g., operator or column change), retaining variants only if they return non-empty results. A second LLM generates the corresponding natural-language question and predicate decomposition for accepted variants, automatically expanding the exemplar bank without additional annotation. The demo interface exposes decomposition, retrieval, synthesis, and execution results to support interactive refinement and continuous improvement.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表