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RF-VoID: Towards Bandwidth-Efficient Exterior Tile Void Detection via Narrowband Radio-Frequency Representation Learning

arXiv:2609.12388v1 Announce Type: cross Abstract: Hidden debonding behind exterior ceramic tiles is a falling-tile hazard, and millimeter-wave radar offers a non-contact way to find it. Conventional interpretation first reconstructs a range profile, so its reliability is bounded by the available bandwidth, yet bandwidth is what sets the cost, the acquisition time, and the regulatory footprint of a deployed system. This work asks whether that bandwidth can be traded for computation. A 4-40 GHz stepped-frequency system scans twelve exterior-wall specimens containing 0.5-1.0 mm air voids at different depths and interfaces, and the bandwidth dependence of A-scan, B-scan, and C-scan interpretation is analyzed to establish the resolution bound. RF-VoID is then proposed, which decides directly on the narrowband complex response: the sub-band is kept in its measured frequency order with amplitude and phase alongside the in-phase and quadrature channels, a dual-branch encoder reads it along the physical frequency axis using relative position encoding and a distance-dependent locality bias, and an inspection-oriented objective handles the class imbalance and the asymmetric error cost of facade screening. Under a mixed-sample protocol the method attains 98.61% accuracy and a 95.84% F1-score with 0.5 GHz of bandwidth, a seventy-two-fold reduction relative to the full sweep, without range-profile reconstruction, deconvolution, or depth-slice selection; on specimens held out entirely from training it remains the strongest of the compared models, with a mean macro F1-score of 62.12% at 0.5 GHz that rises to 68.57% at 1 GHz.
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UniPart: Towards Zero-shot Language-Grounded 3D Part Segmentation for Embodied Interaction

arXiv:2609.12898v1 Announce Type: cross Abstract: Fine-grained robotic manipulation depends on understanding parts, not only whole objects. Existing 3D foundation models tend to be either generalized but object-aware, or part-aware but limited to closed-set taxonomies, which weakens zero-shot transfer. We study text-conditioned 3D part segmentation, where a free-form phrase selects a functional part on point cloud. We introduce UniPart, a feed-forward cross-modal 3D Transformer that conditions CLIP text embedding. To scale supervision, we build LangPart-1M with 160K+ Objaverse assets and 8M text to part pairs using multi-view consistent part generation. We further manually label a high-quality subset, LangPart-4K, for fine-tuning and evaluation. UniPart achieves strong zero-shot results on open-vocabulary part benchmarks and transfers to language-conditioned part grasping in real world.
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PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement

arXiv:2604.23580v2 Announce Type: replace-cross Abstract: Translating natural-language descriptions of physical phenomena into executable simulation code requires both programming expertise and physical reasoning. Current large language models (LLMs) lack this combination: they frequently produce code that runs but simulates the wrong physics. We introduce PhysCodeBench, the first benchmark for this task, with 1,200 expert-validated examples spanning four physical domains. Its evaluation suite, PhysCodeEval, goes beyond executability and visual fidelity to measure physical correctness directly from the engine state via conservation-law residuals and expert-written assertions, and supports cross-engine evaluation to disentangle physics reasoning from API fluency. As a reference method, we propose the Self-Corrective Multi-Agent Refinement Framework (SMRF), which decouples physics-aware error correction from code generation through specialized agents. This design is motivated by our finding that targeted correction, rather than generic iterative refinement, is the key driver of physical accuracy. SMRF nearly triples the physical-assertion pass rate of the best proprietary baseline (70.6\% vs.\ 23.8\%) and retains its advantage under cross-engine transfer.
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A bibliometric analysis of quantitative computed tomography in chronic obstructive pulmonary disease research based on Web of Science: trends, hotspots, and future directions (2005-2025)

J Thorac Dis. 2026 Aug 31;18(8):883. doi: 10.21037/jtd-2026-0807. Epub 2026 Jul 21.

ABSTRACT

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition not fully captured by spirometry. Quantitative computed tomography (QCT) enables objective characterization of emphysema, airway remodeling, and other structural abnormalities, playing key roles in early recognition, phenotyping, and prognosis. Despite growing literature in this field, no comprehensive bibliometric synthesis has mapped the intellectual structure, collaborative networks, or thematic evolution of QCT research in COPD. This study aims to fill this gap by providing a structured overview of the field over the past two decades.

METHODS: A systematic search was performed in the Web of Science Core Collection (WoSCC) using the topic formula: TS=(("quantitative computed tomography" OR "quantitative CT" OR "QCT" OR "CT quantification" OR "quantitative CT assessment") AND ("chronic obstructive pulmonary disease" OR "COPD" OR "chronic obstructive pulmonary disease*")). Publications from 2005 to 2025 were included, limited to English original articles and reviews. Titles and abstracts were independently screened by two reviewers; studies not primarily focusing on QCT-based quantitative analysis in COPD were excluded. Disagreements were resolved through discussion. Bibliometric and visual analyses were conducted using CiteSpace 6.4.R1, VOSviewer 1.6.19, and the R package bibliometrix.

RESULTS: A total of 300 publications (279 original articles, 21 reviews) were included. The United States was the leading contributor in overall output and international collaboration. The University of Iowa was the most productive institution, Hoffman EA was the most prolific author, and the International Journal of Chronic Obstructive Pulmonary Disease was the most productive journal. Keyword and thematic analyses revealed a clear evolutionary trajectory: early research (2005-2012) focused on technical quantification of emphysema and airway abnormalities; a transitional phase (2013-2018) emphasized "phenotypes" and disease heterogeneity; and the recent period (2019-2025) has seen rising attention to prognostic evaluation, mortality prediction, and artificial intelligence-assisted analysis.

CONCLUSIONS: This study confirms a shift from morphologic quantification toward clinically actionable imaging biomarkers. However, the existing literature suffers from several critical gaps: lack of standardized acquisition and analysis protocols, predominance of cross-sectional designs, and insufficient external validation of artificial intelligence models. Future research should prioritize multicenter prospective validation, integration with multi-omics data for endotyping, and development of open-source automated pipelines to facilitate clinical translation.

PMID:42724634 | PMC:PMC13559334 | DOI:10.21037/jtd-2026-0807

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A bibliometric analysis of quantitative computed tomography in chronic obstructive pulmonary disease research based on Web of Science: trends, hotspots, and future directions (2005-2025)

J Thorac Dis. 2026 Aug 31;18(8):883. doi: 10.21037/jtd-2026-0807. Epub 2026 Jul 21.

ABSTRACT

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition not fully captured by spirometry. Quantitative computed tomography (QCT) enables objective characterization of emphysema, airway remodeling, and other structural abnormalities, playing key roles in early recognition, phenotyping, and prognosis. Despite growing literature in this field, no comprehensive bibliometric synthesis has mapped the intellectual structure, collaborative networks, or thematic evolution of QCT research in COPD. This study aims to fill this gap by providing a structured overview of the field over the past two decades.

METHODS: A systematic search was performed in the Web of Science Core Collection (WoSCC) using the topic formula: TS=(("quantitative computed tomography" OR "quantitative CT" OR "QCT" OR "CT quantification" OR "quantitative CT assessment") AND ("chronic obstructive pulmonary disease" OR "COPD" OR "chronic obstructive pulmonary disease*")). Publications from 2005 to 2025 were included, limited to English original articles and reviews. Titles and abstracts were independently screened by two reviewers; studies not primarily focusing on QCT-based quantitative analysis in COPD were excluded. Disagreements were resolved through discussion. Bibliometric and visual analyses were conducted using CiteSpace 6.4.R1, VOSviewer 1.6.19, and the R package bibliometrix.

RESULTS: A total of 300 publications (279 original articles, 21 reviews) were included. The United States was the leading contributor in overall output and international collaboration. The University of Iowa was the most productive institution, Hoffman EA was the most prolific author, and the International Journal of Chronic Obstructive Pulmonary Disease was the most productive journal. Keyword and thematic analyses revealed a clear evolutionary trajectory: early research (2005-2012) focused on technical quantification of emphysema and airway abnormalities; a transitional phase (2013-2018) emphasized "phenotypes" and disease heterogeneity; and the recent period (2019-2025) has seen rising attention to prognostic evaluation, mortality prediction, and artificial intelligence-assisted analysis.

CONCLUSIONS: This study confirms a shift from morphologic quantification toward clinically actionable imaging biomarkers. However, the existing literature suffers from several critical gaps: lack of standardized acquisition and analysis protocols, predominance of cross-sectional designs, and insufficient external validation of artificial intelligence models. Future research should prioritize multicenter prospective validation, integration with multi-omics data for endotyping, and development of open-source automated pipelines to facilitate clinical translation.

PMID:42724634 | PMC:PMC13559334 | DOI:10.21037/jtd-2026-0807

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