FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization

Cross-modality point cloud registration is confronted with significant challenges due to inherent differences in modalities between different sensors. We propose a cross-modality point cloud registration framework FF-LOGO: a cross-modality point cloud registration method with feature filtering and local-global optimization. The cross-modality feature correlation filtering module extracts geometric transformation-invariant features from cross-modality point clouds and achieves point selection by feature matching. We also introduce a cross-modality optimization process, including a local adaptive key region aggregation module and a global modality consistency fusion optimization module. Experimental results demonstrate that our two-stage optimization significantly improves the registration accuracy of the feature association and selection module. Our method achieves a substantial increase in recall rate compared to the current state-of-the-art methods on the 3DCSR dataset, improving from 40.59% to 75.74%. Our code will be available at https://github.com/wangmohan17/FFLOGO.Comment: 7 pages, 2 figure

The feasibility and safety of his-purkinje conduction system pacing in patients with heart failure with severely reduced ejection fraction

ObjectiveThe purpose of this study was to evaluate the feasibility and outcomes of conduction system pacing (CSP) in patients with heart failure (HF) who had a severely reduced left ventricular ejection fraction (LVEF) of less than 30% (HFsrEF).MethodsBetween January 2018 and December 2020, all consecutive HF patients with LVEF < 30% who underwent CSP at our center were evaluated. Clinical outcomes and echocardiographic data [LVEF and left ventricular end-systolic volume (LVESV)], and complications were all recorded. In addition, clinical and echocardiographic (≥5% improvement in LVEF or ≥15% decrease in LVESV) responses were assessed. The patients were classified into a complete left bundle branch block (CLBBB) morphology group and a non-CLBBB morphology group according to the baseline QRS configuration.ResultsSeventy patients (66 ± 8.84 years; 55.7% male) with a mean LVEF of 23.2 ± 3.23%, LVEDd of 67.33 ± 7.47 mm and LVESV of 212.08 ± 39.74 ml were included. QRS configuration at baseline was CLBBB in 67.1% (47/70) of patients and non-CLBBB in 32.9%. At implantation, the CSP threshold was 0.6 ± 0.3 V @ 0.4 ms and remained stable during a mean follow-up of 23.43 ± 11.44 months. CSP resulted in significant LVEF improvement from 23.2 ± 3.23% to 34.93 ± 10.34% (P < 0.001) and significant QRS narrowing from 154.99 ± 34.42 to 130.81 ± 25.18 ms (P < 0.001). Clinical and echocardiographic responses were observed in 91.4% (64/70) and 77.1% (54/70) of patients. Super-response to CSP (≥15% improvement in LVEF or ≥30% decrease in LVESV) was observed in 52.9% (37/70) of patients. One patient died due to acute HF and following severe metabolic disorders. Baseline BNP (odds ratio: 0.969; 95% confidence interval: 0.939–0.989; P = 0.045) was associated with echocardiographic response. The proportions of clinical and echocardiographic responses in the CLBBB group were higher than those in the non-CLBBB group but without significant statistical differences.ConclusionsCSP is feasible and safe in patients with HFsrEF. CSP is associated with a significant improvement in clinical and echocardiographic outcomes, even for patients with non-CLBBB widened QRS

Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models

This paper presents a comprehensive survey of ChatGPT and GPT-4, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing interest in ChatGPT/GPT-4 research, predominantly centered on direct natural language processing applications, while also demonstrating considerable potential in areas ranging from education and history to mathematics, medicine, and physics. This study endeavors to furnish insights into ChatGPT's capabilities, potential implications, ethical concerns, and offer direction for future advancements in this field.Comment: 35 pages, 3 figure

Effect of infill pattern of polylactide acid (PLA) 3D-printed integral sandwich panels under ballistic impact loading

This paper investigates the effect of the infill pattern of polylactide acid (PLA) 3D-printed sandwich panels under ballistic impact loading. Fused deposition modeling (FDM) technique is used to manufacture the PLA 3D-printed integral sandwich panels with four infill patterns: cubic, grid, gyroid, and honeycomb. The ballistic data acquisition system is collected the experimental results with three impact velocities: 109.65, 173.97, and 209.48 m/s. It was revealed that the 3D-printed sandwich panel with cubic infill pattern reached the highest maximum impact load than the other three infill patterns. Moreover, it was highlighted that the sandwich panel with cubic and gyroid infill patterns absorbed 1.41 and 1.15 J and provided better impact resistance characteristics. It is highlighted that the infill pattern plays a vital role in the impact resistance of 3D-printed sandwich structures. Furthermore, it is recommended the three-dimensional (3D) infill pattern, e.g., cubic, gyroid, 3D honeycomb, can provide better impact performance than the two-dimensional (2D) infill pattern

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages