One stone, two birds: A lightweight multidimensional learned index with cardinality support

Innovative learning based structures have recently been proposed to tackle index and cardinality estimation tasks, specifically learned indexes and data driven cardinality estimators. These structures exhibit excellent performance in capturing data distribution, making them promising for integration into AI driven database kernels. However, accurate estimation for corner case queries requires a large number of network parameters, resulting in higher computing resources on expensive GPUs and more storage overhead. Additionally, the separate implementation for CE and learned index result in a redundancy waste by storage of single table distribution twice. These present challenges for designing AI driven database kernels. As in real database scenarios, a compact kernel is necessary to process queries within a limited storage and time budget. Directly integrating these two AI approaches would result in a heavy and complex kernel due to a large number of network parameters and repeated storage of data distribution parameters. Our proposed CardIndex structure effectively killed two birds with one stone. It is a fast multidim learned index that also serves as a lightweight cardinality estimator with parameters scaled at the KB level. Due to its special structure and small parameter size, it can obtain both CDF and PDF information for tuples with an incredibly low latency of 1 to 10 microseconds. For tasks with low selectivity estimation, we did not increase the model's parameters to obtain fine grained point density. Instead, we fully utilized our structure's characteristics and proposed a hybrid estimation algorithm in providing fast and exact results

One-stage partial vertebrectomy, titanium mesh implantation and pedicle screw fixation in the treatment of thoracolumbar burst fractures through a posterior approach

OBJECTIVE: To analyze the clinical results of a partial vertebrectomy with titanium mesh implantation and pedicle screw fixation using a posterior approach to reconstruct the spine in the treatment of thoracolumbar burst fractures. METHOD: From January 2006 to August 2008, 20 patients with severe thoracolumbar fractures were treated.For vertebral bodies associated with one injured intervertebral disk, subtotal vertebrectomy surgery and single-segment fusion were performed. For vertebral bodies with two injured adjacent intervertebral disks, partial vertebrectomy surgery and two-segment fusion were performed. RESULTS: All 20 patients were followed up for 12 to 24 months (average of 18 months). There were no complications such as wound infections, hemopneumothorax or abdominal infections in any of the patients. The neurological status of all of the patients was improved by at least one American Spinal Injury Association grade by the last follow-up. The anterior vertebral body height was an average of 50.77% before surgery, 88.51% after surgery and 87.86% at the last follow up; the sagittal Cobb angle was improved, on average, from 26.15° to 5.39° and was 5.90° at the last follow up. The percentage of spinal stenosis was improved, on average, from 26.07% to 4.93%° and was 6.15% at the last follow up. There were significant differences in the anterior vertebral body height pre- and post-surgery and in the sagittal Cobb angle and the percentage of spinal stenosis (

Improving Molecular Pretraining with Complementary Featurizations

Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery. Recently, prosperous progress has been made in molecular pretraining with different molecular featurizations, including 1D SMILES strings, 2D graphs, and 3D geometries. However, the role of molecular featurizations with their corresponding neural architectures in molecular pretraining remains largely unexamined. In this paper, through two case studies -- chirality classification and aromatic ring counting -- we first demonstrate that different featurization techniques convey chemical information differently. In light of this observation, we propose a simple and effective MOlecular pretraining framework with COmplementary featurizations (MOCO). MOCO comprehensively leverages multiple featurizations that complement each other and outperforms existing state-of-the-art models that solely relies on one or two featurizations on a wide range of molecular property prediction tasks.Comment: 24 pages, work in progres

Blunt injury to the inferior gluteal artery: case report of a rare "near miss" event

Traumatic injuries of the inferior gluteal artery are rare, the majority of which are aneurysms due to sharp or blunt trauma. We report the rare case of a "near miss" event of a patient with an acute hemorrhagic mass in the right buttock caused by blunt trauma to the inferior gluteal artery without "hard" clinical signs of vascular injury. Despite the unusual presentation, diffuse injury of the inferior gluteal artery branches was diagnosed by ultrasonography and angiography. This article highlights the importance of considering an arterial injury following blunt trauma to the buttock with subsequent pain and swelling. Appreciation of this rare injury pattern is necessary in order to facilitate rapid diagnosis and appropriate treatment

RFAConv: Innovating Spatital Attention and Standard Convolutional Operation

Spatial attention has been widely used to improve the performance of convolutional neural networks by allowing them to focus on important information. However, it has certain limitations. In this paper, we propose a new perspective on the effectiveness of spatial attention, which is that it can solve the problem of convolutional kernel parameter sharing. Despite this, the information contained in the attention map generated by spatial attention is not sufficient for large-size convolutional kernels. Therefore, we introduce a new attention mechanism called Receptive-Field Attention (RFA). While previous attention mechanisms such as the Convolutional Block Attention Module (CBAM) and Coordinate Attention (CA) only focus on spatial features, they cannot fully address the issue of convolutional kernel parameter sharing. In contrast, RFA not only focuses on the receptive-field spatial feature but also provides effective attention weights for large-size convolutional kernels. The Receptive-Field Attention convolutional operation (RFAConv), developed by RFA, represents a new approach to replace the standard convolution operation. It offers nearly negligible increment of computational cost and parameters, while significantly improving network performance. We conducted a series of experiments on ImageNet-1k, MS COCO, and VOC datasets, which demonstrated the superiority of our approach in various tasks including classification, object detection, and semantic segmentation. Of particular importance, we believe that it is time to shift focus from spatial features to receptive-field spatial features for current spatial attention mechanisms. By doing so, we can further improve network performance and achieve even better results. The code and pre-trained models for the relevant tasks can be found at https://github.com/Liuchen1997/RFAConv.Comment: 14 pages, 5 figure

National incidence of traumatic fractures in China: a retrospective survey of 512 187 individuals

Background Traumatic fractures place a substantial burden on health-care systems worldwide. Although detailed information about incidence, distribution, and risk factors for traumatic fractures is vital for planning and prevention, in China, national data are unavailable. We aimed to do an up-to-date national survey on the population-weighted incidence of traumatic fractures in China. Methods The China National Fracture Study (CNFS) was a retrospective epidemiological study that recruited a nationally representative sample from eight provinces, 24 urban cities, and 24 rural counties in China using stratified random sampling and the probability proportional to size method. All eligible household members who had lived in their current residence for 6 months or longer were personally interviewed by trained research teams about traumatic fractures of the trunk, arms, or legs (not including the skull, sternum, and ribs) that had occurred in 2014. Telephone surveys were used for participants who were non-contactable after repeated visits. Fracture cases were verified by clinical records, medical history, and radiographs by orthopaedic surgeons and radiologists. We estimated incidence rates for traumatic fractures for the overall population and for subgroups by age and sex, as well as by demographic factors such as ethnic origin, occupation, geographical region, and residency category. We also studied potential associations between fractures and various factors of interest, such as age, ethnic origin, education, smoking, alcohol drinking, sleep time per day, and history of previous fracture. Data were weighted during statistical analysis to ascertain the national incidence rate. This study is registered with the Chinese Clinical Trial Registry, number ChiCTR-EPR-15005878. Findings Between Jan 19, 2015, and May 16, 2015, 535 836 individuals were selected and invited to participate in the study. Questionnaires from 23 649 (4%) individuals were excluded due to missing items, insufficient responses, or logical errors. Following exclusions, 512 187 (96%) individuals participated in the CNFS, consisting of 259 649 (51%) boys and men and 252 538 (49%) girls and women. Of these individuals, 1763 individuals had experienced traumatic fractures during 2014 (n=1833). The population-weighted incidence rate of traumatic fractures of the trunk, arms, or legs was 3·21 (95% CI 2·83–3·59) per 1000 population in 2014 (3·65, 3·12–4·18 in men and 2·75, 2·46–3·04 in women). For all ages, sleeping less than 7 h per day was identified as a risk factor for traumatic fractures. We identified previous fracture history as a risk factor for adults aged 15 years and older. Alcohol consumption incurred a risk effect for men aged 15 years and older and women aged 15–64 years. Interpretation Our results provide detailed information about fracture incidence, distribution, and risk factors, which can now be used as an up-to-date clinical evidence base for national health-care planning and preventive efforts in China and elsewhere. Specific public health policies that focus on decreasing alcohol consumption, prohibiting drunk driving, promoting smoking cessation, and encouraging individuals to obtain sufficient sleep and maintain a healthy bodyweight should be urgently implemented to help reduce the risk of traumatic fractures

Caveolin-1: a critical regulator of lung fibrosis in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive chronic disorder characterized by activation of fibroblasts and overproduction of extracellular matrix (ECM). Caveolin-1 (cav-1), a principal component of caveolae, has been implicated in the regulation of numerous signaling pathways and biological processes. We observed marked reduction of cav-1 expression in lung tissues and in primary pulmonary fibroblasts from IPF patients compared with controls. We also demonstrated that cav-1 markedly ameliorated bleomycin (BLM)-induced pulmonary fibrosis, as indicated by histological analysis, hydroxyproline content, and immunoblot analysis. Additionally, transforming growth factor β1 (TGF-β1), the well-known profibrotic cytokine, decreased cav-1 expression in human pulmonary fibroblasts. cav-1 was able to suppress TGF-β1–induced ECM production in cultured fibroblasts through the regulation of the c-Jun N-terminal kinase (JNK) pathway. Interestingly, highly activated JNK was detected in IPF- and BLM-instilled lung tissue samples, which was dramatically suppressed by ad–cav-1 infection. Moreover, JNK1-null fibroblasts showed reduced smad signaling cascades, mimicking the effects of cav-1. This study indicates a pivotal role for cav-1 in ECM regulation and suggests a novel therapeutic target for patients with pulmonary fibrosis

Stress effects on stability and diffusion behavior of sulfur impurity in nickel: A first-principles study

A systematic investigation regarding the effect of stress on the stability and diffusion behavior of S impurity in Ni was carried out via first-principles methods. A comparison of the formation energy of S in Ni indicated that S more easily forms as a solution atom with increasing S concentration in Ni supercells, but the binding energy showed that as the concentration of S that dissolved into Ni increased, the structure became less stable. The diffusion barrier via the octahedral-tetrahedral-octahedral site path was always lower than that via the octahedral-octahedral site path. The diffusion barrier of single S decreased with increase in tensile stress. S diffusion accelerated under applied tensile stress, which was disadvantageous in suppressing S retention in Ni. These results implied that even at a low concentration, dissolved S still had a tendency of precipitating from the Ni matrix, to further increase the stability of the system. (C) 2014 Elsevier B. V. All rights reserved

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.Comment: 51 pages, 2 figure