Photon-tagged and B-meson-tagged b-jet production at the LHC

Tagged jet measurements in high energy hadronic and nuclear reactions provide constraints on the energy and parton flavor origin of the parton shower that recoils against the tagging particle. Such additional insight can be especially beneficial in illuminating the mechanisms of heavy flavor production in proton-proton collisions at the LHC and their modification in the heavy ion environment, which are not fully understood. With this motivation, we present theoretical results for isolated-photon-tagged and B-meson-tagged b-jet production at center-of-mass energy 5.1 TeV for comparison to the upcoming lead-lead data. We find that photon-tagged b-jets exhibit smaller momentum imbalance shift in nuclear matter, and correspondingly smaller energy loss, than photon-tagged light flavor jets. Our results show that B-meson tagging is most effective in ensuring that the dominant fraction of recoiling jets originate from prompt b-quarks. Interestingly, in this channel the large suppression of the cross section is not accompanied by a significant momentum imbalance shift.Comment: 9 pages, 6 figures, 1 table; typos in text fixed, extended discussion adde

GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute

In recent years, point clouds have become increasingly popular for representing three-dimensional (3D) visual objects and scenes. To efficiently store and transmit point clouds, compression methods have been developed, but they often result in a degradation of quality. To reduce color distortion in point clouds, we propose a graph-based quality enhancement network (GQE-Net) that uses geometry information as an auxiliary input and graph convolution blocks to extract local features efficiently. Specifically, we use a parallel-serial graph attention module with a multi-head graph attention mechanism to focus on important points or features and help them fuse together. Additionally, we design a feature refinement module that takes into account the normals and geometry distance between points. To work within the limitations of GPU memory capacity, the distorted point cloud is divided into overlap-allowed 3D patches, which are sent to GQE-Net for quality enhancement. To account for differences in data distribution among different color omponents, three models are trained for the three color components. Experimental results show that our method achieves state-of-the-art performance. For example, when implementing GQE-Net on the recent G-PCC coding standard test model, 0.43 dB, 0.25 dB, and 0.36 dB Bjontegaard delta (BD)-peak-signal-to-noise ratio (PSNR), corresponding to 14.0%, 9.3%, and 14.5% BD-rate savings can be achieved on dense point clouds for the Y, Cb, and Cr components, respectively.Comment: 13 pages, 11 figures, submitted to IEEE TI

MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image. However, it is difficult for these case studies to fully reflect the performance of MLLM, lacking a comprehensive evaluation. In this paper, we fill in this blank, presenting the first MLLM Evaluation benchmark MME. It measures both perception and cognition abilities on a total of 14 subtasks. In order to avoid data leakage that may arise from direct use of public datasets for evaluation, the annotations of instruction-answer pairs are all manually designed. The concise instruction design allows us to fairly compare MLLMs, instead of struggling in prompt engineering. Besides, with such an instruction, we can also easily carry out quantitative statistics. A total of 10 advanced MLLMs are comprehensively evaluated on our MME, which not only suggests that existing MLLMs still have a large room for improvement, but also reveals the potential directions for the subsequent model optimization.Comment: https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Model

Chinese Expert Consensus on Critical Care Ultrasound Applications at COVID-19 Pandemic

The spread of new coronavirus (SARS-Cov-2) follows a different pattern than previous respiratory viruses, posing a serious public health risk worldwide. World Health Organization (WHO) named the disease as COVID-19 and declared it a pandemic. COVID-19 is characterized by highly contagious nature, rapid transmission, swift clinical course, profound worldwide impact, and high mortality among critically ill patients. Chest X-ray, computerized tomography (CT), and ultrasound are commonly used imaging modalities. Among them, ultrasound, due to its portability and non-invasiveness, can be easily moved to the bedside for examination at any time. In addition, with use of 4G or 5G networks, remote ultrasound consultation can also be performed, which allows ultrasound to be used in isolated medial areas. Besides, the contact surface of ultrasound probe with patients is small and easy to be disinfected. Therefore, ultrasound has gotten lots of positive feedbacks from the frontline healthcare workers, and it has played an indispensable role in the course of COVID-19 diagnosis and follow up

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Coprecipitation synthesis and negative thermal expansion of NbVO5

We develop a coprecipitation synthesis route to prepare NbVO 5 with simple oxide Nb2O5 and NH4VO3 as starting materials. No metal alkoxide or organometallic substance was used in the process. Nano-crystal NbVO5 was obtained by calcination of the coprecipitates at 550°C for 2 h. DSC/TG and XRD investigations indicate that the target compound NbVO5 is completely formed up to 504.5°C and is thermally stable below 658°C. Rietveld XRD refinements give an orthorhombic structure with space group Pnma and lattice parameters, a = 11.8453(2), b = 5.5126(3) and c = 6.9212(2) Å, respectively. In particular, HTXRD determinations show a negative thermal expansion in NbVO 5 with a TEC of -6.63 × 10 -6 °C -1 in the temperature range of RT-600 °C. This fact is ascribed to the tilting of NbO6 octahedra and VO4 tetrahedra in the flexible framework structure. The present synthesis route is facile and easy to be extended to prepare analogues such as TaVO 5, etc

Design, Dynamic Performance and Ecological Efficiency of Fiber-Reinforced Mortars with Different Binder Systems: Ordinary Portland Cement, Limestone Calcined Clay Cement and Alkali-Activated Slag

The dynamic mechanical properties and ecological efficiency of cement-based composites are of vital importance to the development of green building materials and dynamic loadings resistance. In this study, the dynamic mechanical properties, ecological and economic efficiency of fiber-reinforced mortars (FRMs) made with different binder systems, including ordinary Portland cement (OPC), limestone calcined clay cement (LC3), and alkali-activated slag (AAS), were compared. In addition, different rheological parameters were designed to evaluate the dynamic mechanical properties of FRMs. The fiber pull-out testing, Barrett-Joyner-Halenda (BJH) testing, and scanning electron microscope (SEM) were used to reveal the difference in the dynamic mechanical properties of FRMs made with different binder systems. The results showed that the loss factor of FRM made with LC3 was the highest, 70% and 150% higher than that made with OPC and AAS as the plastic viscosity of mixtures was at the same range. In addition, the loss factor of FRMs made with OPC and LC3 improved by 25% and 8.6% respectively as the plastic viscosity was improved to a higher level and caused more uniform fiber distribution. The fiber pull-out and microstructure testing indicated that FRM made with LC3 showed appropriate pore size distributions and good fiber-matrix interfacial properties. The ecological evaluation and cost analysis showed that the EE, ECO2e, and the cost of unit loss factor of FRM made with LC3 were the lowest among three kinds of mortars, showing 48369.1 MJ/m3, 6894.4 kgCO2/m3, and 20288.0 RMB/m3 respectively. Compared with FRM made with OPC, the above parameters were reduced by 55.8%, 66.9% and 46.0% respectively. Therefore, the FRM made with LC3 shows higher potential for designing green building materials with great dynamic loadings resistance, compared with FRMs made with OPC and AAS

Ultrafast high-temperature sintering of high-entropy oxides with refined microstructure and superior lithium-ion storage performance

High-entropy oxides (HEOs) have received significant attention because of their tunable mechanical properties and wide range of functional applications. However, the conventional method used for sintering HEOs requires prolonged processing time, which results in excessive grain growth, thereby compromising their performance. Here, an ultrafast high-temperature sintering (UHS) strategy was adopted, and rock-salt composite (Mg0.2Co0.2Ni0.2Cu0.2Zn0.2)O was selected as model materials. Experimental parameters were tuned to illustrate the influence of applied current and soaking time on the densification process and resulting grain size. Additionally, the electrochemical performance of UHS-synthesized microparticles as anode materials in lithium-ion batteries was investigated. The results show that the ultrafast heating rate results in fine grains with a diameter of ~6–8 μm and density of 95%, which are much smaller and similar to those obtained using the conventional sintering method (25 μm and 96%). Moreover, the high surface area and reactivity of the microparticles, as well as their sluggish diffusion effect and structural stability, contribute to outstanding performance with high capacity (336 mA·h/g at 1 A/g) and ultralong cyclability (1000 cycles). This novel technique offers valuable insights into the densification process of HEOs and other materials and can thus broaden their application range