CKG: Dynamic Representation Based on Context and Knowledge Graph

Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance. As a result, they have achieved state-of-the-art results in a large range of language tasks. However, there exists other valuable semantic information such as similar, opposite, or other possible meanings in external knowledge graphs (KGs). We argue that entities in KGs could be used to enhance the correct semantic meaning of language sentences. In this paper, we propose a new method CKG: Dynamic Representation Based on \textbf{C}ontext and \textbf{K}nowledge \textbf{G}raph. On the one side, CKG can extract rich semantic information of large corpus. On the other side, it can make full use of inside information such as co-occurrence in large corpus and outside information such as similar entities in KGs. We conduct extensive experiments on a wide range of tasks, including QQP, MRPC, SST-5, SQuAD, CoNLL 2003, and SNLI. The experiment results show that CKG achieves SOTA 89.2 on SQuAD compared with SAN (84.4), ELMo (85.8), and BERT$_{Base}$ (88.5)

Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection

While the voxel-based methods have achieved promising results for multi-person 3D pose estimation from multi-cameras, they suffer from heavy computation burdens, especially for large scenes. We present Faster VoxelPose to address the challenge by re-projecting the feature volume to the three two-dimensional coordinate planes and estimating X, Y, Z coordinates from them separately. To that end, we first localize each person by a 3D bounding box by estimating a 2D box and its height based on the volume features projected to the xy-plane and z-axis, respectively. Then for each person, we estimate partial joint coordinates from the three coordinate planes separately which are then fused to obtain the final 3D pose. The method is free from costly 3D-CNNs and improves the speed of VoxelPose by ten times and meanwhile achieves competitive accuracy as the state-of-the-art methods, proving its potential in real-time applications.Comment: 22 pages, 7 figures, submitted to ECCV 202

Exercise training reduces ventricular arrhythmias through restoring calcium handling and sympathetic tone in myocardial infarction mice

Exercise can improve morbidity and mortality in heart failure patients; however, the underlying mechanisms remain to be fully investigated. Thus, we investigated the effects of exercise on cardiac function and ventricular arrhythmias in myocardial infarction (MI) induced heart failure mice. Wild‐type male mice underwent sham‐operation or permanent left coronary artery ligation to induce MI. MI mice were divided into a sedentary (MI‐Sed) and two intervention groups: MI‐Ex (underwent 6‐week treadmill exercise training) and MI‐βb (oral bisoprolol treatment (1 mg/kg/d) without exercise). Cardiac function and structure were assessed by echocardiography and histology. Exercise capacity and cardiopulmonary function was accepted as oxygen consumption at peak exercise (peak VO2). Autonomic nervous system function and the incidence of spontaneous ventricular arrhythmia were evaluated via telemetry recording. mRNA and protein expressions in the left ventricle (LV) were investigated by real‐time PCR and Western blotting. There were no differences in survival rate, MI size, cardiac function and structure, while exercise training improved peak VO2. Compared with MI‐Sed, MI‐Ex, and MI‐βb showed decreased sympathetic tone and lower incidence of spontaneous ventricular arrhythmia. By Western blot, the hyperphosphorylation of CaMKII and RyR2 were restored by exercise and β‐blocker treatment. Furthermore, elevated expression of miR‐1 and decreased expression of its target protein PP2A were recovered by exercise and β‐blocker treatment. Continuous intensive exercise training can suppress ventricular arrhythmias in subacute to chronic phase of MI through restoring autonomic imbalance and impaired calcium handling, similarly to that for β‐blockers

Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homo-dimers, 3 homo-trimers, 13 hetero-dimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their 5 best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% for the targets compared to 8% two years earlier, a remarkable improvement resulting from the wide use of the AlphaFold2 and AlphaFold-Multimer software. Creative use was made of the deep learning inference engines affording the sampling of a much larger number of models and enriching the multiple sequence alignments with sequences from various sources. Wide use was also made of the AlphaFold confidence metrics to rank models, permitting top performing groups to exceed the results of the public AlphaFold-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem

A Small Step Forwards on the Erd˝os-S´os Problem Concerning the Ramsey Numbers R(3, k)

Let ∆s = R(K3, Ks) − R(K3, Ks−1), where R(G, H) is the Ramsey number of graphs G and H defined as the smallest n such that any edge coloring of Kn with two colors contains G in the first color or H in the second color. In 1980, Erd˝os and S´os posed some questions about the growth of ∆s. The best known concrete bounds on ∆s are 3 ≤ ∆s ≤ s, and they have not been improved since the stating of the problem. In this paper we present some constructions, which imply in particular that R(K3, Ks) ≥ R(K3, Ks−1 − e) + 4, and R(3, Ks+t−1) ≥ R(3, Ks+1 − e) + R(3, Kt+1 − e) − 5 for s, t ≥ 3. This does not improve the lower bound of 3 on ∆s, but we still consider it a step towards to understanding its growth. We discuss some related questions and state two conjectures involving ∆s, including the following: for some constant d and all s it holds that ∆s − ∆s+1 ≤ d. We also prove that if the latter is true, then lims→∞ ∆s/s = 0

Prospective Analysis of University Carbon Reduction Based on Photovoltaic Utilization - Taking Jinnan Campus of Nankai University as an Example

In order to reduce campus carbon emissions, the accounting boundary and accounting list of carbon emissions of Nankai University's Jinnan Campus were set. On this basis, the carbon emissions of Nankai University Jinnan Campus were calculated using the emission factor method. Based on the PVsyst simulator, calculations were made to obtain the annual photovoltaic power generation and then to analyze its contribution to the carbon reduction of the campus. Conclusion: Between 2017 and 2020, the carbon emissions generated by buildings were the main influencing factor for campus carbon emissions. The net carbon emissions of Jinnan Campu were 50167.34 tons, 51848.27 tons, 50674.08 tons, and 42330.47 tonss from 2017 to 2020, respectively, reaching their peak in 2018. The total area of campus greenery, water bodies, and currently undeveloped areas is 602400m2. It is possible to consider installing a photovoltaic power generation system in 50% of the area, which is 301200m2. Using PVSyst to simulate the photovoltaic power generation system of Jinnan Campus, it was calculated that its annual power generation is about 28868000 kWh, replacing traditional electricity, which is equivalent to saving 108 tons of standard coal and reducing carbon emissions by 21073.64 tons per year. This can offset more than 40% of the net carbon emissions of the campus, which is very beneficial for the construction of low-carbon campuses. Campus photovoltaic construction relies on the huge roof resources of colleges and universities, and with the help of planning advantages, architectural advantages and energy-use advantages, it forms a photovoltaic power generation system suitable for the characteristics of schools. It not only reduces the initial investment cost, but also improves the economic efficiency and ecological benefits. The method proposed in this study can be applied to quickly and accurately evaluate the potential of campus photovoltaics. By combining more accurate hourly energy consumption data, it is possible to develop a reasonable photovoltaic utilization strategy for the entire campus and various functional clusters. Promoting to various campuses can promote the formation of more renewable energy substitution projects, reduce carbon emissions for campus communities and the entire society