Pricing of Short Circuit Current in High IBR-Penetrated System

With the growing penetration of Inverter-Based Resources (IBRs) in power systems, stability service markets have emerged to incentivize technologies that ensure power system stability and reliability. Among the various challenges faced in power system operation and stability, a prominent issue raised from the increasing integration of large-scale IBRs is the significant reduction of the Short-Circuit Current (SCC) level in the system, which poses a considerable threat to system voltage stability and protection. Thus, a proper market mechanism to incentivize the provision of SCC as a stability service is desired. However, the pricing of this service within the future stability market has not yet been fully developed, due to the nonconvex nature of SCC constraints and the locational property of SCC. To address these problems, this work aims to explore, for the first time, a pricing model for SCC service by incorporating a linearized SCC constraint into the Unit Commitment (UC) problem, to achieve the desired SCC level and extract the shadow price for SCC through different pricing methods

DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs

Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference. This work introduces a new benchmark dataset DialogRE^C+, introducing coreference resolution into the DRE scenario. With the aid of high-quality coreference knowledge, the reasoning of argument relations is expected to be enhanced. In DialogRE^C+ dataset, we manually annotate total 5,068 coreference chains over 36,369 argument mentions based on the existing DialogRE data, where four different coreference chain types namely speaker chain, person chain, location chain and organization chain are explicitly marked. We further develop 4 coreference-enhanced graph-based DRE models, which learn effective coreference representations for improving the DRE task. We also train a coreference resolution model based on our annotations and evaluate the effect of automatically extracted coreference chains demonstrating the practicality of our dataset and its potential to other domains and tasks.Comment: Accepted by NLPCC 202

SketchBodyNet: A Sketch-Driven Multi-faceted Decoder Network for 3D Human Reconstruction

Reconstructing 3D human shapes from 2D images has received increasing attention recently due to its fundamental support for many high-level 3D applications. Compared with natural images, freehand sketches are much more flexible to depict various shapes, providing a high potential and valuable way for 3D human reconstruction. However, such a task is highly challenging. The sparse abstract characteristics of sketches add severe difficulties, such as arbitrariness, inaccuracy, and lacking image details, to the already badly ill-posed problem of 2D-to-3D reconstruction. Although current methods have achieved great success in reconstructing 3D human bodies from a single-view image, they do not work well on freehand sketches. In this paper, we propose a novel sketch-driven multi-faceted decoder network termed SketchBodyNet to address this task. Specifically, the network consists of a backbone and three separate attention decoder branches, where a multi-head self-attention module is exploited in each decoder to obtain enhanced features, followed by a multi-layer perceptron. The multi-faceted decoders aim to predict the camera, shape, and pose parameters, respectively, which are then associated with the SMPL model to reconstruct the corresponding 3D human mesh. In learning, existing 3D meshes are projected via the camera parameters into 2D synthetic sketches with joints, which are combined with the freehand sketches to optimize the model. To verify our method, we collect a large-scale dataset of about 26k freehand sketches and their corresponding 3D meshes containing various poses of human bodies from 14 different angles. Extensive experimental results demonstrate our SketchBodyNet achieves superior performance in reconstructing 3D human meshes from freehand sketches.Comment: 9 pages, to appear in Pacific Graphics 202

Ethyl 6-methyl­sulfanyl-2-phenyl-1H-imidazo[1,2-b]pyrazole-7-carboxyl­ate monohydrate

The title compound, C15H15N3O2S·H2O, has been obtained in a search for new imidazo[1,2-b]pyrazole derivatives with better biological activity. The 1H-imidazo[1,2-b]pyrazole plane forms a dihedral angle of 16.90 (3)° with the benzene ring. π–π inter­actions are indicated by the short distance of 3.643 (2) Å between the centroids of the benzene and imidazole rings. The crystal structure also involves inter­molecular O—H⋯N hydrogen bonds

Learning to Reweight for Graph Neural Network

Graph Neural Networks (GNNs) show promising results for graph tasks. However, existing GNNs' generalization ability will degrade when there exist distribution shifts between testing and training graph data. The cardinal impetus underlying the severe degeneration is that the GNNs are architected predicated upon the I.I.D assumptions. In such a setting, GNNs are inclined to leverage imperceptible statistical correlations subsisting in the training set to predict, albeit it is a spurious correlation. In this paper, we study the problem of the generalization ability of GNNs in Out-Of-Distribution (OOD) settings. To solve this problem, we propose the Learning to Reweight for Generalizable Graph Neural Network (L2R-GNN) to enhance the generalization ability for achieving satisfactory performance on unseen testing graphs that have different distributions with training graphs. We propose a novel nonlinear graph decorrelation method, which can substantially improve the out-of-distribution generalization ability and compares favorably to previous methods in restraining the over-reduced sample size. The variables of the graph representation are clustered based on the stability of the correlation, and the graph decorrelation method learns weights to remove correlations between the variables of different clusters rather than any two variables. Besides, we interpose an efficacious stochastic algorithm upon bi-level optimization for the L2R-GNN framework, which facilitates simultaneously learning the optimal weights and GNN parameters, and avoids the overfitting problem. Experimental results show that L2R-GNN greatly outperforms baselines on various graph prediction benchmarks under distribution shifts

Sex differences in patients with COVID-19: a retrospective cohort study and meta-analysis

BACKGROUND: Accumulated evidence revealed that male was much more likely to higher severity and fatality by SARS-CoV-2 infection than female patients, but few studies and meta-analyses have evaluated the sex differences of the infection and progression of COVID-19 patients. AIM: We aimed to compare the sex differences of the epidemiological and clinical characteristics in COVID-19 patients; and to perform a meta-analysis evaluating the severe rate, fatality rate, and the sex differences of the infection and disease progression in COVID-19 patients. METHODS: We analyzed clinical data of patients in Changchun Infectious Hospital and Center, Changchun, Northeast China; and searched PubMed, Embase, Web of Science, and Cochrane Library without any language restrictions for published articles that reported the data of sex-disaggregated, number of severe, and death patients on the confirmed diagnosis of adult COVID-19 patients. RESULTS: The pooled severe rate and fatality rate of COVID-19 were 22.7% and 10.7%. Male incidence in the retrospective study was 58.1%, and the pooled incidence in male was 54.7%. CONCLUSION: The pooled severe rate in male and female of COVID-19 was 28.2% and 18.8%, the risky of severe and death was about 1.6folds higher in male compared with female, especially for older patients (> 50 y)

Mechanical antihypersensitivity effect induced by repeated spinal administrations of a TRPA1 antagonist or a gap junction decoupler in peripheral neuropathy

Spinal transient receptor potential ankyrin 1 (TRPA1) channel is associated with various pain hypersensitivity conditions. Spinally, TRPA1 is expressed by central terminals of nociceptive nerve fibers and astrocytes. Among potential endogenous agonists of TRPA1 is H2O2 generated by D-amino acid oxidase (DAAO) in astrocytes. Here we studied whether prolonged block of the spinal TRPA1 or astrocytes starting at time of injury attenuates development and/or maintenance of neuropathic hypersensitivity. Additionally, TRPA1 and DAAO mRNA were determined in the dorsal root ganglion (DRG) and spinal dorsal horn (SDH). Experiments were performed in rats with spared nerve injury (SNI) and chronic intrathecal catheter. Drugs were administered twice daily for the first seven injury days or only once seven days after injury. Mechanical hypersensitivity was assessed with monofilaments. Acute and prolonged treatment with Chembridge-5861528 (a TRPA1 antagonist), carbenoxolone (an inhibitor of activated astrocytes), or gabapentin (a comparison drug) attenuated tactile allodynia-like responses evoked by low (2 g) stimulus. However, antihypersensitivity effect of these compounds was short of significance at a high (15 g) stimulus intensity. No preemptive effects were observed. In healthy controls, carbenoxolone failed to prevent hypersensitivity induced by spinal cinnamaldehyde, a TRPA1 agonist TRPA1 and DAAO mRNA in the DRG but not SDH were slightly increased in SNI, independent of drug treatment The results indicate that prolonged peri-injury block of spinal TRPA1 or inhibition of spinal astrocyte activation attenuates maintenance but not development of mechanical (tactile allodynia-like) hypersensitivity after nerve injury. (C) 2016 Elsevier Inc. All rights reserved.Peer reviewe