Tetra­kis(μ-4-azido­benzoato-κ2 O:O′)bis­[(N,N-dimethyl­formamide-κO)copper(II)]

The binuclear title compound, [Cu2(C7H4N3O2)4(C3H7NO)2], is a discrete metal–organic compound having a paddle-wheel-type structure. The Cu⋯Cu distance is 2.6366 (5) Å and an inversion center is located at the mid-point of this bond. The CuII cation is coordinated by four carboxyl­ate O atoms from four 4-azido­benzoate ligands, and one O atom from a dimethyl­formamide mol­ecule, forming an overall distorted octahedral geometry when the Cu⋯Cu bond is also considered

A Day-ahead Optimal Economic Dispatch Schedule for Multi Energy Interconnected Region

AbstractThe energy supply center of the multi energy interconnected region is an energy station, which contains many types of energy supply equipment to match the cold, heating and power loads. This paper proposed a day-ahead optimal economic dispatch model for multi energy interconnected region based on centralized and interconnected energy exchange framework. In the model, the constraints of regional network topology are taken into account. The model is solved by the interior point method in this paper. A case study shows that by performing the schedule made by the dispatch model, the daily operation cost of the multi energy interconnected region decreasing remarkably, thus demonstrates the effectiveness of the proposed economic dispatch schedule

Causality-based Dual-Contrastive Learning Framework for Domain Generalization

Domain Generalization (DG) is essentially a sub-branch of out-of-distribution generalization, which trains models from multiple source domains and generalizes to unseen target domains. Recently, some domain generalization algorithms have emerged, but most of them were designed with non-transferable complex architecture. Additionally, contrastive learning has become a promising solution for simplicity and efficiency in DG. However, existing contrastive learning neglected domain shifts that caused severe model confusions. In this paper, we propose a Dual-Contrastive Learning (DCL) module on feature and prototype contrast. Moreover, we design a novel Causal Fusion Attention (CFA) module to fuse diverse views of a single image to attain prototype. Furthermore, we introduce a Similarity-based Hard-pair Mining (SHM) strategy to leverage information on diversity shift. Extensive experiments show that our method outperforms state-of-the-art algorithms on three DG datasets. The proposed algorithm can also serve as a plug-and-play module without usage of domain labels

A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose

Existing deep learning-based human mesh reconstruction approaches have a tendency to build larger networks in order to achieve higher accuracy. Computational complexity and model size are often neglected, despite being key characteristics for practical use of human mesh reconstruction models (e.g. virtual try-on systems). In this paper, we present GTRS, a lightweight pose-based method that can reconstruct human mesh from 2D human pose. We propose a pose analysis module that uses graph transformers to exploit structured and implicit joint correlations, and a mesh regression module that combines the extracted pose feature with the mesh template to reconstruct the final human mesh. We demonstrate the efficiency and generalization of GTRS by extensive evaluations on the Human3.6M and 3DPW datasets. In particular, GTRS achieves better accuracy than the SOTA pose-based method Pose2Mesh while only using 10.2% of the parameters (Params) and 2.5% of the FLOPs on the challenging in-the-wild 3DPW dataset. Code will be publicly available

EMShepherd: Detecting Adversarial Samples via Side-channel Leakage

Deep Neural Networks (DNN) are vulnerable to adversarial perturbations-small changes crafted deliberately on the input to mislead the model for wrong predictions. Adversarial attacks have disastrous consequences for deep learning-empowered critical applications. Existing defense and detection techniques both require extensive knowledge of the model, testing inputs, and even execution details. They are not viable for general deep learning implementations where the model internal is unknown, a common 'black-box' scenario for model users. Inspired by the fact that electromagnetic (EM) emanations of a model inference are dependent on both operations and data and may contain footprints of different input classes, we propose a framework, EMShepherd, to capture EM traces of model execution, perform processing on traces and exploit them for adversarial detection. Only benign samples and their EM traces are used to train the adversarial detector: a set of EM classifiers and class-specific unsupervised anomaly detectors. When the victim model system is under attack by an adversarial example, the model execution will be different from executions for the known classes, and the EM trace will be different. We demonstrate that our air-gapped EMShepherd can effectively detect different adversarial attacks on a commonly used FPGA deep learning accelerator for both Fashion MNIST and CIFAR-10 datasets. It achieves a 100% detection rate on most types of adversarial samples, which is comparable to the state-of-the-art 'white-box' software-based detectors

Therapeutic effect of co-administered salvianolate and atorvastatin calcium on coronary heart disease patients with angina pectoris, and their blood lipid levels

Purpose: To investigate the clinical effect of combination of salvianolate and atorvastatin on blood lipids of coronary heart disease patients with angina pectoris (CHD-AP).Method: Patients with CHD-AP (n = 104) from January 2016 to January 2017 were randomly assigned to two groups: control group treated with atorvastatin (10 mg/day), and study group was administered atorvastatin (10 mg/day, oral) plus salvianolate (200 mg/day in 5 % glucose, iv). Palpitation, chest distress, improvement in myocardial ischemia, myocardial function, and hemodynamics were determined and used to assess treatment effectiveness in the two groups. Differences in blood lipid profiles were also investigated.Results: Improvement in palpitation, chest distress, myocardial ischemia and myocardial function in the study group were significantly higher than in the control group (p < 0.05). In the study group, total cholesterol (TC), low density lipoprotein cholesterol (LDL-C) and triacylglycerols (TGs) significantly decreased, relative to the control group (p < 0.05).Conclusion: Treatment of CHD-AP patients with combination of salvianolate and atorvastatin significantly ameliorates coronary heart disease and angina pectoris, and also reduces their blood lipid levels.Keywords: Salvianolate, Atorvastatin, Coronary heart disease, Angina pectoris, Blood lipid

Multivariable Fuzzy Control Based Mobile Robot Odor Source Localization via Semitensor Product

In order to take full advantage of the multisensor information, a MIMO fuzzy control system based on semitensor product (STP) is set up for mobile robot odor source localization (OSL). Multisensor information, such as vision, olfaction, laser, wind speed, and direction, is the input of the fuzzy control system and the relative searching strategies, such as random searching (RS), nearest distance-based vision searching (NDVS), and odor source declaration (OSD), are the outputs. Fuzzy control rules with algebraic equations are given according to the multisensor information via STP. Any output can be updated in the proposed fuzzy control system and has no influence on the other searching strategies. The proposed MIMO fuzzy control scheme based on STP can reach the theoretical system of the mobile robot OSL. Experimental results show the efficiency of the proposed method