中国奥突寄蝇属Austrophasiopsis一新种记述(双翅目:寄蝇科)

记述采自中国云南西北部梅里雪山和西藏东南部寄蝇亚科莱寄蝇族奥突寄蝇属1新种(双翅目:寄蝇科):薛奥突寄蝇Austrophasiopsis xuewanqi sp.nov.;提供了新种的形态描述和特征图;给出了奥突寄蝇属的鉴别特征,并编制了该属4种类检索表。

Research on the Freezing Phenomenon of Quantum Correlation by Machine Learning

Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics,especially in the field of quantum information.Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods,and all these quantification methods exhibit the peculiar freezing phenomenon.The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon,rather than only study the conditions which generate this phenomenon under a certain quantum system.In essence,this is a classification problem.Machine learning has become an effective method for researchers to study classification and feature generation.In this work,we prove that the machine learning can solve the problem of X form quantum states,which is a problem of physical significance.Subsequently,we apply the density-based spatial clustering of applications with noise(DBSCAN)algorithm and the decision tree to divide quantum states into two different groups.Our goal is to classify the quantum correlations of quantum states into two classes:one is the quantum correlation with freezing phenomenon for both Rènyi discord(α=2)and the geometric discord(Bures distance),the other is the quantum correlation of non-freezing phenomenon.The results demonstrate that the machine learning method has reasonable performance in quantum correlation research.

The Controllability of Quantum Correlation under Geometry and Entropy Discords

Quantum correlation plays a critical role in the maintenance of quantum information processing and nanometer device design.In the past two decades,several quantitative methods had been proposed to study the quantum correlation of certain open quantum systems,including the geometry and entropy style discord methods.However,there are differences among these quantification methods,which promote a deep understanding of the quantum correlation.In this paper,a novel time-dependent three environmental open system model is established to study the quantum correlation.This system model interacts with two independent spin-environments(two spin-environments are connected to the other spin-environment)respectively.We have calculated and compared the changing properties of the quantum correlation under three kinds of geometry and two entropy discords,especially for the freezing phenomenon.At the same time,some original and novel changing behaviors of the quantum correlation under different timedependent parameters are studied,which is helpful to achieve the optimal revival of the quantum discord and the similar serrated form of the freezing phenomenon.Finally,it shows the controllability of the freezing correlation and the robustness of these methods by adjusting time-dependent parameters.This work provides a new way to control the quantum correlation and design nanospintronic devices.

Key technologies of artificial intelligence in electric power customer service

As the demand for customer service continues to increase,more companies are attempting to apply artificial intelligence technology in the field of customer service,enabling intelligent customer service,reducing customer service pressure,and reducing operating costs.Currently,the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions,and complex user expressions are difficult to understand.To solve the problem of low accuracy of multi-round dialogue semantic understanding,this paper proposes a semantic understanding model based on the fusion of a convolutional neural network(CNN)and attention.The model builds an“intention-slot”joint model based on the“encoding–decoding”framework and uses hidden semantic information that combines intent recognition and slot filling,avoiding the problem of information loss in traditional isolated tasks,and achieving end-to-end semantic understanding.Additionally,an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text,thereby increasing the accuracy of semantic understanding.Finally,by applying the model to electric power intelligent customer service,we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.

Quantum Generative Model with Variable-Depth Circuit

In recent years,an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm.The hybrid quantum-classical framework,which is constructed by a variational quantum circuit(VQC)and an optimizer,plays a key role in the latest quantum machine learning studies.Nevertheless,in these hybrid-framework-based quantum machine learning models,the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems.There are also few studies focused on comparing the performance of quantum generative models with different loss functions.In this study,we address the inflexibility problem by adopting the variable-depth VQC model to automatically change the structure of the quantum circuit according to the qBAS score.The basic idea behind the variable-depth VQC is to consider the depth of the quantum circuit as a parameter during the training.Meanwhile,we compared the performance of the variable-depth VQC model based on four widely used statistical distances set as the loss functions,including Kullback-Leibler divergence(KL-divergence),Jensen-Shannon divergence(JS-divergence),total variation distance,and maximum mean discrepancy.Our numerical experiment shows a promising result that the variable-depth VQC model works better than the original VQC in the generative learning tasks.

健康数据科学驱动的中西医融合研究

中、西医作为两种不同的理论体系,在传统的学科融合研究中较难建立统一的知识与认识体系。随着大数据时代的到来,“健康数据科学”这一新兴交叉学科的蓬勃发展为变革中西医融合研究范式提供了新的契机。本文从数据密集型科学研究范式,健康数据科学、环境健康和中西医结合医学的跨学科背景出发,在宏观视角下运用“三因制宜”的观点,论述多疾病谱与多维度数据驱动的知识发现问题,为推动中西医结合的理论发展与知识融合提供新模式与新途径,也为健康数据科学在医学中的应用提供可推广范例。

Suppressing the donor-like effect via fast extrusion engineering for high thermoelectric performance of polycrystalline Bi_(2)Te_(2.79)Se_(0.21)

Bi2Te3-based alloys are the most mature commercial thermoelectric(TE)materials for the cooling application near room temperature.However,the poor mechanical properties of the commercial zone melting(ZM)ingot severely limits the further application.Meanwhile,due to the donor-like effect,the robust polycrystalline n-type bulks usually have low TE performance near room temperature.Herein,based on the commercial ZM ingots,a high figure of merit(zT)of 1.0 at 320 K and good mechanical properties are achieved via the hot extrusion.The dynamic recrystallization in the hot-extrusion process can suppress the donor-like effect and refine the large ZM grains into fine-equiaxed grains.Moreover,the obtained polycrystalline Bi2Te2.79Se0.21 has good preferential orientation and high carrier mobility(m).The high m and the weaken donor-like effect maintain the high power factor(PF)of 43.1 mW cm^(-1)K^(-2)in the hot-extruded ZM sample.Due to the enhanced phonon scattering,the total thermal conductivity ktot decreased to 1.35 W·m^(-1)·K^(-1).To demonstrate the good mechanical properties of the extruded ZM sample,micro TE dices with the cross sections of 300μm×300 mm and 200μm×200 mm are successfully cut from the extrusion sample.This study provided a fast and low-cost extrusion technique to improve the TE and mechanical properties of the commercial ZM ingot at room temperature.

A naturally occurring CD8^＋CD122^＋ T-cell subset as a memory-like Treg family

Despite extensive studies on CD4^＋CD25^＋ regulatory T cells （Tregs） during the past decade, the progress on their clinical translation remains stagnant. Mounting evidence suggests that naturally occurring CD8^＋CD122^＋ T cells are also Tregs with the capacity to inhibit T-cell responses and suppress autoimmunity as well as alloimmunity. In fact, they are memory-like Tregs that resemble a central memory T cell （TcM） phenotype. The mechanisms underlying their suppression are still not well understood, although they may include IL-IO production. We have recently demonstrated that programmed death-1 （PD-1） expression distinguishes between regulatory and memory CD8^＋CD122^＋ T cells and that CD8^＋CD122^＋ Tregs undergo faster homeostatic proliferation and are more potent in the suppression of allograft rejection than conventional CD4^＋CD25^＋ Tregs. These findings may open a new line of investigation for accelerating effective Treg therapies in the clinic. In this review, we summarize the significant progress in this promising field of CD8^＋CD122^＋ Treg research and discuss their phenotypes, suppressive roles in autoimmunity and alloimmunity, functional requirements, mechanisms of action and potential applications in the clinic.

Curvature Estimates for Irreducible Symmetric Spaces

由使用真实简单谎言代数学的分类，我们由大小写得到限制的根大小写的摆平的长度的最大值，；因此得到部分弯曲的上面的界限紧缩的类型的无法缩减的 Riemannian 对称的空格。作为应用，这篇论文证实 Sampson 是在大多数情况中推测军士合同类型的无法缩减的 Riemannian 对称的空格。

The Partial Positivity of the Curvature in Riemannian Symmetric Spaces

在这篇论文，部分确实(分别地，否定性) 所有无法缩减的 Riemannian 的弯曲，对称的空格被决定。从抽象的根系统和最大的分系统的分类，作者在古典类型并且在非凡的类型给计算对称的空格。