Falling behind the Rest? China and the Gender Gap Index

China’s rank falling in the Global Gender Gap Index of the World Economic Forum has aroused the domestic scholar’s controversy. Based on the data provided by the Global Gender Gap Report, this article will describe the gender inequality in China by comparing its overall index scores and scores in the fields of economic participation and opportunity, education attainment, health and survival, and political empowerment with other countries, and then examining the reasons for China’s falling in rank through the score changes of sub-dimensions and indicators. Analysis of the data suggests that China has not kept up with the rate of improvement in the overall index, and in the four fields, compared to the original 112 countries, the upper-middle income countries, and the Asian and Pacific countries. Over the 13 years covered by the report, China’s score experienced a rapid improvement from 2006 to 2009 and a decline after 2013. China’s high sex ratio at birth, further expansion of gender inequality in active life expectancy, and an enlarged gender gap in secondary education caused China’s lagging overall score and ranking. In addition, the inclusion of measures such as secondary education enrollment, political empowerment, and other indicators also led to the backward ranking of China to some extent

A New Time Series Similarity Measurement Method Based on Fluctuation Features

Time series similarity measurement is one of the fundamental tasks in time series data mining, and there are many studies on time series similarity measurement methods. However, the majority of them only calculate the distance between equal-length time series, and also cannot adequately reflect the fluctuation features of time series. To solve this problem, a new time series similarity measurement method based on fluctuation features is proposed in this paper. Firstly, the fluctuation features extraction method of time series is introduced. By defining and identifying fluctuation points, the fluctuation points sequence is obtained to represent the original time series for subsequent analysis. Then, a new similarity measurement (D_SM) is put forward to calculate the distance between different fluctuation points sequences. This method can calculate the distance of unequal-length time series, and it includes two main steps: similarity matching and the distance calculation based on similarity matching. Finally, the experiments are performed on some public time series using agglomerative hierarchical clustering based on D_SM. Compared to some traditional time series similarity measurements, the clustering results show that the proposed method can effectively distinguish time series with similar shapes from different classes and get a visible improvement in clustering accuracy in terms of F-Measure

Numerical analysis on magnetic leakage field of pipeline defect

Pipeline magnetic flux leakage inspection, mainly used for pipeline defect detection, is an important means of inner examination technology on pipeline. Flux leakage testing can't obtain valid defect identification signals by one hundred percent because of magnetization of the magnetic leakage field, the measured shape, size and location of pipeline defects, materials and operating conditions, and lift-off value, pole pitch and the length of steel brush during the measurement as well as forged pipe fittings such as welding seam, stiffener, flange and tee on the pipeline to be tested. This article reaches a conclusion that magnetic flux density distribution is influenced by the depth and width of defect through respectively researching magnetic leakage field of individual defect and double defects (thickness type) by finite-element method. It also conducts the numerical analysis on pipeline welding seam, stiffener, flange (increased wall thickness type) and tee (compound) leakage magnetic field in detection conditions of the same direction, and concludes their distribution rules of magnetic flux density. The characteristic parameters of distinguishing defect magnetic flux leakage field and the part of the pipeline magnetic flux leakage, derived from analysis and comparison of results on defective pipeline and conduit joint, stiffener, flange and tee magnetic flux leakage, provide a foundation of qualitative identification for accurately recognizing pipeline defect and eliminating the impact of other ancillary fittings on a pipe on pipeline magnetic flux leakage, and they can also offer infallible data to pipeline maintenance as a basis of quantitative analysis

On compression rate of quantum autoencoders: Control design, numerical and experimental realization

Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information. In this paper, we establish an upper bound of the compression rate for a given quantum autoencoder and present a learning control approach for training the autoencoder to achieve the maximal compression rate. The upper bound of the compression rate is theoretically proven using eigen-decomposition and matrix differentiation, which is determined by the eigenvalues of the density matrix representation of the input states. Numerical results on 2-qubit and 3-qubit systems are presented to demonstrate how to train the quantum autoencoder to achieve the theoretically maximal compression, and the training performance using different machine learning algorithms is compared. Experimental results of a quantum autoencoder using quantum optical systems are illustrated for compressing two 2-qubit states into two 1-qubit states

Tomography of Quantum States from Structured Measurements via quantum-aware transformer

Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices. However, the specific structure of quantum measurements for characterizing a quantum state has been neglected in previous work. In this paper, we explore the similarity between highly structured sentences in natural language and intrinsically structured measurements in QST. To fully leverage the intrinsic quantum characteristics involved in QST, we design a quantum-aware transformer (QAT) model to capture the complex relationship between measured frequencies and density matrices. In particular, we query quantum operators in the architecture to facilitate informative representations of quantum data and integrate the Bures distance into the loss function to evaluate quantum state fidelity, thereby enabling the reconstruction of quantum states from measured data with high fidelity. Extensive simulations and experiments (on IBM quantum computers) demonstrate the superiority of the QAT in reconstructing quantum states with favorable robustness against experimental noise

Hierarchical Clustering of Time Series Based on Linear Information Granules

Time series clustering is one of the main tasks in time series data mining. In this paper, a new time series clustering algorithm is proposed based on linear information granules. First, we improve the identification method of fluctuation points using threshold set, which represents the main trend information of the original time series. Then using fluctuation points as segmented nodes, we segment the original time series into several information granules, and linear function is used to represent the information granules. With information granulation, a granular time series consisting of several linear information granules replaces the original time series. In order to cluster time series, we then propose a linear information granules based segmented matching distance measurement (LIG_SMD) to calculate the distance between every two granular time series. In addition, hierarchical clustering method is applied based on the new distance (LIG_SMD_HC) to get clustering results. Finally, some public and real datasets about time series are experimented to examine the effectiveness of the proposed algorithm. Specifically, Euclidean distance based hierarchical clustering (ED_HC) and Dynamic Time Warping distance based hierarchical clustering (DTW_HC) are used as the compared algorithms. Our results show that LIG_SMD_HC is better than ED_HC and DTW_HC in terms of F-Measure and Accuracy

Dual-attention Focused Module for Weakly Supervised Object Localization

The research on recognizing the most discriminative regions provides referential information for weakly supervised object localization with only image-level annotations. However, the most discriminative regions usually conceal the other parts of the object, thereby impeding entire object recognition and localization. To tackle this problem, the Dual-attention Focused Module (DFM) is proposed to enhance object localization performance. Specifically, we present a dual attention module for information fusion, consisting of a position branch and a channel one. In each branch, the input feature map is deduced into an enhancement map and a mask map, thereby highlighting the most discriminative parts or hiding them. For the position mask map, we introduce a focused matrix to enhance it, which utilizes the principle that the pixels of an object are continuous. Between these two branches, the enhancement map is integrated with the mask map, aiming at partially compensating the lost information and diversifies the features. With the dual-attention module and focused matrix, the entire object region could be precisely recognized with implicit information. We demonstrate outperforming results of DFM in experiments. In particular, DFM achieves state-of-the-art performance in localization accuracy in ILSVRC 2016 and CUB-200-2011.Comment: 8 pages, 6 figures and 4 table

In Situ OH Generation From O2- and H2O2 Plays a Critical Role in Plasma Induced Cell Death

Reactive oxygen and nitrogen species produced by cold atmospheric plasma (CAP) are considered to be the most important species for biomedical applications, including cancer treatment. However, it is not known which species exert the greatest biological effects, and the nature of their interactions with tumor cells remains ill-defined. These questions were addressed in the present study by exposing human mesenchymal stromal and LP-1 cells to reactive oxygen and nitrogen species produced by CAP and evaluating cell viability. Superoxide anion (O2-) and hydrogen peroxide (H2O2) were the two major species present in plasma, but their respective concentrations were not sufficient to cause cell death when used in isolation; however, in the presence of iron, both species enhanced the cell death-inducing effects of plasma. We propose that iron containing proteins in cells catalyze O2- and H2O2 into the highly reactive OH radical that can induce cell death. The results demonstrate how reactive species are transferred to liquid and converted into the OH radical to mediate cytotoxicity and provide mechanistic insight into the molecular mechanisms underlying tumor cell death by plasma treatment

The Antitumor Effects of Plasma-Activated Saline on Muscle-Invasive Bladder Cancer Cells in Vitro and in Vivo Demonstrate Its Feasibility as a Potential Therapeutic Approach

Muscle-invasive bladder cancer (MIBC) is a fast-growing and aggressive malignant tumor in urinary system. Since chemotherapy and immunotherapy are only useable with a few MIBC patients, the clinical treatment of MIBC still faces challenges. Here, we examined the feasibility of plasma-activated saline (PAS) as a fledgling therapeutic strategy for MIBC treatment. Our data showed that plasma irradiation could generate a variety of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in saline. In vivo tests revealed that pericarcinomatous tissue injection with PAS was effective at preventing subcutaneous bladder tumor growth, with no side effects to the visceral organs after long-term administration, as well as having no obvious influence on the various biochemistry indices of the blood in mice. The in vitro studies indicated that adding 30% PAS in cell culture media causes oxidative damage to the bladder transitional cells T24 and J82 through enhancing the intracellular ROS level, and eventually induces cancer cells\u27 apoptosis by activating the ROS-mediated Fas/CD95 pathway. Therefore, for an intracavity tumor, these initial observations suggest that the soaking of the tumor tissue with PAS by intravesical perfusion may be a novel treatment option for bladder cancer