The internal logic o f intelligent surveillance technology

In the era of deep intelligence, intelligent surveillance technology not only enables social governance and economic development, but also generates complex and diverse value confl icts and ethical risks. Therefore, only by deeply understanding the internal logic of intelligent surveillance technology, can we accurately grasp its development trend and ensure that the technology is good. Its internal logic is combination matrix, phenomenon coding, domain selection and translational evolution, through revealing its internal logic, in order to provide theoretical reference for intelligent surveillance technology governance

(E)-N′-[4-(Dimethyl­amino)­benzyl­idene]-4-methyl­benzohydrazide methanol monosolvate

In the title compound, C17H19N3O·CH3OH, the hydrazone mol­ecule exists in a trans geometry with respect to the methyl­idene unit and the dihedral angle between the two substituted benzene rings is 42.6 (2)°. In the crystal, the components are linked through N—H⋯O and O—H⋯O hydrogen bonds, forming [100] chains of alternating hydrazone and methanol mol­ecules

Methods for labeling error detection in microarrays based on the effect of data perturbation on the regression model

Abstract Motivation: Mislabeled samples often appear in gene expression profile because of the similarity of different sub-type of disease and the subjective misdiagnosis. The mislabeled samples deteriorate supervised learning procedures. The LOOE-sensitivity algorithm is an approach for mislabeled sample detection for microarray based on data perturbation. However, the failure of measuring the perturbing effect makes the LOOE-sensitivity algorithm a poor performance. The purpose of this article is to design a novel detection method for mislabeled samples of microarray, which could take advantage of the measuring effect of data perturbations. Results: To measure the effect of data perturbation, we define an index named perturbing influence value (PIV), based on the support vector machine (SVM) regression model. The Column Algorithm (CAPIV), Row Algorithm (RAPIV) and progressive Row Algorithm (PRAPIV) based on the PIV value are proposed to detect the mislabeled samples. Experimental results obtained by using six artificial datasets and five microarray datasets demonstrate that all proposed methods in this article are superior to LOOE-sensitivity. Moreover, compared with the simple SVM and CL-stability, the PRAPIV algorithm shows an increase in precision and high recall. Availability: The program and source code (in JAVA) are publicly available at http://ccst.jlu.edu.cn/CSBG/PIVS/index.htm Contact: [email protected]; [email protected]

Correlation between intercalated magnetic layers and superconductivity in pressurized EuFe2(As0.81P0.19)2

We report comprehensive high pressure studies on correlation between intercalated magnetic layers and superconductivity in EuFe2(As0.81P0.19)2 single crystal through in-situ high pressure resistance, specific heat, X-ray diffraction and X-ray absorption measurements. We find that an unconfirmed magnetic order of the intercalated layers coexists with superconductivity in a narrow pressure range 0-0.5GPa, and then it converts to a ferromagnetic (FM) order at pressure above 0.5 GPa, where its superconductivity is absent. The obtained temperature-pressure phase diagram clearly demonstrates that the unconfirmed magnetic order can emerge from the superconducting state. In stark contrast, the superconductivity cannot develop from the FM state that is evolved from the unconfirmed magnetic state. High pressure X-ray absorption (XAS) measurements reveal that the pressure-induced enhancement of Eu's mean valence plays an important role in suppressing the superconductivity and tuning the transition from the unconfirmed magnetic state to a FM state. The unusual interplay among valence state of Eu ions, magnetism and superconductivity under pressure may shed new light on understanding the role of the intercalated magnetic layers in Fe-based superconductors

Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems