Enhanced Discrete Multi-modal Hashing: More Constraints yet Less Time to Learn (Extended Abstract)

This paper proposes a novel method, Enhanced Discrete Multi-modal Hashing (EDMH), which learns binary codes and hash functions simultaneously from the pairwise similarity matrix of data for large-scale cross-view retrieval. EDMH distinguishes itself from existing methods by considering not just the binarization constraint but also the balance and decorrelation constraints. Although those additional discrete constraints make the optimization problem of EDMH look a lot more complicated, we are actually able to develop a fast iterative learning algorithm in the alternating optimization framework for it, as after introducing a couple of auxiliary variables each subproblem of optimization turns out to have closed-form solutions. It has been confirmed by extensive experiments that EDMH can consistently deliver better retrieval performances than state-of-the-art MH methods at lower computational costs

Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim

With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, and lower collection efficiency. Moreover, public remote data sources have restrictions on user settings, such as access to IP, frequency, and bandwidth. In order to satisfy users’ demand for accessing public remote sensing data collection nodes and effectively increase the data collection speed, this paper proposes a TSCD-TSA dynamic task scheduling algorithm that combines the BP neural network prediction algorithm with PSO-based task scheduling algorithms. Comparative experiments were carried out using the proposed task scheduling algorithms on an acquisition task using data from Sentinel2. The experimental results show that the MAX-MAX-PSO dynamic task scheduling algorithm has a smaller fitness value and a faster convergence speed

Effect of directional solidification rate on the microstructure and properties of deformation-processed Cu-7Cr-0.1Ag in situ composites

The influence of directional solidification rate on the microstructure, mechanical properties and conductivity of deformation-processed Cu-7Cr-0.1Ag in situ composites produced by thermo-mechanical processing was systematically investigated. The microstructure was analyzed by optical microscopy and scanning electronic microscopy. The mechanical properties and conductivity were evaluated by tensile-testing machine and micro-ohmmeter, respectively. The results indicate that the size, shape and distribution of second-phase Cr grains are significantly different in the Cu-7Cr-0.1Ag alloys with different growth rates. At a growth rate of 200 μm s-1, the Cr grains transform into fine Cr fiber-like grains parallel to the pulling direction from the Cr dendrites. The tensile strength of the Cu-7Cr-0.1Ag in situ composites from the directional solidification (DS) alloys is significantly higher than that from the as-cast alloy, while the conductivity of the in situ composites from the DS alloys is slightly lower than that from the as-cast alloy. The following combinations of tensile strength, elongation to fracture and conductivity of the Cu-7Cr-0.1Ag in situ composites from the DS alloy with a growth rate of 200 μm s-1 and a cumulative cold deformation strain of 8 after isochronic aging treatment for 1 h can be obtained respectively as: (i) 1067 MPa, 2.9% and 74.9% IACS; or (ii) 1018 MPa, 3.0%, and 76.0% IACS or (iii) 906 MPa, 3.3% and 77.6% IACS

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

A study of two Chinese patients with tetrasomy and pentasomy 15q11q13 including Prader-Willi/Angelman syndrome critical region present with developmental delays and mental impairment

BACKGROUND: The proximal chromosome 15q is prone to unequal crossover, leading to rearrangements. Although 15q11q13 duplications are common in patients with developmental delays and mental impairment, 15q aneusomies resulting in greater or equal to 4 copies of 15q11q13 are rare and no pentasomy 15q11q13 has been reported in the literature. Thus far, all reported high copy number 15q11q13 cases are from the West populations and no such study in Chinese patients have been documented. Dosage-response pattern of high copy number 15q11q13 on clinical presentations is still a subject for further study. CASE PRESENTATION: In this study, we characterized two Han Chinese patients with high copy number 15q11q13. Using chromosome banding, high resolution SNP-based cytogenomic array, Fluorescence in situ hybridization, and PCR-based microsatellite analysis, we identified two patients with tetrasomy 15q11q13 and pentasomy 15q11q13. Both 15q11q13 aneusomies resulted from a maternally inherited supernumerary marker chromosome 15, and each was composed of two different sized 15q11q13 segments covering the Prader-Willi/Angelman critical region: one being about 10 Mb with breakpoints at BP1 and BP5 regions on 15q11 and 15q13, respectively, and another about 8 Mb in size with breakpoints at BP1 and BP4 regions on 15q. Both patients presented with similar clinical features that included neurodevelopmental delays, mental impairment, speech and autistic behavior, and mild dysmorphism. The patient with pentasomy 15q11q13 was more severely affected than the patient with tetrasomy 15q11q13. Low birth weight was noted in patient with pentasomy 15q1q13. CONCLUSIONS: To the best of our knowledge, this is the first case of pentasomy 15q11q13 and the first study of high copy number 15q11q13 in Han Chinese patients. Our findings demonstrate that patients with tetrasomy and pentasomy of chromosome 15q11q13 share similar spectrum of phenotypes reported in other high copy number 15q11q13 patients in the West, and positive correlation between 15q11q13 copy number and degree of severity of clinical phenotypes. Low birth weight observed in the pentasomy 15q11q13 patient was not reported in other patients with high copy number 15q11q13. Additional studies would be necessary to further characterize high copy number 15q11q13 aneusomies