The SpeakIn System Description for CNSRC2022

This report describes our speaker verification systems for the tasks of the CN-Celeb Speaker Recognition Challenge 2022 (CNSRC 2022). This challenge includes two tasks, namely speaker verification(SV) and speaker retrieval(SR). The SV task involves two tracks: fixed track and open track. In the fixed track, we only used CN-Celeb.T as the training set. For the open track of the SV task and SR task, we added our open-source audio data. The ResNet-based, RepVGG-based, and TDNN-based architectures were developed for this challenge. Global statistic pooling structure and MQMHA pooling structure were used to aggregate the frame-level features across time to obtain utterance-level representation. We adopted AM-Softmax and AAM-Softmax combined with the Sub-Center method to classify the resulting embeddings. We also used the Large-Margin Fine-Tuning strategy to further improve the model performance. In the backend, Sub-Mean and AS-Norm were used. In the SV task fixed track, our system was a fusion of five models, and two models were fused in the SV task open track. And we used a single system in the SR task. Our approach leads to superior performance and comes the 1st place in the open track of the SV task, the 2nd place in the fixed track of the SV task, and the 3rd place in the SR task.Comment: 4 page

SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model

The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with annotating Remote Sensing (RS) images, a large amount of valuable RS data remains unlabeled, particularly at the pixel level. In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances, surpassing existing high-resolution RS segmentation datasets in size by several orders of magnitude. It provides object category, location, and instance information that can be used for semantic segmentation, instance segmentation, and object detection, either individually or in combination. We also provide a comprehensive analysis of SAMRS from various aspects. Moreover, preliminary experiments highlight the importance of conducting segmentation pre-training with SAMRS to address task discrepancies and alleviate the limitations posed by limited training data during fine-tuning. The code and dataset will be available at https://github.com/ViTAE-Transformer/SAMRS.Comment: Accepted by NeurIPS 2023 Datasets and Benchmarks Trac

Analysis of skin influence in identification of heroin using singular value decomposition

AbstractIn this paper, the influence of skin in energy-dispersive X-ray diffraction (EDXRD) spectrum of heroin was studied using singular value decomposition (SVD). The spectra of pure heroin, skin and heroin covered by skin were organized as matrices for SVD after truncation and smoothing. It was demonstrated that the two largest singular values and their corresponding left and right singular vectors of each matrix could reconstruct the matrix in the permissible error and contained enough information of the matrix. We extracted the two largest singular values of each matrix as two dimensions of the feature point of the corresponding spectrum. The feature points of different samples were clustered and a linear relationship was proved to be between and movement of feature point and thickness of component of skin, such as fat and muscle. This indicated that the method of SVD may be suitable for identification of heroin covered by skin

The influence of X-ray wavelength and the simulative human skin and muscle obstruction on the detection of human body-hidden drugs by non-intrusive X-ray diffraction method

AbstractIn order to detect the body-hidden drugs non-intrusively and rapidly, the influence of the X-ray wavelength and covering of the simulative skin and muscle on the detection of methamphetamine sample by synchrotron radiation X-ray diffraction (SR-XRD) technique have been investigated. Synchrotron radiation based X-ray with three different wavelengths (1.29 Å, 1.54 Å, 1.80 Å) has been chosen as the X-ray source. The results indicate that the intensities as well as the number of the diffraction peaks of methamphetamine sample covered by simulative muscle decreased with the increasing of the X-ray wavelength from 1.29 Åto 1.80 Å. In addition, the intensities of the diffraction peaks for methamphetamine will be seriously affected by the covered simulative skin or muscle due to the X-ray absorption. Furthermore, the absorption of X-ray by the simulative muscle seems much stronger than that of the simulative skin. Moreover, the specific molecular structure of the methamphetamine sample has been obtained by X-ray diffraction method

Preparation and Characterization of Folate Targeting Magnetic Nanomedicine Loaded with Cisplatin

We used Aldehyde sodium alginate (ASA) as modifier to improve surfactivity and stability of magnetic nanoparticles, and folate acid (FA) as targeting molecule. Fe3O4 nanoparticles were prepared by chemical coprecipitation method. FA was activated and coupled with diaminopolyethylene glycol (NH2-PEG-NH2). ASA was combined with Fe3O4 nanoparticles, and FA-PEG was connected with ASA by Schiff’s base formation. Then Cl- in cisplatin was replaced by hydroxyl group in ASA, and FA- and ASA-modified cisplatin-loaded magnetic nanomedicine (CDDP-FA-ASA-MNPs) was prepared. This nanomedicine was characterized by transmission electron microscopy, dynamic lighterring scattering, phase analysis light scattering and vibrating sample magnetometer. The uptake of magnetic nanomedicine by nasopharyngeal and laryngeal carcinoma cells with folate receptor positive or negative expression were observed by Prussian blue iron stain and transmission electron microscopy. We found that CDDP-FA-ASA-MNPs have good water-solubility and stability. Mean diameter of Fe3O4 core was 8.17 ± 0.24 nm, hydrodynamic diameters was 110.90±1.70 nm, and zeta potential was -26.45±1.26 mV. Maximum saturation magnetization was 22.20 emu/g. CDDP encapsulation efficiency was 49.05±1.58% (mg/mg), and drug loading property was 14.31±0.49% (mg/mg). In vitro, CDDP-FA-ASA-MNPs were selectively taken up by HNE-1 cells and Hep-2 cells, which express folate receptor positively

FDTD analysis of transient fault induced travelling-wave propagation for multi-branch distribution networks

Many methods are available to analyze the process of the travelling-wave propagation. Among these methods, the Finite Difference Time Domain (FDTD) method has a distinct advantage in calculating dynamic process of the travelling wave propagation in the time domain and is thus applied to the field of power system protection for researching transient fault induced travelling-wave propagation. The novelty of this paper is that the attenuation law of the traveling wave signal affected by the fork junction in the multi-branch distribution network is summarized and the cause of failure in the fault location based on the incipient travelling wave front method in distribution networks is found

Genome-wide identification and functional exploration of the legume lectin genes in Brassica napus and their roles in Sclerotinia disease resistance

As one of the largest classes of lectins, legume lectins have a variety of desirable features such as antibacterial and insecticidal activities as well as anti-abiotic stress ability. The Sclerotinia disease (SD) caused by the soil-borne fungus Sclerotinia sclerotiorum is a devastating disease affecting most oil crops such as Brassica napus. Here, we identified 130 legume lectin (LegLu) genes in B. napus, which could be phylogenetically classified into seven clusters. The BnLegLu gene family has been significantly expanded since the whole-genome duplication (WGD) or segmental duplication. Gene structure and conserved motif analysis suggested that the BnLegLu genes were well conserved in each cluster. Moreover, relative to those genes only containing the legume lectin domain in cluster VI–VII, the genes in cluster I–V harbored a transmembrane domain and a kinase domain linked to the legume lectin domain in the C terminus. The expression of most BnLegLu genes was relatively low in various tissues. Thirty-five BnLegLu genes were responsive to abiotic stress, and 40 BnLegLu genes were strongly induced by S. sclerotiorum, with a most significant up-regulation of 715-fold, indicating their functional roles in SD resistance. Four BnLegLu genes were located in the candidate regions of genome-wide association analysis (GWAS) results which resulted from a worldwide rapeseed population consisting of 324 accessions associated with SD. Among them, the positive role of BnLegLus-16 in SD resistance was validated by transient expression in tobacco leaves. This study provides important information on BnLegLu genes, particularly about their roles in SD resistance, which may help targeted functional research and genetic improvement in the breeding of B. napus

MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis

Retrieval of atmospheric CFC-11 and CFC-12 from high-resolution FTIR observations at Hefei and comparisons with other independent datasets

peer reviewedAbstract. Synthetic halogenated organic chlorofluorocarbons (CFCs) play an important role in stratospheric ozone depletion and contribute significantly to the greenhouse effect. In this work, the mid-infrared solar spectra measured by ground-based high-resolution Fourier transform infrared spectroscopy (FTIR) were used to retrieve atmospheric CFC-11 (CCl3F) and CFC-12 (CCl2F2) at Hefei, China. The CFC-11 columns observed from January 2017 to December 2020 and CFC-12 columns from September 2015 to December 2020 show a similar annual decreasing trend and seasonal cycle, with an annual rate of -0.47±0.06 % yr−1 and -0.68±0.03 % yr−1, respectively. So the decline rate of CFC-11 is significantly lower than that of CFC-12. CFC-11 total columns were higher in summer, and CFC-12 total columns were higher in summer and autumn. Both CFC-11 and CFC-12 total columns reached the lowest in spring. Further, FTIR data of NDACC (Network for the Detection of Atmospheric Composition Change) candidate station Hefei were compared with the ACE-FTS (Atmospheric Chemistry Experiment Fourier transform spectrometer) satellite data, WACCM (Whole Atmosphere Community Climate Model) data, and the data from other NDACC-IRWG (InfraRed Working Group) stations (St. Petersburg, Jungfraujoch, and Réunion). The mean relative difference between the vertical profiles observed by FTIR and ACE-FTS is -5.6±3.3 % and 4.8±0.9 % for CFC-11 and CFC-12 for an altitude of 5.5 to 17.5 km, respectively. The results demonstrate that our FTIR data agree relatively well with the ACE-FTS satellite data. The annual decreasing rate of CFC-11 measured from ACE-FTS and calculated by WACCM is -1.15±0.22 % yr−1 and -1.68±0.18 % yr−1, respectively. The interannual decreasing rates of atmospheric CFC-11 obtained from ACE-FTS and WACCM data are higher than that from FTIR observations. Also, the annual decreasing rate of CFC-12 from ACE-FTS and WACCM is -0.85±0.15 % yr−1 and -0.81±0.05 % yr−1, respectively, close to the corresponding values from the FTIR measurements. The total columns of CFC-11 and CFC-12 at the Hefei and St. Petersburg stations are significantly higher than those at the Jungfraujoch and Réunion (Maïdo) stations, and the two values reached the maximum in local summer or autumn and the minimum in local spring or winter at the four stations. The seasonal variability at the three stations in the Northern Hemisphere is higher than that at the station in the Southern Hemisphere