Microwave millisecond spike emission and its associated phenomena during the impulsive phase of large flares

A tentative model is proposed to account for some features of the microwave millisecond spike emission and its links with the physical processes of associated phenomena during the impulsive phase of large flares by comparing the optical, radio, and X-ray observations on May 16, 1981 to those on October 12, 1981

Application of Weighted Support Vector Machines to Network Intrusion Detection

Support Vector Machines(SVMs) have succeeded in many classification fields. Some researchers have tried to apply SVMs to Intrusion Detection recently and got desirable results. By analyzing C-SVM theoretically and experimentally, we found that C-SVM had some properties which showed C-SVM was not most suitable for Network Intrusion Detection. First, C-SVM has different classification error rates on different classes if the sizes of training classes are uneven. Second, C-SVM is over-dependent on every training sample, even if the samples are duplicated. Third, C-SVM does not make a difference between training samples. According to these characteristics of C-SVM and the fact that the size of network normal data is always much larger than that of intrusion data and the fact that the importance of attack data is different from each other, an extended C-SVM, termed weighted C-SVM is proposed in this paper. Weighed C-SVM introduces two parameters, class weights and sample weights. Class weights are used to adjust false negative rate and false positive rate of each intrusion class. And sample weights are used to emphasize importance of some intrusion samples. Experiments showed that Weighted C-SVM was more effective than C-SVM in network intrusion detection systems

Blowup of solution for a reaction diffusion equation with memory and multiple nonlinearities

In this paper, the blow-up of solution for the initial boundary value problem of a class of reaction diffusion equation with memory and multiple nonlinearities is studied. Using a differential inequalities, we obtain sufficient conditions for the blow-up of solutions in a finite time interval under suitable conditions on memory and nonlinearities term and for vanishing initial energy

Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models

Exploring the application of powerful large language models (LLMs) on the fundamental named entity recognition (NER) task has drawn much attention recently. This work aims to investigate the possibilities of pushing the boundary of zero-shot NER with LLM via a training-free self-improving strategy. We propose a self-improving framework, which utilize an unlabeled corpus to stimulate the self-learning ability of LLMs on NER. First, we use LLM to make predictions on the unlabeled corpus and obtain the self-annotated data. Second, we explore various strategies to select reliable samples from the self-annotated dataset as demonstrations, considering the similarity, diversity and reliability of demonstrations. Finally, we conduct inference for the test query via in-context learning with the selected self-annotated demonstrations. Through comprehensive experimental analysis, our study yielded the following findings: (1) The self-improving framework further pushes the boundary of zero-shot NER with LLMs, and achieves an obvious performance improvement; (2) Iterative self-improving or naively increasing the size of unlabeled corpus does not guarantee improvements; (3) There might still be space for improvement via more advanced strategy for reliable entity selection

Dynamic Anchor Learning for Arbitrary-Oriented Object Detection

Arbitrary-oriented objects widely appear in natural scenes, aerial photographs, remote sensing images, etc., thus arbitrary-oriented object detection has received considerable attention. Many current rotation detectors use plenty of anchors with different orientations to achieve spatial alignment with ground truth boxes, then Intersection-over-Union (IoU) is applied to sample the positive and negative candidates for training. However, we observe that the selected positive anchors cannot always ensure accurate detections after regression, while some negative samples can achieve accurate localization. It indicates that the quality assessment of anchors through IoU is not appropriate, and this further lead to inconsistency between classification confidence and localization accuracy. In this paper, we propose a dynamic anchor learning (DAL) method, which utilizes the newly defined matching degree to comprehensively evaluate the localization potential of the anchors and carry out a more efficient label assignment process. In this way, the detector can dynamically select high-quality anchors to achieve accurate object detection, and the divergence between classification and regression will be alleviated. With the newly introduced DAL, we achieve superior detection performance for arbitrary-oriented objects with only a few horizontal preset anchors. Experimental results on three remote sensing datasets HRSC2016, DOTA, UCAS-AOD as well as a scene text dataset ICDAR 2015 show that our method achieves substantial improvement compared with the baseline model. Besides, our approach is also universal for object detection using horizontal bound box. The code and models are available at https://github.com/ming71/DAL.Comment: Accepted to AAAI 2021. The code and models are available at https://github.com/ming71/DA

MicroRNA-140-5p inhibits cellular proliferation, migration and invasion by downregulating AKT/STAT3/NF-κB pathway in breast carcinoma cells

MicroRNA-140-5p (miR-140-5p) plays a pivotal role in human cancers. However, its role and molecular mechanisms in breast carcinoma are not fully explored. Using miR-140-5p transfected breast cancer cell line MDA-MB-231, several in vitro experiments were performed and described in this paper. They consist of the cell proliferation assay, wound healing assay, transwell assay, colony formation assays and qRTPCR. Expression levels of target proteins were determined using western blotting. In addition, experiments on animal models were performed to study the possible role of miR-140-5p in tumorigenesis of breast carcinoma cells. The induction of experimental breast tumor in mice model was achieved through the incorporation of MDA-MB-231 tumor cells subcutaneously into the middle left side of the mice. The results showed that miR-140-5p up-regulation significantly suppresses proliferation, cellular invasion and migration of breast carcinoma cells. Furthermore, miR-140-5p up-regulation stops breast cancer cells at G0/G1 phase. The results of the animal model indicated that up-regulation of miR-140-5p suppresses its tumorigenic ability. Moreover, we also found that miR-140-5p up-regulation reduces the phosphorylation level of STAT3, p65, and AKT. In addition, miR-140-5p overexpression significantly decreases CDK2 expression while increasing E-cadherin expression level. These data revealed that miR-140-5p suppressed tumor progression of breast carcinoma cells through inhibition of the AKT/STAT3/NF-κB pathway. Taken the present study results together, we can conclude that miR-140-5p may act as a novel target in microRNA-targeting anticancer strategy for the treatment of breast cancer