PetriBaR: A MATLAB Toolbox for Petri Nets Implementing Basis Reachability Approaches

This paper presents a MATLAB toolbox, called PetriBaR, for the analysis and control of Petri nets. PetriBaR is a package of functions devoted to basic Petri net analysis (including the computation of T-invariants, siphons, reachability graph, etc.), monitor design, reachability analysis, state estimation, fault diagnosis, and opacity verification. In particular, the functions for reachability analysis, state estimation, fault diagnosis, and opacity verification exploit the construction of the Basis Reachability Graph to avoid the exhaustive enumeration of the reachable set, thus leading to significant advantages in terms of computational complexity. All functions of PetriBaR are introduced in detail clarifying the syntax to be used to run them. Finally, they are illustrated via a series of numerical examples. PetriBaR is available online for public access

Reducing Domain Gap in Frequency and Spatial domain for Cross-modality Domain Adaptation on Medical Image Segmentation

Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain. In medical image segmentation field, most existing UDA methods depend on adversarial learning to address the domain gap between different image modalities, which is ineffective due to its complicated training process. In this paper, we propose a simple yet effective UDA method based on frequency and spatial domain transfer uner multi-teacher distillation framework. In the frequency domain, we first introduce non-subsampled contourlet transform for identifying domain-invariant and domain-variant frequency components (DIFs and DVFs), and then keep the DIFs unchanged while replacing the DVFs of the source domain images with that of the target domain images to narrow the domain gap. In the spatial domain, we propose a batch momentum update-based histogram matching strategy to reduce the domain-variant image style bias. Experiments on two cross-modality medical image segmentation datasets (cardiac, abdominal) show that our proposed method achieves superior performance compared to state-of-the-art methods.Comment: accepted at Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23

Video anomaly detection and localization by local motion based joint video representation and OCELM

Nowadays, human-based video analysis becomes increasingly exhausting due to the ubiquitous use of surveillance cameras and explosive growth of video data. This paper proposes a novel approach to detect and localize video anomalies automatically. For video feature extraction, video volumes are jointly represented by two novel local motion based video descriptors, SL-HOF and ULGP-OF. SL-HOF descriptor captures the spatial distribution information of 3D local regions’ motion in the spatio-temporal cuboid extracted from video, which can implicitly reflect the structural information of foreground and depict foreground motion more precisely than the normal HOF descriptor. To locate the video foreground more accurately, we propose a new Robust PCA based foreground localization scheme. ULGP-OF descriptor, which seamlessly combines the classic 2D texture descriptor LGP and optical flow, is proposed to describe the motion statistics of local region texture in the areas located by the foreground localization scheme. Both SL-HOF and ULGP-OF are shown to be more discriminative than existing video descriptors in anomaly detection. To model features of normal video events, we introduce the newly-emergent one-class Extreme Learning Machine (OCELM) as the data description algorithm. With a tremendous reduction in training time, OCELM can yield comparable or better performance than existing algorithms like the classic OCSVM, which makes our approach easier for model updating and more applicable to fast learning from the rapidly generated surveillance data. The proposed approach is tested on UCSD ped1, ped2 and UMN datasets, and experimental results show that our approach can achieve state-of-the-art results in both video anomaly detection and localization task.This work was supported by the National Natural Science Foundation of China (Project nos. 60970034, 61170287, 61232016)

Hyperparameter selection of one-class support vector machine by self-adaptive data shifting

With flexible data description ability, one-class Support Vector Machine (OCSVM) is one of the most popular and widely-used methods for one-class classification (OCC). Nevertheless, the performance of OCSVM strongly relies on its hyperparameter selection, which is still a challenging open problem due to the absence of outlier data. This paper proposes a fully automatic OCSVM hyperparameter selection method, which requires no tuning of additional hyperparameter, based on a novel self-adaptive “data shifting” mechanism: Firstly, by efficient edge pattern detection (EPD) and “negatively” shifting edge patterns along the negative direction of estimated data density gradient, a constrained number of high-quality pseudo outliers are self-adaptively generated at more desirable locations, which readily avoids two major difficulties in previous outlier generation methods. Secondly, to avoid time-consuming cross-validation and enhance robustness to noise in the given training data, a pseudo target set is generated for model validation by “positively” shifting each given target datum along the positive direction of data density gradient. Experiments on synthetic and benchmark datasets demonstrate the effectiveness of the proposed method.This work was sponsored by the National Natural Science Foundation of China (Project no. 61170287, 61232016)

Recent progress in anodic oxidation of TiO2 nanotubes and enhanced photocatalytic performance: a short review

© 2021 World Scientific Publishing Company. This is the accepted version of the final published version found at https://doi.org/10.1142/S1793292021300024By adjusting the oxidation voltage, electrolyte, anodizing time and other parameters, TiO2 nanotubes with high aspect ratio can be prepared by oxidation in organic system because anodic oxidation method has the advantage of simple preparation process, low material cost and controllable morphology. Low material cost and controllable morphology by anodizing. This review focuses on the influence of anodizing parameters on the morphology of TiO2 nanotube arrays prepared by anodizing. In order to improve the photocatalytic activity of TiO2 nanotubes under visible light and prolong the life of photo-generated carriers, the research status of improving the photocatalytic activity of TiO2 nanotubes in recent years is reviewed. This review focuses on the preparation and modification of TiO2 nanotubes by anodic oxidation, which is helpful to understand the best structure of TiO2 nanotubes and the appropriate modification methods, thus guiding the application of TiO2 nanotubes in practical photocatalysis. Finally, the development of TiO2 nanotubes is prospected.Peer reviewe

Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events

As a vital topic in media content interpretation, video anomaly detection (VAD) has made fruitful progress via deep neural network (DNN). However, existing methods usually follow a reconstruction or frame prediction routine. They suffer from two gaps: (1) They cannot localize video activities in a both precise and comprehensive manner. (2) They lack sufficient abilities to utilize high-level semantics and temporal context information. Inspired by frequently-used cloze test in language study, we propose a brand-new VAD solution named Video Event Completion (VEC) to bridge gaps above: First, we propose a novel pipeline to achieve both precise and comprehensive enclosure of video activities. Appearance and motion are exploited as mutually complimentary cues to localize regions of interest (RoIs). A normalized spatio-temporal cube (STC) is built from each RoI as a video event, which lays the foundation of VEC and serves as a basic processing unit. Second, we encourage DNN to capture high-level semantics by solving a visual cloze test. To build such a visual cloze test, a certain patch of STC is erased to yield an incomplete event (IE). The DNN learns to restore the original video event from the IE by inferring the missing patch. Third, to incorporate richer motion dynamics, another DNN is trained to infer erased patches' optical flow. Finally, two ensemble strategies using different types of IE and modalities are proposed to boost VAD performance, so as to fully exploit the temporal context and modality information for VAD. VEC can consistently outperform state-of-the-art methods by a notable margin (typically 1.5%-5% AUROC) on commonly-used VAD benchmarks. Our codes and results can be verified at github.com/yuguangnudt/VEC_VAD.Comment: To be published as an oral paper in Proceedings of the 28th ACM International Conference on Multimedia (ACM MM '20). 9 pages, 7 figure

Comprehensive Analysis and Functional Studies of WRKY Transcription Factors in Nelumbo nucifera

The WRKY family is one of the largest transcription factor (TF) families in plants and plays central roles in modulating plant stress responses and developmental processes, as well as secondary metabolic regulations. Lotus (Nelumbo nucifera) is an aquatic crop that has significant food, ornamental and pharmacological values. Here, we performed an overview analysis of WRKY TF family members in lotus, and studied their functions in environmental adaptation and regulation of lotus benzylisoquinoline alkaloid (BIA) biosynthesis. A total of 65 WRKY genes were identified in the lotus genome and they were well clustered in a similar pattern with their Arabidopsis homologs in seven groups (designated I, IIa-IIe, and III), although no lotus WRKY was clustered in the group IIIa. Most lotus WRKYs were functionally paired, which was attributed to the recently occurred whole genome duplication in lotus. In addition, lotus WRKYs were regulated dramatically by salicilic acid (SA), jasmonic acid (JA), and submergence treatments, and two lotus WRKYs, NnWRKY40a and NnWRKY40b, were significantly induced by JA and promoted lotus BIA biosynthesis through activating BIA biosynthetic genes. The investigation of WRKY TFs for this basal eudicot reveals new insights into the evolution of the WRKY family, and provides fundamental information for their functional studies and lotus breeding

Expression Profiling of Transcriptome and Its Associated Disease Risk in Yang Deficiency Constitution of Healthy Subjects

Objectives. Differences among healthy subjects and associated disease risks are of substantial interest in clinical medicine. According to the theory of “constitution-disease correlation” in traditional Chinese medicine, we try to find out if there is any connection between intolerance of cold in Yang deficiency constitution and molecular evidence and if there is any gene expression basis in specific disorders. Methods. Peripheral blood mononuclear cells were collected from Chinese Han individuals with Yang deficiency constitution (n=20) and balanced constitution (n=8) (aged 18–28) and global gene expression profiles were determined between them using the Affymetrix HG-U133 Plus 2.0 array. Results. The results showed that when the fold change was ≥1.2 and q ≤ 0.05, 909 genes were upregulated in the Yang deficiency constitution, while 1189 genes were downregulated. According to our research differential genes found in Yang deficiency constitution were usually related to lower immunity, metabolic disorders, and cancer tendency. Conclusion. Gene expression disturbance exists in Yang deficiency constitution, which corresponds to the concept of constitution and gene classification. It also suggests people with Yang deficiency constitution are susceptible to autoimmune diseases, enteritis, arthritis, metabolism disorders, and cancer, which provides molecular evidence for the theory of “constitution-disease correlation.

Magnon-mediated interlayer coupling in an all-antiferromagnetic junction

The interlayer coupling mediated by fermions in ferromagnets brings about parallel and anti-parallel magnetization orientations of two magnetic layers, resulting in the giant magnetoresistance, which forms the foundation in spintronics and accelerates the development of information technology. However, the interlayer coupling mediated by another kind of quasi-particle, boson, is still lacking. Here we demonstrate such a static interlayer coupling at room temperature in an antiferromagnetic junction Fe2O3/Cr2O3/Fe2O3, where the two antiferromagnetic Fe2O3 layers are functional materials and the antiferromagnetic Cr2O3 layer serves as a spacer. The N\'eel vectors in the top and bottom Fe2O3 are strongly orthogonally coupled, which is bridged by a typical bosonic excitation (magnon) in the Cr2O3 spacer. Such an orthogonally coupling exceeds the category of traditional collinear interlayer coupling via fermions in ground state, reflecting the fluctuating nature of the magnons, as supported by our magnon quantum well model. Besides the fundamental significance on the quasi-particle-mediated interaction, the strong coupling in an antiferromagnetic magnon junction makes it a realistic candidate for practical antiferromagnetic spintronics and magnonics with ultrahigh-density integration.Comment: 19 pages, 4 figure

Diagnostic value of FNAC combined with BRAFV600E mutation detection in Hashimoto’s thyroiditis complicated with papillary thyroid carcinoma

BackgroundThis study aimed to analyze the effect of preoperative fine needle aspiration cytology (FNAC) combined with BRAFV600E mutation detection as compared to that of fine needle aspiration cytology alone on the diagnostic performance of papillary thyroid carcinoma (PTC) combined with Hashimoto’s thyroiditis (HT).MethodPatients with thyroid nodules in Hashimoto’s thyroiditis, who underwent fine-needle aspiration cytology examination and BRAFV600E mutation detection in the puncture eluate at the outpatient clinic, were selected. Finally, 122 patients received surgical treatment and were included in the study. We used postoperative pathological results as the gold standard. Accordingly, we compared the sensitivity, specificity and accuracy of preoperative FNAC alone and FNAC combined with BRAFV600E mutation detection in for the diagnosis of PTC combined with HT.ResultsFor PTC patients with HT, the sensitivity of FNAC diagnosis was 93.69%, the specificity was 90.90% and the accuracy was 93.44%. However, the sensitivity, specificity and accuracy of FNAC combined with BRAFV600E mutation detection were 97.30%, 90.90% and 96.72%, respectively. Therefore, combined detection can improve the sensitivity and accuracy of diagnosis (p<0.05).ConclusionFNAC combined with eluent BRAFV600E mutation detection can improve the sensitivity and accuracy of diagnosis of PTC in the background of HT