The behavior of simulated annealing in stochastic optimization

In this thesis we examine the performance of simulated annealing (SA) on various response surfaces. The main goals of the study are to evaluate the effectiveness of SA for stochastic optimization, develop modifications to SA in an attempt to improve its performance, and to evaluate whether artificially adding noise to a deterministic response surface might improve the performance of SA. SA is applied to several different response surfaces with different levels of complexity. We first experiment with two basic approaches of computing the performance measure for stochastic surfaces, constant sample size and variable sample size. We found that the constant sample size performed best. At the same time we also show that artificially adding noise may improve the performance of SA on more complex deterministic response surfaces. We develop a hybrid version of SA in which the genetic algorithm is embedded within SA. The effectiveness of the hybrid approach is not conclusive and needs further investigation. Finally, we conclude with a brief discussion on the strengths and weaknesses of the proposed method and an outline of future directions

New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification

In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP

Impact of hypertensive disorders of pregnancy on maternal and neonatal outcomes of twin gestation: a systematic review and meta-analysis

BackgroundThe impact of hypertensive disorders of pregnancy (HDP) on outcomes of twin gestations is not clear. We aimed to collate data via this meta-analysis to examine how HDP alters maternal and neonatal outcomes of twin gestations.MethodsStudies comparing pregnancy outcomes of twin gestations based on HDP and published on the databases of PubMed, CENTRAL, Scopus, Web of Science, and Embase between 1 January 2000 to 20 March 2023 were eligible for inclusion.ResultsTwelve studies were included. A cumulative of 355,129 twin gestations were analyzed in the current meta-analysis. The pooled analysis found that the presence of HDP increases the risk of preterm birth (OR: 1.86 95% CI: 1.36, 2.55 I2 = 99%) and cesarean section in twin gestations (OR: 1.36 95% CI: 1.20, 1.54 I2 = 89%). Meta-analysis showed a significantly increased risk of low birth weight (OR: 1.30 95% CI: 1.10, 1.55 I2 = 97%), small for gestational age (OR: 1.30 95% CI: 1.09, 1.55 I2 = 96%) and neonatal intensive care unit admissions (OR: 1.77 95% CI: 1.43, 2.20 I2 = 76%) with HDP in twin gestations. There was no difference in the incidence of 5-min Apgar scores <7 (OR: 1.07 95% CI: 0.87, 1.38 I2 = 79%) but a lower risk of neonatal death (OR: 0.39 95% CI: 0.25, 0.61 I2 = 62%) with HDP.ConclusionHDP increases the risk of preterm birth, cesarean sections, low birth weight, SGA, and NICU admission in twin gestations. Contrastingly, the risk of neonatal death is reduced with HDP. Further studies are needed to corroborate the current results.Systematic Review RegistrationPROSPERO (CRD42023407725)

Real-Time Detection of Application-Layer DDoS Attack Using Time Series Analysis

Distributed denial of service (DDoS) attacks are one of the major threats to the current Internet, and application-layer DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. Consequently, neither intrusion detection systems (IDS) nor victim server can detect malicious packets. In this paper, a novel approach to detect application-layer DDoS attack is proposed based on entropy of HTTP GET requests per source IP address (HRPI). By approximating the adaptive autoregressive (AAR) model, the HRPI time series is transformed into a multidimensional vector series. Then, a trained support vector machine (SVM) classifier is applied to identify the attacks. The experiments with several databases are performed and results show that this approach can detect application-layer DDoS attacks effectively

SpikeBERT: A Language Spikformer Trained with Two-Stage Knowledge Distillation from BERT

Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way. However, the network architectures of existing SNNs for language tasks are too simplistic, and deep architectures have not been fully explored, resulting in a significant performance gap compared to mainstream transformer-based networks such as BERT. To this end, we improve a recently-proposed spiking transformer (i.e., Spikformer) to make it possible to process language tasks and propose a two-stage knowledge distillation method for training it, which combines pre-training by distilling knowledge from BERT with a large collection of unlabelled texts and fine-tuning with task-specific instances via knowledge distillation again from the BERT fine-tuned on the same training examples. Through extensive experimentation, we show that the models trained with our method, named SpikeBERT, outperform state-of-the-art SNNs and even achieve comparable results to BERTs on text classification tasks for both English and Chinese with much less energy consumption

Target Tracking System Constructed by ELM-AE and Transfer Representation Learning

In the target tracking algorithm, the feature model’s ability to quickly learn image features and the ability to adapt to changes in target features during tracking has always been one of the main research directions of target tracking algorithms. Especially for discriminative target trackers based on image block learning, these two points have become decisive factors affecting the efficiency and robustness of the tracker. However, the performance of most existing similar algorithms on these two abilities cannot achieve satisfactory results. To solve this problem, an efficient and robust feature model is proposed. The feature model first uses extreme learning machine autoencoder (ELM-AE) to quickly perform random feature mapping on complex image features of the target and background image blocks, and then uses the transfer learning ability of transfer representation learning (TRL) to improve the adaptability of random feature space. The feature model is named transfer representation learning with ELM-AE (TRL-ELM-AE). Compared with original complex image features, this model can provide the classifier with more compact and expressive shared features, so that the classifier can learn and classify more quickly and efficiently. In addition, in the target tracking process, the target and background usually change continuously over time. Although the feature migration capability of TRL can already adapt to this, in order to further improve the robustness of the tracker, a strategy of dynamically updating training samples is adopted. Through a large number of experimental and analysis results on the 11 target tracking challenge scenarios proposed by OTB, it is proven that the proposed target tracker has significant advantages over the existing target tracker

Pathological phenotypes and in vivo DNA cleavage by unrestrained activity of a phosphorothioate-based restriction system in Salmonella

Prokaryotes protect their genomes from foreign DNA with a diversity of defence mechanisms, including a widespread restriction–modification (R–M) system involving phosphorothioate (PT) modification of the DNA backbone. Unlike classical R–M systems, highly partial PT modification of consensus motifs in bacterial genomes suggests an unusual mechanism of PT-dependent restriction. In Salmonella enterica, PT modification is mediated by four genes dptB–E, while restriction involves additional three genes dptF–H. Here, we performed a series of studies to characterize the PT-dependent restriction, and found that it presented several features distinct with traditional R–M systems. The presence of restriction genes in a PT-deficient mutant was not lethal, but instead resulted in several pathological phenotypes. Subsequent transcriptional profiling revealed the expression of > 600 genes was affected by restriction enzymes in cells lacking PT, including induction of bacteriophage, SOS response and DNA repair-related genes. These transcriptional responses are consistent with the observation that restriction enzymes caused extensive DNA cleavage in the absence of PT modifications in vivo. However, overexpression of restriction genes was lethal to the host in spite of the presence PT modifications. These results point to an unusual mechanism of PT-dependent DNA cleavage by restriction enzymes in the face of partial PT modification.National Natural Science Foundation (China) (Grant 31170085)National Natural Science Foundation (China) (Grant 31070058)Ministry of Science and Technology of the People's Republic of China (973 and 863 Programs)China Scholarship CouncilNational Science Foundation (U.S.) (Grant CHE-1019990)Shanghai Municipal Council of Science and Technology. Shanghai Pujiang Program (Grant 12PJD021

Preparation of p-type ZnMgO thin films by Sb doping method

We report on Sb-doped p-type Zn0.95Mg0.05O thin films grown by pulsed laser deposition. The Sb-doped Zn0.95Mg0.05O films show an acceptable p-type conductivity with a resistivity of 126 Ω cm, a Hall mobility of 1.71 cm2 V−1 s−1 and a hole concentration of 2.90 × 1016 cm−3 at room temperature. Secondary ion mass spectroscopy confirms that Sb has been incorporated into the Zn0.95Mg0.05O films. Guided by x-ray photoemission spectroscopy analysis and a model for large-size-mismatched group-V dopants in ZnO, an SbZn–2VZn complex is believed to be the most possible acceptor in the Sb-doped p-type Zn0.95Mg0.05O thin films.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58103/2/d7_14_020.pd