Structural stability of Supersonic solutions to the Euler-Poisson system

The well-posedness for the supersonic solutions of the Euler-Poisson system for hydrodynamical model in semiconductor devices and plasmas is studied in this paper. We first reformulate the Euler-Poisson system in the supersonic region into a second order hyperbolic-elliptic coupled system together with several transport equations. One of the key ingredients of the analysis is to obtain the well-posedness of the boundary value problem for the associated linearized hyperbolic-elliptic coupled system, which is achieved via a delicate choice of multiplier to gain energy estimate. The nonlinear structural stability of supersonic solution in the general situation is established by combining the iteration method with the estimate for hyperbolic-elliptic system and the transport equations together.Comment: The paper was revised substantially in this new version. In particular, we constructed the new multiplier under general conditions on the background solution

TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain

BackgroundGene prioritization (gene ranking) aims to obtain the centrality of genes, which is critical for cancer diagnosis and therapy since keys genes correspond to the biomarkers or targets of drugs. Great efforts have been devoted to the gene ranking problem by exploring the similarity between candidate and known disease-causing genes. However, when the number of disease-causing genes is limited, they are not applicable largely due to the low accuracy. Actually, the number of disease-causing genes for cancers, particularly for these rare cancers, are really limited. Therefore, there is a critical needed to design effective and efficient algorithms for gene ranking with limited prior disease-causing genes.ResultsIn this study, we propose a transfer learning based algorithm for gene prioritization (called TLGP) in the cancer (target domain) without disease-causing genes by transferring knowledge from other cancers (source domain). The underlying assumption is that knowledge shared by similar cancers improves the accuracy of gene prioritization. Specifically, TLGP first quantifies the similarity between the target and source domain by calculating the affinity matrix for genes. Then, TLGP automatically learns a fusion network for the target cancer by fusing affinity matrix, pathogenic genes and genomic data of source cancers. Finally, genes in the target cancer are prioritized. The experimental results indicate that the learnt fusion network is more reliable than gene co-expression network, implying that transferring knowledge from other cancers improves the accuracy of network construction. Moreover, TLGP outperforms state-of-the-art approaches in terms of accuracy, improving at least 5%.ConclusionThe proposed model and method provide an effective and efficient strategy for gene ranking by integrating genomic data from various cancers

Characterization Of Neuronal Groups Regulating Sexual And Agonistic Behavior In Male Chicken (Gallus Gallus)

The study aimed to understand the neuronal regulation of male sexual and agonistic behavior in broiler breeders. First, brain structures associated with sexual and agonistic behavior were identified by mapping Fos expression. The ventromedial subnucleus of medial portion of bed nucleus of the stria teriminalis (BSTM2) was specifically activated by male courtship behavior. The medial preoptic nucleus (POM) and lateral septum (SL) were associated with both sexual and agonistic behaviors. The bed nucleus of the pallial commissure (NCPa) and the paraventricular nucleus (PVN) were closely related to stress. Second, Fos-ir neurons were phenotyped by double labeling Fos with aromatase (ARO) and arginine vasotocin (AVT). Male courtship behavior significantly increased co-expressions of ARO+Fos and AVT+Fos in the BSTM2. In the POM, inter-male interactions decreased visible ARO cell counts while the opposite-sex interaction increased co-expression of ARO+Fos. The caudo-lateral POM (clPOM) and caudo-medial POM (cmPOM) were involved in sexual and agonistic behavior, respectively. Immunostaining for AVT, galanin, serotonin and gonadotropin-releasing hormone (GnRH) were compared between aggressive and non-aggressive males to test their behavior functions. Galanin fiber density in the SL was higher in aggressive males than non-aggressive males and galanin-ir cell counts in the BSTM were negatively associated with aggressive behaviors. Density of vasotocinergic fibers in the SL correlated with sexual behavior. Finally, gene expression of estrogen-producing enzyme aromatase, AVT, corticortropin-releasing hormone (CRH) and their receptors were investigated in males of two chicken lines that differed in their Sociality. Social line had higher P450 expression in the rPOM and clPOM, more ESR1 expression in clPOM and more ESR2 in the NCPa. Higher expression of AVPR1A/VT4R in the rPOM, clPOM, BSTM1, BSTM2 and SL1 were found in the Social line than in the aSocial line. In the cmPOM, higher expression of CRHR1 was found in the Social line and interaction with females only reduced the expression in this line of chickens. Highly level of CRH gene expression was revealed in the NCPa, which strongely supported its role in stress responses. In conclusion, the differential expression of neuromodulators (estrogen, AVT) and their receptors in the brain may contribute to variation in sociosexuality in male broilers

A survey on vulnerability of federated learning: A learning algorithm perspective

Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning

3D interactive coronary artery segmentation using random forests and Markov random field optimization

Coronary artery segmentation plays a vital important role in coronary disease diagnosis and treatment. In this paper, we present a machine learning based interactive coronary artery segmentation method for 3D computed tomography angiography images. We first apply vessel diffusion to reduce noise interference and enhance the tubular structures in the images. A few user strokes are required to specify region of interest and background. Various image features for detecting the coronary arteries are then extracted in a multi-scale fashion, and are fed into a random forests classifier, which assigns each voxel with probability values of being coronary artery and background. The final segmentation is carried through an MRF based optimization using primal dual algorithm. A connectivity component analysis is carried out as post processing to remove isolated, small regions to produce the segmented coronary arterial vessels. The proposed method requires limited user interference and achieves robust segmentation results

MEMS-Based Endoscopic Optical Coherence Tomography

Early cancer detection has been playing an important role in reducing cancer mortality. Optical coherence tomography (OCT), due to its micron-scale resolution, has the ability to detect cancerous tissues at their early stages. For internal organs, endoscopic probes are needed as the penetration depth of OCT is about 1–3 mm. MEMS technology has the advantages of fast speed, small size, and low cost, and it has been widely used as the scanning engine in endoscopic OCT probes. Research results have shown great potential for OCT in endoscopic imaging by incorporating MEMS scanning mirrors. Various MEMS-OCT designs are introduced, and their imaging results are reviewed in the paper