Estimation of a change-point in the mean function of functional data

The paper develops a comprehensive asymptotic theory for the estimation of a change-point in the mean function of functional observations. We consider both the case of a constant change size, and the case of a change whose size approaches zero, as the sample size tends to infinity. We show how the limit distribution of a suitably defined change-point estimator depends on the size and location of the change. The theoretical insights are confirmed by a simulation study which illustrates the behavior of the estimator in finite samples.     

INTERACTION BETWEEN PHYTOCHROME AND THE BLUE LIGHT PHOTORECEPTOR SYSTEM IN Mougeotia*

Abstract— Face-to-profile chloroplast movement in Mougeotia was induced by sequences of strong blue and red short irradiations. This type of response occured only when blue light was applied prior to or simultaneously with red light, and far-red irradiation was necessary after the sequence to cancel the remaining gradient of the far-red absorbing form of phytochrome P_fr. The dependence of the response magnitude on blue and red light sequences was studied for a wide range of light durations and dark intervals. The relationship between the response and the dark interval points to the lack of direct coupling between phytochrome and blue-absorbing “cryptochrome”. It was postulated that a photoproduct having a life-time of2–3 min is formed by the blue-light-mediated reaction. This photoproduct interacts with phytochrome during its transformation or with its final P_fr form.     

An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.