Convolutions of heavy-tailed random variables and applications to portfolio diversification and MA(1) time series

The paper characterizes first and second order tail behavior of convolutions of i.i.d. heavy tailed random variables with support on the real line. The result is applied to the problem of risk diversification in portfolio analysis and to the estimation of the parameter in a MA(1) model

(E)-N′-(2,5-Dimethoxy­benzyl­idene)-2-(8-quinol­yloxy)acetohydrazide methanol solvate

The two mol­ecules in the asymmetric unit of the title compound, C20H19N3O4·CH4O, are paired via O—H⋯(O,N), N—H⋯O, and C—H⋯O hydrogen bonds. The mol­ecular skeleton of the acetohydrazide mol­ecule is close to planar; the benzene and quinoline mean planes form a dihedral angle of 3.9 (3)°. The crystal packing exhibits weak inter­molecular C—H⋯O hydrogen bonds and π–π inter­actions, indicated by short distances of 3.668 (3) Å, between the centroids of N-containing six-membered rings from neighbouring acetohydrazide mol­ecules

Using a bootstrap method to choose the sample fraction in tail index estimation

Tail index estimation depends for its accuracy on a precise choice of the sample fraction, i.e. the number of extreme order statistics on which the estimation is based. A complete solution to the sample fraction selection is given by means of a two step subsample bootstrap method. This method adaptively determines the sample fraction that minimizes the asymptotic mean squared error. Unlike previous methods, prior knowledge of the second order parameter is not required. In addition, we are able to dispense with the need for a prior estimate of the tail index which already converges roughly at the optimal rate. The only arbitrary choice of parameters is the number of Monte Carlo replications

A bootstrap-based method to achieve optimality on estimating the extreme-value index

Estimators of the extreme-value index are based on a set of upper order statistics. We present an adaptive method to choose the number of order statistics involved in an optimal way, balancing variance and bias components. Recently this has been achieved for the similar but somewhat less involved case of regularly varying tails (Drees and Kaufmann(1997); Danielsson et al.(1996)). The present paper follows the line of proof of the last mentioned paper

Mining Architectural Information: A Systematic Mapping Study

Context: Mining Software Repositories (MSR) has become an essential activity in software development. Mining architectural information to support architecting activities, such as architecture understanding and recovery, has received a significant attention in recent years. However, there is an absence of a comprehensive understanding of the state of research on mining architectural information. Objective: This work aims to identify, analyze, and synthesize the literature on mining architectural information in software repositories in terms of architectural information and sources mined, architecting activities supported, approaches and tools used, and challenges faced. Method: A Systematic Mapping Study (SMS) has been conducted on the literature published between January 2006 and November 2021. Results: Of the 79 primary studies finally selected, 8 categories of architectural information have been mined, among which architectural description is the most mined architectural information; 12 architecting activities can be supported by the mined architectural information, among which architecture understanding is the most supported activity; 81 approaches and 52 tools were proposed and employed in mining architectural information; and 4 types of challenges in mining architectural information were identified. Conclusions: This SMS provides researchers with promising future directions and help practitioners be aware of what approaches and tools can be used to mine what architectural information from what sources to support various architecting activities.Comment: 68 pages, 5 images, 15 tables, Manuscript submitted to a Journal (2022

A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, in terms of reconstruction error, perceptual quality and reconstruction speed for both 3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the method proposed is approximately twice as small, allowing to preserve anatomical structures more faithfully. Using our method, each image can be reconstructed in 23 ms, which is fast enough to enable real-time applications

Nodal Promotes Glioblastoma Cell Growth

Nodal is a member of the transforming growth factor-β (TGF-β) superfamily that plays critical roles during embryogenesis. Recent studies in ovarian, breast, prostate, and skin cancer cells suggest that Nodal also regulates cell proliferation, apoptosis, and invasion in cancer cells. However, it appears to exert both tumor-suppressing and tumor-promoting effects, depending on the cell type. To further understand the role of Nodal in tumorigenesis, we examined the effect of Nodal in glioblastoma cell growth and spheroid formation using U87 cell line. Treatment of U87 with recombinant Nodal significantly increased U87 cell growth. In U87 cells stably transfected with the plasmid encoding Nodal, Smad2 phosphorylation was strongly induced and cell growth was significantly enhanced. Overexpression of Nodal also resulted in tight spheroid formation. On the other hand, the cells stably transfected with Nodal siRNA formed loose spheroids. Nodal is known to signal through activin receptor-like kinase 4 (ALK4) and ALK7 and the Smad2/3 pathway. To determine which receptor and Smad mediate the growth promoting effect of Nodal, we transfected siRNAs targeting ALK4, ALK7, Smad2, or Smad3 into Nodal-overexpressing cells and observed that cell growth was significantly inhibited by ALK4, ALK7, and Smad3 siRNAs. Taken together, these findings suggest that Nodal may have tumor-promoting effects on glioblastoma cells and these effects are mediated by ALK4, ALK7, and Smad3

Ground-to-Aerial Person Search: Benchmark Dataset and Approach

In this work, we construct a large-scale dataset for Ground-to-Aerial Person Search, named G2APS, which contains 31,770 images of 260,559 annotated bounding boxes for 2,644 identities appearing in both of the UAVs and ground surveillance cameras. To our knowledge, this is the first dataset for cross-platform intelligent surveillance applications, where the UAVs could work as a powerful complement for the ground surveillance cameras. To more realistically simulate the actual cross-platform Ground-to-Aerial surveillance scenarios, the surveillance cameras are fixed about 2 meters above the ground, while the UAVs capture videos of persons at different location, with a variety of view-angles, flight attitudes and flight modes. Therefore, the dataset has the following unique characteristics: 1) drastic view-angle changes between query and gallery person images from cross-platform cameras; 2) diverse resolutions, poses and views of the person images under 9 rich real-world scenarios. On basis of the G2APS benchmark dataset, we demonstrate detailed analysis about current two-step and end-to-end person search methods, and further propose a simple yet effective knowledge distillation scheme on the head of the ReID network, which achieves state-of-the-art performances on both of the G2APS and the previous two public person search datasets, i.e., PRW and CUHK-SYSU. The dataset and source code available on \url{https://github.com/yqc123456/HKD_for_person_search}.Comment: Accepted by ACM MM 202