Optimal model averaging for single-index models with divergent dimensions

This paper offers a new approach to address the model uncertainty in (potentially) divergent-dimensional single-index models (SIMs). We propose a model-averaging estimator based on cross-validation, which allows the dimension of covariates and the number of candidate models to increase with the sample size. We show that when all candidate models are misspecified, our model-averaging estimator is asymptotically optimal in the sense that its squared loss is asymptotically identical to that of the infeasible best possible averaging estimator. In a different situation where correct models are available in the model set, the proposed weighting scheme assigns all weights to the correct models in the asymptotic sense. We also extend our method to average regularized estimators and propose pre-screening methods to deal with cases with high-dimensional covariates. We illustrate the merits of our method via simulations and two empirical applications.<br/

Improving tensor regression by optimal model averaging

Tensors have broad applications in neuroimaging, data mining, digital marketing, etc. CANDECOMP/PARAFAC (CP) tensor decomposition can effectively reduce the number of parameters to gain dimensionality-reduction and thus plays a key role in tensor regression. However, in CP decomposition, there is uncertainty which rank to use. In this article, we develop a model averaging method to handle this uncertainty by weighting the estimators from candidate tensor regression models with different ranks. When all candidate models are misspecified, we prove that the model averaging estimator is asymptotically optimal. When correct models are included in the candidate models, we prove the consistency of parameters and the convergence of the model averaging weight. Simulations and empirical studies illustrate that the proposed method has superiority over the competition methods and has promising applications

A General Implicit Framework for Fast NeRF Composition and Rendering

A variety of Neural Radiance Fields (NeRF) methods have recently achieved remarkable success in high render speed. However, current accelerating methods are specialized and incompatible with various implicit methods, preventing real-time composition over various types of NeRF works. Because NeRF relies on sampling along rays, it is possible to provide general guidance for acceleration. To that end, we propose a general implicit pipeline for composing NeRF objects quickly. Our method enables the casting of dynamic shadows within or between objects using analytical light sources while allowing multiple NeRF objects to be seamlessly placed and rendered together with any arbitrary rigid transformations. Mainly, our work introduces a new surface representation known as Neural Depth Fields (NeDF) that quickly determines the spatial relationship between objects by allowing direct intersection computation between rays and implicit surfaces. It leverages an intersection neural network to query NeRF for acceleration instead of depending on an explicit spatial structure.Our proposed method is the first to enable both the progressive and interactive composition of NeRF objects. Additionally, it also serves as a previewing plugin for a range of existing NeRF works.Comment: 7 pages for main conten

Knee-point-conscious battery aging trajectory prediction of lithium-ion based on physics-guided machine learning

Early prediction of aging trajectories of lithium-ion (Li-ion) batteries is critical for cycle life testing, quality control, and battery health management. Although data-driven machine learning (ML) approaches are well suited for this task, unfortunately, relying solely on data is exceedingly time-consuming and resource-intensive, even in accelerated aging with complex aging mechanisms. This challenge is rooted in the highly complex and time-varying degradation mechanisms of Li-ion battery cells. We propose a novel method based on physics-guided machine learning (PGML) to overcome this issue. First, electrode-level physical information is incorporated into the model training process to predict the aging trajectory’s knee point (KP). The relationship between the identified KP and the accelerated aging behavior is then explored, and an aging trajectory prediction algorithm is developed. The prior knowledge of aging mechanisms enables a transfer of valuable physical insights to yield accurate KP predictions with small data and weak correlation feature relationship. Based on a Li[NiCoMn]O\ua02\ua0cell dataset, we demonstrate that only 14 cells are needed to train a PGML model for achieving a lifetime prediction error of 2.02% using the data of the first 50 cycles. In contrast, at least 100 cells are needed to reach this level of accuracy without the physical insights

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were "silently" patched by the vendors when releasing later versions of these products; in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with

International medical schools have insufficient training addressing LGBTQ+ health needs.

Background: The LGBTQ+ community constitutes a significant proportion of society with unique health needs. However, healthcare services and doctors often inadequately address their needs, with insufficient training proposed as a major contributory factor. This international observational study aimed to investigate the level of training in LGBTQ+ medicine during medical school.Method: Following validation with LGBTQ+ organisations, a survey was created to assess medical students’ knowledge, sources of understanding, and areas for improvement for LGBTQ+ health issues in the curricula. The survey consisted of multiple-choice and Likertscale questions. Following a pilot, the online survey was disseminated at two medical schools in London and Singapore. Findings: 330 respondents completed the survey, with comparable absolute numbers from both universities. At least one-third of respondents were unclear on terminologies such as ‘out-of-the-closet’ and ‘men who have sex with men’. Additionally, respondents lacked knowledge of clinical topics such as conversion therapy. 84.2% of respondents expressed inadequacy in learning about LGBTQ+ medicine at university, with only 27.9% of respondents indicating they learnt general LGBTQ+ issues from medical curricula. Sexual health (90.9%) was well-learnt at medical schools, whilst many other topics such as genderaffirming care were not learnt (56.7%). Conclusions: This study highlights the lack of training surrounding LGBTQ+ medicine that medical schools provide for students, with much information gathered from outside sources. Medical school curricula should be reviewed to better incorporate important issues surrounding LGBTQ+ medicine. This would better equip the next generation of doctors to address the LGBTQ+ community’s health needs

Identification of random amplified polymorphic DNA (RAPD) marker of Ph-3 gene for late blight resistance in tomato

Late blight is a highly destructive disease of tomato worldwide. Host resistance is the most effective method for disease control. The application of molecular markers is an efficient way to identify host resistance for breeding programs. In this study, bulked segregant analysis (BSA) was used to search for random amplified polymorphic DNA (RAPD) markers linked to the late blight resistance gene Ph-3, using an F2 population (147 individuals) derived from a cross of tomato lines CLN2037 (resistant) and T2-03 (susceptible). Two hundred and thirty decamer primers with arbitrary sequences were chosen for polymerase chain reaction amplification. One RAPD marker CCPB272-03740 (primer sequence GGTCGATCTG) was found to be tightly linked to the resistance gene Ph-3 and was located 5.8 cm from the resistance gene. Marker CCPB272-03740 is the first marker of gene Ph-3 based on PCR reaction.Key words: Tomato, late blight, random amplified polymorphic DNA (RAPD) marker, gene Ph-3