‘Again’ separation in Italian

In Italian, ri- ‘again’ can be separated from the constituent it is semantically attached to and challenges the structural account for the ambiguity of ‘again’-type elements. To address this issue, this paper proposes a solution through aspectual agreement and suggests a movement and reconstruction analysis for the separation effect of ri-. It also provides supporting evidence for this analysis through Coordinate Structure Constraint and Relativized Minimality

Enhancing the understanding of hydrogen evolution and oxidation reaction on Pt(111) through ab initio simulations on electrode/electrolyte kinetics

The hydrogen oxidation reaction (HOR) and hydrogen evolution reaction (HER) play an important role in hydrogen based energy conversion. Recently, the frustrating performance in alkaline media raised debates on the relevant mechanism, especially on the role of surface hydroxyl (OH*). We present a full pH range electrode/electrolyte kinetics simulation for HER/HOR on Pt(111), with the potential-related rate constants been calculated with density functional theory methods. The polarization curves agree well with the experimental observations. The stability of OH* is found to be unlikely an effective activity descriptor since it is irrelevant to the onset potential of HOR/HER. Degree of rate control analyses reveal that the alkaline current is controlled jointly by Tafel and Volmer steps, while the acidic current solely by Tafel step, which explains the observed pH-dependent kinetics. Therefore, it is also possible to reduce the overpotential of alkaline HER/HOR by accelerating the Tafel step besides tuning the hydrogen binding energy.Comment: 4 pages, 4 figure

The Proximal Operator of the Piece-wise Exponential Function and Its Application in Compressed Sensing

This paper characterizes the proximal operator of the piece-wise exponential function $1\!-\!e^{-|x|/\sigma}$ with a given shape parameter $\sigma\!>\!0$, which is a popular nonconvex surrogate of $\ell_0$-norm in support vector machines, zero-one programming problems, and compressed sensing, etc. Although Malek-Mohammadi et al. [IEEE Transactions on Signal Processing, 64(21):5657--5671, 2016] once worked on this problem, the expressions they derived were regrettably inaccurate. In a sense, it was lacking a case. Using the Lambert W function and an extensive study of the piece-wise exponential function, we have rectified the formulation of the proximal operator of the piece-wise exponential function in light of their work. We have also undertaken a thorough analysis of this operator. Finally, as an application in compressed sensing, an iterative shrinkage and thresholding algorithm (ISTA) for the piece-wise exponential function regularization problem is developed and fully investigated. A comparative study of ISTA with nine popular non-convex penalties in compressed sensing demonstrates the advantage of the piece-wise exponential penalty

ERStruct: An Eigenvalue Ratio Approach to Inferring Population Structure from Sequencing Data

Inference of population structure from genetic data plays an important role in population and medical genetics studies. The traditional EIGENSTRAT method has been widely used for computing and selecting top principal components that capture population structure information (Price et al., 2006). With the advancement and decreasing cost of sequencing technology, whole-genome sequencing data provide much richer information about the underlying population structures. However, the EIGENSTRAT method was originally developed for analyzing array-based genotype data and thus may not perform well on sequencing data for two reasons. First, the number of genetic variants $p$ is much larger than the sample size $n$ in sequencing data such that the sample-to-marker ratio $n/p$ is nearly zero, violating the assumption of the Tracy-Widom test used in the EIGENSTRAT method. Second, the EIGENSTRAT method might not be able to handle the linkage disequilibrium (LD) well in sequencing data. To resolve those two critical issues, we propose a new statistical method called ERStruct to estimate the number of latent sub-populations based on sequencing data. We propose to use the ratio of successive eigenvalues as a more robust testing statistic, and then we approximate the null distribution of our proposed test statistic using modern random matrix theory. Simulation studies found that our proposed ERStruct method has outperformed the traditional Tracy-Widom test on sequencing data. We further use two public data sets from the HapMap 3 and the 1000 Genomes Projects to demonstrate the performance of our ERStruct method. We also implement our ERStruct in a MATLAB toolbox which is now publicly available on github through https://github.com/bglvly/ERStruct

Using machine learning techniques and brain MRI scans for detection of Alzheimer’s disease

Dementia is a clinical syndrome characterized by cognitive and behavioral impairment: it mostly affects people who are aged 65 years and over. Dementia results from several diseases, of which Alzheimer’s disease (AD) accounts for up to 80% of all dementia diagnoses. Magnetic Resonance Imaging (MRI) is one of the most widely used methods to diagnose AD but due to low efficiency of manual analysis, machine learning algorithms have been developed to diagnose AD using medical imaging data. In this study, unsupervised learning strategies were used to cluster the two diagnostic status, a healthy status called cognitively normal (CN), and AD, using brain structural MRI scans. First, we detected the abnormal regions between CN and AD using two-sample t-tests, and then employed an unsupervised learning neural network to extract features from brain MRI images. In the final stage, unsupervised learning (clustering) was implemented to discriminate between CN and AD data based on the extracted features. The approach was tested on 429 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had baseline brain structural MRI scans: 231 CN and 198 AD. In the study, we found that the abnormal regions around the hippocampus were indicated based on two-sample t-test (p<0.0001), and the proposed methods using the abnormal regions yield the clustering results for CN vs. AD (accuracy=0.8163, specificity=0.7863, sensitivity=0.8436, and precision=0.8411 [mean values based on 10 runs])

Using Unsupervised Learning Methods to Analyse Magnetic Resonance Imaging (MRI) Scans for the Detection of Alzheimer’s Disease

Background: Alzheimer’s disease (AD) is the most common cause of dementia, characterised by behavioural and cognitive impairment. The manual diagnosis of AD by doctors is time-consuming and can be ineffective, so machine learning methods are increasingly being proposed to diagnose AD in many recent studies. Most research developing machine learning algorithms to diagnose AD use supervised learning to classify magnetic resonance imaging (MRI) scans. However, supervised learning requires a considerable volume of labelled data and MRI scans are difficult to label. The aim of this thesis was therefore to use unsupervised learning methods to differentiate between MRI scans from people who were cognitively normal (CN), people with mild cognitive impairment (MCI), and people with AD. Objectives: This study applied a statistical method and unsupervised learning methods to discriminate scans from (1) people with CN and with AD; (2) people with stable mild cognitive impairment (sMCI) and with progressive mild cognitive impairment (pMCI); (3) people with CN and with pMCI, using a limited number of labelled structural MRI scans. Methods: Two-sample t-tests were used to detect the regions of interest (ROIs) between each of the two groups (CN vs. AD; sMCI vs. pMCI; CN vs. pMCI), and then an unsupervised learning neural network was employed to extract features from the regions. Finally, a clustering algorithm was implemented to discriminate between each of the two groups based on the extracted features. The approach was tested on baseline brain structural MRI scans from 715 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), of which 231 were CN, 198 had AD, 152 had sMCI, and 134 were pMCI. The results were evaluated by calculating the overall accuracy, the sensitivity, specificity, and positive and negative predictive values. Results: The abnormal regions around the lower parts of the limbic system were indicated as AD-relevant regions based on the two-sample t-test (p<0.001), and the proposed method yielded an overall accuracy of 0.842 for discriminating between CN and AD, an overall accuracy of 0.672 for discriminating between sMCI and pMCI, and an overall accuracy of 0.776 for discriminating between CN and pMCI. Conclusion: The study combined statistical and unsupervised learning methods to identify scans of people with different stages of AD. This method can detect AD-relevant regions and could be used to accurately diagnose stages of AD; it has the advantage that it does not require large amounts of labelled MRI scans. The performances of the three discriminations were all comparable to those of previous state-of-the-art studies. The research in this thesis could be implemented in the future to help in the automatic diagnosis of AD and provide a basis for diagnosing sMCI and pMCI