Aggregation Algorithm Vs. Average for Time Series Prediction

Learning with expert advice as a scheme of on-line learning has been very successfully applied to various learning problems due to its strong theoretical basis. In this paper, for the purpose of times se- ries prediction, we investigate the application of Aggregation Algorithm, which a generalisation of the famous weighted majority algorithm. The results of the experiments done, show that the Aggregation Algorithm performs very well in comparison to average

Clonal Composition of Human Adrenocortical Neoplasms

The mechanisms of tumorigenesis of adrenocortical neoplasms are still not understood. Tumor formation may be the result of spontaneous transformation of adrenocortical cells by somatic mutations. Another factor stimulating adrenocortical cell growth and potentially associated with formation of adrenal adenomas and, less frequently, carcinomas is the chronic elevation of proopiomelanocortin-derived peptides in diseases like ACTH-dependent Cushing's syndrome and congenital adrenal hyperplasia. To further investigate the pathogenesis of adrenocortical neoplasms, we studied the clonal composition of such tumors using X-chromosome inactivation analysis of the highly polymorphic region Xcen-Xp11.4 with the hybridization probe M27ß, which maps to a variable number of tandem repeats on the X-chromsome. In addition, polymerase chain reaction amplification of a phosphoglycerokinase gene polymorphism was performed. After DNA extraction from tumorous adrenal tissue and normal leukocytes in parallel, the active X-chromosome of each sample was digested with the methylation-sensitive restriction enzyme HpaII. A second digestion with an appropriate restriction enzyme revealed the polymorphism of the region Xcen-Xp11.4 and the phosphoglycerokinase locus. Whereas in normal polyclonal tissue both the paternal and maternal alleles are detected, a monoclonal tumor shows only one of the parental alleles. A total of 21 female patients with adrenal lesions were analyzed; 17 turned out to be heterozygous for at least one of the loci. Our results were as follows: diffuse (n = 4) and nodular (n = 1) adrenal hyperplasia in patients with ACTH-dependent Cushing's syndrome, polyclonal pattern; adrenocortical adenomas (n = 8), monoclonal (n = 7), as well as polyclonal (n = 1); adrenal carcinomas (n = 3), monoclonal pattern. One metastasis of an adrenocortical carcinoma showed a pattern most likely due to tumor-associated loss of methylation. In the special case of a patient with bilateral ACTH-independent macronodular hyperplasia, diffuse hyperplastic areas and a small nodule showed a polyclonal pattern, whereas a large nodule was monoclonal. We conclude that most adrenal adenomas and carcinomas are monoclonal, whereas diffuse and nodular adrenal hyperplasias are polyclonal. The clonal composition of ACTH-independent massive macronodular hyperplasia seems to be heterogeneous, consisting of polyclonal and monoclonal areas

Competitive Regularised Regression

Regularised regression uses sparsity and variance to reduce the complexity and over-fitting of a regression model. The present paper introduces two novel regularised linear regression algorithms: Competitive Iterative Ridge Regression (CIRR) and Online Shrinkage via Limit of Gibbs Sampler (OSLOG) for fast and reliable prediction on "Big Data" without making distributional assumption on the data. We use the technique of competitive analysis to design them and show their strong theoretical guarantee. Furthermore, we compare their performance against some neoteric regularised regression methods such as On-line Ridge Regression (ORR) and the Aggregating Algorithm for Regression (AAR). The comparison of the algorithms is done theoretically, focusing on the guarantee on the performance on cumulative loss, and empirically to show the advantages of CIRR and OSLOG

Online Bayesian Shrinkage Regression

The present work introduces a new online regression method that extends the Shrinkage via Limit of Gibbs sampler (SLOG) in the context of online learning. In particular, we theoretically demonstrate that the proposed Online SLOG (OSLOG) is derived using the Bayesian framework without resorting to the Gibbs sampler. We also state the performance guarantee of OSLOG

One-sided Cauchy-Stieltjes Kernel Families

This paper continues the study of a kernel family which uses the Cauchy-Stieltjes kernel in place of the celebrated exponential kernel of the exponential families theory. We extend the theory to cover generating measures with support that is unbounded on one side. We illustrate the need for such an extension by showing that cubic pseudo-variance functions correspond to free-infinitely divisible laws without the first moment. We also determine the domain of means, advancing the understanding of Cauchy-Stieltjes kernel families also for compactly supported generating measures

Subtype-selective alpha-i adrenoceptor alkylation in the rat kidney and its effect on the vascular presser response

ABSTRACT ABBREVIATiON

Scalable online learning for flink: SOLMA library

Driven by the needs of Flink to expand the offline engine to a hybrid one, a new machine learning (ML) library, called SOLMA is proposed. This library aims to cover online learning algorithms for data streams. In this setting, data streams are processed sequentially example by example. SOLMA, which is under development, currently contains two classes of algorithms: (i) basic streaming routines such as online sampling, online PCA, online statistical moments and (ii) advanced online ML algorithms covering in particular classification, regression and drift/anomaly detection and handling. This paper briefly highlights the concepts underlying SOLMA

People of the British Isles: preliminary analysis of genotypes and surnames in a UK control population

There is a great deal of interest in fine scale population structure in the UK, both as a signature of historical immigration events and because of the effect population structure may have on disease association studies. Although population structure appears to have a minor impact on the current generation of genome-wide association studies, it is likely to play a significant part in the next generation of studies designed to search for rare variants. A powerful way of detecting such structure is to control and document carefully the provenance of the samples involved. Here we describe the collection of a cohort of rural UK samples (The People of the British Isles), aimed at providing a well-characterised UK control population that can be used as a resource by the research community as well as providing fine scale genetic information on the British population. So far, some 4,000 samples have been collected, the majority of which fit the criteria of coming from a rural area and having all four grandparents from approximately the same area. Analysis of the first 3,865 samples that have been geocoded indicates that 75% have a mean distance between grandparental places of birth of 37.3km, and that about 70% of grandparental places of birth can be classed as rural. Preliminary genotyping of 1,057 samples demonstrates the value of these samples for investigating fine scale population structure within the UK, and shows how this can be enhanced by the use of surnames

Heart failure during the COVID-19 pandemic: clinical, diagnostic, management, and organizational dilemmas

The coronavirus 2019 (COVID-19) infection pandemic has affected the care of patients with heart failure (HF). Several consensus documents describe the appropriate diagnostic algorithm and treatment approach for patients with HF and associated COVID-19 infection. However, few questions about the mechanisms by which COVID can exacerbate HF in patients with high-risk (Stage B) or symptomatic HF (Stage C) remain unanswered. Therefore, the type of HF occurring during infection is poorly investigated. The diagnostic differentiation and management should be focused on the identification of the HF phenotype, underlying causes, and subsequent tailored therapy. In this framework, the relationship existing between COVID and onset of acute decompensated HF, isolated right HF, and cardiogenic shock is questioned, and the specific management is mainly based on local hospital organization rather than a standardized model. Similarly, some specific populations such as advanced HF, heart transplant, patients with left ventricular assist device (LVAD), or valve disease remain under investigated. In this systematic review, we examine recent advances regarding the relationships between HF and COVID-19 pandemic with respect to epidemiology, pathogenetic mechanisms, and differential diagnosis. Also, according to the recent HF guidelines definition, we highlight different clinical profile identification, pointing out the main concerns in understudied HF populations.© 2022 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology