Macrostate Data Clustering

We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable macroscopic states (macrostates) of a diffusive system. A "minimum uncertainty criterion" determines the linear transformation from eigenvectors to cluster-defining window functions. Eigenspectrum gap and cluster certainty conditions identify the proper number of clusters. The physically motivated fuzzy representation and associated uncertainty analysis distinguishes macrostate clustering from spectral partitioning methods. Macrostate data clustering solves a variety of test cases that challenge other methods.Comment: keywords: cluster analysis, clustering, pattern recognition, spectral graph theory, dynamic eigenvectors, machine learning, macrostates, classificatio

A New Solution to the Relative Orientation Problem using only 3 Points and the Vertical Direction

This paper presents a new method to recover the relative pose between two images, using three points and the vertical direction information. The vertical direction can be determined in two ways: 1- using direct physical measurement like IMU (inertial measurement unit), 2- using vertical vanishing point. This knowledge of the vertical direction solves 2 unknowns among the 3 parameters of the relative rotation, so that only 3 homologous points are requested to position a couple of images. Rewriting the coplanarity equations leads to a simpler solution. The remaining unknowns resolution is performed by an algebraic method using Grobner bases. The elements necessary to build a specific algebraic solver are given in this paper, allowing for a real-time implementation. The results on real and synthetic data show the efficiency of this method

Asymmetry through time dependency

Given a single network of interactions, asymmetry arises when the links are directed. For example, if protein A upregulates protein B and protein B upregulates protein C, then (in the absence of any further relationships between them) A may affect C but not vice versa. This type of imbalance is reflected in the associated adjacency matrix, which will lack symmetry. A different type of imbalance can arise when interactions appear and disappear over time. If A meets B today and B meets C tomorrow, then (in the absence of any further relationships between them) A may pass a message or disease to C, but not vice versa. Hence, even when each interaction is a two-way exchange, the effect of time ordering can introduce asymmetry. This observation is very closely related to the fact that matrix multiplication is not commutative. In this work, we describe a method that has been designed to reveal asymmetry in static networks and show how it may be combined with a measure that summarizes the potential information flow between nodes in the temporal case. This results in a new method that quantifies the asymmetry arising through time ordering. We show by example that the new tool can be used to visualize and quantify the amount of asymmetry caused by the arrow of time

History of clinical transplantation

How transplantation came to be a clinical discipline can be pieced together by perusing two volumes of reminiscences collected by Paul I. Terasaki in 1991-1992 from many of the persons who were directly involved. One volume was devoted to the discovery of the major histocompatibility complex (MHC), with particular reference to the human leukocyte antigens (HLAs) that are widely used today for tissue matching.1 The other focused on milestones in the development of clinical transplantation.2 All the contributions described in both volumes can be traced back in one way or other to the demonstration in the mid-1940s by Peter Brian Medawar that the rejection of allografts is an immunological phenomenon.3,4 © 2008 Springer New York

A population-scale temporal case–control evaluation of COVID-19 disease phenotype and related outcome rates in patients with cancer in England (UKCCP)

Patients with cancer are at increased risk of hospitalisation and mortality following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, the SARS-CoV-2 phenotype evolution in patients with cancer since 2020 has not previously been described. We therefore evaluated SARS-CoV-2 on a UK populationscale from 01/11/2020-31/08/2022, assessing case-outcome rates of hospital assessment(s), intensive care admission and mortality. We observed that the SARS-CoV-2 disease phenotype has become less severe in patients with cancer and the non-cancer population. Case-hospitalisation rates for patients with cancer dropped from 30.58% in early 2021 to 7.45% in 2022 while case-mortality rates decreased from 20.53% to 3.25%. However, the risk of hospitalisation and mortality remains 2.10x and 2.54x higher in patients with cancer, respectively. Overall, the SARS-CoV-2 disease phenotype is less severe in 2022 compared to 2020 but patients with cancer remain at higher risk than the non-cancer population. Patients with cancer must therefore be empowered to live more normal lives, to see loved ones and families, while also being safeguarded with expanded measures to reduce the risk of transmission

Excavation and support design of the Dicle-Kralkizi water tunnel: an overview

Tunneling projects have their uniqueness in terms of engineering problems. The expertise gained from analyzing these projects establishes a sound basis for future application. This paper conveys experiences gained during the construction and support of the design of the Dicle-Kralkizi water tunnel, Turkey. Tunnel stability problems including overbreaks and surface subsidence are evaluated. An analysis of the breakdowns, factors controlling advance rate and the overall performance of tunnel are covered. The accumulated information presented here is believed to be useful and reliable for a successful tunnel excavation in similar formations. (C) 2004 Elsevier Ltd. All rights reserved