On Convergence of the Inexact Rayleigh Quotient Iteration with the Lanczos Method Used for Solving Linear Systems

For the Hermitian inexact Rayleigh quotient iteration (RQI), the author has established new local general convergence results, independent of iterative solvers for inner linear systems. The theory shows that the method locally converges quadratically under a new condition, called the uniform positiveness condition. In this paper we first consider the local convergence of the inexact RQI with the unpreconditioned Lanczos method for the linear systems. Some attractive properties are derived for the residuals, whose norms are $\xi_{k+1}$'s, of the linear systems obtained by the Lanczos method. Based on them and the new general convergence results, we make a refined analysis and establish new local convergence results. It is proved that the inexact RQI with Lanczos converges quadratically provided that $\xi_{k+1}\leq\xi$ with a constant $\xi\geq 1$. The method is guaranteed to converge linearly provided that $\xi_{k+1}$ is bounded by a small multiple of the reciprocal of the residual norm $\|r_k\|$ of the current approximate eigenpair. The results are fundamentally different from the existing convergence results that always require $\xi_{k+1}<1$, and they have a strong impact on effective implementations of the method. We extend the new theory to the inexact RQI with a tuned preconditioned Lanczos for the linear systems. Based on the new theory, we can design practical criteria to control $\xi_{k+1}$ to achieve quadratic convergence and implement the method more effectively than ever before. Numerical experiments confirm our theory.Comment: 20 pages, 8 figures. arXiv admin note: text overlap with arXiv:0906.223

Performance Evaluation - Annual Report Year 3

This report describes the work done and results obtained in third year of the CATNETS project. Experiments carried out with the different configurations of the prototype are reported and simulation results are evaluated with the CATNETS metrics framework. The applicability of the Catallactic approach as market model for service and resource allocation in application layer networks is assessed based on the results and experience gained both from the prototype development and simulations. --Grid Computing

Determination of protein binding affinities within hydrogel-based molecularly imprinted polymers (HydroMIPs)

Hydrogel-based molecularly imprinted polymers (HydroMIPs) were prepared for several proteins (haemoglobin, myoglobin and catalase) using a family of acrylamide-based monomers. Protein affinity towards the HydroMIPs was investigated under equilibrium conditions and over a range of concentrations using specific binding with Hill slope saturation profiles. We report HydroMIP binding affinities, in terms of equilibrium dissociation constants (Kd) within the micro-molar range (25 ± 4 mM, 44 ± 3 mM, 17 ± 2 mM for haemoglobin, myoglobin and catalase respectively within a polyacrylamide-based MIP). The extent of non-specific binding or cross-selectivity for non-target proteins has also been assessed. It is concluded that both selectivity and affinity for both cognate and non-cognate proteins towards the MIPs were dependent on the concentration and the complementarity of their structures and size. This is tentatively attributed to the formation of protein complexes during both the polymerisation and rebinding stages at high protein concentrations. We have used atomic force spectroscopy to characterize molecular interactions in the MIP cavities using protein-modified AFM tips. Attractive and repulsive force curves were obtained for the MIP and NIP (non-imprinted polymer) surfaces (under protein loaded or unloaded states). Our force data suggest that we have produced selective cavities for the template protein in the MIPs and we have been able to quantify the extent of non-specific protein binding on, for example, a non-imprinted polymer (NIP) control surface

The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation

Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative development of MT systems. While considerable progress has been made on estimating a single scalar quality score, current metrics lack the informativeness of more detailed schemes that annotate individual errors, such as Multidimensional Quality Metrics (MQM). In this paper, we help fill this gap by proposing AutoMQM, a prompting technique which leverages the reasoning and in-context learning capabilities of large language models (LLMs) and asks them to identify and categorize errors in translations. We start by evaluating recent LLMs, such as PaLM and PaLM-2, through simple score prediction prompting, and we study the impact of labeled data through in-context learning and finetuning. We then evaluate AutoMQM with PaLM-2 models, and we find that it improves performance compared to just prompting for scores (with particularly large gains for larger models) while providing interpretability through error spans that align with human annotations.Comment: 19 page

Performance evaluation - annual report year 3

This report describes the work done and results obtained in third year of the CATNETS project. Experiments carried out with the different configurations of the prototype are reported and simulation results are evaluated with the CATNETS metrics framework. The applicability of the Catallactic approach as market model for service and resource allocation in application layer networks is assessed based on the results and experience gained both from the prototype development and simulations

Inflammatory cytokines and risk of coronary heart disease: new prospective study and updated meta-analysis.

AIMS: Because low-grade inflammation may play a role in the pathogenesis of coronary heart disease (CHD), and pro-inflammatory cytokines govern inflammatory cascades, this study aimed to assess the associations of several pro-inflammatory cytokines and CHD risk in a new prospective study, including meta-analysis of prospective studies. METHODS AND RESULTS: Interleukin-6 (IL-6), IL-18, matrix metalloproteinase-9 (MMP-9), soluble CD40 ligand (sCD40L), and tumour necrosis factor-α (TNF-α) were measured at baseline in a case-cohort study of 1514 participants and 833 incident CHD events within population-based prospective cohorts at the Danish Research Centre for Prevention and Health. Age- and sex-adjusted hazard ratios (HRs) for CHD per 1-SD higher log-transformed baseline levels were: 1.37 (95% CI: 1.21-1.54) for IL-6, 1.26 (1.11-1.44) for IL-18, 1.30 (1.16-1.46) for MMP-9, 1.01 (0.89-1.15) for sCD40L, and 1.13 (1.01-1.27) for TNF-α. Multivariable adjustment for conventional vascular risk factors attenuated the HRs to: 1.26 (1.08-1.46) for IL-6, 1.12 (0.95-1.31) for IL-18, 1.21 (1.05-1.39) for MMP-9, 0.93 (0.78-1.11) for sCD40L, and 1.14 (1.00-1.31) for TNF-α. In meta-analysis of up to 29 population-based prospective studies, adjusted relative risks for non-fatal MI or CHD death per 1-SD higher levels were: 1.25 (1.19-1.32) for IL-6; 1.13 (1.05-1.20) for IL-18; 1.07 (0.97-1.19) for MMP-9; 1.07 (0.95-1.21) for sCD40L; and 1.17 (1.09-1.25) for TNF-α. CONCLUSIONS: Several different pro-inflammatory cytokines are each associated with CHD risk independent of conventional risk factors and in an approximately log-linear manner. The findings lend support to the inflammation hypothesis in vascular disease, but further studies are needed to assess causality.This work was supported by a grant from the British Heart Foundation (RG/08/014), the U.K. Medical Research Council, and the U.K. National Institute of Health Research Cambridge Biomedical Research Centre.This is the accepted manuscript. The final version is available from OUP at http://eurheartj.oxfordjournals.org/content/35/9/578

Genetic and environmental determinants of diastolic heart function

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets

Natriuretic peptides and integrated risk assessment for cardiovascular disease. an individual-participant-data meta-analysis

BACKGROUND: Guidelines for primary prevention of cardiovascular diseases focus on prediction of coronary heart disease and stroke. We assessed whether or not measurement of N-terminal-pro-B-type natriuretic peptide (NT-proBNP) concentration could enable a more integrated approach than at present by predicting heart failure and enhancing coronary heart disease and stroke risk assessment. METHODS: In this individual-participant-data meta-analysis, we generated and harmonised individual-participant data from relevant prospective studies via both de-novo NT-proBNP concentration measurement of stored samples and collection of data from studies identified through a systematic search of the literature (PubMed, Scientific Citation Index Expanded, and Embase) for articles published up to Sept 4, 2014, using search terms related to natriuretic peptide family members and the primary outcomes, with no language restrictions. We calculated risk ratios and measures of risk discrimination and reclassification across predicted 10 year risk categories (ie, <5%, 5% to <7·5%, and ≥7·5%), adding assessment of NT-proBNP concentration to that of conventional risk factors (ie, age, sex, smoking status, systolic blood pressure, history of diabetes, and total and HDL cholesterol concentrations). Primary outcomes were the combination of coronary heart disease and stroke, and the combination of coronary heart disease, stroke, and heart failure. FINDINGS: We recorded 5500 coronary heart disease, 4002 stroke, and 2212 heart failure outcomes among 95 617 participants without a history of cardiovascular disease in 40 prospective studies. Risk ratios (for a comparison of the top third vs bottom third of NT-proBNP concentrations, adjusted for conventional risk factors) were 1·76 (95% CI 1·56-1·98) for the combination of coronary heart disease and stroke and 2·00 (1·77-2·26) for the combination of coronary heart disease, stroke, and heart failure. Addition of information about NT-proBNP concentration to a model containing conventional risk factors was associated with a C-index increase of 0·012 (0·010-0·014) and a net reclassification improvement of 0·027 (0·019-0·036) for the combination of coronary heart disease and stroke and a C-index increase of 0·019 (0·016-0·022) and a net reclassification improvement of 0·028 (0·019-0·038) for the combination of coronary heart disease, stroke, and heart failure. INTERPRETATION: In people without baseline cardiovascular disease, NT-proBNP concentration assessment strongly predicted first-onset heart failure and augmented coronary heart disease and stroke prediction, suggesting that NT-proBNP concentration assessment could be used to integrate heart failure into cardiovascular disease primary prevention

ENIGMA-anxiety working group : rationale for and organization of large-scale neuroimaging studies of anxiety disorders

Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders