The development of the HUPO-PSI's (Proteomics Standards Initiative) standard data formats and MIAPE (Minimum Information About a Proteomics Experiment) guidelines should improve proteomics data sharing within the scientific community. Proteomics journals have encouraged the use of these standards and guidelines to improve the quality of experimental reporting and ease the evaluation and publication of manuscripts. However, there is an evident lack of bioinformatics tools specifically designed to create and edit standard file formats and reports, or embed them within proteomics workflows. In this article, we describe a new web-based software suite (The ProteoRed MIAPE web toolkit) that performs several complementary roles related to proteomic data standards. Firstly, it can verify the reports fulfill the minimum information requirements of the corresponding MIAPE modules, highlighting inconsistencies or missing information. Secondly, the toolkit can convert several XML-based data standards directly into human readable MIAPE reports stored within the ProteoRed MIAPE repository. Finally, it can also perform the reverse operation, allowing users to export from MIAPE reports into XML files for computational processing, data sharing or public database submission. The toolkit is thus the first application capable of automatically linking the PSI's MIAPE modules with the corresponding XML data exchange standards, enabling bidirectional conversions. This toolkit is freely available at http://www.proteored.org/MIAPE/.

7 May 2020 , Volume 96, Number 3

6 November 2019

Although some digital platforms now have an impact on more people’s lives than does any one state authority, the international community has been slow to hold to account these platforms’ activities by reference to human rights law. This paper examines how human rights frameworks should guide digital technology.

Research Event

Event participants

Charles Wanguhu, Coordinator, Kenya CSO Platform on Oil and Gas
Ndanga Kamau, Oil and Gas Policy Adviser, Oxfam Kenya
John Ochola, Chairman, Kenya CSO Platform on Oil and Gas / EcoNews Africa
Simon Thompson, Chairman, Tullow Oil
Chair: Alex Vines, Research Director, Area Studies and International Law; Head, Africa Programme, Chatham House 

In recent years, questions of whether, when, and how to return failed asylum seekers and other migrants to their origin countries have dominated migration debates in many countries. These issues were also taken up in the negotiation of the Global Compact for Safe, Orderly, and Regular Migration, moving the discussion beyond the typical bilateral one. This policy brief outlines how states might more constructively work together on returns and reintegration programs.

MPI has completed an analysis of the major provisions in the 2013 framework, comparing them to provisions of the legislation the Senate considered in 2006 and 2007. 

This fact sheet is formatted as a chart comparing the framework of comprehensive immigration reform outlined in the 2013 Senate immigration bill against provisions included in bills considered by the Senate in 2006 and 2007.

This report analyzes how governments ought to best allocate their resources to address the risks associated with migration—the "immigration harms" that undermine the positive economic and social benefits of immigration—including choosing which threats to tackle and where to prioritize enforcement efforts. Immigration policymakers can learn from other public policy regulation efforts to ensure that regulatory actions advance the public interest.

This publication outlines the Commonwealth Child Safe Framework requirements and guidance for its implementation.The primary audience for this publication is Commonwealth entities. This publication has been developed to assist entities to understand and implement the requirements of the Commonwealth Child Safe Framework.

"The HIV resilience framework will be of relevance to a wide range of people involved at all levels of the HIV response. We hope it provides encouragement for government decision makers and policy developers to pursue consistent and enabling policy and legislative environments and work cooperatively between jurisdictions and across departments to achieve best practice. Our consultation suggests that consistent legislation with the goal of supporting strong health outcomes, making disclosure safe, and eliminating stigmatising laws is crucial to creating environments in which people with HIV feel safe to access testing, treatment, and care. The HIV resilience framework also contains valuable insights for those who have responsibility for HIV health program management and service delivery, providing guidance to help ensure that programs, services, interventions or strategies supporting people living with HIV are developed and delivered in a way that is mindful of the interlinked twin goals of building personal resilience and eliminating stigma and discrimination." --President's message (page 6).

"A special challenge of hard rock exploration is to identify targets of interest within complex geological settings. Interpretation of the geology can be made from direct geological observations and knowledge of the area, and from 2D or 3D seismic surveys. These interpretations can be developed into 3D geological models that provide the basis for predictions as to likely targets for drilling and/or mining. To verify these predictions we need to simulate 3D seismic wave propagation in the proposed geological models and compare the simulation results to seismic survey data. To achieve this we convert geological surfaces created in an interpretation software package into discretised block models representing the different lithostratigraphic units, and segment these into discrete volumes to which appropriate density and seismic velocity values are assigned. This approach allows us to scale models appropriately for desired wave propagation parameters and to go from local to global geological models and vice versa. Then we use these digital models with forward modelling codes to undertake numerous 3D acoustic wave simulations. Simulations are performed with single shot and with exploding reflector (located on extracted geological surface) configurations" -- Summary.

Gun control -- Australia.

Bath : And sold by Underwood, London, 1825.

A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation. While being conceptually and computationally appealing, this method involves the problematic need to also split the prior for the local inferences; these weakened priors may not provide enough regularization for each separate computation, thus eliminating one of the key advantages of Bayesian methods. To resolve this dilemma while still retaining the generalizability of the underlying local inference method, we apply the idea of expectation propagation (EP) as a framework for distributed Bayesian inference. The central idea is to iteratively update approximations to the local likelihoods given the state of the other approximations and the prior. The present paper has two roles: we review the steps that are needed to keep EP algorithms numerically stable, and we suggest a general approach, inspired by EP, for approaching data partitioning problems in a way that achieves the computational benefits of parallelism while allowing each local update to make use of relevant information from the other sites. In addition, we demonstrate how the method can be applied in a hierarchical context to make use of partitioning of both data and parameters. The paper describes a general algorithmic framework, rather than a specific algorithm, and presents an example implementation for it.

Graph learning from data is a canonical problem that has received substantial attention in the literature. Learning a structured graph is essential for interpretability and identification of the relationships among data. In general, learning a graph with a specific structure is an NP-hard combinatorial problem and thus designing a general tractable algorithm is challenging. Some useful structured graphs include connected, sparse, multi-component, bipartite, and regular graphs. In this paper, we introduce a unified framework for structured graph learning that combines Gaussian graphical model and spectral graph theory. We propose to convert combinatorial structural constraints into spectral constraints on graph matrices and develop an optimization framework based on block majorization-minimization to solve structured graph learning problem. The proposed algorithms are provably convergent and practically amenable for a number of graph based applications such as data clustering. Extensive numerical experiments with both synthetic and real data sets illustrate the effectiveness of the proposed algorithms. An open source R package containing the code for all the experiments is available at https://CRAN.R-project.org/package=spectralGraphTopology.

When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper. In particular, we propose a fast, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem. The proposed algorithm, Group Alternating Direction Method of Multipliers (GADMM) is based on the Alternating Direction Method of Multipliers (ADMM) framework. The key novelty in GADMM is that it solves the problem in a decentralized topology where at most half of the workers are competing for the limited communication resources at any given time. Moreover, each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with a lower amount of communication overhead in each exchange. We prove that GADMM converges to the optimal solution for convex loss functions, and numerically show that it converges faster and more communication-efficient than the state-of-the-art communication-efficient algorithms such as the Lazily Aggregated Gradient (LAG) and dual averaging, in linear and logistic regression tasks on synthetic and real datasets. Furthermore, we propose Dynamic GADMM (D-GADMM), a variant of GADMM, and prove its convergence under the time-varying network topology of the workers.

Given a multivariate data set, sparse principal component analysis (SPCA) aims to extract several linear combinations of the variables that together explain the variance in the data as much as possible, while controlling the number of nonzero loadings in these combinations. In this paper we consider 8 different optimization formulations for computing a single sparse loading vector; these are obtained by combining the following factors: we employ two norms for measuring variance (L2, L1) and two sparsity-inducing norms (L0, L1), which are used in two different ways (constraint, penalty). Three of our formulations, notably the one with L0 constraint and L1 variance, have not been considered in the literature. We give a unifying reformulation which we propose to solve via a natural alternating maximization (AM) method. We show the the AM method is nontrivially equivalent to GPower (Journ'{e}e et al; JMLR 11:517--553, 2010) for all our formulations. Besides this, we provide 24 efficient parallel SPCA implementations: 3 codes (multi-core, GPU and cluster) for each of the 8 problems. Parallelism in the methods is aimed at i) speeding up computations (our GPU code can be 100 times faster than an efficient serial code written in C++), ii) obtaining solutions explaining more variance and iii) dealing with big data problems (our cluster code is able to solve a 357 GB problem in about a minute).

The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of non-expert stakeholders a challenge. Here we explore the applicability of deep learning approaches by adapting a pre-trained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the UK where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides and tunnelling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exists to construct a balanced training data set, and the deformation signals are slower and more localised than in previous applications. We propose three enhancement methods to tackle these problems: i) spatial interpolation with modified matrix completion, ii) a synthetic training dataset based on the characteristics of real UK velocity map, and iii) enhanced over-wrapping techniques. Using velocity maps spanning 2015-2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.

Pier Luigi Conti, Daniela Marella, Fulvia Mecatti, Federico Andreis.

Source: Bernoulli, Volume 26, Number 2, 1044--1069.

Abstract:
In this paper, a class of resampling techniques for finite populations under $pi $ps sampling design is introduced. The basic idea on which they rest is a two-step procedure consisting in: (i) constructing a “pseudo-population” on the basis of sample data; (ii) drawing a sample from the predicted population according to an appropriate resampling design. From a logical point of view, this approach is essentially based on the plug-in principle by Efron, at the “sampling design level”. Theoretical justifications based on large sample theory are provided. New approaches to construct pseudo populations based on various forms of calibrations are proposed. Finally, a simulation study is performed.

PR Montague
Mar 1, 1996; 16:1936-1947
Articles

Welcoming remarks by Mr Fernando Restoy, Chairman, Financial Stability Institute, Bank for International Settlements, at the FSI-IADI conference on crisis management, resolution and deposit insurance: what's next and how to prepare, Basel, 4 September 2019.

Press release about the Basel Committee publishing consultation paper on revisions to the credit valuation adjustment risk framework, 28 November 2019.

Press release about the Basel Committee launching the consolidated Basel Framework, 16 December 2019.

Press release about Basel Committee assesses members' implementation of the Net Stable Funding Ratio and large exposures framework, 19 March 2020

RIGHT now, Shahbaz Majeed should be heading to Glencoe. Or flying over central Scotland. Or making his way to Harris which he has been trying to get back to since his first visit in 2018. Or maybe visiting some other part of Scotland so he can capture it in a photograph. Instead, he is at home in Dundee (where he is web development manager at the University of Dundee), “climbing the walls,” and looking forward to life after lockdown.

Dried up talent

Children born very preterm (VPT) are at high risk of educational delay. School readiness has been identified as a potentially useful clinical framework for early detection of those at greatest risk. However, evidence to support its predictive validity is limited.

VPT preschoolers are at risk of impairment across the 5 American Academy of Pediatrics school readiness domains. The number of domains affected predicted likelihood of later learning problems, supporting the utility of schoolreadiness frameworks for identifying children needing surveillance and/or support. (Read the full article)

Use the coupon code below to snag the 55-inch model for just $1,249 (down from $1,999.99) or the 65-inch model for $1,549 (down from $2,799.99).

 Review of Securities Lending and Borrowing (SLB) Framework

Full Article