Accelerating Parallel Tempering: Quantile Tempering Algorithm (QuanTA)

Using MCMC to sample from a target distribution, $\pi(x)$ on a $d$-dimensional state space can be a difficult and computationally expensive problem. Particularly when the target exhibits multimodality, then the traditional methods can fail to explore the entire state space and this results in a bias sample output. Methods to overcome this issue include the parallel tempering algorithm which utilises an augmented state space approach to help the Markov chain traverse regions of low probability density and reach other modes. This method suffers from the curse of dimensionality which dramatically slows the transfer of mixing information from the auxiliary targets to the target of interest as $d \rightarrow \infty$. This paper introduces a novel prototype algorithm, QuanTA, that uses a Gaussian motivated transformation in an attempt to accelerate the mixing through the temperature schedule of a parallel tempering algorithm. This new algorithm is accompanied by a comprehensive theoretical analysis quantifying the improved efficiency and scalability of the approach; concluding that under weak regularity conditions the new approach gives accelerated mixing through the temperature schedule. Empirical evidence of the effectiveness of this new algorithm is illustrated on canonical examples

Mboat7 down-regulation by hyper-insulinemia induces fat accumulation in hepatocytes

BACKGROUND: Naturally occurring variation in Membrane-bound O-acyltransferase domain-containing 7 (MBOAT7), encoding for an enzyme involved in phosphatidylinositol acyl-chain remodelling, has been associated with fatty liver and hepatic disorders. Here, we examined the relationship between hepatic Mboat7 down-regulation and fat accumulation. METHODS: Hepatic MBOAT7 expression was surveyed in 119 obese individuals and in experimental models. MBOAT7 was acutely silenced by antisense oligonucleotides in C57Bl/6 mice, and by CRISPR/Cas9 in HepG2 hepatocytes. FINDINGS: In obese individuals, hepatic MBOAT7 mRNA decreased from normal liver to steatohepatitis, independently of diabetes, inflammation and MBOAT7 genotype. Hepatic MBOAT7 levels were reduced in murine models of fatty liver, and by hyper-insulinemia. In wild-type mice, Mboat7 was down-regulated by refeeding and insulin, concomitantly with insulin signalling activation. Acute hepatic Mboat7 silencing promoted hepatic steatosis in vivo and enhanced expression of fatty acid transporter Fatp1. MBOAT7 deletion in hepatocytes reduced the incorporation of arachidonic acid into phosphatidylinositol, consistently with decreased enzymatic activity, determining the accumulation of saturated triglycerides, enhanced lipogenesis and FATP1 expression, while FATP1 deletion rescued the phenotype. INTERPRETATION: MBOAT7 down-regulation by hyper-insulinemia contributes to hepatic fat accumulation, impairing phosphatidylinositol remodelling and up-regulating FATP1. FUNDING: LV was supported by MyFirst Grant AIRC n.16888, Ricerca Finalizzata Ministero della Salute RF-2016-02,364,358, Ricerca corrente Fondazione IRCCS Ca\u27 Granda Ospedale Maggiore Policlinico; LV and AG received funding from the European Union Programme Horizon 2020 (No. 777,377) for the project LITMUS- Liver Investigation: Testing Marker Utility in Steatohepatitis . MM was supported by Fondazione Italiana per lo Studio del Fegato (AISF) \u27Mario Coppo\u27 fellowship

Catalyzing Collaboration: The Developing Infrastructure for Federal Public Private Partnerships

There is growing interest on the part of government, philanthropy and business to work together to achieve greater impact. Partnerships that span the sectors have the potential to achieve more than any sector can achieve on its own by leveraging the strengths of each. However, such partnerships also give rise to added costs and entail greater risks. To address these challenges, offices of strategic partnerships are emerging at the federal level to provide an infrastructure to catalyze cross-sectoral partnerships. This report examines 21 such offices in federal departments and agencies whose purpose is to facilitate and accelerate partnerships with philanthropy and business -- ranging from the Department of Housing and Urban Development and the Department of Education, to the Department of State and the Agency for International Development, to the Department of Homeland Security and the Federal Emergency Management Agency. The formation of these offices has been driven by champions within government -- many with prior experience in philanthropy or business -- that have witnessed the power of working collaboratively with other sectors. Their actions have often been reinforced by executive orders and other directives conducive to their growth. In the case of those offices that have been created in the last few years, they have also been encouraged by the examples of their more established counterparts

Scalable Data Augmentation for Deep Learning

Scalable Data Augmentation (SDA) provides a framework for training deep learning models using auxiliary hidden layers. Scalable MCMC is available for network training and inference. SDA provides a number of computational advantages over traditional algorithms, such as avoiding backtracking, local modes and can perform optimization with stochastic gradient descent (SGD) in TensorFlow. Standard deep neural networks with logit, ReLU and SVM activation functions are straightforward to implement. To illustrate our architectures and methodology, we use P\'{o}lya-Gamma logit data augmentation for a number of standard datasets. Finally, we conclude with directions for future research

yaImpute: An R Package for kNN Imputation

This article introduces yaImpute, an R package for nearest neighbor search and imputation. Although nearest neighbor imputation is used in a host of disciplines, the methods implemented in the yaImpute package are tailored to imputation-based forest attribute estimation and mapping. The impetus to writing the yaImpute is a growing interest in nearest neighbor imputation methods for spatially explicit forest inventory, and a need within this research community for software that facilitates comparison among different nearest neighbor search algorithms and subsequent imputation techniques. yaImpute provides directives for defining the search space, subsequent distance calculation, and imputation rules for a given number of nearest neighbors. Further, the package offers a suite of diagnostics for comparison among results generated from different imputation analyses and a set of functions for mapping imputation results.

Collaborative research on V/STOL control system/cockpit display tradeoffs under the NASA/MOD joint aeronautical program

Summarized here are activities that have taken place from 1979 to the present in a collaborative program between NASA Ames Research Center and the Royal Aerospace Establishment (now Defence Research Agency), Bedford on flight control system and cockpit display tradeoffs for low-speed and hover operations of future V/STOL aircraft. This program was created as Task 8A of the Joint Aeronautical Program between NASA in the United States and the Ministry of Defence (Procurement Executive) in the United Kingdom. The program was initiated based on a recognition by both parties of the strengths of the efforts of their counterparts and a desire to participate jointly in future simulation and flight experiments. In the ensuing years, teams of NASA and RAE engineers and pilots have participated in each other's simulation experiments to evaluate control and display concepts and define design requirements for research aircraft. Both organizations possess Harrier airframes that have undergone extensive modification to provide in-flight research capabilities in the subject areas. Both NASA and RAE have profited by exchanges of control/display concepts, design criteria, fabrication techniques, software development and validation, installation details, and ground and flight clearance techniques for their respective aircraft. This collaboration has permitted the two organizations to achieve jointly substantially more during the period than if they had worked independently. The two organizations are now entering the phase of flight research for the collaborative program as currently defined