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Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise prediction of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

We study quantum interior point methods (QIPMs) for second-order cone programming (SOCP), guided by the example use case of portfolio optimization (PO). We provide a complete quantum circuit-level description of the algorithm from problem input to problem output, making several improvements to the implementation of the QIPM. We report the number of logical qubits and the quantity/depth of non-Clifford T-gates needed to run the algorithm, including constant factors. The resource counts we find depend on instance-specific parameters, such as the condition number of certain linear systems within the problem. To determine the size of these parameters, we perform numerical simulations of small PO instances, which lead to concrete resource estimates for the PO use case. Our numerical results do not probe large enough instance sizes to make conclusive statements about the asymptotic scaling of the algorithm. However, already at small instance sizes, our analysis suggests that, due primarily to large constant pre-factors, poorly conditioned linear systems, and a fundamental reliance on costly quantum state tomography, fundamental improvements to the QIPM are required for it to lead to practical quantum advantage., Comment: 38 pages, 15 figures

Chronic inflammation accompanies obesity and limits subcutaneous white adipose tissue (WAT) expandability, accelerating the development of insulin resistance and type 2 diabetes mellitus. MicroRNAs (miRNAs) influence expression of many metabolic genes in fat cells, but physiological roles in WAT remain poorly characterized. Here, we report that expression of the miRNA miR-30a in subcutaneous WAT corresponds with insulin sensitivity in obese mice and humans. To examine the hypothesis that restoration of miR-30a expression in WAT improves insulin sensitivity, we injected adenovirus (Adv) expressing miR-30a into the subcutaneous fat pad of diabetic mice. Exogenous miR-30a expression in the subcutaneous WAT depot of obese mice coupled improved insulin sensitivity and increased energy expenditure with decreased ectopic fat deposition in the liver and reduced WAT inflammation. High-throughput proteomic profiling and RNA-Seq suggested that miR-30a targets the transcription factor STAT1 to limit the actions of the proinflammatory cytokine interferon-γ (IFN-γ) that would otherwise restrict WAT expansion and decrease insulin sensitivity. We further demonstrated that miR-30a opposes the actions of IFN-γ, suggesting an important role for miR-30a in defending adipocytes against proinflammatory cytokines that reduce peripheral insulin sensitivity. Together, our data identify a critical molecular signaling axis, elements of which are involved in uncoupling obesity from metabolic dysfunction.

Original summary, description, bibliography and data management plan for a collaborative proposal for Einstein Toolkit support and development submitted by Georgia Tech, Louisiana State University, Rochester Institute of Technology and the University of Illinois at Urbana-Champaign to NSF SSI solicitation 14-520 in 2015. Following some re-budgeting and scope changes, the proposal resulted in funded collaborative project NSF 1550514, NSF 1550551, NSF 1550461, NSF 1550436.

Agglomerative clustering is an effective greedy way to quickly generate graph clusterings of high modularity in a small amount of time. In an effort to use the power offered by multi-core CPU and GPU hardware to solve the clustering problem, we introduce a fine-grained sharedmemory parallel graph coarsening algorithm and use this to implement a parallel agglomerative clustering heuristic on both the CPU and the GPU. This heuristic is able to generate clusterings in very little time: a modularity 0.996 clustering is obtained from a street network graph with 14 million vertices and 17 million edges in 4.6 seconds on the GPU.

With the proliferation of large irregular sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users to ingest, update, query and compute on the topological structure of these relationships represented as set(s) of edges between set(s) of vertices. To store and process Facebook-scale datasets, they must be able to support data sources with billions of edges, update rates of millions of updates per second, and complex analysis kernels. These platforms must provide intuitive interfaces that enable graph experts and novice programmers to write implementations of common graph algorithms. In this paper, we explore a variety of graph analysis and storage platforms. We compare their capabil- ities, interfaces, and performance by implementing and computing a set of real-world graph algorithms on synthetic graphs with up to 256 million edges. In the spirit of full disclosure, several authors are affiliated with the development of STINGER., Comment: WSSSPE13, 4 Pages, 18 Pages with Appendix, 25 figures

DNA sequence analysis is fundamental to life science research. The rapid development of next generation sequencing (NGS) technologies, and the richness and diversity of applications it makes feasible, have created an enormous gulf between the potential of this technology and the development of computational methods to realize this potential. Bridging this gap holds possibilities for broad impacts toward multiple grand challenges and offers unprecedented opportunities for software innovation and research. We argue that NGS-enabled applications need a critical mass of sustainable software to benefit from emerging computing platforms' transformative potential. Accumulating the necessary critical mass will require leaders in computational biology, bioinformatics, computer science, and computer engineering work together to identify core opportunity areas, critical software infrastructure, and software sustainability challenges. Furthermore, due to the quickly changing nature of both bioinformatics software and accelerator technology, we conclude that creating sustainable accelerated bioinformatics software means constructing a sustainable bridge between the two fields. In particular, sustained collaboration between domain developers and technology experts is needed to develop the accelerated kernels, libraries, frameworks and middleware that could provide the needed flexible link from NGS bioinformatics applications to emerging platforms., Comment: 4 pages

The Cell Broad Engine (BE) Processor has unique memory access architecture besides its powerful computing engines. Many computing-intensive applications have been ported to Cell/BE successfully. But memory-intensive applications are rarely investigated except for several micro benchmarks. Since Cell/BE has powerful software visible DMA engine, this paper studies on whether Cell/BE is suit for applica- tions with large amount of random memory accesses. Two benchmarks, GUPS and SSCA#2, are used. The latter is a rather complex one that in representative of real world graph analysis applications. We find both benchmarks have good performance on Cell/BE based IBM QS20/22. Com- pared with 2 conventional multi-processor systems with the same core/thread number, GUPS is about 40-80% fast and SSCA#2 about 17-30% fast. The dynamic load balanc- ing and software pipeline for optimizing SSCA#2 are intro- duced. Based on the experiment, the potential of Cell/BE for random access is analyzed in detail as well as its limita- tions of memory controller, atomic engine and TLB manage- ment.Our research shows although more programming effort are needed, Cell/BE has the potencial for irregular memory access applications., Comment: 8 pages, 10 figures

Bves was discovered in 1999 by two independent laboratories using screens to identify novel genes that were highly expressed in the developing heart (Reese et al., 1999; Andree et al., 2000). As an evolutionarily conserved transmembrane protein, Bves is postulated to play a role in cell adhesion and cell motility. In studies of Bves protein disruption, there have been multiple phenotypes, but few molecular mechanisms have been advanced to explain the underlying cause of these phenotypes. As the molecular function of Bves protein begins to be uncovered, it is now time to review the literature to examine the significance of this work and future directions of study. This review summarizes the literature on this unique protein and explores new and exciting data that support emerging themes on its molecular function.

Synthesis and Assessment Product 3.1 Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research, Robin T. Tokmakian, Naval Postgraduate School

Summary Background: Combined spinal-epidural anesthesia (CSE-A) is reportedly safe and effective for the pediatric population in infraumbilical surgery. Our main purpose was to describe our experience of this technique in neonates and infants undergoing elective major upper abdominal surgery. Methods: Spinal anesthesia was performed in 28 neonates and infants with isobaric bupivacaine 0.5%, 1 mg·kg−1 followed by placement of a caudal epidural catheter to thoracic spinal segments. The catheter tip position was confirmed radiographically. Respiratory and hemodynamic data were collected before and after the CSE-A and throughout the operation, as a measure of anesthetic effectiveness. Complications related to the anesthesia technique were collected as a measure of the anesthetic technique safety. Results: Satisfactory surgical anesthesia was achieved in 24 neonates and infants, four patients were converted to general anesthesia. Respiratory and hemodynamic variables did not change significantly during surgery, compared with baseline values: oxygen saturation (P = 0.07), systolic and diastolic blood pressures (P = 0.143, P = 0.198 respectively), heart rate (P = 0.080) and respiratory rate (P = 0.127). However, twenty infants were fussy during the surgical procedures and were calmed with intravenous midazolam; our patients required oxygen supplementation and transient manual ventilation intraoperatively. Conclusions: Combined spinal-epidural anesthesia could be considered as an effective anesthetic technique for elective major upper abdominal surgery in awake or sedated neonates and infants, and could be used cautiously by a pediatric anesthesiologist as an alternate to general anesthesia in high-risk neonates and infants undergoing upper gastrointestinal surgery.