GraphX: Unifying Data-Parallel and Graph-Parallel Analytics

From social networks to language modeling, the growing scale and importance of graph data has driven the development of numerous new graph-parallel systems (e.g., Pregel, GraphLab). By restricting the computation that can be expressed and introducing new techniques to partition and distribute the graph, these systems can efficiently execute iterative graph algorithms orders of magnitude faster than more general data-parallel systems. However, the same restrictions that enable the performance gains also make it difficult to express many of the important stages in a typical graph-analytics pipeline: constructing the graph, modifying its structure, or expressing computation that spans multiple graphs. As a consequence, existing graph analytics pipelines compose graph-parallel and data-parallel systems using external storage systems, leading to extensive data movement and complicated programming model. To address these challenges we introduce GraphX, a distributed graph computation framework that unifies graph-parallel and data-parallel computation. GraphX provides a small, core set of graph-parallel operators expressive enough to implement the Pregel and PowerGraph abstractions, yet simple enough to be cast in relational algebra. GraphX uses a collection of query optimization techniques such as automatic join rewrites to efficiently implement these graph-parallel operators. We evaluate GraphX on real-world graphs and workloads and demonstrate that GraphX achieves comparable performance as specialized graph computation systems, while outperforming them in end-to-end graph pipelines. Moreover, GraphX achieves a balance between expressiveness, performance, and ease of use

Probing Decoherence with Electromagnetically Induced Transparency in Superconductive Quantum Circuits

Superconductive quantum circuits (SQCs) comprise quantized energy levels that may be coupled via microwave electromagnetic fields. Described in this way, one may draw a close analogy to atoms with internal (electronic) levels coupled by laser light fields. In this Letter, we present a superconductive analog to electromagnetically induced transparency (S-EIT) that utilizes SQC designs of present day experimental consideration. We discuss how S-EIT can be used to establish macroscopic coherence in such systems and, thereby, utilized as a sensitive probe of decoherence.Comment: 5 pages, 3 figure

Dual and triple therapy to prevent mother-to-child transmission of HIV in a resource-limited setting – lessons from a South African programme

Objective. To determine outcomes of pregnant women and their infants at McCord Hospital in Durban, South Africa, where dual and triple therapy to reduce HIV vertical transmission have been used since 2004 despite national guidelines recommending simpler regimens. Method. We retrospectively examined records of all pregnant women attending McCord Hospital for their first antenatal visit between 1 March 2004 and 28 February 2007. Uptake of HIV testing and HIV prevalence were determined, and clinical, immunological and virological outcomes of HIV-positive women and their infants, followed through to 6 months after delivery, were described. Results. The antenatal clinic was attended by 5 303 women; 4 891 (92%) had an HIV test, and 703 (14%) were HIV positive. The HIV-positive women were subsequently followed up: 653 (93%) received antiretroviral therapy or prophylaxis, including 424 (60%) who received triple therapy. Of the 699 live babies delivered, 661 (94%) received prophylaxis. At 6 weeks 571 babies (82%) were brought back for HIV testing; 16 (2.8%) were HIV positive. After 6 months, only 150 women (21%) were receiving follow-up care at the adult HIV clinic. Conclusion. Where a tailored approach to prevention of motherto-child transmission (PMTCT) is used, which attempts to maximise available technology and resources, good short-term transmission outcomes can be achieved. However, longer-term follow-up of mothers’ and babies’ health presents a challenge. Successful strategies to link women to ongoing care are crucial to sustain the gains of PMTCT programmes

Induction of respiratory control in submitochondrial particles by dicyclohexylcarbodiimide

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/33289/1/0000681.pd

BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26x-629x while guaranteeing the same predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201

Impact of time-ordered measurements of the two states in a niobium superconducting qubit structure

Measurements of thermal activation are made in a superconducting, niobium Persistent-Current (PC) qubit structure, which has two stable classical states of equal and opposite circulating current. The magnetization signal is read out by ramping the bias current of a DC SQUID. This ramping causes time-ordered measurements of the two states, where measurement of one state occurs before the other. This time-ordering results in an effective measurement time, which can be used to probe the thermal activation rate between the two states. Fitting the magnetization signal as a function of temperature and ramp time allows one to estimate a quality factor of 10^6 for our devices, a value favorable for the observation of long quantum coherence times at lower temperatures.Comment: 14 pages, 4 figure

Multicentre, randomised controlled trial to investigate the effects of parental touch on relieving acute procedural pain in neonates (Petal)

This is the final version. Available on open access from BMJ Publishing via the DOI in this record. Data availability statement: Data sharing not applicable as no data sets generated and/or analysed for this study. Not applicable.INTRODUCTION: Newborn infants routinely undergo minor painful procedures as part of postnatal care, with infants born sick or premature requiring a greater number of procedures. As pain in early life can have long-term neurodevelopmental consequences and lead to parental anxiety and future avoidance of interventions, effective pain management is essential. Non-pharmacological comfort measures such as breastfeeding, swaddling and sweet solutions are inconsistently implemented and are not always practical or effective in reducing the transmission of noxious input to the brain. Stroking of the skin can activate C-tactile fibres and reduce pain, and therefore could provide a simple and safe parent-led intervention for the management of pain. The trial aim is to determine whether parental touch prior to a painful clinical procedure provides effective pain relief in neonates. METHODS AND ANALYSIS: This is a multicentre randomised controlled trial. A total of 112 neonates born at 35 weeks' gestation or more requiring a blood test in the first week of life will be recruited and randomised to receive parental stroking either preprocedure or postprocedure. We will record brain activity (EEG), cardiac and respiratory dynamics, oxygen saturation and facial expression to provide proxy pain outcome measures. The primary outcome will be the reduction of noxious-evoked brain activity in response to a heel lance. Secondary outcomes will be a reduction in clinical pain scores (Premature Infant Pain Profile-Revised), postprocedural tachycardia and parental anxiety. ETHICS AND DISSEMINATION: The study has been approved by the London-South East Research Ethics Committee (ref: 21/LO/0523). The results will be widely disseminated through peer-reviewed publications, international conferences and via our partner neonatal charities Bliss and Supporting the Sick Newborn And their Parents (SSNAP). If the parental tactile intervention is effective, recommendations will be submitted via the National Health Service clinical guideline adoption process. STUDY STATUS: Commenced September 2021. TRIAL REGISTRATION NUMBER: NCT04901611; 14 135 962.Wellcome TrustBlis