16 research outputs found

    Darwinian Data Structure Selection

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    Data structure selection and tuning is laborious but can vastly improve an application's performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for \emph{every} project in 55 Java projects from DaCapo benchmark, 88 popular projects and 3030 uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves \emph{all} measures for 86%86\% (37/4337/43) of the projects. The median improvement across the best solutions is 4.8%4.8\%, 10.1%10.1\%, 5.1%5.1\% for runtime, memory and CPU usage. These aggregate results understate ARTEMIS's potential impact. Some of the benchmarks it improves are libraries or utility functions. Two examples are gson, a ubiquitous Java serialization framework, and xalan, Apache's XML transformation tool. ARTEMIS improves gson by 16.516.5\%, 1%1\% and 2.2%2.2\% for memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by 23.523.5\%. \emph{Every} client of these projects will benefit from these performance improvements.Comment: 11 page

    Ascertaining price formation in cryptocurrency markets with machine learning

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    The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction. Overall, we demonstrate that promising results are possible for cryptocurrencies on live data, by achieving a consistent 78% accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs. US dollars

    Optimising Darwinian Data Structures on Google Guava

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    Data structure selection and tuning is laborious but can vastly improve application performance and memory footprint. In this paper, we demonstrate how artemis, a multiobjective, cloud-based optimisation framework can automatically find optimal, tuned data structures and how it is used for optimising the Guava library. From the proposed solutions that artemis found, 27.45% of them improve all measures (execution time, CPU usage, and memory consumption). More specifically, artemis managed to improve the memory consumption of Guava by up 13%, execution time by up to 9%, and 4% CPU usage

    Genetic Improvement @ ICSE 2020

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    Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the 42nd ACM/IEEE International Conference on Software Engineering on Friday 3rd July 2020). Topics included industry take up, human factors, explainabiloity (explainability, justifyability, exploitability) and GI benchmarks. We also contrast various recent online approaches (e.g. SBST 2020) to holding virtual computer science conferences and workshops via the WWW on the Internet without face-2-face interaction. Finally we speculate on how the Coronavirus Covid-19 Pandemic will affect research next year and into the future

    Weak Chaos and the "Melting Transition" in a Confined Microplasma System

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    We present results demonstrating the occurrence of changes in the collective dynamics of a Hamiltonian system which describes a confined microplasma characterized by long--range Coulomb interactions. In its lower energy regime, we first detect macroscopically, the transition from a "crystalline--like" to a "liquid--like" behavior, which we call the "melting transition". We then proceed to study this transition using a microscopic chaos indicator called the \emph{Smaller Alignment Index} (SALI), which utilizes two deviation vectors in the tangent dynamics of the flow and is nearly constant for ordered (quasi--periodic) orbits, while it decays exponentially to zero for chaotic orbits as exp((λ1λ2)t)\exp(-(\lambda_{1}-\lambda_{2})t), where λ1>λ2>0\lambda_{1}>\lambda_{2}>0 are the two largest Lyapunov exponents. During the "melting phase", SALI exhibits a peculiar, stair--like decay to zero, reminiscent of "sticky" orbits of Hamiltonian systems near the boundaries of resonance islands. This alerts us to the importance of the Δλ=λ1λ2\Delta\lambda=\lambda_{1}-\lambda_{2} variations in that regime and helps us identify the energy range over which "melting" occurs as a multi--stage diffusion process through weakly chaotic layers in the phase space of the microplasma. Additional evidence supporting further the above findings is given by examining the GALIkGALI_{k} indices, which generalize SALI (=GALI2GALI_{2}) to the case of k>2k>2 deviation vectors and depend on the complete spectrum of Lyapunov exponents of the tangent flow about the reference orbit.Comment: 21 pages, 7 figures, submitted at PR

    Current trends and challenges towards wireless internet

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    Infertility reversed by glucocorticoids and full-term pregnancy in a couple with previously undiagnosed nonclassic congenital adrenal hyperplasia

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    Objective: To report the case of a couple with infertility and two unsuccessful previous attempts of ovarian stimulation for in vitro fertilization (IVF), whose nonclassic congenital adrenal hyperplasia (NC-CAH) due to 21-hydroxylase deficiency (21-OHD) was diagnosed and verified by molecular studies. Design: Case report. Setting: Outpatient practice and academic hospital. Patient(s): A woman with hyperandrogenism, luteal phase deficiency, and polycystic ovaries, and a man with oligospermia, a high rate of abnormal forms of spermatozoa (>95%), decreased sperm motility, and normal testicular volume. Intervention(s): Ultrasonography, semen analysis, endocrinologic assays, corticosteroids. Main Outcome Measure(s): Increased basal and adrenocorticotropic hormone (ACTH) stimulated 17α-hydroxyprogesterone (17-OHP) values were detected in both partners. CYP21A2 genotyping revealed compound heterozygosity in both wife and husband (wife: p.P30L/p.P453S; husband: p.P453S /p.V281L). Result(s): Hydrocortisone, 30 mg/day orally, was administered to both wife and husband. Forty days later, a pregnancy was detected. The prospective mother continued to receive hydrocortisone (25 mg/day) adjusted according to her hormone status. After a full-term uneventful pregnancy, a completely normal female was born. The baby had NC-CAH (genotype p.P30L/p.V281L). Conclusion(s): Nonclassic congenital adrenal hyperplasia, a potential cause of infertility in couples, can be successfully treated with corticosteroids. © 2011 American Society for Reproductive Medicine, Published by Elsevier Inc

    Symbolic dynamics generated by a combination of graphs

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    In this paper we investigate the growth rate of the number of all possible paths in graphs with respect to their length in an exact analytical way. Apart from the typical rates of growth, i.e. exponential or polynomial, we identify conditions for a stretched exponential type of growth. This is made possible by combining two or more graphs over the same alphabet, in order to obtain a discrete dynamical system generated by a triangular map, which can also be interpreted as a discrete nonautonomous system. Since the vertices and the edges of a graph are usually used to depict the states and transitions between states of a discrete dynamical system, the combination of two (or more) graphs can be interpreted as the driving, or perturbation, of one system by another. © 2008 World Scientific Publishing Company.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

    Giant Ectopic Retroesophageal Parathyroid Adenoma Excised Via Cervical Incision: a Case Report

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    Ectopic parathyroid adenomas are presented in 6–16% of patients with primary hyperparathyroidism. We herein report a case of a 6-cm, giant, intrathorasic, retroesophageal parathyroid adenoma that was successfully excised through cervical parathyroidectomy without the need of median sternotomy or thoracotomy. Cervical parathyroidectomy is a safe and feasible approach for giant ectopic mediastinal parathyroid adenomas providing a bilateral neck exploration and a lower proportion of perioperative morbidity. © 2020, Association of Surgeons of India