16 research outputs found
Darwinian Data Structure Selection
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
Java projects from DaCapo benchmark, popular projects and 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 () of the projects. The median improvement across
the best solutions is , , 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 \%, and for
memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by
\%. \emph{Every} client of these projects will benefit from these
performance improvements.Comment: 11 page
Ascertaining price formation in cryptocurrency markets with machine learning
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
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
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
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 , where
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
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 indices, which generalize SALI (=) to the
case of 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
Hyperchaos of arbitrary order generated by a single feedback circuit, and the emergence of chaotic walks
info:eu-repo/semantics/publishe
Infertility reversed by glucocorticoids and full-term pregnancy in a couple with previously undiagnosed nonclassic congenital adrenal hyperplasia
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
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
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