A probabilistic evolutionary optimization approach to compute quasiparticle braids

Topological quantum computing is an alternative framework for avoiding the quantum decoherence problem in quantum computation. The problem of executing a gate in this framework can be posed as the problem of braiding quasiparticles. Because these are not Abelian, the problem can be reduced to finding an optimal product of braid generators where the optimality is defined in terms of the gate approximation and the braid's length. In this paper we propose the use of different variants of estimation of distribution algorithms to deal with the problem. Furthermore, we investigate how the regularities of the braid optimization problem can be translated into statistical regularities by means of the Boltzmann distribution. We show that our best algorithm is able to produce many solutions that approximates the target gate with an accuracy in the order of $10^{-6}$, and have lengths up to 9 times shorter than those expected from braids of the same accuracy obtained with other methods.Comment: 9 pages,7 figures. Accepted at SEAL 201

The interrelation between temperature regimes and fish size in juvenile Atlantic cod (Gadus morhua): effects on growth and feed conversion efficiency

The present paper describes the growth properties of juvenile Atlantic cod (Gadus morhua) reared at 7, 10, 13 and 16 °C, and a group reared under “temperature steps” i.e. with temperature reduced successively from 16 to 13 and 10 °C. Growth rate and feed conversion efficiency of juvenile Atlantic cod were significantly influenced by the interaction of temperature and fish size. Overall growth was highest in the 13 °C and the T-step groups but for different reasons, as the fish at 13 °C had 10% higher overall feeding intake compared to the T-step group, whereas the T-step had 8% higher feeding efficiency. After termination of the laboratory study the fish were reared in sea pens at ambient conditions for 17 months. The groups performed differently when reared at ambient conditions in the sea as the T-step group was 11.6, 11.5, 5.3 and 7.5% larger than 7, 10, 13 and 16 °C, respectively in June 2005. Optimal temperature for growth and feed conversion efficiency decreased with size, indicating an ontogenetic reduction in optimum temperature for growth with increasing size. The results suggest an optimum temperature for growth of juvenile Atlantic cod in the size range 5–50 g dropping from 14.7 °C for 5–10 g juvenile to 12.4 °C for 40–50 g juvenile. Moreover, a broader parabolic regression curve between growth, feed conversion efficiency and temperature as size increases, indicate increased temperature tolerance with size. The study confirms that juvenile cod exhibits ontogenetic variation in temperature optimum, which might partly explain different spatial distribution of juvenile and adult cod in ocean waters. Our study also indicates a physiological mechanism that might be linked to cod migrations as cod may maximize their feeding efficiency by active thermoregulation

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Modellbasierte Operationsplanung in der orthopädischen Chirurgie

Java performance is far from being trivial to benchmark because it is affected by various factors such as the Java application, its input, the virtual machine, the garbage collector, the heap size, etc. In addition, non-determinism at run-time causes the execution time of a Java program to differ from run to run. There are a number of sources of non-determinism such as Just-In-Time (JIT) compilation and optimization in the virtual machine (VM) driven by timerbased method sampling, thread scheduling, garbage collection, and various system effects. There exist a wide variety of Java performance evaluation methodologies used by researchers and benchmarkers. These methodologies differ from each other in a number of ways. Some report average performance over a number of runs of the same experiment; others report the best or second best performance observed; yet others report the worst. Some iterate the benchmark multiple times within a single VM invocation; others consider multiple VM invocations and iterate a single benchmark execution; yet others consider multiple VM invocations and iterate the benchmark multiple times. This paper shows that prevalent methodologies can be misleading, and can even lead to incorrect conclusions. The reason is that the data analysis is not statistically rigorous. In this paper, we present a survey of existing Java performance evaluation methodologies and discuss the importance of statistically rigorous data analysis for dealing with non-determinism. We advocate approaches to quantify startup as well as steady-state performance, and, in addition, we provide the JavaStats software to automatically obtain performance numbers in a rigorous manner. Although this paper focuses on Java performance evaluation, many of the issues addressed in this paper also apply to other programming languages and systems that build on a managed runtime system

Application of Conformal Prediction in QSAR

Part 4: First Conformal Prediction and Its Applications Workshop (COPA 2012)International audienceQSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using statistical learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity. However, predictions from a QSAR model are difficult to assess if their prediction intervals are unknown. In this paper we introduce conformal prediction into the QSAR field to address this issue. We apply support vector machine regression in combination with two nonconformity measures to five datasets of different sizes to demonstrate the usefulness of conformal prediction in QSAR modeling. One of the nonconformity measures provides prediction intervals with almost the same width as the size of the QSAR models’ prediction errors, showing that the prediction intervals obtained by conformal prediction are efficient and useful