Concentration around the mean for maxima of empirical processes

In this paper we give optimal constants in Talagrand's concentration inequalities for maxima of empirical processes associated to independent and eventually nonidentically distributed random variables. Our approach is based on the entropy method introduced by Ledoux.Comment: Published at http://dx.doi.org/10.1214/009117905000000044 in the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Initial Mass Function of the Orion Nebula Cluster across the H-burning limit

We present a new census of the Orion Nebula Cluster (ONC) over a large field of view (>30'x30'), significantly increasing the known population of stellar and substellar cluster members with precisely determined properties. We develop and exploit a technique to determine stellar effective temperatures from optical colors, nearly doubling the previously available number of objects with effective temperature determinations in this benchmark cluster. Our technique utilizes colors from deep photometry in the I-band and in two medium-band filters at lambda~753 and 770nm, which accurately measure the depth of a molecular feature present in the spectra of cool stars. From these colors we can derive effective temperatures with a precision corresponding to better than one-half spectral subtype, and importantly this precision is independent of the extinction to the individual stars. Also, because this technique utilizes only photometry redward of 750nm, the results are only mildly sensitive to optical veiling produced by accretion. Completing our census with previously available data, we place some 1750 sources in the Hertzsprung-Russel diagram and assign masses and ages down to 0.02 solar masses. At faint luminosities, we detect a large population of background sources which is easily separated in our photometry from the bona fide cluster members. The resulting initial mass function of the cluster has good completeness well into the substellar mass range, and we find that it declines steeply with decreasing mass. This suggests a deficiency of newly formed brown dwarfs in the cluster compared to the Galactic disk population.Comment: 16 pages, 18 figures. Accepted for publication in The Astrophysical Journa

DNA strand displacement, strand annealing and strand swapping by the Drosophila Bloom's syndrome helicase

Genetic analysis of the Drosophila Bloom's syndrome helicase homolog (mus309/DmBLM) indicates that DmBLM is required for the synthesis-dependent strand annealing (SDSA) pathway of homologous recombination. Here we report the first biochemical study of DmBLM. Recombinant, epitope-tagged DmBLM was expressed in Drosophila cell culture and highly purified protein was prepared from nuclear extracts. Purified DmBLM exists exclusively as a high molecular weight (∼1.17 MDa) species, is a DNA-dependent ATPase, has 3′→5′ DNA helicase activity, prefers forked substrate DNAs and anneals complementary DNAs. High-affinity DNA binding is ATP-dependent and low-affinity ATP-independent interactions contribute to forked substrate DNA binding and drive strand annealing. DmBLM combines DNA strand displacement with DNA strand annealing to catalyze the displacement of one DNA strand while annealing a second complementary DNA strand

A Tuned and Scalable Fast Multipole Method as a Preeminent Algorithm for Exascale Systems

Among the algorithms that are likely to play a major role in future exascale computing, the fast multipole method (FMM) appears as a rising star. Our previous recent work showed scaling of an FMM on GPU clusters, with problem sizes in the order of billions of unknowns. That work led to an extremely parallel FMM, scaling to thousands of GPUs or tens of thousands of CPUs. This paper reports on a a campaign of performance tuning and scalability studies using multi-core CPUs, on the Kraken supercomputer. All kernels in the FMM were parallelized using OpenMP, and a test using 10^7 particles randomly distributed in a cube showed 78% efficiency on 8 threads. Tuning of the particle-to-particle kernel using SIMD instructions resulted in 4x speed-up of the overall algorithm on single-core tests with 10^3 - 10^7 particles. Parallel scalability was studied in both strong and weak scaling. The strong scaling test used 10^8 particles and resulted in 93% parallel efficiency on 2048 processes for the non-SIMD code and 54% for the SIMD-optimized code (which was still 2x faster). The weak scaling test used 10^6 particles per process, and resulted in 72% efficiency on 32,768 processes, with the largest calculation taking about 40 seconds to evaluate more than 32 billion unknowns. This work builds up evidence for our view that FMM is poised to play a leading role in exascale computing, and we end the paper with a discussion of the features that make it a particularly favorable algorithm for the emerging heterogeneous and massively parallel architectural landscape

PENGARUH SUHU TERHADAP KARAKTERISTIK TEGANGAN TEMBUS AC 50 Hz PADA MINY AK ISOLASI

Ide dasar dari penelitian ini adalah untuk mengetahui pengaruh perubahan suhu terhadap karakteristik tegangan tembus minyak isolasi, yaitu pada minyak isolasi merk Shell Diala B (baru), Electrol (baru). dan Electrol (lama). Berdasarkan data hasi/ pengujian, didapatkan karakteristik hubungan antara besarnya tegangan tembus minyak isolasi dengan perubahan suhu. Besarnya tegangan tembus minyak isolasi relatif mengalami peningkatan pada saat suhu dari minyak isolasi dinaikkan. Kondisi peningkatan tegangan tembus minyak isolasi ini terjadi pada ketiga jenis minyak isolasi mulai dari suhu 28°C sampai dengan 100°C. Tetapi pada kondisi suhu di atas 80°C peningkatan besarnya tegangan tembus minyak isolasi merk Shell Diala B (baru) dan Electrol (bekas) relatif kecil bahkan pada suhu 100°C mengalami sedikit penurunan. Jika ditinjau dari kondisi minyak isolasinya maka minyak isolasi baru memiliki tegangan tembus yang /ebih tinggi dibandingkan dengan minyak isolasi lama

Penanganan Overdispersi Pada Model Regresi Poisson Menggunakan Model Regresi Binomial Negatif

Poisson regression is the most popular tool for modeling the relationship between a discrete data in the response variable and a set of predictors with continue, discrete, categoric or mix data. Response variable with discrete data, however, may overdispersed or underdispersed, not conductive to Poisson regression which assumed that the mean value equals to variance (equidispersed). One of the model that be used to overdispersed the discrete data is a regression model based on mixture distribution namely Poisson-gamma mixture which result negative binomial distribution. This regression model usually known as binomial negative regression. Using Generalized Linier Model (GLM) approach, the given model, parameter estimate, diagnostics, and interpretation of negative binomial regression can be determined