Physical properties and behavior of high-performance concrete at high temperatures

The following report gives an overview of the TC program and some preliminary result

Cloud benchmarking and performance analysis of an HPC application in Amazon EC2

Cloud computing platforms have been continuously evolving. Features such as the Elastic Fabric Adapter (EFA) in the Amazon Web Services (AWS) platform have brought yet another revolution in the High Performance Computing (HPC) world, further accelerating the convergence of HPC and cloud computing. Other public clouds also support similar features further fueling this change. In this paper, we show how and why the performance of a large-scale computational fluid dynamics (CFD) HPC application on AWS competes very closely with the one on Beskow - a Cray XC40 supercomputer at the PDC Center for High-Performance Computing - in terms of cost-efficiency with strong scaling up to 2304 processes. We perform an extensive set of micro and macro bench- marks in both environments and conduct a comparative analysis. Until as recently as 2020 these benchmarks have notoriously yielded unsatisfactory results for the cloud platforms compared with on-premise infrastructures. Our aim is to access the HPC capabilities of the cloud, and in general to demonstrate how researchers can scale and evaluate the performance of their application in the cloud.ENABL

Amino acid metabolism conflicts with protein diversity

The twenty protein coding amino acids are found in proteomes with different relative abundances. The most abundant amino acid, leucine, is nearly an order of magnitude more prevalent than the least abundant amino acid, cysteine. Amino acid metabolic costs differ similarly, constraining their incorporation into proteins. On the other hand, sequence diversity is necessary for protein folding, function and evolution. Here we present a simple model for a cost-diversity trade-off postulating that natural proteomes minimize amino acid metabolic flux while maximizing sequence entropy. The model explains the relative abundances of amino acids across a diverse set of proteomes. We found that the data is remarkably well explained when the cost function accounts for amino acid chemical decay. More than one hundred proteomes reach comparable solutions to the trade-off by different combinations of cost and diversity. Quantifying the interplay between proteome size and entropy shows that proteomes can get optimally large and diverse

Perspectives for analyzing non-linear photo-ionization spectra with deep neural networks trained with synthetic Hamilton matrices

We have constructed deep neural networks, which can map fluctuating photo-electron spectra obtained from noisy pulses to spectra from noise-free pulses. The network is trained on spectra from noisy pulses in combination with random Hamilton matrices, representing systems which could exist but do not necessarily exist. In [Giri et al., Phys. Rev. Lett., 2020, 124, 113201] we performed a purification of fluctuating spectra, that is, mapping them to those from Fourier-limited Gaussian pulses. Here, we investigate the performance of such neural-network-based maps for predicting spectra of double pulses, pulses with a chirp and even partially-coherent pulses from fluctuating spectra generated by noisy pulses. Secondly, we demonstrate that along with purification of a fluctuating double-pulse spectrum, one can estimate the time-delay of the underlying double pulse, an attractive feature for single-shot spectra from SASE FELs. We demonstrate our approach with resonant two-photon ionization, a non-linear process, sensitive to details of the laser pulse

Chemical abundances in bright giants of the globular cluster M62 (NGC 6266)

With the exception of Terzan 5, all the Galactic globular clusters that possess significant metallicity spreads, such as omega Cen and M22, are preferentially the more luminous clusters with extended horizontal branches. Here we present radial velocities and chemical abundances for seven bright giants in the globular cluster M62, a previously little-studied cluster. With M_V = -9.18, M62 is the ninth most luminous Galactic globular cluster and has an extended horizontal branch. Within our sample, we find (i) no evidence for a dispersion in metallicity, [Fe/H], beyond the measurement uncertainties, (ii) star-to-star abundance variations for C, O, Na and Al with the usual correlations between these elements as seen in other globular clusters, and (iii) a global enrichment for the elements Zr, Ba and La at the level [X/Fe] = +0.4 dex. For elements heavier than La, the abundance ratios are consistent with the scaled-solar $r$-process distribution. Below La, the abundances are anomalous when compared to the scaled-solar s-process or r-process distributions. For these elements, the abundance signature in M62 is in agreement with predictions of the s-process from fast-rotating massive stars, although the high [Rb/Y] ratio we measure may be a challenge to this scenario.Comment: Accepted for publication in MNRA

Quantal Consequences of Perturbations Which Destroy Structurally Unstable Orbits in Chaotic Billiards

Non-generic contributions to the quantal level-density from parallel segments in billiards are investigated. These contributions are due to the existence of marginally stable families of periodic orbits, which are structurally unstable, in the sense that small perturbations, such as a slight tilt of one of the segments, destroy them completely. We investigate the effects of such perturbation on the corresponding quantum spectra, and demonstrate them for the stadium billiard

Finding cool subdwarfs using a V-J reduced proper-motion diagram: Stellar parameters for 91 candidates

We present the results of a search for cool subdwarfs for which our candidates were drawn from a V-J reduced proper-motion diagram constructed by Salim & Gould (2002). Kinematic (U, V, and W) and self-consistent stellar parameters (Teff, log g, [Fe/H], and V_t) are derived for 91 candidate subdwarfs based on high resolution spectra. The observed stars span 3900K < Teff < 6200K and -2.63 < [Fe/H] < 0.25 including only 3 giants (log g < 4.0). Of the sample, 77 stars have MgH lines present in their spectra. With more than 56% of our candidate subdwarfs having [Fe/H] < -1.5, we show that the V-J reduced proper-motion diagram readily identifies metal-poor stars.Comment: PASP (in press

Efficient Concept Drift Handling for Batch Android Malware Detection Models

The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the existing literature pays limited attention to addressing this issue, with many advanced Android malware detection approaches, such as Drebin, DroidDet and MaMaDroid, relying on static models. In this work, we show how retraining techniques are able to maintain detector capabilities over time. Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: 1) the frequency with which the models are retrained, and 2) the data used for retraining. In the first experiment, we compare periodic retraining with a more advanced concept drift detection method that triggers retraining only when necessary. In the second experiment, we analyze sampling methods to reduce the amount of data used to retrain models. Specifically, we compare fixed sized windows of recent data and state-of-the-art active learning methods that select those apps that help keep the training dataset small but diverse. Our experiments show that concept drift detection and sample selection mechanisms result in very efficient retraining strategies which can be successfully used to maintain the performance of the static Android malware state-of-the-art detectors in changing environments.Comment: 18 page