Institute of Physics and Technology

This article reviews stability and laminar-turbulent transition in high-speed boundary-layer flows, emphasizing qualitative features of the disturbance spectrum leading to new mechanisms of receptivity and instability. It is shown that the extension of subsonic and low-supersonic stability concepts and transition prediction methods to hypersonic speeds is not straightforward. The discussion focuses on theoretical models providing insights into the physics of instability and helping make proper decisions on transition control strategies.

In the original version of this manuscript, an error was introduced on pp352. '2.7nb:1.6nb' has been corrected to '2.4nb:1.3nb' in the current online and printed version. doi:10.1093/ptep/ptz106.

(c) The Author(s) 2014. This article is published with open access at Springerlink.com.
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New sets of CMS underlying-event parameters ("tunes") are presented for the pythia8 event generator. These tunes use the NNPDF3.1 parton distribution functions (PDFs) at leading (LO), next-to-leading (NLO), or next-to-next-to-leading (NNLO) orders in perturbative quantum chromodynamics, and the strong coupling evolution at LO or NLO. Measurements of charged-particle multiplicity and transverse momentum densities at various hadron collision energies are fit simultaneously to determine the parameters of the tunes. Comparisons of the predictions of the new tunes are provided for observables sensitive to the event shapes at LEP, global underlying event, soft multiparton interactions, and double-parton scattering contributions. In addition, comparisons are made for observables measured in various specific processes, such as multijet, Drell-Yan, and top quark-antiquark pair production including jet substructure observables. The simulation of the underlying event provided by the new tunes is interfaced to a higher-order matrix-element calculation. For the first time, predictions from pythia8 obtained with tunes based on NLO or NNLO PDFs are shown to reliably describe minimum-bias and underlying-event data with a similar level of agreement to predictions from tunes using LO PDF sets.

We propose a simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning. Given a layer, we use non-linear least squares to compute a low-rank CP-decomposition of the 4D convolution kernel tensor into a sum of a small number of rank-one tensors. At the second step, this decomposition is used to replace the original convolutional layer with a sequence of four convolutional layers with small kernels. After such replacement, the entire network is fine-tuned on the training data using standard backpropagation process. We evaluate this approach on two CNNs and show that it is competitive with previous approaches, leading to higher obtained CPU speedups at the cost of lower accuracy drops for the smaller of the two networks. Thus, for the 36-class character classification CNN, our approach obtains a 8.5x CPU speedup of the whole network with only minor accuracy drop (1% from 91% to 90%). For the standard ImageNet architecture (AlexNet), the approach speeds up the second convolution layer by a factor of 4x at the cost of $1\%$ increase of the overall top-5 classification error.

The problem of photon creation from vacuum in an ideal cavity with vibrating walls is studied in the resonance case, when the frequency of vibrations equals twice the frequency of some unperturbed electromagnetic mode. Analytical solutions are obtained in two cases: for the one-dimensional model (scalar electrodynamics) and for the three-dimensional (3D) cavity. In the first example, we have a strong intermode interaction; nonetheless, an explicit solution in terms of the complete elliptic integrals is found. The rate of photon generation in the principal mode rapidly assumes a constant value proportional to the product of the frequency by the dimensionless amplitude of oscillations. The total amount of photons created in all the modes increases in time as ${\mathit{t}}^{2}$. In the second example, the eigenmode spectrum is nonequidistant and the problem can be reduced to the problem of a single harmonic oscillator with a time-dependent frequency. The number of photons in the resonant mode of a 3D cavity increases exponentially in time and the field appears in a highly squeezed state with a strongly oscillating photon distribution function. The problem of detecting the created photons is analyzed in the framework of a simplified model, when a detector is replaced with a harmonic oscillator. It turns out that the presence of the detector changes the picture drastically: both the detector and the field mode occur in highly mixed (nonthermal) quantum states, with identical nonoscillating photon distribution functions. The detector gains exactly half of the total energy of excitation inside the cavity. The estimations show a possibility of creating up to several hundred or even thousand photons, provided that the cavity's Q factor exceeds ${10}^{10}$ and the amplitude of the wall's oscillations is greater than ${10}^{\mathrm{\ensuremath{-}}10}$ cm at a frequency of the order of 10 GHz. \textcopyright{} 1996 The American Physical Society.

Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular structures within hours, while conventional drug discovery pipelines require months of work. In this article, we propose a new generative architecture, entangled conditional adversarial autoencoder, that generates molecular structures based on various properties, such as activity against a specific protein, solubility, or ease of synthesis. We apply the proposed model to generate a novel inhibitor of Janus kinase 3, implicated in rheumatoid arthritis, psoriasis, and vitiligo. The discovered molecule was tested in vitro and showed good activity and selectivity.

In the original version of this manuscript, an error was introduced on pp352. '2.7nb:1.6nb' has been corrected to '2.4nb:1.3nb' in the current online and printed version. doi:10.1093/ptep/ptz106.

A second-mode stability analysis has been performed for a hypersonic boundary layer on a wall covered by a porous coating with equally spaced cylindrical blind microholes. Massive reduction of the second mode amplification is found to be due to the disturbance energy absorption by the porous layer. This stabilization effect was demonstrated by experiments recently conducted on a sharp cone in the T-5 high-enthalpy wind tunnel of the Graduate Aeronautical Laboratories of the California Institute of Technology. Their experimental confirmation of the theoretical predictions underscores the possibility that ultrasonically absorptive porous coatings may be exploited for passive laminar flow control on hypersonic vehicle surfaces.

In a conventional view of the prokaryotic genome organization, promoters precede operons and ribosome binding sites (RBSs) with Shine-Dalgarno consensus precede genes. However, recent experimental research suggesting a more diverse view motivated us to develop an algorithm with improved gene-finding accuracy. We describe GeneMarkS-2, an ab initio algorithm that uses a model derived by self-training for finding species-specific (native) genes, along with an array of precomputed "heuristic" models designed to identify harder-to-detect genes (likely horizontally transferred). Importantly, we designed GeneMarkS-2 to identify several types of distinct sequence patterns (signals) involved in gene expression control, among them the patterns characteristic for leaderless transcription as well as noncanonical RBS patterns. To assess the accuracy of GeneMarkS-2, we used genes validated by COG (Clusters of Orthologous Groups) annotation, proteomics experiments, and N-terminal protein sequencing. We observed that GeneMarkS-2 performed better on average in all accuracy measures when compared with the current state-of-the-art gene prediction tools. Furthermore, the screening of ∼5000 representative prokaryotic genomes made by GeneMarkS-2 predicted frequent leaderless transcription in both archaea and bacteria. We also observed that the RBS sites in some species with leadered transcription did not necessarily exhibit the Shine-Dalgarno consensus. The modeling of different types of sequence motifs regulating gene expression prompted a division of prokaryotic genomes into five categories with distinct sequence patterns around the gene starts.

In this paper we discuss stochastic differential delay equations with Markovian switching. These can be regarded as the result of several stochastic differential delay equations switching among each other according to the movement of a Markov chain. One of the main aims of this paper is to investigate the exponential stability of the equations.

Cross sections for elastic and proton-dissociative photoproduction of J / mesons are measured with the H1 detector in positron-proton collisions at HERA. The data were collected at ep centre-of-mass energies s 318 GeV and s 225 GeV, corresponding to integrated luminosities of L = 130 pb -1 and L = 10.8 pb -1 , respectively. The cross sections are measured as a function of the photon-proton centre-of-mass energy in the range 25 < W p < 110 GeV. Differential cross sections d/dt, where t is the squared four-momentum transfer at the proton vertex, are measured in the range |t| < 1.2 GeV 2 for the elastic process and |t| < 8 GeV 2 for proton dissociation. The results are compared to other measurements.

With use of the travelling-wave geometry approach, integrated superconductor-nanophotonic devices based on silicon nitride nanophotonic waveguide with a superconducting NbN-nanowire suited on top of the waveguide were fabricated. NbN-nanowire was operated as a single-photon counting detector with up to 92 % on-chip detection efficiency in the coherent mode, serving as a highly sensitive IR heterodyne mixer with spectral resolution (f/df) greater than 106 in C-band at 1550 nm wavelength.

Elucidating the nature of the magnetism of a high-temperature superconductor is crucial for establishing its pairing mechanism. The parent compounds of the cuprate and iron-pnictide superconductors exhibit Néel and stripe magnetic order, respectively. However, FeSe, the structurally simplest iron-based superconductor, shows nematic order (Ts=90 K), but not magnetic order in the parent phase, and its magnetic ground state is intensely debated. Here we report inelastic neutron-scattering experiments that reveal both stripe and Néel spin fluctuations over a wide energy range at 110 K. On entering the nematic phase, a substantial amount of spectral weight is transferred from the Néel to the stripe spin fluctuations. Moreover, the total fluctuating magnetic moment of FeSe is ∼60% larger than that in the iron pnictide BaFe2As2. Our results suggest that FeSe is a novel S=1 nematic quantum-disordered paramagnet interpolating between the Néel and stripe magnetic instabilities.

Some gamma-ray bursts (GRBs) have a tera-electron volt (TeV) afterglow, but the early onset of this has not been observed. We report observations with the Large High Altitude Air Shower Observatory (LHAASO) of the bright GRB 221009A, which serendipitously occurred within the instrument's field of view. More than 64,000 photons >0.2 TeV were detected within the first 3000 seconds. The TeV flux began several minutes after the GRB trigger and then rose to a peak ~10 seconds later. This was followed by a decay phase, which became more rapid ~650 seconds after the peak. We interpret the emission using a model of a relativistic jet with half-opening angle of ~0.8°. This is consistent with the core of a structured jet and could explain the high isotropic energy of this GRB.

CATHODE-DIRECTED STREAMER; PULSED POSITIVE CORONA; STRAHLUNG AUS ELEKTRONENLAWINEN; PHOTOIONIZATION; OXYGEN; AIR; DETACHMENT; DISCHARGE; DYNAMICS; IONS

The discrete spectrum of disturbances in high-speed boundary layers is discussed with emphasis on singularities caused by synchronization of the normal modes. Numerical examples illustrate different spectral structures and jumps from one structure to another with small variations of basic flow parameters. It is shown that this singular behavior is due to branching of the dispersion curves in the synchronization region. Depending on the locations of the branch points, the spectrum contains an unstable mode or two. In connection with this, the terminology used for instability of high-speed boundary layers is clarified. It is emphasized that the spectrum branching may cause difficulties in stability analyses based on traditional linear stability theory and parabolized stability equations methods. Multiple-mode considerations and direct numerical simulations are needed to clarify this issue.

The diverse cell types of an organ have a highly structured organization to enable their efficient and correct function. To fully appreciate gene functions in a given cell type, one needs to understand how much, when and where the gene is expressed. Classic bulk RNA sequencing and popular single cell sequencing destroy cell structural organization and fail to provide spatial information. However, the spatial location of gene expression or of the cell in a complex tissue provides key clues to comprehend how the neighboring genes or cells cross talk, transduce signals and work together as a team to complete the job. The functional requirement for the spatial content has been a driving force for rapid development of the spatial transcriptomics technologies in the past few years. Here, we present an overview of current spatial technologies with a special focus on the commercially available or currently being commercialized technologies, highlight their applications by category and discuss experimental considerations for a first spatial experiment.

Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering. In particular, the recently proposed Mult-VAE model, which used the multinomial likelihood variational autoencoders, has shown excellent results for top-N recommendations. In this work, we propose the Recommender VAE (RecVAE) model that originates from our research on regularization techniques for variational autoencoders. RecVAE introduces several novel ideas to improve Mult-VAE, including a novel composite prior distribution for the latent codes, a new approach to setting the beta hyperparameter for the beta-VAE framework, and a new approach to training based on alternating updates. In experimental evaluation, we show that RecVAE significantly outperforms previously proposed autoencoder-based models, including Mult-VAE and RaCT, across classical collaborative filtering datasets, and present a detailed ablation study to assess our new developments. Code and models are available at https://github.com/ilya-shenbin/RecVAE.

We experimentally demonstrate quantum enhanced resolution in confocal fluorescence microscopy exploiting the nonclassical photon statistics of single nitrogen-vacancy color centers in diamond. By developing a general model of superresolution based on the direct sampling of the kth-order autocorrelation function of the photoluminescence signal, we show the possibility to resolve, in principle, arbitrarily close emitting centers.