The variations of temperature, pressure and wind speedvalues: Effects on gravity waves

According to the theoretical studies, gravity waves, which are excited in the lower atmosphere, can transport momentum and energy on to the upper levels. Gravity waves are attenuated by interacting with the large-scale wind motions in the upper part of the middle atmosphere. Different meteorological parameters and atmospheric circulations are known as one of the sources of excitement of gravity waves. The main purpose of this paper is to analyze the onset of some gravity waves (GWs), and seasonal variations of gravity waves over Istanbul. Radiosonda data of Istanbul in troposphere and lower stratosphere (1000 hPa–30 hPa) between 1993 and 1997 is analyzed. Daily, monthly and annual variation of pressure heights, air temperature, horizontal wind speed and deviations from mean values are interpreted. Zonal and meridional wind speed variations show the effects of gravity waves for different pressure levels in the troposphere. These waves lead the meso-scale wave form structures in spring, autumn and winter

Wavelet transforms of meteorological parameters and gravity waves

The main purpose of this paper is to analyze some characteristics of gravity waves (GWs), and seasonal variations of atmospheric waves over Istanbul by using wavelet techniques. Daily radiosonda data of Istanbul in the troposphere and lower stratosphere (1000hPa-30hPa) between 1993 and 1997 have been considered. Wavelet analysis based on a computer simulation of data is generally close to the real data when Daubechies wavelet series are used. Daily, monthly, seasonal and annual variations of pressure heights, air temperature and deviations from mean values have been analyzed. Variations show the effects of gravity waves for different pressure levels in the troposphere. These waves lead to the meso-scale wave-form structures in spring, autumn and winter. As a result of this study, wavelet series and transforms for data construction, definition of some discontinuities and the local effects on the signal have been compared with the results of previous studies. The most similar structure between temperature, turbulence parameters and geo-potential height deviations has been defined at the 500-hPa pressure level

Optimization-Based Peptide Mass Fingerprinting for Protein Mixture Identification

*Motivation:* In current proteome research, peptide sequencing is probably the most widely used method for protein mixture identification. However, this peptide-centric method has its own disadvantages such as the immense volume of tandem Mass Spectrometry (MS) data for sequencing peptides. With the fast development of technology, it is possible to investigate other alternative techniques. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins for more than 15 years. Unfortunately, this technique is less accurate than peptide sequencing method and cannot handle protein mixtures, which hampers the widespread use of PMF technique. If we can remove these limitations, PMF will become a useful tool in protein mixture identification. 
*Results:* We first formulate the problem of PMF protein mixture identification as an optimization problem. Then, we show that the use of some simple heuristics enables us to find good solutions. As a result, we obtain much better identification results than previous methods. Moreover, the result on real MS data can be comparable with that of the peptide sequencing method. Through a comprehensive simulation study, we identify a set of limiting factors that hinder the performance of PMF method in protein mixtures. We argue that it is feasible to remove these limitations and PMF can be a powerful tool in the analysis of protein mixtures

Deep Metric Learning with Chance Constraints

Deep metric learning (DML) aims to minimize empirical expected loss of the pairwise intra-/inter- class proximity violations in the embedding image. We relate DML to feasibility problem of finite chance constraints. We show that minimizer of proxy-based DML satisfies certain chance constraints, and that the worst case generalization performance of the proxy-based methods can be characterized by the radius of the smallest ball around a class proxy to cover the entire domain of the corresponding class samples, suggesting multiple proxies per class helps performance. To provide a scalable algorithm as well as exploiting more proxies, we consider the chance constraints implied by the minimizers of proxy-based DML instances and reformulate DML as finding a feasible point in intersection of such constraints, resulting in a problem to be approximately solved by iterative projections. Simply put, we repeatedly train a regularized proxy-based loss and re-initialize the proxies with the embeddings of the deliberately selected new samples. We apply our method with the well-accepted losses and evaluate on four popular benchmark datasets for image retrieval. Outperforming state-of-the-art, our method consistently improves the performance of the applied losses. Code is available at: https://github.com/yetigurbuz/ccp-dmlComment: Under review at IEEE Transactions on Neural Networks and Learning System

An Empirical Examination of the “Rule of Three”: Strategy Implications for Top Management, Marketers, and Investors

This study represents the first empirical examination of the “Rule of Three,” a theory at odds with several popular notions regarding industry structure and business performance, including the positive linear market share–performance relationship. In general, the findings from more than 160 industries support the Rule of Three and provide five main insights: First, there appears to be a prevalent competitive structure for mature industries in which three “generalist” firms control the market. Second, industries that conform to this structure tend to perform better than industries with a fewer or greater number of generalists. Third, both “specialists” and generalists outperform firms that are “stuck in the middle.” Fourth, the performance benefits of market leadership appear to diminish with excessive market share. Fifth, the Rule of Three industry structure and its influence over firm profitability do not appear to be priced appropriately by financial markets. The authors discuss the implications for multiple stakeholders

Capture on High Curvature Region: Aggregation of Colloidal Particle Bound to Giant Phospholipid Vesicles

A very recent observation on the membrane mediated attraction and ordered aggregation of colloidal particles bound to giant phospholipid vesicles (I. Koltover, J. O. R\"{a}dler, C. R. Safinya, Phys. Rev. Lett. {\bf 82}, 1991(1999)) is investigated theoretically within the frame of Helfrich curvature elasticity theory of lipid bilayer fluid membrane. Since the concave or waist regions of the vesicle possess the highest local bending energy density, the aggregation of colloidal beads on these places can reduce the elastic energy in maximum. Our calculation shows that a bead in the concave region lowers its energy $\sim 20 k_B T$. For an axisymmetrical dumbbell vesicle, the local curvature energy density along the waist is equally of maximum, the beads can thus be distributed freely with varying separation distance.Comment: 12 pages, 2 figures. REVte

On bulk singularities in the random normal matrix model

We extend the method of rescaled Ward identities of Ameur-Kang-Makarov to study the distribution of eigenvalues close to a bulk singularity, i.e. a point in the interior of the droplet where the density of the classical equilibrium measure vanishes. We prove results to the effect that a certain "dominant part" of the Taylor expansion determines the microscopic properties near a bulk singularity. A description of the distribution is given in terms of a special entire function, which depends on the nature of the singularity (a Mittag-Leffler function in the case of a rotationally symmetric singularity).Comment: This version clarifies on the proof of Theorem

Large deformation of spherical vesicle studied by perturbation theory and Surface evolver

With tangent angle perturbation approach the axial symmetry deformation of a spherical vesicle in large under the pressure changes is studied by the elasticity theory of Helfrich spontaneous curvature model.Three main results in axial symmetry shape: biconcave shape, peanut shape, and one type of myelin are obtained. These axial symmetry morphology deformations are in agreement with those observed in lipsome experiments by dark-field light microscopy [Hotani, J. Mol. Biol. 178, (1984) 113] and in the red blood cell with two thin filaments (myelin) observed in living state (see, Bessis, Living Blood Cells and Their Ultrastructure, Springer-Verlag, 1973). Furthermore, the biconcave shape and peanut shape can be simulated with the help of a powerful software, Surface Evolver [Brakke, Exp. Math. 1, 141 (1992) 141], in which the spontaneous curvature can be easy taken into account.Comment: 16 pages, 6 EPS figures and 2 PS figure