Von der Milch zum festen Futter : Von der Abhängigkeit zur Selbständigkeit

Das Neugeborene ist bei Säugetieren und beim Menschen zunächst völlig von der Mutter abhängig, denn die Ernährung des oder der Nachkommen erfolgt erst einmal ganz auf Kosten der Mutter. Mit den Umstellungen auf dem Wege von der Abhängigkeit zur Selbständigkeit befassen sich die hier dargestellten Untersuchungen

apk2vec: Semi-supervised multi-view representation learning for profiling Android applications

Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semi-supervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for largescale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.Comment: International Conference on Data Mining, 201

Free energy for harmonically bound fermions in a magnetic field

An analytic expression is obtained for the free energy of fermions bound in an anisotropic harmonic potential in the presence of an arbitrary magnetic field, at a finite temperature. The specific heat and the magnetic moment are readily calculated. The results consist of two parts, a steady part and an oscillatory one. The latter is similar to the well known de Haas-van Alphen oscillation, but persists in the absence of a magnetic field. Application of the results to the nuclear shell model and surface effects of solids are briefly discussed. © 1987, IOP Publishing Ltd

On the self-similarity of line segments in decaying homogeneous isotropic turbulence

The self-similarity of a passive scalar in homogeneous isotropic decaying turbulence is investigated by the method of line segments (M. Gauding et al., Physics of Fluids 27.9 (2015): 095102). The analysis is based on a highly resolved direct numerical simulation of decaying turbulence. The method of line segments is used to perform a decomposition of the scalar field into smaller sub-units based on the extremal points of the scalar along a straight line. These sub-units (the so-called line segments) are parameterized by their length $\ell$ and the difference $\Delta\phi$ of the scalar field between the ending points. Line segments can be understood as thin local convective-diffusive structures in which diffusive processes are enhanced by compressive strain. From DNS, it is shown that the marginal distribution function of the length~$\ell$ assumes complete self-similarity when re-scaled by the mean length $\ell_m$. The joint statistics of $\Delta\phi$ and $\ell$, from which the local gradient $g=\Delta\phi/\ell$ can be defined, play an important role in understanding the turbulence mixing and flow structure. Large values of $g$ occur at a small but finite length scale. Statistics of $g$ are characterized by rare but strong deviations that exceed the standard deviation by more than one order of magnitude. It is shown that these events break complete self-similarity of line segments, which confirms the standard paradigm of turbulence that intense events (which are known as internal intermittency) are not self-similar

Phase Space Formulation of Quantum Mechanics and the Two Dimensional Electron Gas in a Magnetic Field.

In this dissertation, the theory of the phase-space formulation of quantum mechanics (PSFQM) is discussed and applied to investigations of various low-temperature properties of both two- and three-dimensional electron systems. The origin of an apparent discrepancy recently reported by others between results obtained by the PSFQM and conventional (Schrodinger) quantum mechanics is discussed. A complete agreement is arrived at here by demonstrating the correct way to use the PSFQM. A general formulation is developed, through use of the PSFQM, for determining the free energy of a Fermi gas contained in an arbitrary smooth external potential and in a weak magnetic field, in the low-temperature limit. Explicit formulae are given, which enable one to compute surface and temperature effects on various physical properties (susceptibility, specific heat, etc.) of the system. As an illustration that the general formalism presented can be applied to other kinds of Fermi gas (for example, nucleons) contained in an external potential, the modified Thomas-Fermi theory is extended to include temperature effects. Expressions are derived for the magnetic susceptibility and Fermi energy of a non-interacting two-dimensional electron gas (2DEG) in the strong-magnetic-field limit and for non-zero temperatures. The effect of level broadening on the steady part of the magnetic moment and the specific heat is calculated by deriving an expression for the free energy of a 2DEG in a uniform magnetic field, with an arbitrary Landau level broadening and a finite temperature. Systematic expansions, in powers of 1/B, for the free energy and the density of states, are derived for a degenerate 2DEG in the presence of a strong magnetic field and an arbitrary potential. They are then applied to a system involving random impurities. Level broadenings, induced by long range electron-impurity scatterings alone, are shown to be independent of the magnetic field in the strong magnetic field limit. Broadened Landau levels can have a large variety of shapes. This theory leads to good agreement with the recent experiment on the de Haas-van Alphen effect in Br\sb2-graphite intercalation compounds

Deep Convolution and Correlated Manifold Embedded Distribution Alignment for Forest Fire Smoke Prediction

This paper proposes the deep convolution and correlated manifold embedded distribution alignment (DC-CMEDA) model, which is able to realize the transfer learning classification between and among various small datasets, and greatly shorten the training time. First, pre-trained Resnet50 network is used for feature transfer to extract smoke features because of the difficulty in training small dataset of forest fire smoke; second, a correlated manifold embedded distribution alignment (CMEDA) is proposed to register the smoke features in order to align the input feature distributions of the source and target domains; and finally, a trainable network model is constructed. This model is evaluated in the paper based on satellite remote sensing image and video image datasets. Compared with the deep convolutional integrated long short-term memory (DC-ILSTM) network, DC-CMEDA has increased the accuracy of video images by 1.50 %, and the accuracy of satellite remote sensing images by 4.00 %. Compared the CMEDA algorithm with the ILSTM algorithm, the number of iterations of the former has decreased to 10 times or less, and the algorithm complexity of CMEDA is lower than that of ILSTM. DC-CMEDA has a great advantage in terms of convergence speed. The experimental results show that DC-CMEDA can solve the problem of small sample smoke dataset detection and recognition