Synthesis and Magnetic Characterization of Metal-filled Double-sided Porous Silicon Samples

A magnetic semiconductor/metal nanocomposite with a nanostructured silicon wafer as base material and incorporated metallic nanostructures (Ni, Co, NiCo) is fabricated in two electrochemical steps. First, the silicon template is anodized in an HF-electrolyte to obtain a porous structure with oriented pores grown perpendicular to the surface. This etching procedure is carried out either in forming a sample with a single porous layer on one side or in producing a double-sided specimen with a porous layer on each side. Second, this matrix is used for deposition of transition metals as Ni, Co or an alloy of these. The achieved hybrid material with incorporated Ni- and Co-nanostructures within one sample is investigated magnetically. The obtained results are compared with the ones gained from samples containing a single metal

Electron Microprobe Analysis of Electrolytes in Whole Cultured Epithelial Cells

Microprobe analysis was used to determine electrolyte contents in whole epithelial sheets of A6 cells and to investigate the most critical points of this method. Analysis of dextran standard sections of different thickness revealed that low accelerating voltages of about 10 kV are best suited for whole freeze-dried cells on thick supports, since 5 μm thick sections are not penetrated by 10 kV electrons. Washing of A6 cells for 10 sec with distilled water led to cell swelling of about 40%, but the molar concentration ratios and the concentrations per dry weight (dw) were not altered. Washing for 60 sec with distilled water caused a further increase in cell volume (120%) and loss of cellular K and Cl (90 mmol/kg dw). Washing with isotonic NH4-acetate led to a loss of cell Cl already after 10 sec. To characterize the Na transport compartment, A6 cells cultured on permeable supports were washed for 5 sec with distilled water, freeze-dried, and analyzed. Inhibition of transepithelial Na transport by ouabain increased Na/P from 0.15±0.07 to 0.75±0.03 and Cl/P from 0.21±0.001 to 0.38±0.003 while KIP decreased from 0.83±0.08 to 0.32±0.03. The changes in cell Na and K contents can be explained by K/Na exchange; the increase in Cl content indicates some cell swelling. Since the ouabain-induced changes could be prevented by apical amiloride, the apical membrane provides the most important pathway for Na entry in A6 cells

Analysing Neural Network Topologies: a Game Theoretic Approach

Artificial Neural Networks have shown impressive success in very different application cases. Choosing a proper network architecture is a critical decision for a network\u2019s success, usually done in a manual manner. As a straightforward strategy, large, mostly fully connected architectures are selected, thereby relying on a good optimization strategy to find proper weights while at the same time avoiding overfitting. However, large parts of the final network are redundant. In the best case, large parts of the network become simply irrelevant for later inferencing. In the worst case, highly parameterized architectures hinder proper optimization and allow the easy creation of adverserial examples fooling the network. A first step in removing irrelevant architectural parts lies in identifying those parts, which requires measuring the contribution of individual components such as neurons. In previous work, heuristics based on using the weight distribution of a neuron as contribution measure have shown some success, but do not provide a proper theoretical understanding. Therefore, in our work we investigate game theoretic measures, namely the Shapley value (SV), in order to separate relevant from irrelevant parts of an artificial neural network. We begin by designing a coalitional game for an artificial neural network, where neurons form coalitions and the average contributions of neurons to coalitions yield to the Shapley value. In order to measure how well the Shapley value measures the contribution of individual neurons, we remove low-contributing neurons and measure its impact on the network performance. In our experiments we show that the Shapley value outperforms other heuristics for measuring the contribution of neurons

Investigation of a Mesoporous Silicon Based Ferromagnetic Nanocomposite

A semiconductor/metal nanocomposite is composed of a porosified silicon wafer and embedded ferromagnetic nanostructures. The obtained hybrid system possesses the electronic properties of silicon together with the magnetic properties of the incorporated ferromagnetic metal. On the one hand, a transition metal is electrochemically deposited from a metal salt solution into the nanostructured silicon skeleton, on the other hand magnetic particles of a few nanometres in size, fabricated in solution, are incorporated by immersion. The electrochemically deposited nanostructures can be tuned in size, shape and their spatial distribution by the process parameters, and thus specimens with desired ferromagnetic properties can be fabricated. Using magnetite nanoparticles for infiltration into porous silicon is of interest not only because of the magnetic properties of the composite material due to the possible modification of the ferromagnetic/superparamagnetic transition but also because of the biocompatibility of the system caused by the low toxicity of both materials. Thus, it is a promising candidate for biomedical applications as drug delivery or biomedical targeting

Lessons learned from the 1st Ariel Machine Learning Challenge: Correcting transiting exoplanet light curves for stellar spots

The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is to identify the effects of spots visually and correct for them manually or discard the affected data. This paper explores a first step towards fully automating the efficient and precise derivation of transit depths from transit light curves in the presence of stellar spots. The primary focus of the paper is to present in detail a diverse arsenal of methods for doing so. The methods and results we present were obtained in the context of the 1st Machine Learning Challenge organized for the European Space Agency’s upcoming Ariel mission. We first present the problem, the simulated Ariel-like data and outline the Challenge while identifying best practices for organizing similar challenges in the future. Finally, we present the solutions obtained by the top-5 winning teams, provide their code and discuss their implications. Successful solutions either construct highly non-linear (w.r.t. the raw data) models with minimal preprocessing –deep neural networks and ensemble methods– or amount to obtaining meaningful statistics from the light curves, constructing linear models on which yields comparably good predictive performance

Low-Temperature Preparation of Superparamagnetic CoFe2O4 Microspheres with High Saturation Magnetization

Based on a low-temperature route, monodispersed CoFe2O4 microspheres (MSs) were fabricated through aggregation of primary nanoparticles. The microstructural and magnetic characteristics of the as-prepared MSs were characterized by X-ray diffraction/photoelectron spectroscopy, scanning/transmitting electron microscopy, and vibrating sample magnetometer. The results indicate that the diameters of CoFe2O4 MSs with narrow size distribution can be tuned from over 200 to ~330 nm. Magnetic measurements reveal these MSs exhibit superparamagnetic behavior at room temperature with high saturation magnetization. Furthermore, the mechanism of formation of the monodispersed CoFe2O4 MSs was discussed on the basis of time-dependent experiments, in which hydrophilic PVP plays a crucial role

Software Testing Techniques Revisited for OWL Ontologies

Ontologies are an essential component of semantic knowledge bases and applications, and nowadays they are used in a plethora of domains. Despite the maturity of ontology languages, support tools and engineering techniques, the testing and validation of ontologies is a field which still lacks consolidated approaches and tools. This paper attempts at partly bridging that gap, taking a first step towards the extension of some traditional software testing techniques to ontologies expressed in a widely-used format. Mutation testing and coverage testing, revisited in the light of the peculiar features of the ontology language and structure, can can assist in designing better test suites to validate them, and overall help in the engineering and refinement of ontologies and software based on them