Machining High-Temperature Alloys by Means of Solid Ceramic End Mills

Russian Engineering Research - The cutting of high-temperature alloys is considered. The performance of solid ceramic end mills is assessed by experimental comparison of five different designs with...     

Quantum computing with spin qubits in semiconductor structures

We survey recent work on designing and evaluating quantum computing implementations based on nuclear or bound-electron spins in semiconductor heterostructures at low temperatures and in high magnetic fields. General overview is followed by a summary of results of our theoretical calculations of decoherence time scales and spin-spin interactions. The latter were carried out for systems for which the two-dimensional electron gas provides the dominant carrier for spin dynamics via exchange of spin-excitons in the integer quantum Hall regime.     

Algorithms and software tools for ordering clone libraries: application to the mapping of the genome of Schizosaccharomyces pombe

A complete set of software tools to aid the physical mapping of a genome has been developed and successfully applied to the genomic mapping of the fission yeast Schizosaccharomyces pombe. Two approaches were used for ordering single-copy hybridisation probes: one was based on the simulated annealing algorithm to order all probes, and another on inferring the minimum-spanning subset of the probes using a heuristic filtering procedure. Both algorithms produced almost identical maps, with minor differences in the order of repetitive probes and those having identical hybridisation patterns. A separate algorithm fitted the clones to the established probe order. Approaches for handling experimental noise and repetitive elements are discussed. In addition to these programs and the database management software, tools for visualizing and editing the data are described. The issues of combining the information from different libraries are addressed. Also, ways of handling multiple-copy probes and non-hybridisation data are discussed.     

Mechanical and Corrosion Properties of ZrB2–SiC Composite Ceramics with Oxide Additions

Powder Metallurgy and Metal Ceramics - Hot pressing was used to produce compact ceramic samples with the following composition (wt.%): 60 ZrB2 + 20 SiC + 20 (Al2O3 + 32 t-ZrO2). The tetragonal...     

Influence of hydrogen on the thermoelectric voltage signal in a Pt/WO<Subscript><Emphasis Type="Italic">x</Emphasis></Subscript>/6<Emphasis Type="Italic">H</Emphasis>-SiC/Ni/Pt layered structure

The possibility of detecting H₂ by registering the thermal electromotive force signal, which arises between the surfaces of 6H-SiC plates with a thickness of 400 μm, is established. The working surface of the plates is modified by deposition of a WO _x film and catalytic Pt. An ohmic contact (Ni/Pt) is created on the rear surface of the plate, and this surface is maintained at a stabilized temperature of 350°C. The temperature gradient through the plate thickness arises due to the cooling of the working surface with the air medium. The delivery of H₂ into this medium up to a concentration of 2% gives rise to a 15-fold increase in the electric signal, which considerably exceeds the Pt/WO _x /SiC/Ni/Pt system’s response registered in the usual way by measuring the current–voltage dependence. In this case, an additional power source for the registration of the thermal electromotive force is not required.     

Using artificial neural networks to detect unknown computer worms

Detecting computer worms is a highly challenging task. We present a new approach that uses artificial neural networks (ANN) to detect the presence of computer worms based on measurements of computer behavior. We compare ANN to three other classification methods and show the advantages of ANN for detection of known worms. We then proceed to evaluate ANN’s ability to detect the presence of an unknown worm. As the measurement of a large number of system features may require significant computational resources, we evaluate three feature selection techniques. We show that, using only five features, one can detect an unknown worm with an average accuracy of 90%. We use a causal index analysis of our trained ANN to identify rules that explain the relationships between the selected features and the identity of each worm. Finally, we discuss the possible application of our approach to host-based intrusion detection systems.     

Algorithms for Graphs Embeddable with Few Crossings per Edge

We consider graphs that can be embedded on a surface of bounded genus such that each edge has a bounded number of crossings. We prove that many optimization problems, including maximum independent set, minimum vertex cover, minimum dominating set and many others, admit polynomial time approximation schemes when restricted to such graphs. This extends previous results by Baker and Eppstein to a much broader class of graphs. We also prove that for the considered class of graphs, there are balanced separators of size where n is a number of vertices in the graph. On the negative side, we prove that it is intractable to recognize the graphs embeddable in the plane with at most one crossing per edge.     

Online variance minimization

We consider the following type of online variance minimization problem: In every trial t our algorithms get a covariance matrix C ^t and try to select a parameter vector w ^t−1 such that the total variance over a sequence of trials ?_t=1^T (w^t-1)^T C^tw^t-1\sum_{t=1}^{T} (\boldsymbol {w}^{t-1})^{\top} \boldsymbol {C}^{t}\boldsymbol {w}^{t-1} is not much larger than the total variance of the best parameter vector u chosen in hindsight. Two parameter spaces in ℝ ⁿ are considered—the probability simplex and the unit sphere. The first space is associated with the problem of minimizing risk in stock portfolios and the second space leads to an online calculation of the eigenvector with minimum eigenvalue of the total covariance matrix ?_t=1^T C^t\sum_{t=1}^{T} \boldsymbol {C}^{t}. For the first parameter space we apply the Exponentiated Gradient algorithm which is motivated with a relative entropy regularization. In the second case, the algorithm has to maintain uncertainty information over all unit directions u. For this purpose, directions are represented as dyads uu ^⊤ and the uncertainty over all directions as a mixture of dyads which is a density matrix. The motivating divergence for density matrices is the quantum version of the relative entropy and the resulting algorithm is a special case of the Matrix Exponentiated Gradient algorithm. In each of the two cases we prove bounds on the additional total variance incurred by the online algorithm over the best offline parameter.