Dynamical encoding by networks of competing neuron groups: winnerless competition

Following studies of olfactory processing in insects and fish, we investigate neural networks whose dynamics in phase space is represented by orbits near the heteroclinic connections between saddle regions (fixed points or limit cycles). These networks encode input information as trajectories along the heteroclinic connections. If there are N neurons in the network, the capacity is approximately e(N-1)!, i.e., much larger than that of most traditional network structures. We show that a small winnerless competition network composed of FitzHugh-Nagumo spiking neurons efficiently transforms input information into a spatiotemporal output.     

Pathways to Better Stability of Catalysts for Reforming of Gasoline Fractions

Adsorbents AS-31 and AKD-981 based on zinc oxide and alumina are described. They are used in the purification of hydrogen-containing gas of reforming from hydrogen sulfide at 20–120°C and pressures up to 4 MPa. The second of these adsorbents is capable of forming weak adsorption complexes with hydrogen sulfide and can be recovered 2–3 more times than the first one. To remove sulfur-containing compounds from the gaseous feed of reforming at 350–450°C, the KAS-50 catalyst/adsorbent is proposed, which is prepared by mixing manganese dioxide and aluminum hydroxide. The sulfur capacity of this catalyst is 20%. New platinum–rhenium reforming catalysts KR-200 and KR-201 are proposed, which have higher stability when they work with purified feedstock. The concentration of the active catalyst is the same or lower, and these catalysts show better activity than their predecessors. All catalysts and adsorbents are tested and work in industry.     

Electrodeposited cylindrical iron-nickel films of supracritical thickness I. Method of determining internal macrostresses

An automatic recording apparatus is described for the measurement of the internal macrostresses in cylindrical films in the course of electrodeposition both on stationary and on moving substrates. On the basis of the experimental deformation graph, the formulae are derived for the calculation of the values of the macrostresses for both variants of the experimental method.Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Fizika, No. 2, pp. 41–46, February, 1972.     

Cuts,Trees and ℓ<Subscript>1</Subscript>-Embeddings of Graphs*

Motivated by many recent algorithmic applications, this paper aims to promote a systematic study of the relationship between the topology of a graph and the metric distortion incurred when the graph is embedded into ₁ space. The main results are:1. Explicit constant-distortion embeddings of all series-parallel graphs, and all graphs with bounded Euler number. These are the first natural families known to have constant distortion (strictly greater than 1). Using the above embeddings, algorithms are obtained which approximate the sparsest cut in such graphs to within a constant factor.2. A constant-distortion embedding of outerplanar graphs into the restricted class of ₁-metrics known as dominating tree metrics. A lower bound of (log n) on the distortion for embeddings of series-parallel graphs into (distributions over) dominating tree metrics is also presented. This shows, surprisingly, that such metrics approximate distances very poorly even for families of graphs with low treewidth, and excludes the possibility of using them to explore the finer structure of ₁-embeddability.* A preliminary version of this work appeared in Proceedings of the 40th Annual IEEE Symposium on Foundations of Computer Science, 1999, pp. 399–408. This work was done while the author was at the University of California, Berkeley. Supported in part by NSF grants CCR-9505448 and CCR-9820951.     

A Lower Bound on the Distortion of Embedding Planar Metrics into Euclidean Space

We exhibit a simple infinite family of series-parallel graphs that cannot be metrically embedded into Euclidean space with distortion smaller than