Redox and catalytic chemistry of Ti in titanosilicate molecular sieves: an EPR investigation

An EPR study of Ti3+ in titanosilicate molecular sieves, TS-1, TiMCM-41, ETS-10 and ETS-4 is reported. Ti4+ is reduced to Ti3+ by dry hydrogen above 673 K. Ti ions in TS-1 and TiMCM-41 are located in tetragonally elongated Td and those of ETS-10 and ETS-4 in a tetragonally compressed Oh geometric positions. Reduction at 873 K revealed the presence of two non-equivalent Ti3+ sites in TS-1 and TiMCM-41. Ti4+ ions in a tetrahedral geometry are more difficult to reduce than in an octahedral symmetry. The effects of cation exchange and Pt impregnation, on the geometry and reducibility of titanium in ETS-10, are also examined. Interaction of a tetrahedrally coordinated Ti3+ with O2 or H2O2 results in a diamagnetic titanium(IV) hydroperoxo species. Under the same conditions, an octahedrally coordinated Ti3+ forms a paramagnetic titanium(IV) superoxo species. The higher catalytic activity of TS-1 and TiMCM-41 in selective oxidation reactions is probably a consequence of the formation of the hydroperoxy species on their surface during the catalytic reaction. The presence of Pt in the vicinity of Ti enables the use of H2 and O2 (instead of H2O2) to generate the active hydroperoxy site. The absence of formation of titanium hydroperoxy species in ETS-4 and ETS-10 is the cause of their inactivity in selective oxidation reactions

Exploiting the Synergy Between Gossiping and Structured Overlays

In this position paper we argue for exploiting the synergy between gossip-based algorithms and structured overlay networks (SON). These two strands of research have both aimed at building fault-tolerant, dynamic, self-managing, and large-scale distributed systems. Despite the common goals, the two areas have, however, been relatively isolated. We focus on three problem domains where there is an untapped potential of using gossiping combined with SONs. We argue for applying gossip-based membership for ring-based SONs---such as Chord and Bamboo---to make them handle partition mergers and loopy networks. We argue that small world SONs---such as Accordion and Mercury---are specifically well-suited for gossip-based membership management. The benefits would be better graph-theoretic properties. Finally, we argue that gossip-based algorithms could use the overlay constructed by SONs. For example, many unreliable broadcast algorithms for SONs could be augmented with anti-entropy protocols. Similarly, gossip-based aggregation could be used in SONs for network size estimation and load-balancing purposes

A Probabilistic Analysis of Kademlia Networks

Kademlia is currently the most widely used searching algorithm in P2P (peer-to-peer) networks. This work studies an essential question about Kademlia from a mathematical perspective: how long does it take to locate a node in the network? To answer it, we introduce a random graph K and study how many steps are needed to locate a given vertex in K using Kademlia's algorithm, which we call the routing time. Two slightly different versions of K are studied. In the first one, vertices of K are labelled with fixed IDs. In the second one, vertices are assumed to have randomly selected IDs. In both cases, we show that the routing time is about c*log(n), where n is the number of nodes in the network and c is an explicitly described constant.Comment: ISAAC 201

Knowledge is at the Edge! How to Search in Distributed Machine Learning Models

With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95% accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.Comment: Published in CoopIS 201

Keeping pace with climate change : what is wrong with the evolutionary potential of upper thermal limits?

The potential of populations to evolve in response to ongoing climate change is partly conditioned by the presence of heritable genetic variation in relevant physiological traits. Recent research suggests that Drosophila melanogaster exhibits negligible heritability, hence little evolutionary potential in heat tolerance when measured under slow heating rates that presumably mimic conditions in nature. Here, we study the effects of directional selection for increased heat tolerance using Drosophila as a model system. We combine a physiological model to simulate thermal tolerance assays with multilocus models for quantitative traits. Our simulations show that, whereas the evolutionary response of the genetically determined upper thermal limit (CTmax) is independent of methodological context, the response in knockdown temperatures varies with measurement protocol and is substantially (up to 50%) lower than for CTmax. Realized heritabilities of knockdown temperature may grossly underestimate the true heritability of CTmax. For instance, assuming that the true heritability of CTmax in the base population is h² = 0.25, realized heritabilities of knockdown temperature are around 0.08-0.16 depending on heating rate. These effects are higher in slow heating assays, suggesting that flawed methodology might explain the apparently limited evolutionary potential of cosmopolitan D. melanogaster

An Optimal Broadcast Algorithm for Content-Addressable Networks

International audienceStructured peer-to-peer networks are powerful underlying structures for communication and storage systems in large-scale setting. In the context of the Content-Addressable Network (CAN), this paper addresses the following challenge: how to perform an efficient broadcast while the local view of the network is restricted to a set of neighbours? In existing approaches, either the broadcast is inefficient (there are dupli- cated messages) or it requires to maintain a particular structure among neighbours, e.g. a spanning tree. We define a new broadcast primitive for CAN that sends a minimum number of messages while covering the whole network, without any global knowledge. Currently, no other al- gorithm achieves those two goals in the context of CAN. In this sense, the contribution we propose in this paper is threefold. First, we pro- vide an algorithm that sends exactly one message per recipient without building a global view of the network. Second, we prove the absence of duplicated messages and the coverage of the whole network when using this algorithm. Finally, we show the practical benefits of the algorithm throughout experiments