The topics they are a-changing - characterising topics with time-stamped semantic graphs

DBpedia has become one of the major sources of structured knowledge extracted from Wikipedia. Such structures gradually re-shape the representation of Topics as new events relevant to such topics emerge. Such changes make evident the continuous evolution of topic representations and introduce new challenges to supervised topic classification tasks, since labelled data can rapidly become outdated. Here we analyse topic changes in DBpedia and propose the use of semantic features as a more stable representation of a topic. Our experiments show promising results in understanding how the relevance of features to a topic changes over time.

Stretching the life of Twitter classifiers with time-stamped semantic graphs

Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

Coloring random graphs and maximizing local diversity

We study a variation of the graph coloring problem on random graphs of finite average connectivity. Given the number of colors, we aim to maximize the number of different colors at neighboring vertices (i.e. one edge distance) of any vertex. Two efficient algorithms, belief propagation and Walksat are adapted to carry out this task. We present experimental results based on two types of random graphs for different system sizes and identify the critical value of the connectivity for the algorithms to find a perfect solution. The problem and the suggested algorithms have practical relevance since various applications, such as distributed storage, can be mapped onto this problem.

Resource allocation in sparse graphs

Resource allocation in sparsely connected networks, a representative problem of systems with real variables, is studied using the replica and Bethe approximation methods. An efficient distributed algorithm is devised on the basis of insights gained from the analysis and is examined using numerical simulations,showing excellent performance and full agreement with the theoretical results. The physical properties of the resource allocation model are discussed.

Message passing for task redistribution on sparse graphs

The problem of resource allocation in sparse graphs with real variables is studied using methods of statistical physics. An efficient distributed algorithm is devised on the basis of insight gained from the analysis and is examined using numerical simulations, showing excellent performance and full agreement with the theoretical results.

Solvable model for distribution networks on random graphs

We propose a simple model that captures the salient properties of distribution networks, and study the possible occurrence of blackouts, i.e., sudden failings of large portions of such networks. The model is defined on a random graph of finite connectivity. The nodes of the graph represent hubs of the network, while the edges of the graph represent the links of the distribution network. Both, the nodes and the edges carry dynamical two state variables representing the functioning or dysfunctional state of the node or link in question. We describe a dynamical process in which the breakdown of a link or node is triggered when the level of maintenance it receives falls below a given threshold. This form of dynamics can lead to situations of catastrophic breakdown, if levels of maintenance are themselves dependent on the functioning of the net, once maintenance levels locally fall below a critical threshold due to fluctuations. We formulate conditions under which such systems can be analyzed in terms of thermodynamic equilibrium techniques, and under these conditions derive a phase diagram characterizing the collective behavior of the system, given its model parameters. The phase diagram is confirmed qualitatively and quantitatively by simulations on explicit realizations of the graph, thus confirming the validity of our approach. © 2007 The American Physical Society.

Inference and optimization of real edges on sparse graphs:A statistical physics perspective

Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe approximation and replica method of statistical physics. Equilibrium states of general energy functions involving a large set of real edge variables that interact at the network nodes are obtained in various cases. When applied to the representative problem of network resource allocation, efficient distributed algorithms are also devised. Scaling properties with respect to the network connectivity and the resource availability are found, and links to probabilistic Bayesian approximation methods are established. Different cost measures are considered and algorithmic solutions in the various cases are devised and examined numerically. Simulation results are in full agreement with the theory. © 2007 The American Physical Society.

Chemical synthesis of isotopically labelled (M+4) purine nucleosides and their incorporation into DNA oligomers

Development of accurate and sensitive analytical methods to measure the level of biomarkers, such as 8-oxo-guanine or its corresponding nucleoside, 8-oxo-2’-deoxyguanosine, has become imperative in the study of DNA oxidative damage in vivo. Of the most promising techniques, HPLC-MS/MS, has many attractive advantages. Like any method that employs the MS technique, its accuracy depends on the use of multiply, isotopically-labelled internal standards. This project is aimed at making available such internal standards. The first task was to synthesise the multiply, isotopically-labelled bases (M+4) guanine and (M+4) 8-oxo-guanine. Synthetic routes for both (M+4) guanine and (M+4) 8-oxo-guanine were designed and validated using the unlabelled compounds. The reaction conditions were also optimized during the “dry runs”. The amination of the 4-hydroxy-2,6-dichloropyrimidine, appeared to be very sensitive to the purity of the commercial [15]N benzylamine reagent. Having failed, after several attempts, to obtain the pure reagent from commercial suppliers, [15]N benzylamine was successfully synthesised in our laboratory and used in the first synthesis of (M+4) guanine. Although (M+4) bases can be, and indeed have been used as internal standards in the quantitative analysis of oxidative damage, they can not account for the errors that may occur during the early sample preparation stages. Therefore, internal standards in the form of nucleosides and DNA oligomers are more desirable. After evaluating a number of methods, an enzymatic transglycolization technique was adopted for the transfer of the labelled bases to give their corresponding nucleosides. Both (M+4) 2-deoxyguanosine and (M+4) 8-oxo-2’-deoxyguanosine can be purified on micro scale by HPLC. The challenge came from the purification of larger scale (>50 mg) synthesis of nucleosides. A gel filtration method was successfully developed, which resulted in excellent separation of (M+4) 2’-deoxyguanosine from the incubation mixture. The (M+4) 2’-deoxyguanosine was then fully protected in three steps and successfully incorporated, by solid supported synthesis, into a DNA oligomer containing 18 residues. Thus, synthesis of 8-oxo-deoxyguanosine on a bigger scale for its future incorporation into DNA oligomers is now a possibility resulting from this thesis work. We believe that these internal standards can be used to develop procedures that can make the measurement of oxidative DNA damage more accurate and sensitive.

Competition for shortest paths on sparse graphs

Optimal paths connecting randomly selected network nodes and fixed routers are studied analytically in the presence of a nonlinear overlap cost that penalizes congestion. Routing becomes more difficult as the number of selected nodes increases and exhibits ergodicity breaking in the case of multiple routers. The ground state of such systems reveals nonmonotonic complex behaviors in average path length and algorithmic convergence, depending on the network topology, and densities of communicating nodes and routers. A distributed linearly scalable routing algorithm is also devised. © 2012 American Physical Society.

Research allocation on sparse graphs

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Algorithms for solving large random graphs

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Belief propagation on dense graphs with few strong links

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Development of labelled platelets as radiopharmaceuticals.

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Message-passing for inference and optimization of real variables on sparse graphs

The inference and optimization in sparse graphs with real variables is studied using methods of statistical mechanics. Efficient distributed algorithms for the resource allocation problem are devised. Numerical simulations show excellent performance and full agreement with the theoretical results. © Springer-Verlag Berlin Heidelberg 2006.

Shortest node-disjoint paths on random graphs

A localized method to distribute paths on random graphs is devised, aimed at finding the shortest paths between given source/destination pairs while avoiding path overlaps at nodes. We propose a method based on message-passing techniques to process global information and distribute paths optimally. Statistical properties such as scaling with system size and number of paths, average path-length and the transition to the frustrated regime are analyzed. The performance of the suggested algorithm is evaluated through a comparison against a greedy algorithm. © 2014 IOP Publishing Ltd and SISSA Medialab srl.