Evolving graphs: dynamical models, inverse problems and propagation

Applications such as neuroscience, telecommunication, online social networking, transport and retail trading give rise to connectivity patterns that change over time. In this work, we address the resulting need for network models and computational algorithms that deal with dynamic links. We introduce a new class of evolving range-dependent random graphs that gives a tractable framework for modelling and simulation. We develop a spectral algorithm for calibrating a set of edge ranges from a sequence of network snapshots and give a proof of principle illustration on some neuroscience data. We also show how the model can be used computationally and analytically to investigate the scenario where an evolutionary process, such as an epidemic, takes place on an evolving network. This allows us to study the cumulative effect of two distinct types of dynamics

Status of Lattice QCD

Significant progress has recently been achieved in the lattice gauge theory calculations required for extracting the fundamental parameters of the standard model from experiment. Recent lattice determinations of such quantities as the kaon $B$ parameter, the mass of the $b$ quark, and the strong coupling constant have produced results and uncertainties as good or better than the best conventional determinations. Many other calculations crucial to extracting the fundamental parameters of the standard model from experimental data are undergoing very active development. I review the status of such applications of lattice QCD to standard model phenomenology, and discuss the prospects for the near future.Comment: 20 pages, 8 embedded figures, uuencoded, 2 missing figures. (Talk presented at the Lepton-Photon Symposium, Cornell University, Aug. 10-15, 1993.

Supersymmetric Langevin equation to explore free energy landscapes

The recently discovered supersymmetric generalizations of Langevin dynamics and Kramers equation can be utilized for the exploration of free energy landscapes of systems whose large time-scale separation hampers the usefulness of standard molecular dynamics techniques. The first realistic application is here presented. The system chosen is a minimalist model for a short alanine peptide exhibiting a helix-coil transition.Comment: 9 pages, 9 figures, RevTeX 4 v2: conclusive section enlarged, references adde

Deterministic Digital Clustering of Wireless Ad Hoc Networks

We consider deterministic distributed communication in wireless ad hoc networks of identical weak devices under the SINR model without predefined infrastructure. Most algorithmic results in this model rely on various additional features or capabilities, e.g., randomization, access to geographic coordinates, power control, carrier sensing with various precision of measurements, and/or interference cancellation. We study a pure scenario, when no such properties are available. As a general tool, we develop a deterministic distributed clustering algorithm. Our solution relies on a new type of combinatorial structures (selectors), which might be of independent interest. Using the clustering, we develop a deterministic distributed local broadcast algorithm accomplishing this task in $O(\Delta \log^*N \log N)$ rounds, where $\Delta$ is the density of the network. To the best of our knowledge, this is the first solution in pure scenario which is only polylog$(n)$ away from the universal lower bound $\Omega(\Delta)$, valid also for scenarios with randomization and other features. Therefore, none of these features substantially helps in performing the local broadcast task. Using clustering, we also build a deterministic global broadcast algorithm that terminates within $O(D(\Delta + \log^* N) \log N)$ rounds, where $D$ is the diameter of the network. This result is complemented by a lower bound $\Omega(D \Delta^{1-1/\alpha})$, where $\alpha > 2$ is the path-loss parameter of the environment. This lower bound shows that randomization or knowledge of own location substantially help (by a factor polynomial in $\Delta$) in the global broadcast. Therefore, unlike in the case of local broadcast, some additional model features may help in global broadcast

Parallel Load Balancing on constrained client-server topologies

We study parallel Load Balancing protocols for the client-server distributed model defined as follows. There is a set of n clients and a set of n servers where each client has (at most) a constant number of requests that must be assigned to some server. The client set and the server one are connected to each other via a fixed bipartite graph: the requests of client v can only be sent to the servers in its neighborhood. The goal is to assign every client request so as to minimize the maximum load of the servers. In this setting, efficient parallel protocols are available only for dense topologies. In particular, a simple protocol, named raes, has been recently introduced by Becchetti et al. [1] for regular dense bipartite graphs. They show that this symmetric, non-adaptive protocol achieves constant maximum load with parallel completion time and overall work, w.h.p. Motivated by proximity constraints arising in some client-server systems, we analyze raes over almost-regular bipartite graphs where nodes may have neighborhoods of small size. In detail, we prove that, w.h.p., the raes protocol keeps the same performances as above (in terms of maximum load, completion time, and work complexity, respectively) on any almost-regular bipartite graph with degree. Our analysis significantly departs from that in [1] since it requires to cope with non-trivial stochastic-dependence issues on the random choices of the algorithmic process which are due to the worst-case, sparse topology of the underlying graph

Capillarity Theory for the Fly-Casting Mechanism

Biomolecular folding and function are often coupled. During molecular recognition events, one of the binding partners may transiently or partially unfold, allowing more rapid access to a binding site. We describe a simple model for this flycasting mechanism based on the capillarity approximation and polymer chain statistics. The model shows that flycasting is most effective when the protein unfolding barrier is small and the part of the chain which extends towards the target is relatively rigid. These features are often seen in known examples of flycasting in protein-DNA binding. Simulations of protein-DNA binding based on well-funneled native-topology models with electrostatic forces confirm the trends of the analytical theory