Anisotropic lattice distortions in biogenic aragonite

Composite biogenic materials produced by organisms have a complicated design on a nanometre scale. An outstanding example of organic-inorganic composites is provided by mollusc seashells, whose superior mechanical properties are due to their multi-level crystalline hierarchy and the presence of a small amount (0.1-5 wt%) of organic molecules. The presence of organic molecules, among other characteristics, can influence the coherence length for X-ray scattering in biogenic crystals. Here we show the results of synchrotron high-resolution X-ray powder diffraction measurements in biogenic and non-biogenic (geological) aragonite crystals. On applying the Rietveld refinement procedure to the high-resolution diffraction spectra, we were able to extract the aragonite lattice parameters with an accuracy of 10 p.p.m. As a result, we found anisotropic lattice distortions in biogenic aragonite relative to the geological sample, maximum distortion being 0.1% along the c axis of the orthorhombic unit cell. The organic molecules could be a source of these structural distortions in biogenic crystals. This finding may be important to the general understanding of the biomineralization process and the development of bio-inspired 'smart' materials.     

Measuring microscopic viscosity with optical tweezers as a confocal probe

We demonstrate, what is to the best our knowledge, a new method for studying the motion of a particle trapped by optical tweezers; in this method the trapping beam itself is used as a confocal probe. By studying the response of the particle to periodic motion of the tweezers, we obtain information about the medium viscosity, particle properties, and trap stiffness. We develop the mathematical model, demonstrate experimentally its validity for our system, and discuss advantages of using this method as a new form of scanning photonic force microscopy for applications in which a high spatial and temporal resolution of the medium viscosity is desired.     

Living Bacteria in Thermoresponsive Gel for Treating Fungal Infections

The leading living bacteria formulations currently available are from a limited list of genera and are generally limited to gastrointestinal tract syndromes. A formulation composed of living Bacillus subtilis incorporated in a thermoresponsive hydrogel that hardens after administration on the skin and continuously produces antifungal agents is described. The ability of the formula to support bacteria growth and its mechanical properties and penetrability through the skin are fine‐tuned by varying the ratio between polymer concentrations and bacterial media. The formula penetrates via the stratum corneum and accumulates in the epidermis without penetrating the inner, dermis layer. In vivo results mirror the results seen in vitro: bacillus formulations completely inhibit candida growth, demonstrating clinical effects comparable to those achieved by ketoconazole. LC‐MS/MS analysis of the bacterial formulation confirms the presence of surfactin, the most powerful biosurfactant that possesses a broad antifungal activity. This platform may enable rational design of novel formulations composed of secreting bacteria inside a responsive, smart, hydrogel—which is the prerequisite for producing a successful drug delivery system.     

Approximate distributed top-<Emphasis Type="Italic">k</Emphasis> queries

We consider a distributed system where each node keeps a local count for items (similar to elections where nodes are ballot boxes and items are candidates). A top-k query in such a system asks which are the k items whose global count, across all nodes in the system, is the largest. In this paper, we present a Monte Carlo algorithm that outputs, with high probability, a set of k candidates which approximates the top-k items. The algorithm is motivated by sensor networks in that it focuses on reducing the individual communication complexity. In contrast to previous algorithms, the communication complexity depends only on the global scores and not on the partition of scores among nodes. If the number of nodes is large, our algorithm dramatically reduces the communication complexity when compared with deterministic algorithms. We show that the complexity of our algorithm is close to a lower bound on the cell-probe complexity of any non-interactive top-k approximation algorithm. We show that for some natural global distributions (such as the Geometric or Zipf distributions), our algorithm needs only polylogarithmic number of communication bits per node. An extended abstract of this paper appeared in Proc. 13th Int. Colloquium on Structural Information and Communication Complexity, SIROCCO 2006, Lecture Notes in Computer Science 4056, pp. 319–333.     

Creating a consensus ranking of proposals from reviewers’ partial ordinal rankings

Peer review of research proposals and articles is an essential element in R&D processes worldwide. In most cases, each reviewer evaluates a small subset of the candidate proposals. The review board is then faced with the challenge of creating an overall “consensus” ranking on the basis of many partial rankings. In this paper we propose a branch-and-bound model to support the construction of an aggregate ranking from the partial rankings provided by the reviewers. In a recent paper we proposed ways to allocate proposals to reviewers so as to achieve the maximum possible overlap among the subsets of proposals allocated to different reviewers. Here, we develop a special branch-and-bound algorithm that utilizes the overlap generated through our earlier methods to enable discrimination in ranking the competing proposals. The effectiveness and efficiency of the algorithm is demonstrated with small numerical examples and tested through an extensive simulation experiment.     

Video distribution under multiple constraints

We consider the optimization problem of providing a set of video streams to a set of clients, where each stream has costs in m possible measures (such as communication bandwidth, processing bandwidth, etc.), and each client has its own utility function for each stream. We assume that the server has a budget cap on each of the m cost measures; each client has an upper bound on the utility that can be derived from it, and potentially also upper bounds in each of the m cost measures. The task is to choose which streams the server will provide, and out of this set, which streams each client will receive. The goal is to maximize the overall utility subject to the budget constraints. We give an efficient approximation algorithm with approximation factor of O(m) with respect to the optimal possible utility for any input, assuming that clients have only a bound on their maximal utility. If, in addition, each client has at most m_c capacity constraints, then the approximation factor increases by another factor of O(m_clogn), where n is the input length. We also consider the special case of “small” streams, namely where each stream has cost of at most O(1/logn) fraction of the budget cap, in each measure. For this case we present an algorithm whose approximation ratio is O(logn).     

Efficient algorithms for center problems in cactus networks

Efficient algorithms for solving the center problems in weighted cactus networks are presented. In particular, we have proposed the following algorithms for the weighted cactus networks of size n$n$: an O(nlogn)$O(nlogn)$ time algorithm to solve the 1-center problem, and an O(nlog³n)$O\left(n{log}^{3}n\right)$ time algorithm to solve the weighted continuous 2-center problem. We have also provided improved solutions to the general p$p$-center problems in cactus networks. The developed ideas are then applied to solve the obnoxious 1-center problem in weighted cactus networks.     

The value of agreement a new boosting algorithm

In the past few years unlabeled examples and their potential advantage have received a lot of attention. In this paper a new boosting algorithm is presented where unlabeled examples are used to enforce agreement between several different learning algorithms. Not only do the learning algorithms learn from the given training set but they are supposed to do so while agreeing on the unlabeled examples. Similar ideas have been proposed before (for example, the Co-Training algorithm by Mitchell and Blum), but without a proof or under strong assumptions. In our setting, it is only assumed that all learning algorithms are equally adequate for the tasks. A new generalization bound is presented where the use of unlabeled examples results in a better ratio between training-set size and the resulting classifier's quality and thus reduce the number of labeled examples necessary for achieving it. The extent of this improvement depends on the diversity of the learners—a more diverse group of learners will result in a larger improvement whereas using two copies of a single algorithm gives no advantage at all. As a proof of concept, the algorithm, named Agreement Boost, is applied to two test problems. In both cases, using Agreement Boost results in an up to 40% reduction in the number of labeled examples.     

General Perfectly Periodic Scheduling

In a perfectly periodic schedule, each job must be scheduled precisely every some fixed number of time units after its previous occurrence. Traditionally, motivated by centralized systems, the perfect periodicity requirement is relaxed, the main goal being to attain the requested average rate. Recently, motivated by mobile clients with limited power supply, perfect periodicity seems to be an attractive alternative that allows clients to save energy by reducing their "busy waiting" time. In this case, clients may be willing to compromise their requested service rate in order to get perfect periodicity. In this paper we study a general model of perfectly periodic schedules, where each job has a requested period and a length; we assume that m jobs can be served in parallel for some given m. Job lengths may not be truncated, but granted periods may be different than the requested periods. We present an algorithm which computes schedules such that the worst-case proportion between the requested period and the granted period is guaranteed to be close to the lower bound. This algorithm improves on previous algorithms for perfect schedules in providing a worst-case guarantee rather than an average-case guarantee, in generalizing unit length jobs to arbitrary length jobs, and in generalizing the single-server model to multiple servers.     

Parameter estimation of continuous-time stationary Gaussian processes with rational spectra

This paper considers the problem of estimating the parameters of continuous-time stationary Gaussian processes with rational spectra, from uniformly sampled measurements. The sampled process is shown to be an autoregressive moving-average process, and explicit relationships between the parameters of the continuous-time and the sampled processes are derived. These relationships are then used to derive a lower bound on the variances of biased estimates of the continuous-time parameters, and on the generalized variance of such estimates. It is shown by some examples that the bound on the generalized variance depends on the sampling interval in a nonmonotonic manner. In particular, for each specific set of parameters there exists a sampling interval for which the lower bound is minimized.This work was supported by the Army Research Office under Contract Number DAAG29-83-C-0027.