Taming computational complexity: efficient and parallel SimRank optimizations on undirected graphs

SimRank has been considered as one of the promising link-based ranking algorithms to evaluate similarities of web documents in many modern search engines. In this paper, we investigate the optimization problem of SimRank similarity computation on undirected web graphs. We first present a novel algorithm to estimate the SimRank between vertices in O(n3+ Kn2) time, where n is the number of vertices, and K is the number of iterations. In comparison, the most efficient implementation of SimRank algorithm in [1] takes O(K n3 ) time in the worst case. To efficiently handle large-scale computations, we also propose a parallel implementation of the SimRank algorithm on multiple processors. The experimental evaluations on both synthetic and real-life data sets demonstrate the better computational time and parallel efficiency of our proposed techniques

Log-Euclidean Bag of Words for Human Action Recognition

Representing videos by densely extracted local space-time features has recently become a popular approach for analysing actions. In this paper, we tackle the problem of categorising human actions by devising Bag of Words (BoW) models based on covariance matrices of spatio-temporal features, with the features formed from histograms of optical flow. Since covariance matrices form a special type of Riemannian manifold, the space of Symmetric Positive Definite (SPD) matrices, non-Euclidean geometry should be taken into account while discriminating between covariance matrices. To this end, we propose to embed SPD manifolds to Euclidean spaces via a diffeomorphism and extend the BoW approach to its Riemannian version. The proposed BoW approach takes into account the manifold geometry of SPD matrices during the generation of the codebook and histograms. Experiments on challenging human action datasets show that the proposed method obtains notable improvements in discrimination accuracy, in comparison to several state-of-the-art methods

Synthesis of Al and Ag nanoparticles through ultra-sonic dissociation of thermal evaporation deposited thin films for promising clinical applications as polymer nanocomposite

Nanoparticles (NPs) having well-defined shape, size and clean surface serve as ideal model system to investigate surface/interfacial reactions. Ag and Al NPs are receiving great interest due to their wide applications in bio-medical field, aerospace and space technology as combustible additives in propellants and hydrogen generation. Hence, in this study, we have synthesized Ag and Al NPs using an innovative approach of ultra-sonic dissociation of thin films. Phase and particle size distributions of the Ag and Al NPs have been determined by X-ray diffraction (XRD) and transmission electron microscopy (TEM). Thin film dissociation/dissolution mechanism, hence conversion into NPs has been characterized by SEM- scanning electron microscope. EDXA & ICPMS have been performed for chemical analysis of NPs. Optical properties have been characterized by UV-Vis and PL spectroscopy. These NPs have also been investigated for their anti-bacterial activity against Escherichia coli bacteria. To the best of our knowledge, this is the first time when NPs has been synthesized by ultra-sonic dissociation of thin films. As an application, these NPs were used further for synthesis of nanocomposite polymer membranes, which show excellent activity against bio film formation

Fault-tolerant quantum computation with cluster states

The one-way quantum computing model introduced by Raussendorf and Briegel [Phys. Rev. Lett. 86 (22), 5188-5191 (2001)] shows that it is possible to quantum compute using only a fixed entangled resource known as a cluster state, and adaptive single-qubit measurements. This model is the basis for several practical proposals for quantum computation, including a promising proposal for optical quantum computation based on cluster states [M. A. Nielsen, arXiv:quant-ph/0402005, accepted to appear in Phys. Rev. Lett.]. A significant open question is whether such proposals are scalable in the presence of physically realistic noise. In this paper we prove two threshold theorems which show that scalable fault-tolerant quantum computation may be achieved in implementations based on cluster states, provided the noise in the implementations is below some constant threshold value. Our first threshold theorem applies to a class of implementations in which entangling gates are applied deterministically, but with a small amount of noise. We expect this threshold to be applicable in a wide variety of physical systems. Our second threshold theorem is specifically adapted to proposals such as the optical cluster-state proposal, in which non-deterministic entangling gates are used. A critical technical component of our proofs is two powerful theorems which relate the properties of noisy unitary operations restricted to act on a subspace of state space to extensions of those operations acting on the entire state space.Comment: 31 pages, 54 figure

Nanotechnology: emerging tools for biology and medicine

Historically, biomedical research has been based on two paradigms. First, measurements of biological behaviors have been based on bulk assays that average over large populations. Second, these behaviors have then been crudely perturbed by systemic administration of therapeutic treatments. Nanotechnology has the potential to transform these paradigms by enabling exquisite structures comparable in size with biomolecules as well as unprecedented chemical and physical functionality at small length scales. Here, we review nanotechnology-based approaches for precisely measuring and perturbing living systems. Remarkably, nanotechnology can be used to characterize single molecules or cells at extraordinarily high throughput and deliver therapeutic payloads to specific locations as well as exhibit dynamic biomimetic behavior. These advances enable multimodal interfaces that may yield unexpected insights into systems biology as well as new therapeutic strategies for personalized medicineDamon Runyon Cancer Research Foundation (Merck Fellow, DRG-2065-10)Howard Hughes Medical Institute (Investigator)Lustgarten FoundationNational Institutes of Health (U.S.) (U54CA151884, , Massachusetts Institute of Technology-Harvard Center of Cancer Nanotechnology Excellence)National Institutes of Health (U.S.) (P41- EB002503, BIoMEMS Resource Center

User Intent Prediction in Information-seeking Conversations

Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.Comment: Accepted to CHIIR 201

Thermal resistant environmental barrier coating

A process for preparing a silicon based substrate with a protective coating having improved thermal resistance at temperature up to at least 1500.degree. C., and the resulting article

The ground state of a class of noncritical 1D quantum spin systems can be approximated efficiently

We study families H_n of 1D quantum spin systems, where n is the number of spins, which have a spectral gap \Delta E between the ground-state and first-excited state energy that scales, asymptotically, as a constant in n. We show that if the ground state |\Omega_m> of the hamiltonian H_m on m spins, where m is an O(1) constant, is locally the same as the ground state |\Omega_n>, for arbitrarily large n, then an arbitrarily good approximation to the ground state of H_n can be stored efficiently for all n. We formulate a conjecture that, if true, would imply our result applies to all noncritical 1D spin systems. We also include an appendix on quasi-adiabatic evolutions.Comment: 9 pages, 1 eps figure, minor change