CFT(4) partition functions and the heat kernel on AdS(5)

We consider four-dimensional CFTs which admit a large-N expansion, and whose spectrum contains states whose conformal dimensions do not scale with N. We explicitly reorganise the partition function obtained by exponentiating the one-particle partition function of these states into a heat kernel form for the dual string spectrum on AdS(5). On very general grounds, the heat kernel answer can be expressed in terms of a convolution of the one-particle partition function of the light states in the four-dimensional CFT. (C) 2013 Elsevier B.V. All rights reserved.

Bivariate frequency analysis of floods using a diffusion based kernel density estimator

Recent focus of flood frequency analysis (FFA) studies has been on development of methods to model joint distributions of variables such as peak flow, volume, and duration that characterize a flood event, as comprehensive knowledge of flood event is often necessary in hydrological applications. Diffusion process based adaptive kernel (D-kernel) is suggested in this paper for this purpose. It is data driven, flexible and unlike most kernel density estimators, always yields a bona fide probability density function. It overcomes shortcomings associated with the use of conventional kernel density estimators in FFA, such as boundary leakage problem and normal reference rule. The potential of the D-kernel is demonstrated by application to synthetic samples of various sizes drawn from known unimodal and bimodal populations, and five typical peak flow records from different parts of the world. It is shown to be effective when compared to conventional Gaussian kernel and the best of seven commonly used copulas (Gumbel-Hougaard, Frank, Clayton, Joe, Normal, Plackett, and Student's T) in estimating joint distribution of peak flow characteristics and extrapolating beyond historical maxima. Selection of optimum number of bins is found to be critical in modeling with D-kernel.

Spatially Adaptive Kernel Regression Using Risk Estimation

An important question in kernel regression is one of estimating the order and bandwidth parameters from available noisy data. We propose to solve the problem within a risk estimation framework. Considering an independent and identically distributed (i.i.d.) Gaussian observations model, we use Stein's unbiased risk estimator (SURE) to estimate a weighted mean-square error (MSE) risk, and optimize it with respect to the order and bandwidth parameters. The two parameters are thus spatially adapted in such a manner that noise smoothing and fine structure preservation are simultaneously achieved. On the application side, we consider the problem of image restoration from uniform/non-uniform data, and show that the SURE approach to spatially adaptive kernel regression results in better quality estimation compared with its spatially non-adaptive counterparts. The denoising results obtained are comparable to those obtained using other state-of-the-art techniques, and in some scenarios, superior.

Regional flood frequency analysis using kernel- based fuzzy clustering approach

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. Key Points Kernel-based regionalization approach is presented for flood frequency analysis Kernel procedure to estimate flood quantiles at ungauged sites is developed A set of fuzzy regions is delineated in Ohio, USA

Coconut kernel-derived activated carbon as electrode material for electrical double-layer capacitors

Carbonization of milk-free coconut kernel pulp is carried out at low temperatures. The carbon samples are activated using KOH, and electrical double-layer capacitor (EDLC) properties are studied. Among the several samples prepared, activated carbon prepared at 600 A degrees C has a large surface area (1,200 m(2) g(-1)). There is a decrease in surface area with increasing temperature of preparation. Cyclic voltammetry and galvanostatic charge-discharge studies suggest that activated carbons derived from coconut kernel pulp are appropriate materials for EDLC studies in acidic, alkaline, and non-aqueous electrolytes. Specific capacitance of 173 F g(-1) is obtained in 1 M H2SO4 electrolyte for the activated carbon prepared at 600 A degrees C. The supercapacitor properties of activated carbon sample prepared at 600 A degrees C are superior to the samples prepared at higher temperatures.

Combinatorial effect of rolling and carbonaceous nanoparticles on the evolution of crystallographic texture and structural properties of ultra high molecular weight polyethylene

Ultra high molecular weight polyethylene (PE) is a structural polymer widely used in biomedical implants. The mechanical properties of PE can be improved either by controlled crystalline orientation (texture) or by the addition of reinforcing agents. However, the combinatorial effect has not received much attention. The objective of this study was to characterize the structure and mechanical properties of PE composites incorporating multiwall carbon nanotubes (MWCNT) and reduced graphene oxide (RGO) subjected to hot rolling. The wide angle X-ray diffraction studies revealed that mechanical deformation resulted in a mixture of orthorhombic and monoclinic crystals. Furthermore, the presence of nanoparticles resulted in lower crystallinity in PE with smaller crystallite size, more so in RGO than in MWCNT composites. Rolling strengthened the texture of both orthorhombic and the monoclinic phases in PE. Presence of RGO weakened the texture of both phases of PE after rolling whereas MWCNT only mildly weakened the texture. This resulted in a reduction in the elastic modulus of RGO composites whereas moduli of neat polymer and the MWCNT composite increased after rolling. This study provides new insight into the role of nanoparticles in texture evolution during polymer processing with implications for processing of structural polymer composites.

Belief propagation for minimum weight many-to-one matchings in the random complete graph

In a complete bipartite graph with vertex sets of cardinalities n and n', assign random weights from exponential distribution with mean 1, independently to each edge. We show that, as n -> infinity, with n' = n/alpha] for any fixed alpha > 1, the minimum weight of many-to-one matchings converges to a constant (depending on alpha). Many-to-one matching arises as an optimization step in an algorithm for genome sequencing and as a measure of distance between finite sets. We prove that a belief propagation (BP) algorithm converges asymptotically to the optimal solution. We use the objective method of Aldous to prove our results. We build on previous works on minimum weight matching and minimum weight edge cover problems to extend the objective method and to further the applicability of belief propagation to random combinatorial optimization problems.

THEORY OF NEWFORMS OF HALF-INTEGRAL WEIGHT

We set up the theory of newforms of half-integral weight on Gamma(0)(8N) and Gamma(0)(16N), where N is odd and squarefree. Further, we extend the definition of the Kohnen plus space in general for trivial character and also study the theory of newforms in the plus spaces on Gamma(0)(8N), Gamma(0)(16N), where N is odd and squarefree. Finally, we show that the Atkin-Lehner W-operator W-4 acts as the identity operator on S-2k(new)(4N), where N is odd and squarefree. This proves that S-2k(-)(4) = S-2k(4).

On the Parameterized Complexity of Finding Separators with Non-Hereditary Properties

We study the problem of finding small s-t separators that induce graphs having certain properties. It is known that finding a minimum clique s-t separator is polynomial-time solvable (Tarjan in Discrete Math. 55:221-232, 1985), while for example the problems of finding a minimum s-t separator that induces a connected graph or forms an independent set are fixed-parameter tractable when parameterized by the size of the separator (Marx et al. in ACM Trans. Algorithms 9(4): 30, 2013). Motivated by these results, we study properties that generalize cliques, independent sets, and connected graphs, and determine the complexity of finding separators satisfying these properties. We investigate these problems also on bounded-degree graphs. Our results are as follows: Finding a minimum c-connected s-t separator is FPT for c=2 and W1]-hard for any ca parts per thousand yen3. Finding a minimum s-t separator with diameter at most d is W1]-hard for any da parts per thousand yen2. Finding a minimum r-regular s-t separator is W1]-hard for any ra parts per thousand yen1. For any decidable graph property, finding a minimum s-t separator with this property is FPT parameterized jointly by the size of the separator and the maximum degree. Finding a connected s-t separator of minimum size does not have a polynomial kernel, even when restricted to graphs of maximum degree at most 3, unless .

Hybrid Working Set Algorithm for SVM Learning With a Kernel Coprocessor on FPGA

Support vector machines (SVM) are a popular class of supervised models in machine learning. The associated compute intensive learning algorithm limits their use in real-time applications. This paper presents a fully scalable architecture of a coprocessor, which can compute multiple rows of the kernel matrix in parallel. Further, we propose an extended variant of the popular decomposition technique, sequential minimal optimization, which we call hybrid working set (HWS) algorithm, to effectively utilize the benefits of cached kernel columns and the parallel computational power of the coprocessor. The coprocessor is implemented on Xilinx Virtex 7 field-programmable gate array-based VC707 board and achieves a speedup of upto 25x for kernel computation over single threaded computation on Intel Core i5. An application speedup of upto 15x over software implementation of LIBSVM and speedup of upto 23x over SVMLight is achieved using the HWS algorithm in unison with the coprocessor. The reduction in the number of iterations and sensitivity of the optimization time to variation in cache size using the HWS algorithm are also shown.

Counting zero kernel pairs over a finite field

Helmke et al. have recently given a formula for the number of reachable pairs of matrices over a finite field. We give a new and elementary proof of the same formula by solving the equivalent problem of determining the number of so called zero kernel pairs over a finite field. We show that the problem is, equivalent to certain other enumeration problems and outline a connection with some recent results of Guo and Yang on the natural density of rectangular unimodular matrices over F-qx]. We also propose a new conjecture on the density of unimodular matrix polynomials. (C) 2016 Elsevier Inc. All rights reserved.

Minuscule weight percent of graphene oxide and reduced graphene oxide modified Ag3PO4: new insight into improved photocatalytic activity

Designing and fabricating hybrid systems with a visible light active semiconductor as one of its components is an important research area for the development of highly efficient photocatalysts. Herein, we report visible-light driven photocatalytic activity of graphene oxide (GO) and controllably reduced GO (rGO) modified Ag3PO4 composites fabricated by an in situ method. Concentration of graphene derivatives in GO/rGO-Ag3PO4 composites was in the range of 0.13-0.52 wt% which is very minute compared to those reported previously. The optimal concentration of GO in Ag3PO4 with a kinetics (k = 1.23 +/- 0.04 min(-1)) for the degradation of rhodamine B is 0.26 wt%. GO-Ag3PO4 photocatalysts display an improved catalytic activity compared with pristine and rGOs modified Ag3PO4. In line with this, GO/rGO-Ag3PO4 composites show improved photocatalytic activity for the degradation of 2-chlorophenol compared with Degussa P-25. Our experiments with GO reduced to different extents show that, rGO with more polar functional groups exhibits a higher photocatalytic efficiency. The photocatalytic activity in the presence of different scavengers reveals that holes and O-2(-center dot) reactive species play major roles in the degradation phenomenon. In view of our experimental results and reported theoretical studies, a change in conduction band energy level and variation in the contribution of different charge orbitals (C 2p and O 2p) to the conduction band in the composite favours electron flow from graphene derivatives to the semiconductor, enhancing its photocatalytic response.

Comparison between weight loss of bends in a pneumatic conveyor and erosion rate obtained in a centrifugal erosion tester for the same materials

In order to find a link between results obtained from a laboratory erosion tester and tests carried out on a pneumatic conveyor, a comparison has been made between weight loss from bends on an industrial-scale pneumatic conveyor and erosion rates obtained in a small centrifugal erosion tester, for the same materials. Identical test conditions have been applied to both experiments so that comparable test results have been obtained. The erosion rate of mild steel commonly used as the wall material of conveyor pipes and pipe bends was determined individually on both test rigs. A relationship between weight loss from the bends and erosion rate determined from the tester has been developed. A discussion based on the results and their applicability to the prediction of wear in pneumatic conveyors concludes the paper. © 2004 Elsevier B.V. All rights reserved.

The Stability of a Vertical Single-Walled Carbon Nanotube Under Its Own Weight

It has been reported recently that single carbon nanotubes were attached to AFM tips to act as nanotweezers. In order to investigate its stability, a vertical single-walled carbon nanotube (SWCNT) under its own weight is studied in this paper. The lower end of the carbon nanotube is clamped. Firstly the governing dimensionless numbers are derived by dimensional analysis. Then the theoretical analysis based on an elastic column model is carried out. Two ratios, I.e., the ratio of half wall thickness to radius (t=R) and the ratio of gravity to elastic resilience ($\rho$gR=E), and their influences on the ratio of critical length to radius are discussed. It is found that the relationship between the critical ratio of altitude to radius and ratio of half thickness to radius is approximately linear. As the dimensionless number $\rho$gR=E increases, the compressive force per unit length (weight) becomes larger, thus critical ratio of altitude to radius must become smaller to maintain stability. At last the critical length of SWCNT is calculated. The results of this paper will be helpful for the stability design of nanotweezers-like nanostructures.