Interplay of vector-like top partner multiplets in a realistic mixing set-up

The ATLAS and CMS collaborations at the LHC have performed analyses on the existing data sets, studying the case of one vector-like fermion or multiplet coupling to the standard model Yukawa sector. In the near future, with more data available, these experimental collaborations will start to investigate more realistic cases. The presence of more than one extra vector-like multiplet is indeed a common situation in many extensions of the standard model. The interplay of these vector-like multiplet between precision electroweak bounds, flavour and collider phenomenology is a important question in view of establishing bounds or for the discovery of physics beyond the standard model. In this work we study the phenomenological consequences of the presence of two vector-like multiplets. We analyse the constraints on such scenarios from tree-level data and oblique corrections for the case of mixing to each of the SM generations. In the present work, we limit to scenarios with two top-like partners and no mixing in the down-sector.

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.

Structural studies on a non-toxic homologue of type II RIPs from bitter gourd: Molecular basis of non-toxicity, conformational selection and glycan structure

The structures of nine independent crystals of bitter gourd seed lectin (BGSL), a non-toxic homologue of type II RIPs, and its sugar complexes have been determined. The four-chain, two-fold symmetric, protein is made up of two identical two-chain modules, each consisting of a catalytic chain and a lectin chain, connected by a disulphide bridge. The lectin chain is made up of two domains. Each domain carries a carbohydrate binding site in type II RIPs of known structure. BGSL has a sugar binding site only on one domain, thus impairing its interaction at the cell surface. The adenine binding site in the catalytic chain is defective. Thus, defects in sugar binding as well as adenine binding appear to contribute to the non-toxicity of the lectin. The plasticity of the molecule is mainly caused by the presence of two possible well defined conformations of a surface loop in the lectin chain. One of them is chosen in the sugar complexes, in a case of conformational selection, as the chosen conformation facilitates an additional interaction with the sugar, involving an arginyl residue in the loop. The N-glycosylation of the lectin involves a plant-specific glycan while that in toxic type II RIPs of known structure involves a glycan which is animal as well as plant specific.

Prediction of Indian rainfall during the summer monsoon season on the basis of links with equatorial Pacific and Indian Ocean climate indices

Interannual variation of Indian summer monsoon rainfall (ISMR) is linked to El Nino-Southern oscillation (ENSO) as well as the Equatorial Indian Ocean oscillation (EQUINOO) with the link with the seasonal value of the ENSO index being stronger than that with the EQUINOO index. We show that the variation of a composite index determined through bivariate analysis, explains 54% of ISMR variance, suggesting a strong dependence of the skill of monsoon prediction on the skill of prediction of ENSO and EQUINOO. We explored the possibility of prediction of the Indian rainfall during the summer monsoon season on the basis of prior values of the indices. We find that such predictions are possible for July-September rainfall on the basis of June indices and for August-September rainfall based on the July indices. This will be a useful input for second and later stage forecasts made after the commencement of the monsoon season.

Delineation of homogeneous temperature regions: a two-stage clustering approach

Homogeneous temperature regions are necessary for use in hydrometeorological studies. The regions are often delineated by analysing statistics derived from time series of maximum, minimum or mean temperature, rather than attributes influencing temperature. This practice cannot yield meaningful regions in data-sparse areas. Further, independent validation of the delineated regions for homogeneity in temperature is not possible, as temperature records form the basis to arrive at the regions. To address these issues, a two-stage clustering approach is proposed in this study to delineate homogeneous temperature regions. First stage of the approach involves (1) determining correlation structure between observed temperature over the study area and possible predictors (large-scale atmospheric variables) influencing the temperature and (2) using the correlation structure as the basis to delineate sites in the study area into clusters. Second stage of the approach involves analysis on each of the clusters to (1) identify potential predictors (large-scale atmospheric variables) influencing temperature at sites in the cluster and (2) partition the cluster into homogeneous fuzzy temperature regions using the identified potential predictors. Application of the proposed approach to India yielded 28 homogeneous regions that were demonstrated to be effective when compared to an alternate set of 6 regions that were previously delineated over the study area. Intersite cross-correlations of monthly maximum and minimum temperatures in the existing regions were found to be weak and negative for several months, which is undesirable. This problem was not found in the case of regions delineated using the proposed approach. Utility of the proposed regions in arriving at estimates of potential evapotranspiration for ungauged locations in the study area is demonstrated.

Learning-Based Fast Iterative Convergence of 3-D MoM via Eigen-AGMRES Method

Three-dimensional (3-D) full-wave electromagnetic simulation using method of moments (MoM) under the framework of fast solver algorithms like fast multipole method (FMM) is often bottlenecked by the speed of convergence of the Krylov-subspace-based iterative process. This is primarily because the electric field integral equation (EFIE) matrix, even with cutting-edge preconditioning techniques, often exhibits bad spectral properties arising from frequency or geometry-based ill-conditioning, which render iterative solvers slow to converge or stagnate occasionally. In this communication, a novel technique to expedite the convergence of MoMmatrix solution at a specific frequency is proposed, by extracting and applying Eigen-vectors from a previously solved neighboring frequency in an augmented generalized minimum residual (AGMRES) iterative framework. This technique can be applied in unison with any preconditioner. Numerical results demonstrate up to 40% speed-up in convergence using the proposed Eigen-AGMRES method.