Higgsino Dark Matter in Nonuniversal Gaugino Mass Models

We study two simple and well motivated nonuniversal gaugino mass models, which predict higgsino dark matter. One can account for the observed dark matter relic density along with the observed Higgs boson mass of ~ 125 GeV over a large region of the parameter space of each model, corresponding to higgsino mass of ~ 1 TeV. In each case this parameter region covers the gluino mass range of 2-3 TeV, parts of which can be probed by the 14 TeV LHC experiments. We study these model predictions for LHC in brief and for dark matter detection experiments in greater detail.Comment: 35 pages, 11 figures, pdflatex, new references and a few relevant decay branching ratios added in two tables. Version to appear in Phys Rev

Metacontent based techniques for the regression testing of component based software : a case study

Component based software technologies are viewed as essential for creating the software systems of the future. However the use of externally provided components has serious drawbacks for a wide range of software engineering activities often because of a lack of information about the components. One such drawback involves validation of components. To address this problem previous researchers have proposed the notion of metacontent. Metacontent describes static and dynamic aspects of a component, and consists of information (metadata) about components, and utilities (metamethods) for computing and retrieving such information. In this project we implement three new meta.content based techniques that address the problem of validating component based applications after they have been modified (also known as "regression testing"): a code based approach, a specification based approach, and a hybrid approach that uses information both at the code and at the specification level. We present a case study that applies all three techniques to a real component based system

On Language Clustering: A Non-parametric Statistical Approach

Any approach aimed at pasteurizing and quantifying a particular phenomenon must include the use of robust statistical methodologies for data analysis. With this in mind, the purpose of this study is to present statistical approaches that may be employed in nonparametric nonhomogeneous data frameworks, as well as to examine their application in the field of natural language processing and language clustering. Furthermore, this paper discusses the many uses of nonparametric approaches in linguistic data mining and processing. The data depth idea allows for the centre-outward ordering of points in any dimension, resulting in a new nonparametric multivariate statistical analysis that does not require any distributional assumptions. The concept of hierarchy is used in historical language categorisation and structuring, and it aims to organise and cluster languages into subfamilies using the same premise. In this regard, the current study presents a novel approach to language family structuring based on non-parametric approaches produced from a typological structure of words in various languages, which is then converted into a Cartesian framework using MDS. This statistical-depth-based architecture allows for the use of data-depth-based methodologies for robust outlier detection, which is extremely useful in understanding the categorization of diverse borderline languages and allows for the re-evaluation of existing classification systems. Other depth-based approaches are also applied to processes such as unsupervised and supervised clustering. This paper therefore provides an overview of procedures that can be applied to nonhomogeneous language classification systems in a nonparametric framework.Comment: 18 page

Towards Tight Security Bounds for OMAC, XCBC and TMAC

OMAC -- a single-keyed variant of CBC-MAC by Iwata and Kurosawa -- is a widely used and standardized (NIST FIPS 800-38B, ISO/IEC 29167-10:2017) message authentication code (MAC) algorithm. The best security bound for OMAC is due to Nandi who proved that OMAC's pseudorandom function (PRF) advantage is upper bounded by O(q^2\ell/2^n), where n, q, and \ell, denote the block size of the underlying block cipher, the number of queries, and the maximum permissible query length (in terms of n-bit blocks), respectively. In contrast, there is no attack with matching lower bound. Indeed, the best known attack on OMAC is the folklore birthday attack achieving a lower bound of \Omega(q^2/2^n). In this work, we close this gap for a large range of message lengths. Specifically, we show that OMAC's PRF security is upper bounded by O(q^2/2^n + q\ell^2/2^n). In practical terms, this means that for a 128-bit block cipher, and message lengths up to 64 Gigabyte, OMAC can process up to 264 messages before rekeying (same as the birthday bound). In comparison, the previous bound only allows 248 messages. As a side-effect of our proof technique, we also derive similar tight security bounds for XCBC (by Black and Rogaway) and TMAC (by Kurosawa and Iwata). As a direct consequence of this work, we have established tight security bounds (in a wide range of \ell) for all the CBC-MAC variants, except for the original CBC-MAC

Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled Data

The abstract of a scientific paper distills the contents of the paper into a short paragraph. In the biomedical literature, it is customary to structure an abstract into discourse categories like BACKGROUND, OBJECTIVE, METHOD, RESULT, and CONCLUSION, but this segmentation is uncommon in other fields like computer science. Explicit categories could be helpful for more granular, that is, discourse-level search and recommendation. The sparsity of labeled data makes it challenging to construct supervised machine learning solutions for automatic discourse-level segmentation of abstracts in non-bio domains. In this paper, we address this problem using transfer learning. In particular, we define three discourse categories BACKGROUND, TECHNIQUE, OBSERVATION-for an abstract because these three categories are the most common. We train a deep neural network on structured abstracts from PubMed, then fine-tune it on a small hand-labeled corpus of computer science papers. We observe an accuracy of 75% on the test corpus. We perform an ablation study to highlight the roles of the different parts of the model. Our method appears to be a promising solution to the automatic segmentation of abstracts, where the labeled data is sparse.Comment: to appear in the proceedings of JCDL'202

Garcinol loaded vitamin E TPGS emulsified PLGA nanoparticles: preparation, physicochemical characterization, in vitro and in vivo studies

Garcinol (GAR) is a naturally occurring polyisoprenylated phenolic compound. It has been recently investigated for its biological activities such as antioxidant, anti-inflammatory, anti ulcer, and antiproliferative effect on a wide range of human cancer cell lines. Though the outcomes are very promising, its extreme insolubility in water remains the main obstacle for its clinical application. Herein we report the formulation of GAR entrapped PLGA nanoparticles by nanoprecipitation method using vitamin E TPGS as an emulsifier. The nanoparticles were characterized for size, surface morphology, surface charge, encapsulation efficiency and in vitro drug release kinetics. The MTT assay depicted a high amount of cytotoxicity of GAR-NPs in B16F10, HepG2 and KB cells. A considerable amount of cell apoptosis was observed in B16f10 and KB cell lines. In vivo cellular uptake of fluorescent NPs on B16F10 cells was also investigated. Finally the GAR loaded NPs were radiolabeled with technetium-99m with >95% labeling efficiency and administered to B16F10 melanoma tumor bearing mice to investigate the in vivo deposition at the tumor site by biodistribution and scintigraphic imaging study. In vitro cellular uptake studies and biological evaluation confirm the efficacy of the formulation for cancer treatmen

Hard X-Ray Polarization Catalog for a Five-year Sample of Gamma-Ray Bursts Using AstroSat CZT Imager

The Cadmium Zinc Telluride Imager (CZTI) on board AstroSat has been regularly detecting gamma-ray bursts (GRBs) since its launch in 2015. Its sensitivity to polarization measurements at energies above 100 keV allows CZTI to attempt spectropolarimetric studies of GRBs. Here, we present the first catalog of GRB polarization measurements made by CZTI during its first five years of operation. This includes the time-integrated polarization measurements of the prompt emission of 20 GRBs in the energy range 100-600 keV. The sample includes the bright GRBs that were detected within an angle range of 0 degrees-60 degrees and 120 degrees-180 degrees where the instrument has useful polarization sensitivity and is less prone to systematics. We implement a few new modifications in the analysis to enhance the polarimetric sensitivity of the instrument. The majority of the GRBs in the sample are found to possess less/null polarization across the total bursts' duration in contrast to a small fraction of five GRBs that exhibit high polarization. The low polarization across the bursts might be due either to the burst being intrinsically weakly polarized or to a varying polarization angle within the burst even when it is highly polarized. In comparison to POLAR measurements, CZTI has detected a larger number of cases with high polarization. This may be a consequence of the higher energy window of CZTI observations, which results in the sampling of a shorter duration of burst emissions than POLAR, thereby probing emissions with less temporal variation in polarization properties