Strong Bounds on Sum of Neutrino Masses in a 12 Parameter Extended Scenario with Non-Phantom Dynamical Dark Energy (w(z)≥−1w(z)\geq -1)

We obtained constraints on a 12 parameter extended cosmological scenario including non-phantom dynamical dark energy (NPDDE) with CPL parametrization. We also include the six $\Lambda$CDM parameters, number of relativistic neutrino species ($N_{\textrm{eff}}$) and sum over active neutrino masses ($\sum m_{\nu}$), tensor-to-scalar ratio ($r_{0.05}$), and running of the spectral index ($n_{run}$). We use CMB Data from Planck 2015; BAO Measurements from SDSS BOSS DR12, MGS, and 6dFS; SNe Ia Luminosity Distance measurements from the Pantheon Sample; CMB B-mode polarization data from BICEP2/Keck collaboration (BK14); Planck lensing data; and a prior on Hubble constant ($73.24\pm1.74$ km/sec/Mpc) from local measurements (HST). We have found strong bounds on the sum of the active neutrino masses. For instance, a strong bound of $\sum m_{\nu} <$ 0.123 eV (95\% C.L.) comes from Planck+BK14+BAO. Although we are in such an extended parameter space, this bound is stronger than a bound of $\sum m_{\nu} <$ 0.158 eV (95\% C.L.) obtained in $\Lambda \textrm{CDM}+\sum m_{\nu}$ with Planck+BAO. Varying $A_{\textrm{lens}}$ instead of $r_{0.05}$ however leads to weaker bounds on $\sum m_{\nu}$. Inclusion of the HST leads to the standard value of $N_{\textrm{eff}} = 3.045$ being discarded at more than 68\% C.L., which increases to 95\% C.L. when we vary $A_{\textrm{lens}}$ instead of $r_{0.05}$, implying a small preference for dark radiation, driven by the $H_0$ tension.Comment: 23 pages, 10 figures, matches the published versio

Secret Sharing and Proactive Renewal of Shares in Hierarchical Groups

Secret sharing in user hierarchy represents a challenging area for research. Although a lot of work has already been done in this direc- tion, this paper presents a novel approach to share a secret among a hierarchy of users while overcoming the limitations of the already exist- ing mechanisms. Our work is based on traditional (k +1; n)-threshold secret sharing, which is secure as long as an adversary can compromise not more than k secret shares. But in real life it is often feasible for an adversary to obtain more than k shares over a long period of time. So, in our work we also present a way to overcome this vulnerability, while implementing our hierarchical secret sharing scheme. The use of Elliptic Curve Cryptography makes the computations easier and faster in our work.Comment: 20 Page

A Survey of Cellular Automata: Types, Dynamics, Non-uniformity and Applications

Cellular automata (CAs) are dynamical systems which exhibit complex global behavior from simple local interaction and computation. Since the inception of cellular automaton (CA) by von Neumann in 1950s, it has attracted the attention of several researchers over various backgrounds and fields for modelling different physical, natural as well as real-life phenomena. Classically, CAs are uniform. However, non-uniformity has also been introduced in update pattern, lattice structure, neighborhood dependency and local rule. In this survey, we tour to the various types of CAs introduced till date, the different characterization tools, the global behaviors of CAs, like universality, reversibility, dynamics etc. Special attention is given to non-uniformity in CAs and especially to non-uniform elementary CAs, which have been very useful in solving several real-life problems.Comment: 43 pages; Under review in Natural Computin

Using supertags as source language context in SMT

Recent research has shown that Phrase-Based Statistical Machine Translation (PB-SMT) systems can benefit from two enhancements: (i) using words and POS tags as context-informed features on the source side; and (ii) incorporating lexical syntactic descriptions in the form of supertags on the target side. In this work we present a novel PB-SMT model that combines these two aspects by using supertags as source language contextinformed features. These features enable us to exploit source similarity in addition to target similarity, as modelled by the language model. In our experiments two kinds of supertags are employed: those from Lexicalized Tree-Adjoining Grammar and Combinatory Categorial Grammar. We use a memory-based classification framework that enables the estimation of these features while avoiding problems of sparseness. Despite the differences between these two approaches, the supertaggers give similar improvements. We evaluate the performance of our approach on an English-to-Chinese translation task using a state-of-the-art phrase-based SMT system, and report an improvement of 7.88% BLEU score in translation quality when adding supertags as context-informed features