Upper Bounds for Randic Spread

The Randi´c spread of a simple undirected graph G, sprR(G), is equal to the maximal difference between two eigenvalues of the Randi´c matrix, disregarding the spectral radius [Gomes et al., MATCH Commun. Math. Comput. Chem. 72 (2014) 249–266]. Using a rank-one perturbation on the Randi´c matrix of G it is obtained a new matrix whose matricial spread coincide with sprR(G). By means of this result, upper bounds for sprR(G) are obtained

Privacy and spectral analysis of social network randomization

Social networks are of significant importance in various application domains. Un- derstanding the general properties of real social networks has gained much attention due to the proliferation of networked data. Many applications of networks such as anonymous web browsing and data publishing require relationship anonymity due to the sensitive, stigmatizing, or confidential nature of the relationship. One general ap- proach for this problem is to randomize the edges in true networks, and only release the randomized networks for data analysis. Our research focuses on the development of randomization techniques such that the released networks can preserve data utility while preserving data privacy. Data privacy refers to the sensitive information in the network data. The released network data after a simple randomization could incur various disclosures including identity disclosure, link disclosure and attribute disclosure. Data utility refers to the information, features, and patterns contained in the network data. Many important features may not be preserved in the released network data after a simple randomiza- tion. In this dissertation, we develop advanced randomization techniques to better preserve data utility of the network data while still preserving data privacy. Specifi- cally we develop two advanced randomization strategies that can preserve the spectral properties of the network or can preserve the real features (e.g., modularity) of the network. We quantify to what extent various randomization techniques can protect data privacy when attackers use different attacks or have different background knowl- edge. To measure the data utility, we also develop a consistent spectral framework to measure the non-randomness (importance) of the edges, nodes, and the overall graph. Exploiting the spectral space of network topology, we further develop fraud detection techniques for various collaborative attacks in social networks. Extensive theoretical analysis and empirical evaluations are conducted to demonstrate the efficacy of our developed techniques

The New Heavy Mesons: A Status Report

A survey of the experimental, phenomenological, and theoretical status of the new heavy mesons is presented. States discussed are the $B_c$, $h_c$, $\eta_c'$, $D_s(2317)$, $D_s(2460)$, X(3872), X(3940), Y(3940), Z(3930), and Y(4260). Quark models for spectra, strong decays, and hadronic interactions are reviewed and used to interpret the new states. New results for strong decay models, bound state decays, mesonic molecules, properties of the X(3872), and the chiral doublet model are also presented.Comment: 62 page, 40 figs, 16 tables. v3 corrects typos, adds references. Version to appear in Physics Report

Search for Higgsino pair production in pp collisions at √s = 13 TeV in final states with large missing transverse momentum and two Higgs bosons decaying via H → bb

Results are reported from a search for new physics in 13 TeV proton-proton collisions in the final state with large missing transverse momentum and two Higgs bosons decaying via H → bb. The search uses a data sample accumulated by the CMS experiment at the LHC in 2016, corresponding to an integrated luminosity of 35.9 fb^(-1). The search is motivated by models based on gauge-mediated supersymmetry breaking, which predict the electroweak production of a pair of Higgsinos, each of which can decay via a cascade process to a Higgs boson and an undetected lightest supersymmetric particle. The observed event yields in the signal regions are consistent with the standard model background expectation obtained from control regions in data. Higgsinos in the mass range 230–770 GeV are excluded at 95% confidence level in the context of a simplified model for the production and decay of approximately degenerate Higgsinos

Advances in Discrete Applied Mathematics and Graph Theory

The present reprint contains twelve papers published in the Special Issue “Advances in Discrete Applied Mathematics and Graph Theory, 2021” of the MDPI Mathematics journal, which cover a wide range of topics connected to the theory and applications of Graph Theory and Discrete Applied Mathematics. The focus of the majority of papers is on recent advances in graph theory and applications in chemical graph theory. In particular, the topics studied include bipartite and multipartite Ramsey numbers, graph coloring and chromatic numbers, several varieties of domination (Double Roman, Quasi-Total Roman, Total 3-Roman) and two graph indices of interest in chemical graph theory (Sombor index, generalized ABC index), as well as hyperspaces of graphs and local inclusive distance vertex irregular graphs

Physics at BES-III

This physics book provides detailed discussions on important topics in $\tau$-charm physics that will be explored during the next few years at \bes3 . Both theoretical and experimental issues are covered, including extensive reviews of recent theoretical developments and experimental techniques. Among the subjects covered are: innovations in Partial Wave Analysis (PWA), theoretical and experimental techniques for Dalitz-plot analyses, analysis tools to extract absolute branching fractions and measurements of decay constants, form factors, and CP-violation and \DzDzb-oscillation parameters. Programs of QCD studies and near-threshold tau-lepton physics measurements are also discussed.Comment: Edited by Kuang-Ta Chao and Yi-Fang Wan

Search for Higgsino pair production in pp collisions at root s=13 TeV in final states with large missing transverse momentum and two Higgs bosons decaying via H -> b(b)over bar

Results are reported from a search for new physics in 13 TeV proton-proton collisions in the final state with large missing transverse momentum and two Higgs bosons decaying via H -> b(b)over bar. The search uses a data sample accumulated by the CMS experiment at the LHC in 2016, corresponding to an integrated luminosity of 35.9 fb(-1). The search is motivated by models based on gauge-mediated supersymmetry breaking, which predict the electroweak production of a pair of Higgsinos, each of which can decay via a cascade process to a Higgs boson and an undetected lightest supersymmetric particle. The observed event yields in the signal regions are consistent with the standard model background expectation obtained from control regions in data. Higgsinos in the mass range 230-770 GeV are excluded at 95% confidence level in the context of a simplified model for the production and decay of approximately degenerate Higgsinos.Peer reviewe

Optimal forgery and suppression of ratings for privacy enhancement in recommendation systems

Recommendation systems are information-filtering systems that tailor information to users on the basis of knowledge about their preferences. The ability of these systems to profile users is what enables such intelligent functionality, but at the same time, it is the source of serious privacy concerns. In this paper we investigate a privacy-enhancing technology that aims at hindering an attacker in its efforts to accurately profile users based on the items they rate. Our approach capitalizes on the combination of two perturbative mechanisms—the forgery and the suppression of ratings. While this technique enhances user privacy to a certain extent, it inevitably comes at the cost of a loss in data utility, namely a degradation of the recommendation’s accuracy. In short, it poses a trade-off between privacy and utility. The theoretical analysis of such trade-off is the object of this work. We measure privacy as the Kullback-Leibler divergence between the user’s and the population’s item distributions, and quantify utility as the proportion of ratings users consent to forge and eliminate. Equipped with these quantitative measures, we find a closed-form solution to the problem of optimal forgery and suppression of ratings, an optimization problem that includes, as a particular case, the maximization of the entropy of the perturbed profile. We characterize the optimal trade-off surface among privacy, forgery rate and suppression rate,and experimentally evaluate how our approach could contribute to privacy protection in a real-world recommendation system.Peer ReviewedPostprint (published version