Entanglement detection beyond the CCNR criterion for infinite-dimensions

In this paper, in terms of the relation between the state and the reduced states of it, we obtain two inequalities which are valid for all separable states in infinite-dimensional bipartite quantum systems. One of them provides an entanglement criterion which is strictly stronger than the computable cross-norm or realignment (CCNR) criterion.Comment: 11 page

A characterization of positive linear maps and criteria of entanglement for quantum states

Let $H$ and $K$ be (finite or infinite dimensional) complex Hilbert spaces. A characterization of positive completely bounded normal linear maps from ${\mathcal B}(H)$ into ${\mathcal B}(K)$ is given, which particularly gives a characterization of positive elementary operators including all positive linear maps between matrix algebras. This characterization is then applied give a representation of quantum channels (operations) between infinite-dimensional systems. A necessary and sufficient criterion of separability is give which shows that a state $\rho$ on $H\otimes K$ is separable if and only if $(\Phi\otimes I)\rho\geq 0$ for all positive finite rank elementary operators $\Phi$. Examples of NCP and indecomposable positive linear maps are given and are used to recognize some entangled states that cannot be recognized by the PPT criterion and the realignment criterion.Comment: 20 page

Fermi resonance-algebraic model for molecular vibrational spectra

A Fermi resonance-algebraic model is proposed for molecular vibrations, where a U(2) algebra is used for describing the vibrations of each bond, and Fermi resonances between stretching and bending modes are taken into account. The model for a bent molecule XY_2 and a molecule XY_3 is successfully applied to fit the recently observed vibrational spectrum of the water molecule and arsine (AsH_3), respectively, and results are compared with those of other models. Calculations show that algebraic approaches can be used as an effective method for describing molecular vibrations with small standard deviations

Tevatron Mass Limits for Heavy Quarks Decaying via Flavor Changing Neutral Current

The dimuon and dielectron data from the Tevatron $\bar pp$ collider are used to probe for heavy quarks, which decay dominantly via flavour changing neutral current. Depending on whether the $FCNC$ decay occurs at the tree or loop level, one gets a lower mass limit of 85 or 75 GeV. The former applies to singlet, vector doublet and mirror type quarks while the latter applies to a lefthanded quark doublet of the fourth generation.Comment: 13 pages, TIFR/TH/92-58, Two figures to be supplied on reques

Window on Higgs Boson: Fourth Generation b′b^\prime Decays Revisited

Direct and indirect searches of the Higgs boson suggest that 113 GeV $\lesssim m_H \lesssim$ 170 GeV is likely. With the LEP era over and the Tevatron Run II search via $p\bar p \to WH+X$ arduous, we revisit a case where $WH$ or $ZH +$ jets could arise via strong $b^\prime\bar b^\prime$ pair production. In contrast to 10 years ago, the tight electroweak constraint on $t^\prime$--$b^\prime$ (hence $t^\prime$--$t$) splitting reduces FCNC $b^\prime\to bZ$, $bH$ rates, making $b^\prime\to cW$ naturally competitive. Such a "cocktail solution" is precisely the mix that could evade the CDF search for $b^\prime\to bZ$, and the $b^\prime$ may well be lurking below the top. In light of the Higgs program, this two-in-one strategy should be pursued.Comment: 4 pages, RevTex, 4 eps figures, One more figure, version to be published in Phys. Rev.

Elusive vector glueball

If the vector glueball exists in the mass range that theory suggests, its resonance production cross section can be seen in e+e- annihilation only if the decay width is very narrow (< a few MeV). Otherwise it willbe observed only indirectly through its mixing with psi(2S). A few tests of the glueball-psi' mixing are proposed for future charm factories.Comment: One misleading short sentence delete

Is the Top Quark Really Heavier than the WW Boson?

Scalar induced top decays may drastically suppress $B(t\to \ell\nu + jet)$ and still hide the top below $M_W$. The $p\bar p$ collider experiments should enlarge the scope and study the $m_t - B(t\to\ell\nu j)$ plane. Specific model signatures such as $t\to ch^0\to cb\bar b$ (multiple high $p_T$ $b$-jets) and $t\to bH^+\to bc\bar s$, $b\tau^+\nu$ (with $B(t\to b\tau\nu) \ \raisebox{-.5ex}{\rlap{$\sim$}} \raisebox{.4ex}{$<$}\ 1/3$) should be explored. Without ruling out these possibilities, isolated lepton signals in the future might actually be due to the 4th generation$t^\prime$or$b^\prime$ quark, while top quark and toponium physics could still turn up at LEP-II.Comment: 11 pages (RevTex), 3 figures (not included), NTUTH-93-0

Probing flavor changing interactions in hadron collisions

The subprocess $gg \to t\bar{c}+\bar{t}c$ in the two-Higgs-doublet model with flavor-changing scalar couplings is examined at the one loop level. With perturbative QCD factorization theorem, the corresponding cross sections for hadron-hadron collisions are computed numerically. The results are applicable to the whole mass range of the weakly coupled Higgs bosons. In case we could efficiently exclude the severe backgrounds of the $t\bar{c}(\bar{t}c)$ production signal, probing the flavor-changing top-charm-scalar vertex at hadron colliders would be very promising and accessible experimentally.Comment: LaTex file, 14 pages, 8 EPS figure

Unsupervised Feature Selection with Adaptive Structure Learning

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods