Charmless decays B -> PP, PV, and effects of new strong and electroweak penguins in Topcolor-assisted Technicolor model

Based on the low energy effective Hamiltonian with generalized factorization, we calculate the new physics contributions to the branching ratios and CP-violating asymmetries of the two-body charmless hadronic decays $B \to PP, PV$ from the new strong and electroweak penguin diagrams in the TC2 model. The top-pion penguins dominate the new physics corrections, and both new gluonic and electroweak penguins contribute effectively to most decay modes. For tree-dominated decay modes $B \to \pi \pi, \rho \pi, etc,$ the new physics corrections are less than 10%. For decays $B \to K^{(*)} \pi$, $K^{(*)} \eta$, $etc$, the new physics enhancements can be rather large (from $- 70$ to $\sim 200$) and are insensitive to the variations of $N_c^{eff}$, $k^2$, $\eta$ and $m_{\tilde{\pi}}$ within the reasonable ranges. For decays $B^0 \to \phi \pi$, $\phi \eta^{(')}$, $K^* \bar{K}^0$ and $\rho^+ K^0$, $\delta {\cal B}$ is strongly $N_c^{eff}-$dependent: varying from -90% to $\sim 1680$ in the range of $N_c^{eff}=2-\infty$. The new physics corrections to the CP-violating asymmetries ${\cal A}_{CP}$ vary greatly for different B decay channels. For five measured CP asymmetries of $B \to K \pi, K \eta', \omega \pi$ decays, $\delta {\cal A}_{CP}$ is only about 20% and will be masked by large theoretical uncertainties. The new physics enhancements to interesting $B \to K \eta'$ decays are significant in size ($\sim 50$), insensitive to the variations of input parameters and hence lead to a plausible interpretation for the unexpectedly large $B \to K \eta'$ decay rates. The TC2 model predictions for branching ratios and CP-violating asymmteries of all fifty seven $B \to PP, PV$ decay modes are consistent with the available data within one or two standard deviations.Comment: Latex file, 56 pages with 11 ps and eps figures. to be published in Eur.Phys.J.

Robust and Efficient Hamiltonian Learning

With the fast development of quantum technology, the sizes of both digital and analog quantum systems increase drastically. In order to have better control and understanding of the quantum hardware, an important task is to characterize the interaction, i.e., to learn the Hamiltonian, which determines both static and dynamic properties of the system. Conventional Hamiltonian learning methods either require costly process tomography or adopt impractical assumptions, such as prior information on the Hamiltonian structure and the ground or thermal states of the system. In this work, we present a robust and efficient Hamiltonian learning method that circumvents these limitations based only on mild assumptions. The proposed method can efficiently learn any Hamiltonian that is sparse on the Pauli basis using only short-time dynamics and local operations without any information on the Hamiltonian or preparing any eigenstates or thermal states. The method has a scalable complexity and a vanishing failure probability regarding the qubit number. Meanwhile, it performs robustly given the presence of state preparation and measurement errors and resiliently against a certain amount of circuit and shot noise. We numerically test the scaling and the estimation accuracy of the method for transverse field Ising Hamiltonian with random interaction strengths and molecular Hamiltonians, both with varying sizes and manually added noise. All these results verify the robustness and efficacy of the method, paving the way for a systematic understanding of the dynamics of large quantum systems.Comment: 41 pages, 6 figures, Open source implementation available at https://github.com/zyHan2077/HamiltonianLearnin

A Graph-Based Collision Resolution Scheme for Asynchronous Unsourced Random Access

This paper investigates the multiple-input-multiple-output (MIMO) massive unsourced random access in an asynchronous orthogonal frequency division multiplexing (OFDM) system, with both timing and frequency offsets (TFO) and non-negligible user collisions. The proposed coding framework splits the data into two parts encoded by sparse regression code (SPARC) and low-density parity check (LDPC) code. Multistage orthogonal pilots are transmitted in the first part to reduce collision density. Unlike existing schemes requiring a quantization codebook with a large size for estimating TFO, we establish a \textit{graph-based channel reconstruction and collision resolution (GB-CR$^2$)} algorithm to iteratively reconstruct channels, resolve collisions, and compensate for TFO rotations on the formulated graph jointly among multiple stages. We further propose to leverage the geometric characteristics of signal constellations to correct TFO estimations. Exhaustive simulations demonstrate remarkable performance superiority in channel estimation and data recovery with substantial complexity reduction compared to state-of-the-art schemes.Comment: 6 pages, 6 figures, submitted to IEEE GLOBECOM 202

Sharing Patient Disease Data with Privacy Preservation

When patient data are shared for studying a specific disease, a privacy disclosure occurs as long as an individual is known to be in the shared data. Individuals in such specific disease data are thus subject to higher disclosure risk than those in datasets with different diseases. This problem has been overlooked in privacy research and practice. In this study, we analyze disclosure risks for this problem and identify appropriate risk measures. An efficient algorithm is developed for anonymizing the data. An experimental study is conducted to demonstrate the effectiveness of the proposed approach

Pb2+, Cu2+, Zn2+, Mg2+ and Mn2+ reduce the affinities of flavone, genistein and kaempferol for human serum albumin in vitro

Flavone (Fl), genistein (Gen) and kaempferol (Kol) were studied for their affinities towards human serum albumin (HSA) in the presence and absence of Pb2+,Cu2+,Zn2+,Mg2+ and Mn2+. The fluorescence intensities of HSA decreased with increasing concentration of the three flavonoids. Kaempferol resulted in a blue-shift of the λem of HSA from 336 to 330 nm; flavone showed an obvious red-shift of the λem of HSA from 336 to 342 nm; genistein did not cause an obvious blue-shift or red-shift of the λem of HSA. However, the extents of λem-shifts induced by the flavonoids in the presence of metal ions were much bigger than that in the absence of mental ions. Pb2+,Cu2+,Zn2+,Mg2+ and Mn2+ reduced the quenching constants of the flavonoids for HSA by 14.6% to 60.7% , 28% to 67.9%,3.5% to 59.4%, 23.2% to 63.7% and 14% to 65%, respectively. The affinities of flavone, genistein and kaempferol for HSA decreased about 10.84%, 10.05%and 3.56% in the presence of Pb2+, respectively. Cu2+ decreased the affinities of flavone, genistein and kaempferol for HSA about 14.04%, 5.14%and 8.89%, respectively. Zn2+ decreased the affinities of flavone, genistein and kaempferol for HSA about 3.79%, 0.55% and 3.58%, respectively. Mg2+ decreased the affinities of flavone, genistein and kaempferol for HSA about 16.94%, 2.94%and 7.04%, respectively. Mn2+ decreased the affinities of flavone, genistein and kaempferol for HSA about 14.24%, 3.66% and 4.78%, respectively

A simple method for computing resistance distance

The resistance distance r i j between two vertices v i and v j of a (connected, molecular) graph G is equal to the effective resistance between the respective two points of an electrical network, constructed so as to correspond to G, such that the resistance of any edge is unity. We show how r i j can be computed from the Laplacian matrix L of the graph G: Let L(i) and L(i, j) be obtained from L by deleting its i-th row and column, and by deleting its i-th and j-th rows and columns, respectively. Then r i j = det L(i, j)/ det L(i)

Interconnection Networks with Hypercubic Skeletons

The hypercubic family of interconnection networks, encompassing the hypercube and its derivatives and variants, has a wide range of applications in parallel processing. Various problems in general complex networks can be addressed by choosing a hypercubic network as a skeleton. In this paper, we provide insight into why hypercubic networks are suitable as network skeletons and discuss a mapping scheme to take advantage of the symmetry of such networks for developing efficient algorithms