Determining the CP Violation Angle γ\gamma in BsB_s Decays without Hadronic Uncertainty

We study the rare decays $B_s^0\to D^\pm \pi^\mp$ and $\bar B_s^0\to D^\mp \pi^\pm$, which can occur only via annihilation type $W$ exchange diagrams in the standard model. The time-dependent decay rates of the four channels can provide four CP parameters, which are experimentally measurable. We show that the CKM angle $\phi_3=\gamma$ can be determined from these parameters without any theoretical model dependence. These channels can be measured in future LHCb experiments to provide a clean way for $\gamma$ measurement.Comment: 4 pages, including 2 figures, Revte

Multi-graph-view subgraph mining for graph classification

© 2015, Springer-Verlag London. In this paper, we formulate a new multi-graph-view learning task, where each object to be classified contains graphs from multiple graph-views. This problem setting is essentially different from traditional single-graph-view graph classification, where graphs are collected from one single-feature view. To solve the problem, we propose a cross graph-view subgraph feature-based learning algorithm that explores an optimal set of subgraphs, across multiple graph-views, as features to represent graphs. Specifically, we derive an evaluation criterion to estimate the discriminative power and redundancy of subgraph features across all views, with a branch-and-bound algorithm being proposed to prune subgraph search space. Because graph-views may complement each other and play different roles in a learning task, we assign each view with a weight value indicating its importance to the learning task and further use an optimization process to find optimal weight values for each graph-view. The iteration between cross graph-view subgraph scoring and graph-view weight updating forms a closed loop to find optimal subgraphs to represent graphs for multi-graph-view learning. Experiments and comparisons on real-world tasks demonstrate the algorithm’s superior performance

Multi-graph learning with positive and unlabeled bags

© SIAM. In this paper, we formulate a new multi-graph learning task with only positive and unlabeled bags, where labels are only available for bags but not for individual graphs inside the bag. This problem setting raises significant challenges because bag-of-graph setting does not have features to directly represent graph data, and no negative bags exits for deriving discriminative classification models. To solve the challenge, we propose a puMGL learning framework which relies on two iteratively combined processes for multigraph learning: (1) deriving features to represent graphs for learning; and (2) deriving discriminative models with only positive and unlabeled graph bags. For the former, we derive a subgraph scoring criterion to select a set of informative subgraphs to convert each graph into a feature space. To handle unlabeled bags, we assign a weight value to each bag and use the adjusted weight values to select most promising unlabeled bags as negative bags. A margin graph pool (MGP), which contains some representative graphs from positive bags and identified negative bags, is used for selecting subgraphs and training graph classifiers. The iterative subgraph scoring, bag weight updating, and MGP based graph classification forms a closed loop to find optimal subgraphs and most suitable unlabeled bags for multi-graph learning. Experiments and comparisons on real-world multigraph data demonstrate the algorithm performance. Copyrigh

Implementing universal nonadiabatic holonomic quantum gates with transmons

Geometric phases are well known to be noise-resilient in quantum evolutions/operations. Holonomic quantum gates provide us with a robust way towards universal quantum computation, as these quantum gates are actually induced by nonabelian geometric phases. Here we propose and elaborate how to efficiently implement universal nonadiabatic holonomic quantum gates on simpler superconducting circuits, with a single transmon serving as a qubit. In our proposal, an arbitrary single-qubit holonomic gate can be realized in a single-loop scenario, by varying the amplitudes and phase difference of two microwave fields resonantly coupled to a transmon, while nontrivial two-qubit holonomic gates may be generated with a transmission-line resonator being simultaneously coupled to the two target transmons in an effective resonant way. Moreover, our scenario may readily be scaled up to a two-dimensional lattice configuration, which is able to support large scalable quantum computation, paving the way for practically implementing universal nonadiabatic holonomic quantum computation with superconducting circuits.Comment: v3 Appendix added, v4 published version, v5 published version with correction

BISSIAM: Bispectrum Siamese Network Based Contrastive Learning for UAV Anomaly Detection

In recent years, a surging number of unmanned aerial vehicles (UAVs) are pervasively utilized in many areas. However, the increasing number of UAVs may cause privacy and security issues such as voyeurism and espionage. It is critical for individuals or organizations to manage their behaviors and proactively prevent the misbehaved invasion of unauthorized UAVs through effective anomaly detection. The UAV anomaly detection framework needs to cope with complex signals in noisy-prone environments and to function with very limited labeled samples. This paper proposes BISSIAM, a novel framework that is capable of identifying UAV presence, types, and operation modes. BISSIAM converts UAV signals to bispectrum as the input and exploits a siamese network-based contrastive learning model to learn the vector encoding. A sampling mechanism is proposed for optimizing the sample size involved in the model training whilst ensuring the model accuracy without compromising the training efficiency. Finally, we present a similarity-based fingerprint matching mechanism for detecting unseen UAVs without the need of retraining the whole model. Experimental results show that our approach outperforms other baselines and can reach 92.85% accuracy of UAV type detection in unsupervised learning scenarios, and 91.4% accuracy for detecting the UAV type of the out-of-sample UAVs

Phenomenological Scaling of Rapidity Dependence for Anisotropic Flows in 25 MeV/nucleon Ca + Ca by Quantum Molecular Dynamics Model

Anisotropic flows ($v_1$, $v_2$, $v_3$ and $v_4$) of light fragments up till the mass number 4 as a function of rapidity have been studied for 25 MeV/nucleon $^{40}$Ca + $^{40}$Ca at large impact parameters by Quantum Molecular Dynamics model. A phenomenological scaling behavior of rapidity dependent flow parameters $v_n$ (n = 1, 2, 3 and 4) has been found as a function of mass number plus a constant term, which may arise from the interplay of collective and random motions. In addition, $v_4/{v_2}^2$ keeps almost independent of rapidity and remains a rough constant of 1/2 for all light fragments.Comment: 4 pages, 5 figure

Positron Emission Tomography Imaging of CD105 Expression with a 64Cu-Labeled Monoclonal Antibody: NOTA Is Superior to DOTA

Optimizing the in vivo stability of positron emission tomography (PET) tracers is of critical importance to cancer diagnosis. In the case of 64Cu-labeled monoclonal antibodies (mAb), in vivo behavior and biodistribution is critically dependent on the performance of the bifunctional chelator used to conjugate the mAb to the radiolabel. This study compared the in vivo characteristics of 64Cu-labeled TRC105 (a chimeric mAb that binds to both human and murine CD105), through two commonly used chelators: 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA) and 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA). Flow cytometry analysis confirmed that chelator conjugation of TRC105 did not affect its CD105 binding affinity or specificity. PET imaging and biodistribution studies in 4T1 murine breast tumor-bearing mice revealed that 64Cu-NOTA-TRC105 exhibited better stability than 64Cu-DOTA-TRC105 in vivo, which resulted in significantly lower liver uptake without compromising the tumor targeting efficiency. In conclusion, this study confirmed that NOTA is a superior chelator to DOTA for PET imaging with 64Cu-labeled TRC105

Multiparty Quantum Secret Report

A multiparty quantum secret report scheme is proposed with quantum encryption. The boss Alice and her $M$ agents first share a sequence of ($M$+1)-particle Greenberger--Horne--Zeilinger (GHZ) states that only Alice knows which state each ($M$+1)-particle quantum system is in. Each agent exploits a controlled-not (CNot) gate to encrypt the travelling particle by using the particle in the GHZ state as the control qubit. The boss Alice decrypts the travelling particle with a CNot gate after performing a $\sigma_x$ operation on her particle in the GHZ state or not. After the GHZ states (the quantum key) are used up, the parties check whether there is a vicious eavesdropper, say Eve, monitoring the quantum line, by picking out some samples from the GHZ states shared and measure them with two measuring bases. After confirming the security of the quantum key, they use the GHZ states remained repeatedly for next round of quantum communication. This scheme has the advantage of high intrinsic efficiency for qubits and the total efficiency.Comment: 4 pages, no figure