Representation Class and Geometrical Invariants of Quantum States under Local Unitary Transformations

We investigate the equivalence of bipartite quantum mixed states under local unitary transformations by introducing representation classes from a geometrical approach. It is shown that two bipartite mixed states are equivalent under local unitary transformations if and only if they have the same representation class. Detailed examples are given on calculating representation classes.Comment: 11 page

Angular Reconstruction of a Lead Scintillating-Fiber Sandwiched Electromagnetic Calorimeter

A new method called Neighbor Cell Deposited Energy Ratio (NCDER) is proposed to reconstruct incidence position in a single layer for a 3-dimensional imaging electromagnetic calorimeter (ECAL).This method was applied to reconstruct the ECAL test beam data for the Alpha Magnetic Spectrometer-02 (AMS-02). The results show that this method can achieve an angular resolution of 7.36\pm 0.08 / \sqrt(E) \oplus 0.28 \pm 0.02 degree in the determination of the photons direction, which is much more precise than that obtained with the commonly-adopted Center of Gravity(COG) method (8.4 \pm 0.1 /sqrt(E) \oplus 0.8\pm0.3 degree). Furthermore, since it uses only the properties of electromagnetic showers, this new method could also be used for other type of fine grain sampling calorimeters.Comment: 6 pages, 8 figure

Towards a reliable reconstruction of the power spectrum of primordial curvature perturbation on small scales from GWTC-3

Primordial black holes (PBHs) can be both candidates of dark matter and progenitors of binary black holes (BBHs) detected by the LIGO-Virgo-KAGRA collaboration. Since PBHs could form in the very early Universe through the gravitational collapse of primordial density perturbations, the population of BBHs detected by gravitational waves encodes much information on primordial curvature perturbation. In this work, we take a reliable and systematic approach to reconstruct the power spectrum of the primordial curvature perturbation from GWTC-3, under the hierarchical Bayesian inference framework, by accounting for the measurement uncertainties and selection effects. In addition to just considering the single PBH population model, we also report the results considering the multi-population model, i.e., the mixed PBH and astrophysical black hole binaries model. We find that the maximum amplitude of the reconstructed power spectrum of primordial curvature perturbation can be $\sim2.5\times10^{-2}$ at $\mathcal{O}(10^{5})~\rm Mpc^{-1}$ scales, which is consistent with the PBH formation scenario from inflation at small scales

A Few-Shot Learning-Based Siamese Capsule Network for Intrusion Detection with Imbalanced Training Data

Network intrusion detection remains one of the major challenges in cybersecurity. In recent years, many machine-learning-based methods have been designed to capture the dynamic and complex intrusion patterns to improve the performance of intrusion detection systems. However, two issues, including imbalanced training data and new unknown attacks, still hinder the development of a reliable network intrusion detection system. In this paper, we propose a novel few-shot learning-based Siamese capsule network to tackle the scarcity of abnormal network traffic training data and enhance the detection of unknown attacks. In specific, the well-designed deep learning network excels at capturing dynamic relationships across traffic features. In addition, an unsupervised subtype sampling scheme is seamlessly integrated with the Siamese network to improve the detection of network intrusion attacks under the circumstance of imbalanced training data. Experimental results have demonstrated that the metric learning framework is more suitable to extract subtle and distinctive features to identify both known and unknown attacks after the sampling scheme compared to other supervised learning methods. Compared to the state-of-the-art methods, our proposed method achieves superior performance to effectively detect both types of attacks