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A policymaker discloses public information to interacting agents who also acquire costly private information. More precise public information reduces the precision and cost of acquired private information. Considering this effect, what disclosure rule should the policymaker adopt? We address this question under two alternative assumptions using a linear quadratic Gaussian game with arbitrary quadratic material welfare and convex information costs. First, the policymaker knows the cost of private information and adopts an optimal disclosure rule to maximize the expected welfare. Second, the policymaker is uncertain about the cost and adopts a robust disclosure rule to maximize the worst-case welfare. Depending on the elasticity of marginal cost, an optimal rule is qualitatively the same as that in the case of either a linear information cost or exogenous private information. The worst-case welfare is strictly increasing if and only if full disclosure is optimal under some information costs, which provides a new rationale for central bank transparency.

We study scalar and fermionic crossing two-component dark matter (C2CDM) models with $ U(1)_X $ dark gauge symmetry. This $ U(1)_X $ gauge symmetry is broken via the dark Higgs mechanism of a dark Higgs field $\Phi$ and the dark photon becomes massive. On the other hand, the same dark Higgs field $\Phi$ also serves as a bridge between each component of two DM sectors such that the dark flavor-changing neutral current (DFCNC) interaction between them is induced. Moreover, the mass splitting between these two DM sectors is generated after $ U(1)_X $ gauge symmetry breaking by nonzero $\langle \Phi \rangle$. We discuss the stability of DM candidates and allowed parameter space from the relic density and other constraints in C2CDM models. Some novel signatures with displaced vertices at Belle II are studied. Finally, the possible explanations of 511 keV $\gamma$-ray line and XENON1T excess in C2CDM models are discussed., Comment: 41 pages, 12 figures

Quasi-isometries are mappings on graphs, with distance-distortions parameterized by a multiplicative factor and an additive constant. The distance-distortions of quasi-isometries are in a general form that captures a wide range of distance-approximating graph simplifications. This paper introduces quasi-isometries into the field of graph simplifications, which is becoming increasingly important as large-scale graphs gain more and more prevalence. We discuss some general goals of graph simplification under the framework of quasi-isometries, and investigate several constructions of quasi-isometric graph simplifications, namely one based on maximal independent sets and one based on grouping vertices. For the latter construction, we prove that it preserves the centers and medians of trees., Comment: 31 pages, 7 figures, to be submitted to "Discrete Mathematics and Theoretical Computer Science"

We propose a new approach to constant-roll warm inflation as a generalization of constant-roll inflation. Based on the $\beta$-function formalism, it is shown that constant-roll warm inflation models with a natural end fall into universality classes defined by three different types of $\beta$-functions, under the assumption that radiation energy density is quasi-stable. Given that warm inflation is completely specified by the $\beta$-function and dissipation coefficient ratio, we investigate whether or not the inflation can physically be realized for enough number of e-foldings at the background level for some combinations of the $\beta$-functions and non-trivial dissipation coefficient ratios., Comment: 10 pages, 5 figures

We report the enhanced superconducting properties of double-chain based superconductor Pr$_{2}$Ba$_{4}$Cu$_{7}$O$_{15-\delta}$ synthesized by the citrate pyrolysis technique. %In spite of the polycrystalline bulk samples, we obtained the higher residual resistivity ratios (10-12). The reduction heat treatment in vacuum results in the appearance of superconducting state with $T_\mathrm{c}$=22-24 K, accompanied by the higher residual resistivity ratios. The superconducting volume fractions are estimated from the ZFC data to be 50$\sim55\%$, indicating the bulk superconductivity. We evaluate from the magneto-transport data the temperature dependence of the superconducting critical field, to establish the superconducting phase diagram. The upper critical magnetic field is estimated to be about 35 T at low temperatures from the resistive transition data using the Werthamer-Helfand-Hohenberg formula. The Hall coefficient $R_{H}$ of the 48-h-reduced superconducting sample is determined to be -0.5$\times10^{-3}$ cm$^{3}$/C at 30 K, suggesting higher electron concentration. These findings have a close relationship with homogeneous distributions of the superconducting grains and improved weak links between their superconducting grains in the present synthesis process., Comment: 7 pages, 6 figures, 1 table. arXiv admin note: substantial text overlap with arXiv:1609.00125, arXiv:1512.01300

We document the activities performed during the second MadAnalysis 5 workshop on LHC recasting, that was organised in KIAS (Seoul, Korea) on February 12-20, 2020. We detail the implementation of 12 new ATLAS and CMS searches in the MadAnalysis 5 Public Analysis Database, and the associated validation procedures. Those searches probe the production of extra gauge and scalar/pseudoscalar bosons, supersymmetry, seesaw models and deviations from the Standard Model in four-top production., Comment: 119 pages, 37 figures, 48 tables and 12 new analyses added to the MadAnalysis 5 Public Analysis Database. More information available from https://indico.cern.ch/event/873524/ and http://madanalysis.irmp.ucl.ac.be/wiki/PublicAnalysisDatabase

Anomalous activity recognition deals with identifying the patterns and events that vary from the normal stream. In a surveillance paradigm, these events range from abuse to fighting and road accidents to snatching, etc. Due to the sparse occurrence of anomalous events, anomalous activity recognition from surveillance videos is a challenging research task. The approaches reported can be generally categorized as handcrafted and deep learning-based. Most of the reported studies address binary classification i.e. anomaly detection from surveillance videos. But these reported approaches did not address other anomalous events e.g. abuse, fight, road accidents, shooting, stealing, vandalism, and robbery, etc. from surveillance videos. Therefore, this paper aims to provide an effective framework for the recognition of different real-world anomalies from videos. This study provides a simple, yet effective approach for learning spatiotemporal features using deep 3-dimensional convolutional networks (3D ConvNets) trained on the University of Central Florida (UCF) Crime video dataset. Firstly, the frame-level labels of the UCF Crime dataset are provided, and then to extract anomalous spatiotemporal features more efficiently a fine-tuned 3D ConvNets is proposed. Findings of the proposed study are twofold 1)There exist specific, detectable, and quantifiable features in UCF Crime video feed that associate with each other 2) Multiclass learning can improve generalizing competencies of the 3D ConvNets by effectively learning frame-level information of dataset and can be leveraged in terms of better results by applying spatial augmentation., Comment: 25 pages, 7, figures, 8 Tables, Under Review in Multimedia Tools and Applications