DIVE in the cosmic web: voids with Delaunay Triangulation from discrete matter tracer distributions

We present a novel parameter-free cosmological void finder (\textsc{dive}, Delaunay TrIangulation Void findEr) based on Delaunay Triangulation (DT), which efficiently computes the empty spheres constrained by a discrete set of tracers. We define the spheres as DT voids, and describe their properties, including an universal density profile together with an intrinsic scatter. We apply this technique on 100 halo catalogues with volumes of 2.5\,$h^{-1}$Gpc side each, with a bias and number density similar to the BOSS CMASS Luminous Red Galaxies, performed with the \textsc{patchy} code. Our results show that there are two main species of DT voids, which can be characterised by the radius: they have different responses to halo redshift space distortions, to number density of tracers, and reside in different dark matter environments. Based on dynamical arguments using the tidal field tensor, we demonstrate that large DT voids are hosted in expanding regions, whereas the haloes used to construct them reside in collapsing ones. Our approach is therefore able to efficiently determine the troughs of the density field from galaxy surveys, and can be used to study their clustering. We further study the power spectra of DT voids, and find that the bias of the two populations are different, demonstrating that the small DT voids are essentially tracers of groups of haloes.Comment: 12 pages, 13 figure

Improved Antireflection Properties of an Optical Film Surface with Mixing Conical Subwavelength Structures

Based on finite difference time domain method, an optical film surface with mixing conical subwavelength structures is numerically investigated to improve antireflection property. The mixing conical subwavelength structure is combined with the pure periodic conical subwavelength structures and the added small conical structures in the gap between the pure periodic conical subwavelength structures. The antireflection properties of two types of subwavelength structures with different aspect ratios in spectral range of 400–800 nm are analyzed and compared. It is shown that, for the mixing type, the average reflectance is decreased and the variances of the reflectance are evidently smaller. When the added structure with a better aspect ratio exists, the average reflectance of the surface can be below 0.30%. Obviously, the antireflection properties of the optical film surface with mixing conical subwavelength structures can be improved

Thermally-Switchable Metalenses Based on Quasi-Bound States in the Continuum

Dynamic wavefront shaping with optical metasurfaces has presented a major challenge and inspired a large number of highly elaborate solutions. Here, we experimentally demonstrate thermo-optically reconfigurable, nonlocal metasurfaces using simple device architectures and conventional CMOS-compatible dielectric materials. These metasurfaces support quasi-bound states in the continuum (q-BICs) derived from symmetry breaking and encoded with a spatially varying geometric phase, such that they shape optical wavefront exclusively on spectrally narrowband resonances. Due to the enhanced light-matter interaction enabled by the resonant q-BICs, a slight variation of the refractive index introduced by heating and cooling the entire device leads to a substantial shift of the resonant wavelength and a subsequent change to the optical wavefront associated with the resonance. We experimentally demonstrate a metalens modulator, the focusing capability of which can be thermally turned on and off, and reconfigurable metalenses, which can be thermo-optically switched to produce two distinct focal patterns. Our devices offer a pathway to realize reconfigurable, multifunctional meta-optics using established manufacturing processes and widely available dielectric materials that are conventionally not considered "active" materials due to their small thermo-optic or electro-optic coefficients

Supervised Collective Classification for Crowdsourcing

Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classification algorithm that aims to identify reliable labelers from the training data (e.g., items with known labels). The reliability (i.e., weighting factor) of each labeler is determined via a saddle point algorithm. The results on several crowdsourced data show that supervised methods can achieve better classification accuracy than unsupervised methods, and our proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM) Workshop on Networking and Collaboration Issues for the Internet of Everythin