Determining the luminosity function of Swift long gamma-ray bursts with pseudo-redshifts

The determination of luminosity function (LF) of gamma-ray bursts (GRBs) is of an important role for the cosmological applications of the GRBs, which is however hindered seriously by some selection effects due to redshift measurements. In order to avoid these selection effects, we suggest to calculate pseudo-redshifts for Swift GRBs according to the empirical L-E_p relationship. Here, such a $L-E_p$ relationship is determined by reconciling the distributions of pseudo- and real redshifts of redshift-known GRBs. The values of E_p taken from Butler's GRB catalog are estimated with Bayesian statistics rather than observed. Using the GRB sample with pseudo-redshifts of a relatively large number, we fit the redshift-resolved luminosity distributions of the GRBs with a broken-power-law LF. The fitting results suggest that the LF could evolve with redshift by a redshift-dependent break luminosity, e.g., L_b=1.2\times10^{51}(1+z)^2\rm erg s^{-1}. The low- and high-luminosity indices are constrained to 0.8 and 2.0, respectively. It is found that the proportional coefficient between GRB event rate and star formation rate should correspondingly decrease with increasing redshifts.Comment: 5 pages, 5 figures, accepted for publication in ApJ

Joint constraint on the jet structure from the short GRB population and GRB 170817A

The nearest GRB 170817A provided an opportunity to probe the angular structure of the jet of this short gamma-ray burst (SGRB), by using its off-axis observed afterglow emission. It is investigated that whether the afterglow-constrained jet structures can be consistent with the luminosity of the prompt emission of GRB 170817A. Furthermore, by assuming that all SGRBs including GRB 170817A have the same explosive mechanism and jet structure, we apply the different jet structures into the calculation of the flux and redshfit distributions of the SGRB population, in comparison with the observational distributions of the Swift and Fermi sources. As a result, it is found that the single-Gaussian structure can be basically ruled out, whereas the power-law and two-Gaussian models can in principle survive.Comment: 9 pages,6 figure

Phonon and Raman scattering of two-dimensional transition metal dichalcogenides from monolayer, multilayer to bulk material

Two-dimensional (2D) transition metal dichalcogenide (TMD) nanosheets exhibit remarkable electronic and optical properties. The 2D features, sizable bandgaps, and recent advances in the synthesis, characterization, and device fabrication of the representative MoS$_2$, WS$_2$, WSe$_2$, and MoSe$_2$ TMDs make TMDs very attractive in nanoelectronics and optoelectronics. Similar to graphite and graphene, the atoms within each layer in 2D TMDs are joined together by covalent bonds, while van der Waals interactions keep the layers together. This makes the physical and chemical properties of 2D TMDs layer dependent. In this review, we discuss the basic lattice vibrations of monolayer, multilayer, and bulk TMDs, including high-frequency optical phonons, interlayer shear and layer breathing phonons, the Raman selection rule, layer-number evolution of phonons, multiple phonon replica, and phonons at the edge of the Brillouin zone. The extensive capabilities of Raman spectroscopy in investigating the properties of TMDs are discussed, such as interlayer coupling, spin--orbit splitting, and external perturbations. The interlayer vibrational modes are used in rapid and substrate-free characterization of the layer number of multilayer TMDs and in probing interface coupling in TMD heterostructures. The success of Raman spectroscopy in investigating TMD nanosheets paves the way for experiments on other 2D crystals and related van der Waals heterostructures.Comment: 30 pages, 23 figure

Polytypism and Unexpected Strong Interlayer Coupling of two-Dimensional Layered ReS2

The anisotropic two-dimensional (2D) van der Waals (vdW) layered materials, with both scientific interest and potential application, have one more dimension to tune the properties than the isotropic 2D materials. The interlayer vdW coupling determines the properties of 2D multi-layer materials by varying stacking orders. As an important representative anisotropic 2D materials, multilayer rhenium disulfide (ReS2) was expected to be random stacking and lack of interlayer coupling. Here, we demonstrate two stable stacking orders (aa and a-b) of N layer (NL, N>1) ReS2 from ultralow-frequency and high-frequency Raman spectroscopy, photoluminescence spectroscopy and first-principles density functional theory calculation. Two interlayer shear modes are observed in aa-stacked NL-ReS2 while only one interlayer shear mode appears in a-b-stacked NL-ReS2, suggesting anisotropic-like and isotropic-like stacking orders in aa- and a-b-stacked NL-ReS2, respectively. The frequency of the interlayer shear and breathing modes reveals unexpected strong interlayer coupling in aa- and a-b-NL-ReS2, the force constants of which are 55-90% to those of multilayer MoS2. The observation of strong interlayer coupling and polytypism in multi-layer ReS2 stimulate future studies on the structure, electronic and optical properties of other 2D anisotropic materials

Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation

Monocular depth estimation (MDE) in the self-supervised scenario has emerged as a promising method as it refrains from the requirement of ground truth depth. Despite continuous efforts, MDE is still sensitive to scale changes especially when all the training samples are from one single camera. Meanwhile, it deteriorates further since camera movement results in heavy coupling between the predicted depth and the scale change. In this paper, we present a scale-invariant approach for self-supervised MDE, in which scale-sensitive features (SSFs) are detached away while scale-invariant features (SIFs) are boosted further. To be specific, a simple but effective data augmentation by imitating the camera zooming process is proposed to detach SSFs, making the model robust to scale changes. Besides, a dynamic cross-attention module is designed to boost SIFs by fusing multi-scale cross-attention features adaptively. Extensive experiments on the KITTI dataset demonstrate that the detaching and boosting strategies are mutually complementary in MDE and our approach achieves new State-of-The-Art performance against existing works from 0.097 to 0.090 w.r.t absolute relative error. The code will be made public soon.Comment: Accepted by IEEE Robotics and Automation Letters (RAL

Semi-supervised Cycle-GAN for face photo-sketch translation in the wild

The performance of face photo-sketch translation has improved a lot thanks to deep neural networks. GAN based methods trained on paired images can produce high-quality results under laboratory settings. Such paired datasets are, however, often very small and lack diversity. Meanwhile, Cycle-GANs trained with unpaired photo-sketch datasets suffer from the \emph{steganography} phenomenon, which makes them not effective to face photos in the wild. In this paper, we introduce a semi-supervised approach with a noise-injection strategy, named Semi-Cycle-GAN (SCG), to tackle these problems. For the first problem, we propose a {\em pseudo sketch feature} representation for each input photo composed from a small reference set of photo-sketch pairs, and use the resulting {\em pseudo pairs} to supervise a photo-to-sketch generator $G_{p2s}$. The outputs of $G_{p2s}$ can in turn help to train a sketch-to-photo generator $G_{s2p}$ in a self-supervised manner. This allows us to train $G_{p2s}$ and $G_{s2p}$ using a small reference set of photo-sketch pairs together with a large face photo dataset (without ground-truth sketches). For the second problem, we show that the simple noise-injection strategy works well to alleviate the \emph{steganography} effect in SCG and helps to produce more reasonable sketch-to-photo results with less overfitting than fully supervised approaches. Experiments show that SCG achieves competitive performance on public benchmarks and superior results on photos in the wild.Comment: 11 pages, 11 figures, 5 tables (+ 7 page appendix