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

Diverse origins for non-repeating fast radio bursts: Rotational radio transient sources and cosmological compact binary merger remnants

A large number of fast radio bursts (FRBs) detected with the CHIME telescope have enabled investigations of their energy distributions in different redshift intervals, incorporating the consideration of the selection effects of CHIME. As a result, we obtained a non-evolving energy function (EF) for the high-energy FRBs (HEFRBs) of energies $E\gtrsim2\times0^{38}$ erg, which takes the form of a power law with a low-energy exponential cutoff. On the contrary, the energy distribution of the low-energy FRBs (LEFRBs) obviously cannot be described by the same EF. Including the lowest dispersion measure (DM) samples, the LEFRBs are concentrated towards the Galactic plane and their latitude distribution is similar to that of Galactic rotational radio transients (RRATs). These indications hint that LEFRBs might compose a special type of RRATs, with relatively higher DMs and energies (i.e., $\sim10^{28-31}$ erg for a reference distance of $\sim10$ kpc if they belong to the Milky Way). Finally, we revisit the redshift-dependent event rate of HEFRBs and confirm that they could be produced by the remnants of cosmological compact binary mergers.Comment: 8 pages, 7 figures. Accepted for publication in Astronomy & Astrophysics. Comments are welcom

Robust textual data streams mining based on continuous transfer learning

Copyright © SIAM. In textual data stream environment, concept drift can occur at any time, existing approaches partitioning streams into chunks can have problem if the chunk boundary does not coincide with the change point which is impossible to predict. Since concept drift can occur at any point of the streams, it will certainly occur within chunks, which is called random concept drift. The paper proposed an approach, which is called chunk level-based concept drift method (CLCD), that can overcome this chunking problem by continuously monitoring chunk characteristics to revise the classifier based on transfer learning in positive and unlabeled (PU) textual data stream environment. Our proposed approach works in three steps. In the first step, we propose core vocabulary-based criteria to justify and identify random concept drift. In the second step, we put forward the extension of LELC (PU learning by extracting likely positive and negative microclusters)[ 1], called soft-LELC, to extract representative examples from unlabeled data, and assign a confidence score to each extracted example. The assigned confidence score represents the degree of belongingness of an example towards its corresponding class. In the third step, we set up a transfer learning-based SVM to build an accurate classifier for the chunks where concept drift is identified in the first step. Extensive experiments have shown that CLCD can capture random concept drift, and outperforms state-of-the-art methods in positive and unlabeled textual data stream environments

1-(2-Fluoro­benz­yl)-1-(2-fluoro­benz­yl­oxy)urea

In the title hydroxy­urea derivative, C15H14F2N2O2, the dihedral angle between the two benzene rings is 48.64 (19)°. The urea group forms dihedral angles of 48.1 (2) and 79.2 (2)° with the two benzene rings. In the crystal, inversion dimers linked by pairs of N—H⋯O hydrogen bonds occur, and further N—H⋯O links lead to chains of molecules

4-(4-Bromo­phen­yl)-2,6-diphenyl­pyridine

In the title compound, C23H16BrN, the three benzene rings show a disrotatory counter-rotating arrangement around the central pyridine ring and are twisted with respect to the pyridine ring with dihedral angles of 19.56 (13), 27.54 (13) and 30.51 (13)°

Exploring Straighter Trajectories of Flow Matching with Diffusion Guidance

Flow matching as a paradigm of generative model achieves notable success across various domains. However, existing methods use either multi-round training or knowledge within minibatches, posing challenges in finding a favorable coupling strategy for straight trajectories. To address this issue, we propose a novel approach, Straighter trajectories of Flow Matching (StraightFM). It straightens trajectories with the coupling strategy guided by diffusion model from entire distribution level. First, we propose a coupling strategy to straighten trajectories, creating couplings between image and noise samples under diffusion model guidance. Second, StraightFM also integrates real data to enhance training, employing a neural network to parameterize another coupling process from images to noise samples. StraightFM is jointly optimized with couplings from above two mutually complementary directions, resulting in straighter trajectories and enabling both one-step and few-step generation. Extensive experiments demonstrate that StraightFM yields high quality samples with fewer step. StraightFM generates visually appealing images with a lower FID among diffusion and traditional flow matching methods within 5 sampling steps when trained on pixel space. In the latent space (i.e., Latent Diffusion), StraightFM achieves a lower KID value compared to existing methods on the CelebA-HQ 256 dataset in fewer than 10 sampling steps