BaFe2Se2O as an Iron-Based Mott Insulator with Antiferromagnetic Order

A new compound with a quasi-two-dimensional array of FeSe3O tetrahedra and an orthorombic structure, namely BaFe2Se2O, has been successfully fabricated. Experimental results show that this compound is an insulator and has an antiferromagnetic (AF) transition at 240 K. Band structure calculation reveals the narrowing of Fe 3d bands near the Fermi energy, which leads to the localization of magnetism and the Mott insulating behavior. The large distances between the Fe atoms perhaps are responsible for the characters. Linear response calculation further indicates a strong in-plane AF exchange $J$, this can account for the enhanced magnetic susceptibility (which has a maximum at about 450 K) above the Neel temperature.Comment: submitted to PRL on 2 May 2012, resubmitted to PRB on 31 May 2012, and accepted by PRB on 5 July 201

Polygonal vortex beams in quasi-frequency-degenerate states

We originally demonstrate the vortex beams with patterns of closed polygons [namely polygonal vortex beams (PVBs)] generated by a quasi-frequency-degenerate (QFD) Yb:CALGO laser resonator with astigmatic transformation. The PVBs with peculiar patterns of triangular, square, and parallelogram shapes carrying large orbital angular momentums (OAMs) are theoretically investigated and experimentally obtained in the vicinity of the SU(2) degenerate states of laser resonator. The PVBs in QFD states are compared with the vortex beams with patterns of isolated spots arrays located on the triangle-, square-, and parallelogram-shaped routes [namely polygonalspots-array vortex beams (PSA-VBs)] under normal SU(2) degenerate states. Beam profile shape of PVB or PSA-VB and OAM can be controlled by adjusting the cavity length and the position of pump spot. The simulated and experimental results validate the performance of our method to generate PVB, which is of great potential for promoting novel technologies in particle trapping and beam shaping

OpinSummEval: Revisiting Automated Evaluation for Opinion Summarization

Opinion summarization sets itself apart from other types of summarization tasks due to its distinctive focus on aspects and sentiments. Although certain automated evaluation methods like ROUGE have gained popularity, we have found them to be unreliable measures for assessing the quality of opinion summaries. In this paper, we present OpinSummEval, a dataset comprising human judgments and outputs from 14 opinion summarization models. We further explore the correlation between 24 automatic metrics and human ratings across four dimensions. Our findings indicate that metrics based on neural networks generally outperform non-neural ones. However, even metrics built on powerful backbones, such as BART and GPT-3/3.5, do not consistently correlate well across all dimensions, highlighting the need for advancements in automated evaluation methods for opinion summarization. The code and data are publicly available at https://github.com/A-Chicharito-S/OpinSummEval/tree/main.Comment: preprint, included 2 more metrics compared with the previous submissio