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In the rapidly evolving field of healthcare, the integration of artificial intelligence (AI) has become a pivotal component in the automation of clinical workflows, ushering in a new era of efficiency and accuracy. This study focuses on the transformative capabilities of the fine-tuned KoELECTRA model in comparison to the GPT-4 model, aiming to facilitate automated information extraction from thyroid operation narratives. The current research landscape is dominated by traditional methods heavily reliant on regular expressions, which often face challenges in processing free-style text formats containing critical details of operation records, including frozen biopsy reports. Addressing this, the study leverages advanced natural language processing (NLP) techniques to foster a paradigm shift towards more sophisticated data processing systems. Through this comparative study, we aspire to unveil a more streamlined, precise, and efficient approach to document processing in the healthcare domain, potentially revolutionizing the way medical data is handled and analyzed., Comment: 9 pages, 2 figures, 3 tables

The Kitaev model, a honeycomb network of spins with bond-dependent anisotropic interactions, is a rare example of having a quantum spin liquid ground state. Although most Kitaev model candidate materials eventually order magnetically due to inevitable non-Kitaev terms, their bond-dependent anisotropy manifests in unusual spin dynamics. It has recently been suggested that bond-dependent anisotropy can stabilise novel magnetic phases and exotic spin dynamics on the geometrically frustrated triangular lattice. However, few materials have been identified with simultaneous geometric frustration and bond-dependent anisotropy. Here, we report a frustrated triangular lattice with bond-dependent anisotropy in the cobalt-based triangular van der Waals antiferromagnet CoI2. Its momentum and energy-resolved spin dynamics exhibit substantial magnon breakdown and complex level repulsion, as measured by inelastic neutron scattering. A thorough examination of excitations in both the paramagnetic and magnetically ordered states reveals that the bond-dependent anisotropy is the origin of the spiral order and the magnon breakdown found in CoI2. Our result paves the way toward a new research direction for the Kitaev model with geometrical frustration., Comment: 13 pages, 4 figures, Nature Physics

Type IV radio burst has been studied for over 50 years. However, the specifics of the radio emission mechanisms is still an open question. In order to provide more information about the emission mechanisms, we studied a moving type IV radio burst with fine structures (spike group) by using the high resolution capability of Low-Frequency Array (LOFAR) on Aug 25, 2014\textbf{ (SOLA-D-21-00188)}. We present a comparison of Nan\c{c}ay RadioHeliograph (NRH) and the first LOFAR imaging data of type IV radio burst. The degree of circular polarization (DCP) is calculated at frequencies in the range 20$\sim$180 MHz using LOFAR data, and it was found that the value of DCP gradually increased during the event, with values of 10\%$\sim$20\%. LOFAR interferometric data were combined with white light observations in order to track the propagation of this type IV. The kinematics shows a westward motion of the radio sources, slower than the CME leading edge. The dynamic spectrum of LOFAR shows a large number of fine structures with duration of less than 1s and high brightness temperature ($T_\mathrm{B}$), i.e. $10^{12}$$\sim$$10^{13}$ K. The gradual increase of DCP supports gyrosynchrotron emission as the most plausible mechanism for the type IV. However, coherent emissions such as Electron Cyclotron Maser (ECM) instability can be responsible for small scale fine structures. Countless fine structures altogether were responsible for such high $T_\mathrm{B}$.

With the enhancement of Machine Learning, many tools are being designed to assist developers to easily create their Machine Learning models. In this paper, we propose a novel method for auto creation of such custom models for constraint devices using transfer learning without the need to write any machine learning code. We share the architecture of our automatic model creation tool and the CNN Model created by it using pretrained models such as YAMNet and MobileNetV2 as feature extractors. Finally, we demonstrate accuracy and memory footprint of the model created from the tool by creating an Automatic Image and Audio classifier and report the results of our experiments using Stanford Cars and ESC-50 dataset., Comment: 7 pages, 10 figures

Chromium thiophosphate (CrPS4), a monoclinic crystal of C_2^3 space group, is a ternary layered semiconductor with an optical bandgap of 1.4 eV and exhibits antiferromagnetism below 36 K. Despite its potential in optoelectronic and magnetic applications, the symmetry of its lattice vibrations has not been systematically studied. In this work, we performed a polarized Raman spectroscopy of bulk CrPS4 using three different excitation wavelengths of 457, 514, and 633 nm. High-quality crystals grown by the chemical vapor transport method were mechanically exfoliated or polished to expose three orthogonal crystallographic facets. Polarized Raman spectra were obtained in parallel and cross configurations by rotating samples about the surface normal to each of the facets. Among 33 Raman active modes (16 A and 17 B) at the Brillouin zone center, 19 (8) peaks observed in the parallel (cross) configuration were assigned as A (B) modes. Complex-valued Raman tensors were determined for 7 major A modes using the angle-dependent Raman spectra of the three facets. The results can also be used in determining the crystallographic orientations of CrPS4 unequivocally.

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data.