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The recently discovered LaFe2As2 superconducting compound, member of the 122 family of iron pnictide superconductors, becomes superconducting below Tc=13K, yet its nominal doping apparently places it in the extreme overdoped limit, where superconductivity should be suppressed. In this work, we investigate the normal state of magneto- and thermo-electric transport and specific heat of this compound. The experimental data are consistent with the presence of highly compensated electron and hole bands, with around 0.42 electrons per unit cell just above Tc, and high effective masses around 3m0. The temperature dependence of transport properties strongly resembles that of conventional superconductors, pointing to a key role of electron-phonon coupling. From these evidences, LaFe2As2 can be regarded as the connecting compound between unconventional and conventional superconductors.

The camera of the Large-Sized Telescopes (LSTs) of the Cherenkov Telescope Array Observatory (CTAO) consists of 1855 pixels that are grouped into 265 high-performance photomultiplier tube (PMT) modules. Each module comprises a seven-light-guide plate, seven PMT units, a slow control board, and a readout board with a trigger board. %In this paper we describe The requirements for the PMT modules include various aspects, such as photon detection efficiency, dynamic range, buffer depth, and test pulse functionality. We have developed a high-performance PMT module that fulfills all these requirements. Mass-production and quality control (QC) of modules for all four LSTs of the northern CTAO have been completed. Here we report on the technical details of each element of the module and its performance, together with the methods and results of QC measurements., Comment: Published in NIM A

In this paper, we propose a novel low-light image enhancement method aimed at improving the performance of recognition models. Despite recent advances in deep learning, the recognition of images under low-light conditions remains a challenge. Although existing low-light image enhancement methods have been developed to improve image visibility for human vision, they do not specifically focus on enhancing recognition model performance. Our proposed low-light image enhancement method consists of two key modules: the Global Enhance Module, which adjusts the overall brightness and color balance of the input image, and the Pixelwise Adjustment Module, which refines image features at the pixel level. These modules are trained to enhance input images to improve downstream recognition model performance effectively. Notably, the proposed method can be applied as a frontend filter to improve low-light recognition performance without requiring retraining of downstream recognition models. Experimental results demonstrate that our method improves the performance of pretrained recognition models under low-light conditions and its effectiveness., Comment: accepted to VISAPP2025

We propose an image-adaptive object detection method for adverse weather conditions such as fog and low-light. Our framework employs differentiable preprocessing filters to perform image enhancement suitable for later-stage object detections. Our framework introduces two differentiable filters: a B\'ezier curve-based pixel-wise (BPW) filter and a kernel-based local (KBL) filter. These filters unify the functions of classical image processing filters and improve performance of object detection. We also propose a domain-agnostic data augmentation strategy using the BPW filter. Our method does not require data-specific customization of the filter combinations, parameter ranges, and data augmentation. We evaluate our proposed approach, called Enhanced Robustness by Unified Image Processing (ERUP)-YOLO, by applying it to the YOLOv3 detector. Experiments on adverse weather datasets demonstrate that our proposed filters match or exceed the expressiveness of conventional methods and our ERUP-YOLO achieved superior performance in a wide range of adverse weather conditions, including fog and low-light conditions., Comment: Accepted to WACV 2025

We investigated the temperature (T) dependence of the irreversibility field Hirr(T) in high-critical-temperature cuprate Bi2+xSr2-xCa1-yYyCu2O8+d (Bi-2212) single crystals over a wide range of hole doping level (p). Hirr(T) was evaluated by measuring the magnetization hysteresis loop. The value of Hirr(T) extrapolated to T = 0 K [Hirr(0)], is either equal to or sets the lower boundary for the upper critical field at T = 0 K [Hc2(0)]. Tc shows a parabolic p-dependence (peak at p = 0.16), whereas Hirr(0) increases monotonically with p by approximately one order of magnitude, from 19 T for the most underdoped sample (p = 0.065, Tc = 24 K) to 209 T for the most overdoped sample (p = 0.200, Tc = 75 K). The present results qualitatively agree with Hc2(0) values evaluated from the specific heat measurements. The observed p-dependence of Hirr(0) in Bi-2212 is distinct from those in YBa2Cu3O7-d and HgBa2CuO6+d, in which a pronounced dip structure appears in the underdoped region. Considering that the dip structures observed in these two systems are likely associated with the formation of competing orders (most likely field-induced charge orders), the present results indicate that the influence of the competing order in Bi-2212 is less prominent than that in the other two systems., Comment: 32 pages, 9 figures, 1 table

Lanthanides ($Ln$) are notoriously difficult to intercalate into graphite. We investigated the possibility of using Na to catalyze the formation of $Ln$-intercalated graphite and successfully synthesized $Ln$C$_6$ ($Ln$ = Sm, Eu, and Yb) significantly rapidly in high yields. The synthesis process involves the formation of the reaction intermediate NaC$_x$, through the mixing of Na and C, which subsequently reacts with $Ln$ upon heating to form $Ln$C$_6$. Well-sintered $Ln$C$_6$ pellets with low residual Na concentrations ($Ln$:Na = 98:2) were fabricated by the two-step method. The pellets enabled the evaluation of $Ln$C$_6$ by powder X-ray diffraction and electrical resistivity measurements. This study highlights the versatility of the Na-catalyzed method and lays the foundation for the rapid mass production of $Ln$C$_6$, with potential applications in superconducting and rechargeable battery materials., Comment: 16 pages, 7 figures

This paper proposes a deep feature extractor for iris recognition at arbitrary resolutions. Resolution degradation reduces the recognition performance of deep learning models trained by high-resolution images. Using various-resolution images for training can improve the model's robustness while sacrificing recognition performance for high-resolution images. To achieve higher recognition performance at various resolutions, we propose a method of resolution-adaptive feature extraction with automatically switching networks. Our framework includes resolution expert modules specialized for different resolution degradations, including down-sampling and out-of-focus blurring. The framework automatically switches them depending on the degradation condition of an input image. Lower-resolution experts are trained by knowledge-distillation from the high-resolution expert in such a manner that both experts can extract common identity features. We applied our framework to three conventional neural network models. The experimental results show that our method enhances the recognition performance at low-resolution in the conventional methods and also maintains their performance at high-resolution.

The detection of the short gamma-ray burst (SGRB) 050709 by the HETE-2 satellite opened a new window into understanding the nature of SGRBs, offering clues about their emission mechanism and progenitors, with the crucial aid of optical follow-up observations. Here, we revisit the prompt emission of GRB 050709. Our analysis reveals an initial hard spike ~200 ms long, followed by a subsequent soft tail emission lasting ~300 ms. These components could be common among other SGRBs originating from binary neutron merger events, such as GW/GRB 170817A. Detailed temporal and spectral analyses indicate that the soft tail emission might be attributed to the cocoon formed by the relativistic jet depositing energy into the surrounding material. We find the necessary cocoon parameters at the breakout, as consistent with numerical simulation results. We compared the physical parameters of this cocoon with those of other SGRBs. The relatively higher cocoon pressure and temperature in GRB 050709 may indicate a more on-axis jet compared to GRB 170817A and GRB 150101B., Comment: 13 pages, 6 figures. Accepted for publication in PASJ

Grain alignment is a key factor that determines the performance of a superconducting bulk. In this study, the spark plasma texturing (SPT) technique was used to fabricate a CaKFe$_4$As$_4$ superconducting bulk. X-ray diffraction and electron backscatter diffraction revealed that the $c$-axes of the CaKFe$_4$As$_4$ grains in the SPT bulk are aligned, demonstrating that the SPT technique is effective in achieving $c$-axis texture. In addition, chemical composition analysis showed that oxide impurities, which affect the grain boundary characteristics that determine the inter-grain critical current density ($J_c$), are randomly distributed in the SPT bulk. Magnetization measurements showed high $J_c$ values of the SPT bulk, reaching 127 kA cm$^{-2}$ and 26 kA cm$^{-2}$ at 4.2 K under a self-field and magnetic field of 5 T, respectively. These results suggest that the SPT technique is a promising approach for obtaining a high-performance superconducting bulk material for high-field applications.

A high density CaKFe$_4$As$_4$ bulk sample was successfully synthesized using a spark plasma sintering (SPS) technique. The density of the synthesized sample was 5.02 g cm$^{-3}$, corresponding to 96.2% of the theoretical density of CaKFe$_4$As$_4$. Moreover, a reasonably high Vickers hardness of 1 GPa was measured. The electrical resistivity of the SPS bulk sample was as low as approximately 600 $\mu\Omega$ cm at 300 K, which is smaller than that of the ordinary sintered polycrystalline sample by nearly one order of magnitude, and exhibited a sharp superconducting transition, with the transition width $\Delta\textit{T}_c$ less than 2 K, indicating an improved grain connectivity. The critical current density of the SPS bulk sample, as calculated from the magnetization hysteresis loops (magnetic $\textit{J}_c$), reached 18 kA cm$^{-2}$ at 4.2 K under 5 T, which is the highest among the iron-based superconductor polycrystalline samples reported thus far., Comment: 13 pages, 6 figures, 1 table

We developed an FPGA-based high-speed readout system for a complementary metal-oxide-semiconductor (CMOS) image sensor to observe soft X-ray transients in future satellite missions, such as HiZ-GUNDAM. Our previous research revealed that the CMOS image sensor has low-energy X-ray detection capability (0.4-4 keV) and strong radiation tolerance, which satisfies the requirements of the HiZ-GUNDAM mission. However, CMOS sensors typically have small pixel sizes (e.g., $\sim$10 ${\rm \mu m}$), resulting in large volumes of image data. GSENSE400BSI has 2048$\times$2048 pixels, producing 6 Mbyte per frame. These large volumes of observed raw image data cannot be stored in a satellite bus system with a limited storage size. Therefore, only X-ray photon events must be extracted from the raw image data. Furthermore, the readout time of CMOS image sensors is approximately ten times faster than that of typical X-ray CCDs, requiring faster event extraction on a timescale of $\sim$0.1 s. To address these issues, we have developed an FPGA-based image signal processing system capable of high-speed X-ray event extraction onboard without storing raw image data. The developed compact system enabled mounting on a CubeSat mission, facilitating early in-orbit operation demonstration. Here, we present the design and results of the performance evaluation tests of the proposed FPGA-based readout system. Utilizing X-ray irradiation experiments, the results of the X-ray event extraction with the onboard and offline processing methods were consistent, validating the functionality of the proposed system.

Some short gamma-ray bursts (SGRBs) exhibit a short duration and spectral hard emission (referred to as a "hard spike") followed by a slightly longer soft emission (known as a "soft tail"). We identified nine SGRBs with known redshift in the \textit{Swift}/BAT gamma-ray burst catalog by specifically searching for the soft tail. We found that spectra of these SGRBs can be described as a cutoff power-law model for both hard spike and soft tail, and both show time variation keeping the $E_{\rm peak}$--$L_{\rm iso}$ correlation. This suggests that the emission mechanism of both phenomena is identical. Furthermore, we found a trend of luminosity evolution as a function of redshift. This phenomenon suggests that these bursts originate from sources that have intrinsically bright and/or energy density concentrated within a narrower jet at higher redshift. We demonstrate that the average jet opening angle, derived from the jet break, can be explained by considering a model based on a strongly redshift-dependent jet opening angle.

$\textbf{Background}$: Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on unseen data require expensive and time-intensive labels. $\textbf{Methods}$: We use a "Masked Siamese Network" (MSN) to identify novel phenomena in unseen data and predict polyp detector performance. MSN is trained to predict masked out regions of polyp images, without any labels. We test MSN's ability to be trained on data only from Israel and detect unseen techniques, narrow-band imaging (NBI) and chromendoscoy (CE), on colonoscopes from Japan (354 videos, 128 hours). We also test MSN's ability to predict performance of Computer Aided Detection (CADe) of polyps on colonoscopies from both countries, even though MSN is not trained on data from Japan. $\textbf{Results}$: MSN correctly identifies NBI and CE as less similar to Israel whitelight than Japan whitelight (bootstrapped z-test, |z| > 496, p < 10^-8 for both) using the label-free Frechet distance. MSN detects NBI with 99% accuracy, predicts CE better than our heuristic (90% vs 79% accuracy) despite being trained only on whitelight, and is the only method that is robust to noisy labels. MSN predicts CADe polyp detector performance on in-domain Israel and out-of-domain Japan colonoscopies (r=0.79, 0.37 respectively). With few examples of Japan detector performance to train on, MSN prediction of Japan performance improves (r=0.56). $\textbf{Conclusion}$: Our technique can identify distribution shifts in clinical data and can predict CADe detector performance on unseen data, without labels. Our self-supervised approach can aid in detecting when data in practice is different from training, such as between hospitals or data has meaningfully shifted from training. MSN has potential for application to medical image domains beyond colonoscopy.

In recent years, significant progress has been made in image recognition technology based on deep neural networks. However, improving recognition performance under low-light conditions remains a significant challenge. This study addresses the enhancement of recognition model performance in low-light conditions. We propose an image-adaptive learnable module which apply appropriate image processing on input images and a hyperparameter predictor to forecast optimal parameters used in the module. Our proposed approach allows for the enhancement of recognition performance under low-light conditions by easily integrating as a front-end filter without the need to retrain existing recognition models designed for low-light conditions. Through experiments, our proposed method demonstrates its contribution to enhancing image recognition performance under low-light conditions., Comment: accepted to VISAPP2024

$\textbf{Background and aims}$: Artificial Intelligence (AI) Computer-Aided Detection (CADe) is commonly used for polyp detection, but data seen in clinical settings can differ from model training. Few studies evaluate how well CADe detectors perform on colonoscopies from countries not seen during training, and none are able to evaluate performance without collecting expensive and time-intensive labels. $\textbf{Methods}$: We trained a CADe polyp detector on Israeli colonoscopy videos (5004 videos, 1106 hours) and evaluated on Japanese videos (354 videos, 128 hours) by measuring the True Positive Rate (TPR) versus false alarms per minute (FAPM). We introduce a colonoscopy dissimilarity measure called "MAsked mediCal Embedding Distance" (MACE) to quantify differences between colonoscopies, without labels. We evaluated CADe on all Japan videos and on those with the highest MACE. $\textbf{Results}$: MACE correctly quantifies that narrow-band imaging (NBI) and chromoendoscopy (CE) frames are less similar to Israel data than Japan whitelight (bootstrapped z-test, |z| > 690, p < $10^{-8}$ for both). Despite differences in the data, CADe performance on Japan colonoscopies was non-inferior to Israel ones without additional training (TPR at 0.5 FAPM: 0.957 and 0.972 for Israel and Japan; TPR at 1.0 FAPM: 0.972 and 0.989 for Israel and Japan; superiority test t > 45.2, p < $10^{-8}$). Despite not being trained on NBI or CE, TPR on those subsets were non-inferior to Japan overall (non-inferiority test t > 47.3, p < $10^{-8}$, $\delta$ = 1.5% for both). $\textbf{Conclusion}$: Differences that prevent CADe detectors from performing well in non-medical settings do not degrade the performance of our AI CADe polyp detector when applied to data from a new country. MACE can help medical AI models internationalize by identifying the most "dissimilar" data on which to evaluate models.