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It has been theoretically predicted that the \ExB drift caused by the spontaneously generated potential in scrape-off-layers (SOLs) and divertors in tokamaks is of a similar size to the poloidal component of the parallel flow and turbulent flow, thereby it significantly impacts on the plasma transport there. Many experiments indeed have implied the role of the electric potential, however, its direct observation through its \ExB flow measurement has never been realized because the drift velocity ($10^2$--$10^3$ m/s) is significantly below the detection limit of existing diagnostics. To realize a cross-field ion flow measurement, variety of systematic uncertainties of the system must be narrowed down. Here, we develop a conceptual design of the Doppler spectrometry that enables to measure the impurity flows with $10^2$-m/s accuracy, based on an in-situ wavelength-calibration techniques developed in astrophysics field, the iodine-cell method. We discuss its properties and applicability. In particular, the scaling relation of the wavelength accuracy and various spectroscopic parameters is newly presented, which suggests the high importance of the wavelength resolution of the system. Based on transport simulations for the JT-60SA divertor, the feasibility of the system is assessed.

The chiral perturbation theory (ChPT) of pions is extended to include vector mesons as well as pertinent degrees of freedom. By counting the typical momentum scale of vector mesons as order of $Q$ and vector meson masses as of the order of $\Lambda_\chi$, a consistent theory could be obtained. The explicit renormalization procedure of the theory is presented for the form factors of the pion up to one-loop accuracy. The resulting theory prediction for the form factors is in good agreement with the experimental data for a wide range of momentum transfers. The vector-meson dominance mechanism is also discussed in the systematic framework of ChPT.

The elastic p-12C scattering at low energies is studied by using a cluster effective field theory (EFT), where the low-lying resonance states (s1/2, p3/2, d5/2) of 13N are treated as pertinent degrees of freedom. The low-energy constants of the Lagrangian are expressed in terms of the Coulomb-modified effective range parameters, which are determined to reproduce the experimental data for the differential cross-sections. The resulting theoretical predictions agree very well with the experimental data. The resulting theory is shown to give us almost identical phase shifts as obtained from the R-matrix approach. The role of the ground state of 13N below the threshold and the next-to-leading order in the EFT power counting are also discussed., Comment: 17 pages, 6 figures

We estimate the Big Bang nucleosynthesis (BBN) constraint on the majoron in the mass range between $1\,{\rm MeV}$ to $10\,{\rm GeV}$ which dominantly decays into the standard model neutrinos. When the majoron lifetime is shorter than $1\,{\rm sec}$, the injected neutrinos mainly heat up background plasma, which alters the relation between photon temperature and background neutrino temperature. For a lifetime longer than $1\,{\rm sec}$, most of the injected neutrinos directly contribute to the protons-to-neutrons conversion. In both cases, deuterium and helium abundances are enhanced, while the constraint from the deuterium is stronger than that from the helium. $^7{\rm Li}$ abundance gets decreased as a consequence of additional neutrons, but the parameter range that fits the observed $^7{\rm Li}$ abundance is excluded by the deuterium constraint. We also estimate other cosmological constraints and compare them with the BBN bound., Comment: 12 pages, 3 figures, v2: major revision made by including correct n/p conversion induced by modified thermal neutrinos, slight changes in the result, references added, version accepted in PRD

Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning-based methods in real-world scenarios is hindered by the scarcity of high-quality datasets. In this paper, we argue that embracing poor datasets is viable and aim to explicitly define the challenges associated with using these datasets. To delve into this topic, we analyze the characteristics of high-quality datasets, namely large-scale images and desired annotation, and contrast them with the \emph{limited} and \emph{imperfect} nature of poor datasets. Challenges arise when the training datasets deviate from these characteristics. To provide a comprehensive understanding, we propose a novel and informative taxonomy that categorizes these challenges. Furthermore, we offer a brief overview of existing studies and approaches that address these challenges. We believe that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications. To facilitate the progress, we finally describe several outstanding questions and point out potential future directions. Although our primary focus is on plant disease recognition, we emphasize that the principles of embracing and analyzing poor datasets are applicable to a wider range of domains, including agriculture., Comment: revision v1 in perspetive style

The inclusion of nucleonic exchange energy has been a long-standing challenge for the relativistic density functional theory (RDFT) in nuclear physics. We propose an orbital-dependent relativistic Kohn-Sham density functional theory to incorporate the exchange energy with local Lorentz scalar and vector potentials. The relativistic optimized effective potential equations for the local exchange potentials are derived and solved efficiently. The obtained binding energies and charge radii for nuclei are benchmarked with the results given by the traditional relativistic Hartree-Fock approach, which involves complicated nonlocal potentials. It demonstrates that the present framework is not only accurate but also efficient., Comment: 6 pages, 4 figures

The Seoul Radio Astronomy Observatory (SRAO) operates a 6.1-meter radio telescope on the Gwanak campus of Seoul National University. We present the efforts to reform SRAO to a Very Long Baseline Interferometry (VLBI) station, motivated by recent achievements by millimeter interferometer networks such as Event Horizon Telescope, East Asia VLBI Network, and Korean VLBI Network (KVN). For this goal, we installed a receiver that had been used in the Combined Array for Research in Millimeter-wave Astronomy and a digital backend, including an H-maser clock. The existing hardware and software were also revised, which had been dedicated only to single-dish operations. After several years of preparations and test observations in 1 and 3-millimeter bands, a fringe was successfully detected toward 3C 84 in 86 GHz in June 2022 for a baseline between SRAO and KVN Ulsan station separated by 300 km. Thanks to the dual frequency operation of the receiver, the VLBI observations will soon be extended to the 1 mm band and verify the frequency phase referencing technique between 1 and 3-millimeter bands., Comment: 6 pages, 9 figures, 2 tables. Published in JKAS (received: 2022 Oct 16; accepted: 2022 Nov 22)

Semantic image synthesis (SIS) aims to produce photorealistic images aligning to given conditional semantic layout and has witnessed a significant improvement in recent years. Although the diversity in image-level has been discussed heavily, class-level mode collapse widely exists in current algorithms. Therefore, we declare a new requirement for SIS to achieve more photorealistic images, variation-aware, which consists of inter- and intra-class variation. The inter-class variation is the diversity between different semantic classes while the intra-class variation stresses the diversity inside one class. Through analysis, we find that current algorithms elusively embrace the inter-class variation but the intra-class variation is still not enough. Further, we introduce two simple methods to achieve variation-aware semantic image synthesis (VASIS) with a higher intra-class variation, semantic noise and position code. We combine our method with several state-of-the-art algorithms and the experimental result shows that our models generate more natural images and achieves slightly better FIDs and/or mIoUs than the counterparts. Our codes and models will be publicly available., Comment: 12 pages, 3 figures, 5 tables

Materials whose luminescence can be switched by optical stimulation drive technologies ranging from superresolution imaging1-4, nanophotonics5, and optical data storage6-8, to targeted pharmacology, optogenetics, and chemical reactivity9. These photoswitchable probes, including organic fluorophores and proteins, are prone to photodegradation, and often require phototoxic doses of ultraviolet (UV) or visible light. Colloidal inorganic nanoparticles have significant stability advantages over existing photoswitchable materials, but the ability to switch emission bidirectionally, particularly with NIR light, has not been reported with nanoparticles. Here, we present 2-way, near-infrared (NIR) photoswitching of avalanching nanoparticles (ANPs), showing full optical control of upconverted emission using phototriggers in the NIR-I and NIR-II spectral regions useful for subsurface imaging. Employing single-step photodarkening10-13 and photobrightening12,14-18, we demonstrate indefinite photoswitching of individual nanoparticles (>1000 cycles over 7 h) in ambient or aqueous conditions without measurable photodegradation. Critical steps of the photoswitching mechanism are elucidated by modeling and by measuring the photon avalanche properties of single ANPs in both bright and dark states. Unlimited, reversible photoswitching of ANPs enables indefinitely rewritable 2D and 3D multi-level optical patterning of ANPs, as well as optical nanoscopy with sub-{\AA} localization superresolution that allows us to distinguish individual ANPs within tightly packed clusters., Comment: 15 pages, 5 figures

Deep learning has been achieving decent performance in computer vision requiring a large volume of images, however, collecting images is expensive and difficult in many scenarios. To alleviate this issue, many image augmentation algorithms have been proposed as effective and efficient strategies. Understanding current algorithms is essential to find suitable methods or develop novel techniques for given tasks. In this paper, we perform a comprehensive survey on image augmentation for deep learning with a novel informative taxonomy. To get the basic idea why we need image augmentation, we introduce the challenges in computer vision tasks and vicinity distribution. Then, the algorithms are split into three categories; model-free, model-based, and optimizing policy-based. The model-free category employs image processing methods while the model-based method leverages trainable image generation models. In contrast, the optimizing policy-based approach aims to find the optimal operations or their combinations. Furthermore, we discuss the current trend of common applications with two more active topics, leveraging different ways to understand image augmentation, such as group and kernel theory, and deploying image augmentation for unsupervised learning. Based on the analysis, we believe that our survey gives a better understanding helpful to choose suitable methods or design novel algorithms for practical applications., Comment: Revision

Post-neutron emission fission product mass distributions are calculated by using pre-neutron emission fission product yields (FPYs) and neutron multiplicity. A semi-empirical model is used to calculate the pre-neutron FPY, first. Then the neutron multiplicity for each fission fragment mass is used to convert the pre-neutron FPY to the post-neutron FPY. In doing so, assumptions are made for the probability for a pre-emission fission fragment with a mass number $A^*$ to decay to a post-emission fragment with a mass number $A$. The resulting post-neutron FPYs are compared with the data available. The systems where the experimental data of not only the pre- and post-neutron FPY but also neutron multiplicity are available are the thermal neutron-induced fission of $^{233}$U, $^{235}$U and $^{239}$Pu. Thus, we applied the model calculations to these systems and compared the calculation results with those from the GEF and the data from the ENDF and the EXFOR libraries. Both the pre- and post-neutron fission product mass distributions calculated by using the semi-empirical model and the neutron multiplicity reproduce the overall features of the experimental data.