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We report the 77Se-nuclear magnetic resonance (NMR) results of trivalent Yb zigzag chain compound YbAgSe2, which is a sister compound of YbCuS2. The 77Se-NMR spectrum was reproduced by considering two different Se sites with negative Knight shifts and three-axis anisotropy. Above the Neel temperature TN, the Knight shift is proportional to the bulk magnetic susceptibility. Below TN, the extremely broad signal with weak intensity and the relatively sharp signal coexist, suggesting that one is strongly influenced by internal magnetic fields and the other remains relatively unaffected by these fields in the magnetic ordered state. The nuclear spin-lattice relaxation rate 1/T1 remains almost constant above TN and abruptly decreases below TN. In contrast to YbCuS2, a T-linear behavior of 1/T1 at low temperatures was not observed at least down to 1.0 K in YbAgSe2. Our results indicate that the gapless excitation is unique to YbCuS2, or is immediately suppressed in the magnetic fields., Comment: 5 pages, 3 figures

We present results from the characterization of a vertically stacked amorphous selenium (aSe)-based photodetector for use in cryogenic environments. aSe has been identified as an ideal photoconductor that can efficiently convert vacuum ultraviolet (VUV) light to charges even at cryogenic temperatures. We have designed and fabricated an aSe device in vertical geometry with top and bottom metal electrodes that produces an electric field perpendicular to the substrate. The top-metal contact has an open design that results in a large fraction of the aSe thin film surface to be active for photodetection. Our experiments show that the vertically stacked aSe device detects light from a Xenon flash lamp in a vacuum environment and can produce measurable signals at \(\sim \)130K. We also demonstrate a significant enhancement in the amplitude of the photoinduced signal by growing graphene on the top-metal contact and the aSe thin film. Our results provide the first demonstration of a vertical aSe based VUV photodetector that utilizes the wide-band optical transparency of graphene top-electrode. Our results could open the doorway to a potentially game-changing solution of an integrated charge and light sensor that can be employed in future large-scale time projection chambers with pixelated anode planes.

Galaxy-scale strong gravitational lenses are valuable objects for a variety of astrophysical and cosmological applications. Strong lensing galaxies are rare, so efficient search methods, such as convolutional neural networks, are often used on large imaging datasets. In this work, we apply a new technique to improve the performance of supervised neural networks by subtracting the central (lensing) galaxy light from both the training and test datasets. We use multiband imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) as our training and test datasets. By subtracting the lensing galaxy light, we increase the contrast of the lensed source compared to the original imaging data. We also apply the light subtraction to non-lenses in order to compare them to the light-subtracted lenses. Residual features resulting from poor light subtraction can adversely affect the performance of networks trained on the subtracted images alone. We find that combining the light-subtracted images with the original gri-band images for training and classification can overcome this and improve the overall classification accuracy. We find the area under the receiver operating characteristic curve can be improved to 0.841 using the combination of the fiducial images and light-subtracted images, compared to 0.808 for the fiducial imaging dataset alone. This may be a promising technique for improving future lens searches using CNNs.

We report the re-measurement of the $a$-axis spin susceptibility component in an early-stage sample of the spin-triplet superconductor UTe$_2$ with the transition temperature of $T_{\rm SC}$ = 1.6 K. Using Knight-shift measurements along the $b$ axis and at a 10-degree tilt from the $b$ axis towards the $a$ axis, we accurately determined the $a$-axis component without directly measuring the $a$-axis Knight shift. Our results reveal a decrease of approximately 3\% in the $a$-axis spin susceptibility in the superconducting state under $a$-axis magnetic field $\mu_0 H_a \sim 0.1$ T, indicating that the spin susceptibility decreases similarly in both early-stage and ultraclean samples with $T_{\rm SC}$ = 2.1 K. The previously reported absence of the reduction in Knight shift is attributed to the missing of signal from the superconducting region and to the detection of residual signals from the non-superconducting region instead. We also found that the decrease in the $a$-axis spin susceptibility is immediately suppressed with increasing the $a$-axis magnetic field and is estimated to be completely suppressed at around 1.5 T due to superconducting spin rotation., Comment: 5 pages, 3 figures

The advent of large astronomical surveys has made available large and complex data sets. However, the process of discovery and interpretation of each potentially new astronomical source is, many times, still handcrafted. In this context, machine learning algorithms have emerged as a powerful tool to mine large data sets and lower the burden on the domain expert. Active learning strategies are specially good in this task. In this report, we used the PineForest algorithm to search for superluminous supernova (SLSN) candidates in the Zwicky Transient Facility. We showcase how the use of previously confirmed sources can provide important information to boost the convergence of the active learning algorithm. Starting from a data set of $\sim$14 million objects, and using 8 previously confirmed SLSN light curves as priors, we scrutinized 120 candidates and found 8 SLSN candidates, 2 of which have not been reported before (AT 2018moa and AT 2018mob). These results demonstrate how existing spectroscopic samples can be used to improve the efficiency of active learning strategies in searching for rare astronomical sources., Comment: 16 pages, 2 tables, 8 figures

SNAD is an international project with a primary focus on detecting astronomical anomalies within large-scale surveys, using active learning and other machine learning algorithms. The work carried out by SNAD not only contributes to the discovery and classification of various astronomical phenomena but also enhances our understanding and implementation of machine learning techniques within the field of astrophysics. This paper provides a review of the SNAD project and summarizes the advancements and achievements made by the team over several years., Comment: 14 pages, 4 figures

We present coniferest, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently, static outlier detection analysis is supported via the Isolation forest algorithm. Moreover, Active Anomaly Discovery (AAD) and Pineforest algorithms are available to tackle active anomaly detection problems. The algorithms and package performance are evaluated on a series of synthetic datasets. We also describe a few success cases which resulted from applying the package to real astronomical data in active anomaly detection tasks within the SNAD project., Comment: 13 pages, 1 figure

Acceleration of positive muons from thermal energy to $100~$keV has been demonstrated. Thermal muons were generated by resonant multi-photon ionization of muonium atoms emitted from a sheet of laser-ablated aerogel. The thermal muons were first electrostatically accelerated to $5.7~$keV, followed by further acceleration to 100 keV using a radio-frequency quadrupole. The transverse normalized emittance of the accelerated muons in the horizontal and vertical planes were $0.85 \pm 0.25 ~\rm{(stat.)}~^{+0.22}_{-0.13} ~\rm{(syst.)}~\pi~$mm$\cdot$mrad and $0.32\pm 0.03~\rm{(stat.)} ^{+0.05}_{-0.02} ~\rm{(syst.)}~\pi~$mm$\cdot$mrad, respectively. The measured emittance values demonstrated phase space reduction by a factor of $2.0\times 10^2$ (horizontal) and $4.1\times 10^2$ (vertical) allowing good acceleration efficiency. These results pave the way to realize the first-ever muon accelerator for a variety of applications in particle physics, material science, and other fields.

FAMU is an INFN-led muonic atom physics experiment based at the RIKEN-RAL muon facility at the ISIS Neutron and Muon Source (United Kingdom). The aim of FAMU is to measure the hyperfine splitting in muonic hydrogen to determine the value of the proton Zemach radius with accuracy better than 1%.The experiment has a scintillating-fibre hodoscope for beam monitoring and data normalisation. In order to carry out muon flux estimation, low-rate measurements were performed to extract the single-muon average deposited charge. Then, detector simulation in Geant4 and FLUKA allowed a thorough understanding of the single-muon response function, crucial for determining the muon flux. This work presents the design features of the FAMU beam monitor, along with the simulation and absolute calibration measurements in order to enable flux determination and enable data normalisation.

We study how to generalize the visuomotor policy of a mobile manipulator from the perspective of visual observations. The mobile manipulator is prone to occlusion owing to its own body when only a single viewpoint is employed and a significant domain shift when deployed in diverse situations. However, to the best of the authors' knowledge, no study has been able to solve occlusion and domain shift simultaneously and propose a robust policy. In this paper, we propose a robust imitation learning method for mobile manipulators that focuses on task-related viewpoints and their spatial regions when observing multiple viewpoints. The multiple viewpoint policy includes attention mechanism, which is learned with an augmented dataset, and brings optimal viewpoints and robust visual embedding against occlusion and domain shift. Comparison of our results for different tasks and environments with those of previous studies revealed that our proposed method improves the success rate by up to 29.3 points. We also conduct ablation studies using our proposed method. Learning task-related viewpoints from the multiple viewpoints dataset increases robustness to occlusion than using a uniquely defined viewpoint. Focusing on task-related regions contributes to up to a 33.3-point improvement in the success rate against domain shift.

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

A search for proton decay into $e^+/\mu^+$ and a $\eta$ meson has been performed using data from a 0.373 Mton$\cdot$year exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear $\eta$ interaction cross section, resulting in a factor of two reduction in uncertainties from this source and $\sim$10\% increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of $1.4\times\mathrm{10^{34}~years}$ for $p\rightarrow e^+\eta$ and $7.3\times\mathrm{10^{33}~years}$ for $p\rightarrow \mu^+\eta$ at the 90$\%$ C.L. were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date.

Humans can perform complex tasks with long-term objectives by planning, reasoning, and forecasting outcomes of actions. For embodied agents to achieve similar capabilities, they must gain knowledge of the environment transferable to novel scenarios with a limited budget of additional trial and error. Learning-based approaches, such as deep RL, can discover and take advantage of inherent regularities and characteristics of the application domain from data, and continuously improve their performances, however at a cost of large amounts of training data. This thesis explores the development of data-driven techniques for spatial reasoning and planning tasks, focusing on enhancing learning efficiency, interpretability, and transferability across novel scenarios. Four key contributions are made. 1) CALVIN, a differential planner that learns interpretable models of the world for long-term planning. It successfully navigated partially observable 3D environments, such as mazes and indoor rooms, by learning the rewards and state transitions from expert demonstrations. 2) SOAP, an RL algorithm that discovers options unsupervised for long-horizon tasks. Options segment a task into subtasks and enable consistent execution of the subtask. SOAP showed robust performances on history-conditional corridor tasks as well as classical benchmarks such as Atari. 3) LangProp, a code optimisation framework using LLMs to solve embodied agent problems that require reasoning by treating code as learnable policies. The framework successfully generated interpretable code with comparable or superior performance to human-written experts in the CARLA autonomous driving benchmark. 4) Voggite, an embodied agent with a vision-to-action transformer backend that solves complex tasks in Minecraft. It achieved third place in the MineRL BASALT Competition by identifying action triggers to segment tasks into multiple stages., Comment: DPhil Thesis - Engineering Science, University of Oxford. Original copy available at https://ora.ox.ac.uk/objects/uuid:19489c19-dc5a-464a-831d-bbf887687c41

In recent years, it has been observed that the center of gravity for the volume of higher education has shifted to the Global South. However, research indicates a widening disparity in the quality of higher education between the Global South and the Global North. Although investments in higher education within the Global South have increased, the rapid surge in student numbers has resulted in a decline in public expenditure per student. For instance, the student-to-teacher ratio in the Global South is significantly higher compared to that in the Global North, which poses a substantial barrier to the implementation of creative education. In response, Telkom University in Indonesia has embarked on an experiment to enhance the quality of learning and teaching by integrating large language models (LLMs) such as ChatGPT into five of its courses-Mathematics, English, Computing, Computer Systems, and Creative Media. This article elucidates the ongoing experimental plan and explores how the integration of LLMs could contribute to addressing the challenges currently faced by higher education in the Global South.

Based on a recent simulation study [Masubuchi et al., Macromolecules, 56, 9359 (2023)], the cycle rank plays a significant role in determining the fracture characteristics of network polymers. However, the study only considered energy-minimized networks without the effects of thermal agitation. We conducted Brownian dynamics simulations at various stretch rates to address this gap. The results showed that even with Brownian motion, the strain and stress at the break obtained for different node functionalities and conversion ratios exhibited master curves if plotted against cycle rank. These master curves were dependent on the strain rate, with the curves tending to approach those observed in energy-minimized simulations as the strain rate decreased, even though the fracture process was affected by the competition against Brownian motion, elongation, and bond degradation., Comment: 12 pages, 4 figures

In the task of anomaly detection in modern time-domain photometric surveys, the primary goal is to identify astrophysically interesting, rare, and unusual objects among a large volume of data. Unfortunately, artifacts -- such as plane or satellite tracks, bad columns on CCDs, and ghosts -- often constitute significant contaminants in results from anomaly detection analysis. In such contexts, the Active Anomaly Discovery (AAD) algorithm allows tailoring the output of anomaly detection pipelines according to what the expert judges to be scientifically interesting. We demonstrate how the introduction real-bogus scores, obtained from a machine learning classifier, improves the results from AAD. Using labeled data from the SNAD ZTF knowledge database, we train four real-bogus classifiers: XGBoost, CatBoost, Random Forest, and Extremely Randomized Trees. All the models perform real-bogus classification with similar effectiveness, achieving ROC-AUC scores ranging from 0.93 to 0.95. Consequently, we select the Random Forest model as the main model due to its simplicity and interpretability. The Random Forest classifier is applied to 67 million light curves from ZTF DR17. The output real-bogus score is used as an additional feature for two anomaly detection algorithms: static Isolation Forest and AAD. While results from Isolation Forest remained unchanged, the number of artifacts detected by the active approach decreases significantly with the inclusion of the real-bogus score, from 27 to 3 out of 100. We conclude that incorporating the real-bogus classifier result as an additional feature in the active anomaly detection pipeline significantly reduces the number of artifacts in the outputs, thereby increasing the incidence of astrophysically interesting objects presented to human experts., Comment: 8 pages, 6 figures

Cerebrovascular disease often requires multiple imaging modalities for accurate diagnosis, treatment, and monitoring. Computed Tomography Angiography (CTA) and Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) are two common non-invasive angiography techniques, each with distinct strengths in accessibility, safety, and diagnostic accuracy. While CTA is more widely used in acute stroke due to its faster acquisition times and higher diagnostic accuracy, TOF-MRA is preferred for its safety, as it avoids radiation exposure and contrast agent-related health risks. Despite the predominant role of CTA in clinical workflows, there is a scarcity of open-source CTA data, limiting the research and development of AI models for tasks such as large vessel occlusion detection and aneurysm segmentation. This study explores diffusion-based image-to-image translation models to generate synthetic CTA images from TOF-MRA input. We demonstrate the modality conversion from TOF-MRA to CTA and show that diffusion models outperform a traditional U-Net-based approach. Our work compares different state-of-the-art diffusion architectures and samplers, offering recommendations for optimal model performance in this cross-modality translation task.

We propose ratio divergence (RD) learning for discrete energy-based models, a method that utilizes both training data and a tractable target energy function. We apply RD learning to restricted Boltzmann machines (RBMs), which are a minimal model that satisfies the universal approximation theorem for discrete distributions. RD learning combines the strength of both forward and reverse Kullback-Leibler divergence (KLD) learning, effectively addressing the "notorious" issues of underfitting with the forward KLD and mode-collapse with the reverse KLD. Since the summation of forward and reverse KLD seems to be sufficient to combine the strength of both approaches, we include this learning method as a direct baseline in numerical experiments to evaluate its effectiveness. Numerical experiments demonstrate that RD learning significantly outperforms other learning methods in terms of energy function fitting, mode-covering, and learning stability across various discrete energy-based models. Moreover, the performance gaps between RD learning and the other learning methods become more pronounced as the dimensions of target models increase., Comment: 14 pages, 19 figures

We discuss a QCD-scale composite axion model arising from dark QCD coupled to QCD. The presently proposed scenario not only solves the strong CP problem, but also is compatible with the preheating setup for the QCD baryogenesis. The composite axion is phenomenologically required to mimic the QCD pion, but can generically be flavorful, which could be testable via the induced flavor changing processes at experiments. Another axionlike particle (ALP) is predicted to achieve the axion relaxation mechanism, which can phenomenologically act as the conventional QCD axion. This ALP can be ultralight, having the mass less than 1 eV, to be a dark matter candidate. The QCD $\times$ dark QCD symmetry structure constrains dark QCD meson spectra, so that the dark $\eta'$-like meson would only be accessible at the collider experiments. Still, the Belle II and Electron ion collider experiments can have a high enough sensitivity to probe the dark $\eta'$-like meson in the diphoton channel, which dominantly arises from the mixing with the QCD $\eta'$ and the pionic composite axion. We also briefly address nontrivial cosmological aspects, such as those related to the dark-chiral phase transition, the dark matter production, and an ultraviolet completion related to the ultralight ALP., Comment: 15 pages, 1 figure; discussions on experimental and astrophysical limits revised"

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

Recent developments in laser-excited photoemission electron microscopy (laser-PEEM) advance the visualization of electronic nematicity and nematic domain structures in iron-based superconductors. In FeSe and BaFe$_2$(As$_{0.87}$P$_{0.13}$)$_2$ superconductors, it has been reported that the thickness of the electronic nematic domain walls is unexpectedly long, leading to the formation of mesoscopic nematicity wave [T. Shimojima $\textit{et al.}$, Science $\textbf{373}$ (2021) 1122]. This finding demonstrates that the nematic coherence length $\xi_{\rm nem}$ can be decoupled from the lattice domain wall. Here, we report that the electronic domain wall thickness shows a distinct variation in related materials: it is similarly long in FeSe$_{0.9}$S$_{0.1}$ whereas it is much shorter in undoped BaFe$_2$As$_2$. We find a correlation between the thick domain walls and the non-Fermi liquid properties of normal-state resistivity above the nematic transition temperature. This suggests that the nematic coherence length can be enhanced by underlying spin-orbital fluctuations responsible for the anomalous transport properties., Comment: 5 pages, 3 figures

We present symplectic structures on the shape space of unparameterized space curves that generalize the classical Marsden-Weinstein structure. Our method integrates the Liouville 1-form of the Marsden-Weinstein structure with Riemannian structures that have been introduced in mathematical shape analysis. We also derive Hamiltonian vector fields for several classical Hamiltonian functions with respect to these new symplectic structures., Comment: 30 pages, 2 figures

Rotational motion of charges in plasmonic nanostructures plays an important role in transferring angular momentum between light and matter on the nanometer scale. Although sophisticated control of rotational charge motion has been achieved using spatially structured light, its extension to simultaneous excitation of the same charge motion in multiple nanostructures is not straightforward. In this study, we perform model calculations to show that spatially homogeneous circularly polarized (CP) light can excite rotational charge motions with a high degrees of freedom by exploiting the rotational symmetry of the plasmonic structure and that of the plasmon mode. Finite-difference time-domain simulations demonstrate selective excitation of rotational charge motion for both isolated nanoplates and periodic array structures, showing that complex charge rotations can be manipulated by plane CP waves in a wide range of plasmonic structures., Comment: 9 pages, 5 figures

This work compares ways of extending Reinforcement Learning algorithms to Partially Observed Markov Decision Processes (POMDPs) with options. One view of options is as temporally extended action, which can be realized as a memory that allows the agent to retain historical information beyond the policy's context window. While option assignment could be handled using heuristics and hand-crafted objectives, learning temporally consistent options and associated sub-policies without explicit supervision is a challenge. Two algorithms, PPOEM and SOAP, are proposed and studied in depth to address this problem. PPOEM applies the forward-backward algorithm (for Hidden Markov Models) to optimize the expected returns for an option-augmented policy. However, this learning approach is unstable during on-policy rollouts. It is also unsuited for learning causal policies without the knowledge of future trajectories, since option assignments are optimized for offline sequences where the entire episode is available. As an alternative approach, SOAP evaluates the policy gradient for an optimal option assignment. It extends the concept of the generalized advantage estimation (GAE) to propagate option advantages through time, which is an analytical equivalent to performing temporal back-propagation of option policy gradients. This option policy is only conditional on the history of the agent, not future actions. Evaluated against competing baselines, SOAP exhibited the most robust performance, correctly discovering options for POMDP corridor environments, as well as on standard benchmarks including Atari and MuJoCo, outperforming PPOEM, as well as LSTM and Option-Critic baselines. The open-sourced code is available at https://github.com/shuishida/SoapRL.

We propose a hybrid inflationary scenario based on eight-flavor hidden QCD with the hidden colored fermions being in part gauged under $U(1)_{B-L}$. This hidden QCD is almost scale-invariant, so-called walking, and predicts the light scalar meson (the walking dilaton) associated with the spontaneous scale breaking, which develops the Coleman-Weinberg (CW) type potential as the consequence of the nonperturbative scale anomaly, hence plays the role of an inflaton of the small-field inflation. The $U(1)_{B-L}$ Higgs is coupled to the walking dilaton inflaton, which is dynamically induced from the so-called bosonic seesaw mechanism. We explore the hybrid inflation system involving the walking dilaton inflaton and the $U(1)_{B-L}$ Higgs as a waterfall field. We find that observed inflation parameters tightly constrain the $U(1)_{B-L}$ breaking scale as well as the walking dynamical scale to be $\sim 10^9$ GeV and $\sim 10^{14}$ GeV, respectively, so as to make the waterfall mechanism worked. The lightest walking pion mass is then predicted to be around 500 GeV. Phenomenological perspectives including embedding of the dynamical electroweak scalegenesis and possible impacts on the thermal leptogenesis are also addressed., Comment: 14 pages, 1 figure; a version to be published in JHEP

Although the reverse non-equilibrium molecular dynamics (RNEMD) simulation method has been widely employed, the range of applicability is yet to be discussed. In this study, for the first time, we systematically examine the method against an unentangled melt of the Kremer-Grest type chain. The simulation results indicate that as the shear rate increases, the temperature and density become inhomogeneous. However, the average viscosity remains consistent with the results obtained using the SLLOD method under homogeneous temperature and density. We also confirm that the temperature-density inhomogeneity does not significantly affect polymer conformation.

GroundBIRD is a ground-based cosmic microwave background (CMB) experiment for observing the polarization pattern imprinted on large angular scales ($\ell > 6$ ) from the Teide Observatory in Tenerife, Spain. Our primary scientific objective is a precise measurement of the optical depth $\tau$ ($\sigma(\tau) \sim 0.01$) to the reionization epoch of the Universe to cross-check systematic effects in the measurements made by previous experiments. GroundBIRD observes a wide sky area in the Northern Hemisphere ($\sim 40\%$ of the full sky) while continuously rotating the telescope at a high speed of up to 20 rotations per minute (rpm) to overcome the fluctuations of atmospheric radiation. We have adopted the NbTiN/Al hybrid microwave kinetic inductance detectors (MKIDs) as focal plane detectors. We observe two frequency bands centered at 145 GHz and 220 GHz. The 145 GHz band picks up the peak frequency of the CMB spectrum. The 220 GHz band helps accurate removal of the contamination of thermal emission from the Galactic interstellar dust. The MKID arrays (138 MKIDs for 145GHz and 23 MKIDs for 220GHz) were designed and optimized so as to minimize the contamination of the two-level-system noise and maximize the sensitivity. The MKID arrays were successfully installed in May 2023 after the performance verification tests were performed at a laboratory. GroundBIRD has been upgraded to use the full MKID arrays, and scientific observations are now underway. The telescope is automated, so that all observations are performed remotely. Initial validations, including polarization response tests and observations of Jupiter and the moon, have been completed successfully. We are now running scientific observations., Comment: Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan; paper number 13102-7, Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy XII

Relaxor-ferroelectrics display exceptional dielectric properties resulting from the underlying random dipolar fields induced by strong chemical inhomogeneity. An unusual structural aspect of relaxors is a skin-effect where the near-surface region in single crystals exhibit structures and critical phenomena that differ from the bulk. Relaxors are unique in that this skin effect extends over a macroscopic lengthscale of $\sim$ 100$\mu$m whereas usual surface layers only extend over a few unit cells (or $\sim$ nm). We present a muon spectroscopy study of Pb(Fe$_{1/2}$Nb$_{1/2}$)O$_{3}$ (PFN) which displays ferroelectric order, including many relaxor-like dielectric properties such as a frequency broadened dielectric response, and antiferromagnetism with spatially short-range polar correlations and hence can be termed a multiferroic. In terms of the magnetic behavior determined by the Fe$^{3+}$ ($S=5/2$, $L\approx0$) ions, PFN has been characterized as a unique example of a "cluster spin-glass". We use variable momentum muon spectroscopy to study the depth dependence of the slow magnetic relaxations in a large 1 cm$^{3}$ crystal of PFN. Zero-field positive muon spin relaxation is parameterized using a stretched exponential, indicative of a distribution of relaxation rates of the Fe$^{3+}$ spins. This bandwidth of frequencies changes as a function of muon momentum, indicative of a change in the Fe$^{3+}$ relaxation rates as a function of muon implantation depth in our single crystal. Using negative muon elemental analysis, we find small-to-no measurable change in the Fe$^{3+}$/Nb$^{5+}$ concentration with depth implying that chemical concentration alone cannot account for the change in the relaxational dynamics. PFN displays an analogous magnetic skin effect reported to exist in the structural properties of relaxor-ferroelectrics., Comment: 32 pages, 13 figures

The strength distributions of the Isoscalar Giant Monopole Resonance (ISGMR) and Isoscalar Giant Quadrupole Resonance (ISGQR) in 142,146-150Nd have been determined via inelastic alpha-particle scattering with the Grand Raiden (GR) Spectrometer at the Research Center for Nuclear Physics (RCNP), Japan. In the deformed nuclei 146-150Nd, the ISGMR strength distributions exhibit a splitting into two components, while the nearly spherical nucleus 142Nd displays a single peak in the ISGMR strength distribution. A noteworthy achievement in this study is the first-time detection of overtones in the Isoscalar Giant Quadrupole Resonance (ISGQR) strength distributions within Nd isotopes at an excitation energy around 25 MeV obtained through Multipole Decomposition Analysis (MDA)., Comment: Accepted for publication in Physics Letters B

Xtend is one of the two telescopes onboard the X-ray imaging and spectroscopy mission (XRISM), which was launched on September 7th, 2023. Xtend comprises the Soft X-ray Imager (SXI), an X-ray CCD camera, and the X-ray Mirror Assembly (XMA), a thin-foil-nested conically approximated Wolter-I optics. A large field of view of $38^{\prime}\times38^{\prime}$ over the energy range from 0.4 to 13 keV is realized by the combination of the SXI and XMA with a focal length of 5.6 m. The SXI employs four P-channel, back-illuminated type CCDs with a thick depletion layer of 200 $\mu$m. The four CCD chips are arranged in a 2$\times$2 grid and cooled down to $-110$ $^{\circ}$C with a single-stage Stirling cooler. Before the launch of XRISM, we conducted a month-long spacecraft thermal vacuum test. The performance verification of the SXI was successfully carried out in a course of multiple thermal cycles of the spacecraft. About a month after the launch of XRISM, the SXI was carefully activated and the soundness of its functionality was checked by a step-by-step process. Commissioning observations followed the initial operation. We here present pre- and post-launch results verifying the Xtend performance. All the in-orbit performances are consistent with those measured on ground and satisfy the mission requirement. Extensive calibration studies are ongoing., Comment: 10 pages, 8 figures. Proceedings of SPIE Astronomical Telescopes and Instrumentation 2024

XRISM (X-Ray Imaging and Spectroscopy Mission) is an astronomical satellite with the capability of high-resolution spectroscopy with the X-ray microcalorimeter, Resolve, and wide field-of-view imaging with the CCD camera, Xtend. The Xtend consists of the mirror assembly (XMA: X-ray Mirror Assembly) and detector (SXI: Soft X-ray Imager). The components of SXI include CCDs, analog and digital electronics, and a mechanical cooler. After the successful launch on September 6th, 2023 (UT) and subsequent critical operations, the mission instruments were turned on and set up. The CCDs have been kept at the designed operating temperature of $-110^\circ$C ~after the electronics and cooling system were successfully set up. During the initial operation phase, which continued for more than a month after the critical operations, we verified the observation procedure, stability of the cooling system, all the observation options with different imaging areas and/or timing resolutions, and operations for protection against South Atlantic Anomaly. We optimized the operation procedure and observation parameters including the cooler settings, imaging areas for the specific modes with higher timing resolutions, and event selection algorithm. We summarize our policy and procedure of the initial operations for SXI. We also report on a couple of issues we faced during the initial operations and lessons learned from them., Comment: 10 pages, 8 figures, Proceedings of SPIE Astronomical Telescopes + Instrumentation 2024

Despite many attempts, the relation between fracture and structure of polymer networks is yet to be clarified. For this problem, a recent study for phantom chain simulations [Macromolecules, 56, 9359 (2023)] has demonstrated that the fracture characteristics obtained for polymer networks with various node functionalities and conversion ratios lie on master curves if they are plotted against cycle rank. In this study, we extended the simulation to the effect of prepolymer concentration on the relationships between cycle rank and fracture characteristics within the concentration range of 1

Foundation models are increasingly ubiquitous in our daily lives, used in everyday tasks such as text-image searches, interactions with chatbots, and content generation. As use increases, so does concern over the disparities in performance and fairness of these models for different people in different parts of the world. To assess these growing regional disparities, we present World Wide Dishes, a mixed text and image dataset consisting of 765 dishes, with dish names collected in 131 local languages. World Wide Dishes has been collected purely through human contribution and decentralised means, by creating a website widely distributed through social networks. Using the dataset, we demonstrate a novel means of operationalising capability and representational biases in foundation models such as language models and text-to-image generative models. We enrich these studies with a pilot community review to understand, from a first-person perspective, how these models generate images for people in five African countries and the United States. We find that these models generally do not produce quality text and image outputs of dishes specific to different regions. This is true even for the US, which is typically considered to be more well-resourced in training data - though the generation of US dishes does outperform that of the investigated African countries. The models demonstrate a propensity to produce outputs that are inaccurate as well as culturally misrepresentative, flattening, and insensitive. These failures in capability and representational bias have the potential to further reinforce stereotypes and disproportionately contribute to erasure based on region. The dataset and code are available at https://github.com/oxai/world-wide-dishes/.

We investigate bilayer and trilayer systems composed of topologically distinct, vertically stacked layers based on the Benalcazar-Bernevig-Hughes model. We have identified a topological phase transition that significantly alters the number of the topological corner states in these systems. Additionally, we find that traditional nested Wilson loop analysis inaccurately classifies certain phases, leading us to evaluate multipole chiral numbers (MCNs) as a more appropriate topological invariant. This approach accurately identifies topological phases and the number of MCNs coincides with the number of corner modes, enhancing our understanding of topological insulators and opening avenues for further applications., Comment: 10 pages, 7 figures

The development of algorithms for automation of subtasks during robotic surgery can be accelerated by the availability of realistic simulation environments. In this work, we focus on one aspect of the realism of a surgical simulator, which is the positional accuracy of the robot. In current simulators, robots have perfect or near-perfect accuracy, which is not representative of their physical counterparts. We therefore propose a pair of neural networks, trained by data collected from a physical robot, to estimate both the controller error and the kinematic and non-kinematic error. These error estimates are then injected within the simulator to produce a simulated robot that has the characteristic performance of the physical robot. In this scenario, we believe it is sufficient for the estimated error used in the simulation to have a statistically similar distribution to the actual error of the physical robot. This is less stringent, and therefore more tenable, than the requirement for error compensation of a physical robot, where the estimated error should equal the actual error. Our results demonstrate that error injection reduces the mean position and orientation differences between the simulated and physical robots from 5.0 mm / 3.6 deg to 1.3 mm / 1.7 deg, respectively, which represents reductions by factors of 3.8 and 2.1., Comment: 6 page paper

Swarm robots, inspired by the emergence of animal herds, are robots that assemble a large number of modules and self-organize themselves to form specific morphologies and exhibit specific functions. These modular robots perform relatively simple actions and controls, and create macroscopic morphologies and functions through the interaction of a large number of modular robots. This research focuses on such self-organizing robots or swarm robots. The proposed algorithm is a model that applies the Turing pattern, one of the self-organization models, to make a group of modules accumulate and stay within a certain region. The proposed method utilizes the area within the spots of the Turing pattern as the aggregation region of the modules. Furthermore, it considers the value corresponding to the concentration distribution within the spotted pattern of the Turing pattern model (referred to as the potential value in this research), identifies the center of the region (spotted pattern), and makes it the center of the module group. By controlling the modules in the direction of the higher potential value, it succeeds in maintaining the shape of the module group as a whole while moving. The algorithm was validated using a two-dimensional simulation model. The unit module robot was assumed to have the following properties: 1) limited self-drive, 2) no module identifier, 3) information exchange only with adjacent modules, 4) no coordinate system, and 5) only simple arithmetic and memory functions. Using these modules, the devised algorithm was able to achieve not only the creation of static forms but also the realization of the following movements: 1) modules accumulate and grow, 2) modules move to the light source, 3) exit the gap while maintaining its shape, and 4) self-replication.

As the importance of comprehensive evaluation in workshop courses increases, there is a growing demand for efficient and fair assessment methods that reduce the workload for faculty members. This paper presents an evaluation conducted with Large Language Models (LLMs) using actual student essays in three scenarios: 1) without providing guidance such as rubrics, 2) with pre-specified rubrics, and 3) through pairwise comparison of essays. Quantitative analysis of the results revealed a strong correlation between LLM and faculty member assessments in the pairwise comparison scenario with pre-specified rubrics, although concerns about the quality and stability of evaluations remained. Therefore, we conducted a qualitative analysis of LLM assessment comments, showing that: 1) LLMs can match the assessment capabilities of faculty members, 2) variations in LLM assessments should be interpreted as diversity rather than confusion, and 3) assessments by humans and LLMs can differ and complement each other. In conclusion, this paper suggests that LLMs should not be seen merely as assistants to faculty members but as partners in evaluation committees and outlines directions for further research.

Workshop courses designed to foster creativity are gaining popularity. However, even experienced faculty teams find it challenging to realize a holistic evaluation that accommodates diverse perspectives. Adequate deliberation is essential to integrate varied assessments, but faculty often lack the time for such exchanges. Deriving an average score without discussion undermines the purpose of a holistic evaluation. Therefore, this paper explores the use of a Large Language Model (LLM) as a facilitator to integrate diverse faculty assessments. Scenario-based experiments were conducted to determine if the LLM could integrate diverse evaluations and explain the underlying pedagogical theories to faculty. The results were noteworthy, showing that the LLM can effectively facilitate faculty discussions. Additionally, the LLM demonstrated the capability to create evaluation criteria by generalizing a single scenario-based experiment, leveraging its already acquired pedagogical domain knowledge., Comment: The final version appears in the proceedings of the 32nd International Conference on Computers in Education (ICCE 2024)

Efficient algorithms are being developed to search for strong gravitational lens systems owing to increasing large imaging surveys. Neural networks have been successfully used to discover galaxy-scale lens systems in imaging surveys such as the Kilo Degree Survey, Hyper-Suprime Cam (HSC) Survey and Dark Energy Survey over the last few years. Thus, it has become imperative to understand how some of these networks compare, their strengths and the role of the training datasets as most of the networks make use of supervised learning algorithms. In this work, we present the first-of-its-kind systematic comparison and benchmarking of networks from four teams that have analysed the HSC Survey data. Each team has designed their training samples and developed neural networks independently but coordinated apriori in reserving specific datasets strictly for test purposes. The test sample consists of mock lenses, real (candidate) lenses and real non-lenses gathered from various sources to benchmark and characterise the performance of each of the network. While each team's network performed much better on their own constructed test samples compared to those from others, all networks performed comparable on the test sample with real (candidate) lenses and non-lenses. We also investigate the impact of swapping the training samples amongst the teams while retaining the same network architecture. We find that this resulted in improved performance for some networks. These results have direct implications on measures to be taken for lens searches with upcoming imaging surveys such as the Rubin-Legacy Survey of Space and Time, Roman and Euclid., Comment: 13 pages, 8 figures

The Super-Kamiokande and T2K collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of $19.7(16.3) \times 10^{20}$ protons on target in (anti)neutrino mode, the analysis finds a 1.9$\sigma$ exclusion of CP-conservation (defined as $J_{CP}=0$) and a preference for the normal mass ordering., Comment: 12 pages, 4 figures

We propose a counterpart of the classical Rollnik-class of potentials for fractional and massive relativistic Laplacians, and describe this space in terms of appropriate Riesz potentials. These definitions rely on precise resolvent estimates. We show that Coulomb-type potentials are elements of fractional Rollnik-class up to but not including the critical singularity of the Hardy potential. For the operators with fractional exponent $\alpha = 1$ there exists no fractional Rollnik potential, however, in low dimensions we make sense of these classes as limiting cases by using $\Gamma$-convergence. In a second part of the paper we derive detailed results on the self-adjointness and spectral properties of relativistic Schr\"odinger operators obtained under perturbations by fractional Rollnik potentials. We also define an extended fractional Rollnik-class which is the maximal space for the Hilbert-Schmidt property of the related Birman-Schwinger operators.

Feynman diagrams are an essential tool for simulating strongly correlated electron systems. However, stochastic quantum Monte Carlo (QMC) sampling suffers from the sign problem, e.g., when solving a multiorbital quantum impurity model. Recently, two approaches have been proposed for efficient numerical treatment of Feynman diagrams: Tensor Cross Interpolation (TCI) for replacing the stochastic sampling and the Quantics Tensor Train (QTT) representation for compressing space-time dependence. Combining these approaches, we find low-rank structures in weak-coupling Feynman diagrams for a multiorbital electron-phonon model and demonstrate their efficient numerical integrations with exponential resolution in time and exponential convergence of error with respect to computational cost., Comment: 6 pages, 3 figures + 5 pages, 6 figures

Library-based methods are known to be very effective for fast motion planning by adapting an experience retrieved from a precomputed library. This article presents CoverLib, a principled approach for constructing and utilizing such a library. CoverLib iteratively adds an experience-classifier-pair to the library, where each classifier corresponds to an adaptable region of the experience within the problem space. This iterative process is an active procedure, as it selects the next experience based on its ability to effectively cover the uncovered region. During the query phase, these classifiers are utilized to select an experience that is expected to be adaptable for a given problem. Experimental results demonstrate that CoverLib effectively mitigates the trade-off between plannability and speed observed in global (e.g. sampling-based) and local (e.g. optimization-based) methods. As a result, it achieves both fast planning and high success rates over the problem domain. Moreover, due to its adaptation-algorithm-agnostic nature, CoverLib seamlessly integrates with various adaptation methods, including nonlinear programming-based and sampling-based algorithms.

Aiming to simulate the radiation damage effect on a dual alpha+beta phase Ti-6Al-4V alloy utilized as high-intensity accelerator beam window material, a series of irradiation experiments were conducted with a 2.8 MeV-Fe^2+ ion beam in several dpa regions at room temperature. The nano-indentation hardness increased steeply at 1 dpa and unchanged up to 10 dpa, due to the saturation of defect clusters and tangled dislocations in the dominant alpha-phase matrix with a size of 2~3 nm and a density of about 1x10^23 m^-3. In contrast in the intergranular beta-phase, larger loops of 20~30 nm diameter were observed with much less density of about 5x10^20 m^-3. The diffraction pattern showed rectilinear diffuse streaks between the beta-phase reflections, corresponding to the omega-phase precursor, without dose dependency in its intensity. FFT/I-FFT analysis of the HREM revealed a sub-nanometer-sized lattice disorder with local fluctuations, not discrete but continuous, and homogeneously distributed within the matrix beta-phase stably against the irradiation. The significantly low dislocation density and the absence of phase transformation in the beta-phase matrix could be attributed either to the strong sink effect expected for this distinctive sub-nanometer-sized homogeneous lattice disorder or to the anomalous point defect recombination induced by the high mobility of vacancies, both of which are originated from the metastable omega-phase precursors specifically formed in the beta(BCC) phase of group-4 transition metals., Comment: 36 pages, 15 figures

Context. Transient astronomical events that exhibit no discernible association with a host galaxy are commonly referred to as hostless. These rare phenomena are associated with extremely energetic events, and they can offer unique insights into the properties and evolution of stars and galaxies. However, the sheer number of transients captured by contemporary high-cadence astronomical surveys renders the manual identification of all potential hostless transients impractical. Therefore, creating a systematic identification tool is crucial for studying these elusive events. Aims. We present the ExtragaLactic alErt Pipeline for Hostless AstroNomical Transients (ELEPHANT), a framework for filtering hostless transients in astronomical data streams. Methods. We used Fink to access all the ZTF alerts produced between January/2022 and December/2023, selecting only those associated with extragalactic transients. We then processed the associated stamps using a sequence of image analysis techniques to retrieve hostless candidates. Results. We find that less than 2% of all analyzed transients are potentially hostless. Among them, approximately 10% have a spectroscopic class reported on TNS, with Type Ia supernova being the most common class, followed by SLSN. Among the hostless candidates retrieved by our pipeline, there was SN 2018ibb, which has been proposed to be a PISN candidate; and SN 2022ann, one of only five known SNe Icn. When no class is reported on TNS, the dominant classes are QSO and SN candidates, the former obtained from SIMBAD and the latter inferred using the Fink ML classifier. Conclusions. ELEPHANT represents an effective strategy to filter extragalactic events within large and complex astronomical alert streams. There are many applications for which this pipeline will be useful, ranging from transient selection for follow-up to studies of transient environments., Comment: 15 pages, 11 figures

In a previous work of 2014 on a quantum system governed by the repulsive Hamiltonian, the author proved uniqueness for short-range interactions described by a scattering operator consisting of regular and singular parts. In this paper, the singular part is assumed to have much stronger singularities and the same uniqueness theorem is proved. By applying the time-dependent method invented by Enss and Weder in 1995, the high-velocity limit for a wider class of the scattering operator with stronger singularities also uniquely determines the interactions of a multi-dimensional system.

Preceding a core-collapse supernova, various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande via inverse beta decay interactions. Once these pre-supernova neutrinos are observed, an early warning of the upcoming core-collapse supernova can be provided. In light of this, KamLAND and Super-Kamiokande, both located in the Kamioka mine in Japan, have been monitoring pre-supernova neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and Super-Kamiokande on pre-supernova neutrino detection. A pre-supernova alert system combining the KamLAND detector and the Super-Kamiokande detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-supernova neutrino signal from a 15 M$_{\odot}$ star within 510 pc of the Earth, at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hours in advance., Comment: Resubmitted to ApJ. 22 pages, 16 figures, for more information about the combined pre-supernova alert system, see https://www.lowbg.org/presnalarm/

The upcoming Legacy Survey of Space and Time (LSST) at the Vera Rubin Observatory is expected to detect a few million transients per night, which will generate a live alert stream during the entire 10 years of the survey. This will be distributed via community brokers whose task is to select subsets of the stream and direct them to scientific communities. Given the volume and complexity of data, machine learning (ML) algorithms will be paramount for this task. We present the infrastructure tests and classification methods developed within the {\sc Fink} broker in preparation for LSST. This work aims to provide detailed information regarding the underlying assumptions, and methods, behind each classifier, enabling users to make informed follow-up decisions from {\sc Fink} photometric classifications. Using simulated data from the Extended LSST Astronomical Time-series Classification Challenge (ELAsTiCC), we showcase the performance of binary and multi-class ML classifiers available in {\sc Fink}. These include tree-based classifiers coupled with tailored feature extraction strategies, as well as deep learning algorithms. We introduce the CBPF Alert Transient Search (CATS), a deep learning architecture specifically designed for this task. Results show that {\sc Fink} classifiers are able to handle the extra complexity which is expected from LSST data. CATS achieved $97\%$ accuracy on a multi-class classification while our best performing binary classifier achieve $99\%$ when classifying the Periodic class. ELAsTiCC was an important milestone in preparing {\sc Fink} infrastructure to deal with LSST-like data. Our results demonstrate that {\sc Fink} classifiers are well prepared for the arrival of the new stream; this experience also highlights that transitioning from current infrastructures to Rubin will require significant adaptation of currently available tools., Comment: 18 pages, 29 figures. Submitted to A&A

Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10\,s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that is critical for understanding the dynamics of the supernova explosion mechanism. In order to solve this problem, two new DAQ modules were developed to aid in the observation of very nearby supernovae. The first of these, the SN module, is designed to save only the number of hit PMTs during a supernova burst and the second, the Veto module, prescales the high rate neutrino events to prevent the QBEE from overflowing based on information from the SN module. In the event of a very nearby supernova, these modules allow SK to reconstruct the time evolution of the neutrino event rate from beginning to end using both QBEE and SN module data. This paper presents the development and testing of these modules together with an analysis of supernova-like data generated with a flashing laser diode. We demonstrate that the Veto module successfully prevents DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability of the new modules. During normal running the Veto module is found to issue DAQ vetos a few times per month resulting in a total dead time less than 1\,ms, and does not influence ordinary operations. Additionally, using simulation data we find that supernovae closer than 800~pc will trigger Veto module resulting in a prescaling of the observed neutrino data., Comment: 28 pages, 18 figures. Submitted to PTEP