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This paper presents a design for a drop tower test to evaluate a numerical model for a structurally reconfigurable spacecraft with actuatable joints, referred to as a transformable spacecraft. A mock-up robot for a 3U-sized transformable spacecraft is designed to fit in a limited time and space of the microgravity environment available in the drop tower. The robot performs agile reorientation, referred to as nonholonomic attitude control, by actuating joints in a particular manner. To adapt to the very short duration of microgravity in the drop tower test, a successive joint actuation maneuver is optimized to maximize the amount of attitude reorientation within the time constraint. The robot records the angular velocity history of all four bodies, and the data is analyzed to evaluate the accuracy of the numerical model. We confirm that the constructed numerical model sufficiently replicates the robot's motion and show that the post-experiment model corrections further improve the accuracy of the numerical simulations. Finally, the difference between this drop tower test and the actual orbit demonstration is discussed to show the prospect., Comment: 22 pages, 20 figures

We investigated the magnetic and magnetoelastic properties of MnSc$_2$Se$_4$ single crystals at low temperature under a magnetic field directed along the crystallographic [111] axis. The magnetization data at low temperature show a linear increase with magnetic field, until saturation is reached above 15 T. In ultrasound, a longitudinal acoustic mode shows a softening in field, which is absent for a transverse acoustic mode. We discuss these results using a microscopic model based on the framework of linear spin-wave theory. The magnetic and magnetoelastic data are qualitatively reproduced by considering magnon-phonon interactions arising from exchange-striction coupling between the crystal lattice and spin-wave fluctuations in the zero-temperature limit.

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

We present a novel deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next generation multi-ton scale liquid Xenon-based direct detection experiment, DARWIN. We train an anomaly detector comprising a variational autoencoder and a classifier on extensive, high-dimensional simulated detector response data and construct a one-dimensional anomaly score optimised to reject the background only hypothesis in the presence of an excess of non-background-like events. We benchmark the procedure with a sensitivity study that determines its power to reject the background-only hypothesis in the presence of an injected WIMP dark matter signal, outperforming the classical, likelihood-based background rejection test. We show that our neural networks learn relevant energy features of the events from low-level, high-dimensional detector outputs, without the need to compress this data into lower-dimensional observables, thus reducing computational effort and information loss. For the future, our approach lays the foundation for an efficient end-to-end pipeline that eliminates the need for many of the corrections and cuts that are traditionally part of the analysis chain, with the potential of achieving higher accuracy and significant reduction of analysis time., Comment: 10 Figures, 3 Tables, 23 Pages (incl. references)

It is shown that the first-order term of the asymptotic bias of the posterior mean is removed by a suitable choice of a prior density. In regular statistical models including exponential families, and linear and logistic regression models, such a prior is given by the squared Jeffreys prior. We also explain the relationship between the proposed prior distribution, the moment matching prior, and the prior distribution that reduces the bias term of the posterior mode.

We will explore the dynamical property of non-equilibrium phenomena induced by two-dimensional holographic conformal field theory (2d holographic CFT) Hamiltonian on the curved spacetime by studying the time dependence of the entanglement entropy and mutual information. Here, holographic CFT is the CFT having the gravity dual. We will start from the boundary and thermofield double states, evolve the systems in Euclidean time with the Hamiltonian on the curved background, and then evolve them in real-time with the same Hamiltonian. We found that the early- and late-time entanglement structure depends on the curved background, while the entanglement growth does not, and is linear. Furthermore, in the gravity dual for the thermofield double state, this entanglement growth is due to the linear growth of the wormhole, while in the one for the boundary state, it is due to the in-falling of the end of the world brane to the black hole. We discussed the low temperature system can be regarded as the dynamical system induced by the multi-joining quenches. We also discussed the effective description of the high temperature system, called line tension picture., Comment: 48 pages+appendices, 3 figures,references added, matched with published version

Identifying the exotic quantum spin liquid phase in Kitaev magnets has garnered great research interests and remains a significant challenge. In experiments, most of the proposed candidate materials exhibit an antiferromagnetic (AFM) order at low temperatures, thus the challenge transforms into the searching for a field-driven disordered phase that is distinct from the partially polarized paramagnetic phase after suppressing the AFM order. Recently, Na$_2$Co$_2$TeO$_6$ has been proposed as one of the prime candidates, where the Kitaev interaction is realized by the high-spin $t^{5}_{2g}e^2_g$ configuration, and spin-orbit entangled $J_{\rm eff} = 1/2$ state in a bond-edge shared honeycomb lattice. In this study, we identify an emergent intermediate disordered phase induced by an external field along the $c$-axis of the honeycomb plane. This phase is characterized through magnetization and magnetocaloric effect experiments in high magnetic fields. To explain the experimental results, we propose an effective spin model with large AFM Kitaev interaction, which yields results in good agreement with both our findings and previously reported data. We determine that the effective $K$-$J$-$\Gamma$-$\Gamma'$ model for Na$_2$Co$_2$TeO$_6$ is nearly dual to that of $\alpha$-RuCl$_3$ under an unitary transformation. Given the insignificant fragility of Na$_2$Co$_2$TeO$_6$ sample, further high-field experiments can be conducted to explore this intermediate-field quantum spin disordered phase., Comment: 12 pages, 8 figures

The thermodynamics and the entanglement properties of two-dimensional conformal field theories ($2$d CFTs) on curved backgrounds are studied. By means of conformal mapping we study the equivalent system on flat space governed by the deformed Hamiltonian, which is a spatial integral of the Hamiltonian density modulated by an enveloping function. Focusing on holographic CFTs, we observe Hawking-Page like phase transition for the thermal and the entanglement entropy as we vary the background metric. We also compute the mutual information to study the information theoretic correlation between parts of the curved spacetime. The gravity dual of 2d CFTs on curved background is also discussed., Comment: 38 pages, 12 figures

Predictive biomarkers of treatment response are lacking for metastatic clear cell renal cell carcinoma (ccRCC), a tumor type that is treated with angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a HIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is arguably the best candidate to predict anti-angiogenic (AA) response. However, the clinical adoption of transcriptomic assays faces several challenges including standardization, time delay, and high cost. Further, ccRCC tumors are highly heterogenous, and sampling multiple areas for sequencing is impractical. Here we present a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides. To overcome the lack of interpretability, one of the biggest limitations of typical DL models, our model produces a visual vascular network which is the basis of the model's prediction. To test its reliability, we applied this model to multiple cohorts including a clinical trial dataset. Our model accurately predicts the RNA-based Angioscore on multiple independent cohorts (spearman correlations of 0.77 and 0.73). Further, the predictions help unravel meaningful biology such as association of angiogenesis with grade, stage, and driver mutation status. Finally, we find our model can predict response to AA therapy, in both a real-world cohort and the IMmotion150 clinical trial. The predictive power of our model vastly exceeds that of CD31, a marker of vasculature, and nearly rivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based Angioscore at a fraction of the cost. By providing a robust yet interpretable prediction of the Angioscore from histopathology slides alone, our approach offers insights into angiogenesis biology and AA treatment response., Comment: 19 pages, 4 Figures

In recent years, the field of generative artificial intelligence, particularly in the domain of image generation, has exerted a profound influence on society. Despite the capability of AI to produce images of high quality, the augmentation of users' drawing abilities through the provision of drawing support systems emerges as a challenging issue. In this study, we propose that a cognitive factor, specifically, the size of the canvas, may exert a considerable influence on the outcomes of imitative drawing sketches when utilizing reference images. To investigate this hypothesis, a web based drawing interface was utilized, designed specifically to evaluate the effect of the canvas size's proportionality to the reference image on the fidelity of the drawings produced. The findings from our research lend credence to the hypothesis that a drawing interface, featuring a canvas whose dimensions closely match those of the reference image, markedly improves the precision of user-generated sketches., Comment: 5 pages, 6 figures, accepted in NICOGRAPH International 2024

Magnetic-field control of fundamental optical properties is a crucial challenge in the engineering of multifunctional microdevices. Van der Waals (vdW) magnets retaining a magnetic order even in atomically thin layers, offer a promising platform for hosting exotic magneto-optical functionalities owing to their strong spin-charge coupling. Here, we demonstrate that a giant optical anisotropy can be controlled by magnetic fields in the vdW magnet FePS$_3$. The giant linear dichroism ($\sim$11%), observed below $T_{\text{N}}\!\sim\!120$ K, is nearly fully suppressed in a wide energy range from 1.6 to 2.0 eV, following the collapse of the zigzag magnetic order above 40 T. This remarkable phenomenon can be explained as a result of symmetry changes due to the spin order, enabling minority electrons of Fe$^{2+}$ to hop in a honeycomb lattice. The modification of spin-order symmetry by external fields provides a novel route for controllable anisotropic optical micro-devices., Comment: 5 pages, 4 figures

In emergencies such as disasters, the number of voice calls (VoIP sessions) increases rapidly for a variety of purposes. Thus, a control server near a disaster area may not be able to connect to VoIP sessions due to congestion. To solve this problem, a Call Admission Control (CAC) is needed to determine whether a VoIP session requesting a connection can be accepted or rejected. A CAC has the purpose of guaranteeing the connection quality and communication quality of VoIP sessions. One conventional method classifies VoIP sessions into three classes (emergency VoIP sessions, VoIP sessions from the disaster area, and VoIP sessions from outside the disaster area) by focusing on the outgoing location and offers a CAC with a priority level for each. However, a conventional CAC method cannot be applied to VoIP networks because reception control is designed for Public Switched Telephone Networks (PSTN). When conventional methods are applied to VoIP networks, the connection quality is guaranteed, however the communication quality cannot be guaranteed because the packet dropping probability is not considered. In this paper, we propose a three-dimensional CAC that controls three classes of VoIP sessions and guarantees both communication and connection quality in VoIP networks during emergencies. A conventional CAC method and our proposed CAC method are evaluated in terms of the call blocking probability, which guarantees the connection quality, and packet dropping probability, which guarantees the communication quality, to show the effectiveness of the proposed method., Comment: 25 pages, 31 figures

The conditional text-to-image diffusion models have garnered significant attention in recent years. However, the precision of these models is often compromised mainly for two reasons, ambiguous condition input and inadequate condition guidance over single denoising loss. To address the challenges, we introduce two innovative solutions. Firstly, we propose a Spatial Guidance Injector (SGI) which enhances conditional detail by encoding text inputs with precise annotation information. This method directly tackles the issue of ambiguous control inputs by providing clear, annotated guidance to the model. Secondly, to overcome the issue of limited conditional supervision, we introduce Diffusion Consistency Loss (DCL), which applies supervision on the denoised latent code at any given time step. This encourages consistency between the latent code at each time step and the input signal, thereby enhancing the robustness and accuracy of the output. The combination of SGI and DCL results in our Effective Controllable Network (ECNet), which offers a more accurate controllable end-to-end text-to-image generation framework with a more precise conditioning input and stronger controllable supervision. We validate our approach through extensive experiments on generation under various conditions, such as human body skeletons, facial landmarks, and sketches of general objects. The results consistently demonstrate that our method significantly enhances the controllability and robustness of the generated images, outperforming existing state-of-the-art controllable text-to-image models.

In this paper, we analyze and discuss ShadowFormer in preparation for the NTIRE2023 Shadow Removal Challenge [1], implementing five key improvements: image alignment, the introduction of a perceptual quality loss function, the semi-automatic annotation for shadow detection, joint learning of shadow detection and removal, and the introduction of new data augmentation technique "CutShadow" for shadow removal. Our method achieved scores of 0.196 (3rd out of 19) in LPIPS and 7.44 (4th out of 19) in the Mean Opinion Score (MOS)., Comment: This version is a brief technical report submitted to the organizers, and there are still some points to be added; please wait for updates until May 2024. The code can be found here (https://github.com/Yuki-11/NTIRE2023_ShadowRemoval_IIM_TTI)

Sine-skewed circular distributions are identifiable and have easily-computable trigonometric moments and a simple random number generation algorithm, whereas they are known to have relatively low levels of asymmetry. This study proposes a new family of circular distributions that can be skewed more significantly than that of existing models. It is shown that a subfamily of the proposed distributions is identifiable with respect to parameters and all distributions in the subfamily have explicit trigonometric moments and a simple random number generation algorithm. The maximum likelihood estimation for model parameters is considered and its finite sample performances are investigated by numerical simulations. Some real data applications are illustrated for practical purposes.