Dataset distillation (DD) condenses large datasets into compact yet informative substitutes, preserving performance comparable to the original dataset while reducing storage, transmission costs, and computational consumption. However, previous DD methods mainly focus on distilling information from images, often overlooking the semantic information inherent in the data. The disregard for context hinders the model's generalization ability, particularly in tasks involving complex datasets, which may result in illogical outputs or the omission of critical objects. In this study, we integrate vision-language methods into DD by introducing text prototypes to distill language information and collaboratively synthesize data with image prototypes, thereby enhancing dataset distillation performance. Notably, the text prototypes utilized in this study are derived from descriptive text information generated by an open-source large language model. This framework demonstrates broad applicability across datasets without pre-existing text descriptions, expanding the potential of dataset distillation beyond traditional image-based approaches. Compared to other methods, the proposed approach generates logically coherent images containing target objects, achieving state-of-the-art validation performance and demonstrating robust generalization. Source code and generated data are available in this https URL

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.

Formulating non-Abelian gauge theories as a tensor network is known to be challenging due to the internal degrees of freedom that result in the degeneracy in the singular value spectrum. In two dimensions, it is straightforward to 'trace out' these degrees of freedom with the use of character expansion, giving a reduced tensor network where the degeneracy associated with the internal symmetry is eliminated. In this work, we show that such an index loop also exists in higher dimensions in the form of a closed tensor network we call the 'armillary sphere'. This allows us to completely eliminate the matrix indices and reduce the overall size of the tensors in the same way as is possible in two dimensions. This formulation allows us to include significantly more representations with the same tensor size, thus making it possible to reach a greater level of numerical accuracy in the tensor renormalization group computations.

Dataset distillation (DD) aims to generate a compact yet informative dataset that achieves performance comparable to the original dataset, thereby reducing demands on storage and computational resources. Although diffusion models have made significant progress in dataset distillation, the generated surrogate datasets often contain samples with label inconsistencies or insufficient structural detail, leading to suboptimal downstream performance. To address these issues, we propose a detector-guided dataset distillation framework that explicitly leverages a pre-trained detector to identify and refine anomalous synthetic samples, thereby ensuring label consistency and improving image quality. Specifically, a detector model trained on the original dataset is employed to identify anomalous images exhibiting label mismatches or low classification confidence. For each defective image, multiple candidates are generated using a pre-trained diffusion model conditioned on the corresponding image prototype and label. The optimal candidate is then selected by jointly considering the detector's confidence score and dissimilarity to existing qualified synthetic samples, thereby ensuring both label accuracy and intra-class diversity. Experimental results demonstrate that our method can synthesize high-quality representative images with richer details, achieving state-of-the-art performance on the validation set.

The unsuppressed CP violation in QCD is a problem in the standard model. If we have some mechanism to guarantee real determinants of the quark mass matrices, the vanishing physical vacuum angle $\bar \theta$ indicates the CP invariance at the fundamental level. Thus, the small ${\bar \theta}$ is technically natural, since we have an enhanced CP symmetry in the limit of the vanishing $\bar \theta =0$. In fact, it was proved that the vacuum angle is never renormalized up to the four-loop level once it is fixed at 0 value at some high energy scale. The purpose of this paper is to construct a model which guarantees the real determinants of the quark mass matrices assuming a non-invertible symmetry.

We present a symmetry-adapted extension of sample-based quantum diagonalization (SQD) that rigorously embeds space-group symmetry into the many-body subspace sampled by quantum hardware. The method is benchmarked on the two-leg ladder Hubbard model using both molecular orbital and momentum bases. Energy convergence is shown to be improved in the momentum basis compared to the molecular orbital basis for both the spin-quintet ground state and the spin-singlet excited state. We clarify the relationship between the compactness of the many-body wave function and the sparsity of the representation matrices of symmetry operations. Furthermore, the enhancement of the superconducting correlation function due to the Coulomb interaction is demonstrated. Our method highlights the importance of symmetry structure in random-sampling quantum simulation of correlated systems

Binding energy (BE) is a critical parameter in astrochemical modeling, governing the retention of species on interstellar dust grains and their subsequent chemical evolution. However, conventional models often rely on single-valued BEs, overlooking the intrinsic distribution arising from diverse adsorption sites. In this study, we present BEs for monohydric alcohols, thiols, and their plausible precursors, including aldehydes and thioaldehydes. We incorporate a distribution of BEs to capture the realistic variation in adsorption strengths. The quantum chemical calculations provide a range of BE values rather than a single estimate, ensuring a more precise description of molecular diffusion and surface chemistry. The BE trend of analogous species provides qualitative insight into the dominant reaction pathways and key precursors that drive the formation of larger molecules under interstellar conditions. Oxygen-bearing species generally exhibit higher BEs than their sulfur analogues, primarily due to stronger interactions, further influencing molecular adsorption and reactivity. We implemented BE distributions in astrochemical models, revealing significant effects on predicted abundances and establishing a more accurate framework for future astrochemical modeling.

We analyze radiative corrections to the Starobinsky model of inflation arising from self-interactions of the inflaton, and from its Yukawa couplings, $y$, to matter fermions, and dimensionful trilinear couplings, $\kappa$, to scalar fields, which could be responsible for reheating the Universe after inflation. The inflaton self-interactions are found to be of higher order in the Hubble expansion rate during inflation, and hence unimportant for CMB observations. In contrast, matter couplings to the Starobinsky inflaton can have significant effects on the spectral index of scalar CMB perturbations, $n_s$, and on the tensor-to-scalar ratio, $r$. Using a renormalization-group improved analysis of the effective inflationary potential, we find that the Planck measurement of $n_s$ constrains the inflaton coupling to light fermions in the Einstein frame: y < 4.5 \times 10^{-4}, corresponding to an upper limit on the reheating temperature T_{\rm RH} < 2 \times 10^{11}~{\rm GeV}, whereas the ACT DR6 measurement of $n_s$ corresponds to 3.8 \times 10^{-4} < y < 5.6 \times 10^{-4} and 1.7 \times 10^{11} ~{\rm GeV} < T_{\rm RH} < 2.8 \times 10^{11}~{\rm GeV}, while the upper limits on $r$ provide weaker constraints. Planck data also imply a constraint on a trilinear inflaton coupling to light scalars in the Einstein frame: $\kappa \leq 4 \times 10^{12}~{\rm GeV}$, corresponding to $T_{\rm RH} \leq 4.2 \times 10^{13}~{\rm GeV}$. We further present constraints on inflaton couplings to massive fermions and scalars, and analyze constraints on couplings in the Jordan frame.

We study the hydrodynamic theories with approximate symmetries in the recently developed effective action approach on the Schwinger-Keldysh (SK) contour. We employ the method of spurious symmetry transformation for small explicit symmetry-breaking parameters to systematically constrain symmetry-breaking effects in the non-equilibrium effective action for hydrodynamics. We apply our method to the hydrodynamic theory of chiral symmetry in Quantum Chromodynamics (QCD) at finite temperature and density and its explicit breaking by quark masses. We show that the spurious symmetry and the Kubo-Martin-Schwinger (KMS) relation dictate that the Ward-Takahashi identity for the axial symmetry, i.e., the partial conservation of axial vector current (PCAC) relation, contains a relaxational term proportional to the axial chemical potential, whose kinetic coefficient is at least of the second order in the quark mass. In the phase where the chiral symmetry is spontaneously broken, and the pseudo-Nambu-Goldstone pions appear as hydrodynamic variables, this relaxation effect is subleading compared to the conventional pion mass term in the PCAC relation, which is of the first order in the quark mass. On the other hand, in the chiral symmetry-restored phase, we show that our relaxation term, which is of the second order in the quark mass, becomes the leading contribution to the axial charge relaxation. Therefore, the leading axial charge relaxation mechanism is parametrically different in the quark mass across a chiral phase transition.

GREX-PLUS (Galaxy Reionization EXplorer and PLanetary Universe Spectrometer) is a mission candidate for a JAXA's strategic L-class mission to be launched in the 2030s. Its primary sciences are two-fold: galaxy formation and evolution and planetary system formation and evolution. The GREX-PLUS spacecraft will carry a 1.2 m primary mirror aperture telescope cooled down to 50 K. The two science instruments will be onboard: a wide-field camera in the 2-8 $\mu$m wavelength band and a high resolution spectrometer with a wavelength resolution of 30,000 in the 10-18 $\mu$m band. The GREX-PLUS wide-field camera aims to detect the first generation of galaxies at redshift $z>15$. The GREX-PLUS high resolution spectrometer aims to identify the location of the water ``snow line'' in proto-planetary disks. Both instruments will provide unique data sets for a broad range of scientific topics including galaxy mass assembly, origin of supermassive blackholes, infrared background radiation, molecular spectroscopy in the interstellar medium, transit spectroscopy for exoplanet atmosphere, planetary atmosphere in the Solar system, and so on.

We revisit a supersymmetric flavor model based on the symmetries $SU(2)_L \times A_4 \times Z_3 \times U(1)_R$, which extends the original Altarelli and Feruglio construction by introducing flavon and driving superfields responsible for the spontaneous breaking of the flavor symmetry in order to obtain non-zero reactor angle. The vacuum alignments of flavon fields are achieved through the minimization of the scalar potential derived from the superpotential. This setup leads to specific mass matrices for the charged leptons and neutrinos that are consistent with current experimental data, including the measured values of the lepton mixing angles and neutrino mass squared differences. We investigate whether the model can simultaneously accommodate successful thermal leptogenesis. In particular, we analyze the CP asymmetry generated in the decay of heavy Majorana neutrinos, the resulting lepton asymmetry, and its conversion to the baryon asymmetry through the electroweak sphalerons. However the CP asymmetry is zero, since the Dirac neutrino mass matrix is simple texture in the leading order for our model. Then we consider the next-to-leading order in Yukawa interactions of the Dirac neutrinos. Therefore, we can realize the baryon asymmetry of the universe at the present universe. By numerically scanning the parameter space, we identify the regions consistent with both neutrino oscillation data and the observed baryon asymmetry. In the specific case such that one of the couplings for the right-handed Majorana neutrinos is real parameter, the predicted lightest neutrino mass is at least $5$ meV and $15$ meV for the normal and inverted neutrino mass hierarchies, respectively. In addition, the range of the Majorana phases may be tested in future experiments.

We consider quantum electrodynamics with chiral four-Fermi interactions in the functional renormalization group approach. In gauge theories, the functional flow equation for the effective action is accompanied by the quantum master equation that governs the underlying gauge symmetry. Beyond perturbation theory, fully gauge-consistent solutions are very difficult to obtain. We devise a systematic expansion scheme in which the solutions of the flow equation also solve the Quantum Master Equation. In the present work we apply this construction within the lowest order corrections in the photon two-point functions. In this truncation we discuss the phase structure in terms of the gauge and four-Fermi couplings based on a numerical solution of the system.

This study investigated the relation between the surface density of star formation rate (SFR) ($\Sigma_{\mathrm{SFR}}$), stellar mass ($\Sigma_{M_{\ast}}$), and molecular gas mass ($\Sigma_{M_\mathrm{mol}}$) on nearly 1 kpc scales averaged over concentric tilted rings using the $^{12}$CO $J=1-0$ mapping data of 92 nearby galaxies obtained in the CO Multi-line Imaging of Nearby Galaxies (COMING) project. We categorized these galaxies into three groups based on the deviation of each global SFR from the star-forming main sequence (MS), $\Delta$MS: upper MS (UMS), MS, and lower MS (LMS). UMS galaxies tend to be less massive or barred spiral galaxies, exhibiting molecular gas fraction ($f_{\mathrm{gas}}$) comparable to those of MS galaxies but higher star formation efficiency (SFE). In contrast, the LMS galaxies tend to be massive or active galaxies hosting an active galactic nucleus (AGN). Their $f_{\mathrm{gas}}$ values are lower than those of MS galaxies, and their SFEs are slightly lower or comparable to those of MS galaxies in the inner region. These trends indicate that enhanced SFE contributes to higher $\Delta$MS values, whereas reduced $f_{\mathrm{gas}}$ results in lower $\Delta$MS values. The less prominent bulge or the presence of a bar structure in UMS galaxies induces disk-wide star formation, consequently increasing the SFE. In LMS galaxies, the molecular gas is exhausted, and their star formation activity is low. Environmental effects, such as tidal gas stripping, may also reduce gas supply from the outer regions. Furthermore, our sample galaxies show that both the specific star formation rate (sSFR) and $f_{\mathrm{gas}}$ decrease in the central region in LMS galaxies but did not change in the same region in UMS galaxies. These results seem to support the inside-out quenching of star formation although the dominant cause of depletion remains uncertain.

In the application of brain-computer interface (BCI), being able to accurately decode brain signals is a critical task. For the multi-class classification task of brain signal ECoG, how to improve the classification accuracy is one of the current research hotspots. ECoG acquisition uses a high-density electrode array and a high sampling frequency, which makes ECoG data have a certain high similarity and data redundancy in the temporal domain, and also unique spatial pattern in spatial domain. How to effectively extract features is both exciting and challenging. Previous work found that visual-related ECoG can carry visual information via frequency and spatial domain. Based on this finding, we focused on using deep learning to design frequency and spatial feature extraction modules, and proposed a Bi-Band ECoGNet model based on deep learning. The main contributions of this paper are: 1) The Bi-BCWT (Bi-Band Channel-Wise Transform) neural network module is designed to replace the time-consume method MST, this module greatly improves the model calculation and data storage efficiency, and effectively increases the training speed; 2) The Bi-BCWT module can effectively take into account the information both in low-frequency and high-frequency domain, which is more conducive to ECoG multi-classification tasks; 3) ECoG is acquired using 2D electrode array, the newly designed 2D Spatial-Temporal feature encoder can extract the 2D spatial feature better. Experiments have shown that the unique 2D spatial data structure can effectively improve classification accuracy; 3) Compared with previous work, the Bi-Band ECoGNet model is smaller and has higher performance, with an accuracy increase of 1.24%, and the model training speed is increased by 6 times, which is more suitable for BCI applications.

We introduce the task of human action anomaly detection (HAAD), which aims to identify anomalous motions in an unsupervised manner given only the pre-determined normal category of training action samples. Compared to prior human-related anomaly detection tasks which primarily focus on unusual events from videos, HAAD involves the learning of specific action labels to recognize semantically anomalous human behaviors. To address this task, we propose a normalizing flow (NF)-based detection framework where the sample likelihood is effectively leveraged to indicate anomalies. As action anomalies often occur in some specific body parts, in addition to the full-body action feature learning, we incorporate extra encoding streams into our framework for a finer modeling of body subsets. Our framework is thus multi-level to jointly discover global and local motion anomalies. Furthermore, to show awareness of the potentially jittery data during recording, we resort to discrete cosine transformation by converting the action samples from the temporal to the frequency domain to mitigate the issue of data instability. Extensive experimental results on two human action datasets demonstrate that our method outperforms the baselines formed by adapting state-of-the-art human activity AD approaches to our task of HAAD.

Simulators of animal movements play a valuable role in studying behavior. Advances in imitation learning for robotics have expanded possibilities for reproducing human and animal movements. A key challenge for realistic multi-animal simulation in biology is bridging the gap between unknown real-world transition models and their simulated counterparts. Because locomotion dynamics are seldom known, relying solely on mathematical models is insufficient; constructing a simulator that both reproduces real trajectories and supports reward-driven optimization remains an open problem. We introduce a data-driven simulator for multi-animal behavior based on deep reinforcement learning and counterfactual simulation. We address the ill-posed nature of the problem caused by high degrees of freedom in locomotion by estimating movement variables of an incomplete transition model as actions within an RL framework. We also employ a distance-based pseudo-reward to align and compare states between cyber and physical spaces. Validated on artificial agents, flies, newts, and silkmoth, our approach achieves higher reproducibility of species-specific behaviors and improved reward acquisition compared with standard imitation and RL methods. Moreover, it enables counterfactual behavior prediction in novel experimental settings and supports multi-individual modeling for flexible what-if trajectory generation, suggesting its potential to simulate and elucidate complex multi-animal behaviors.

High-resolution 3D point clouds are highly effective for detecting subtle structural anomalies in industrial inspection. However, their dense and irregular nature imposes significant challenges, including high computational cost, sensitivity to spatial misalignment, and difficulty in capturing localized structural differences. This paper introduces a registration-based anomaly detection framework that combines multi-prototype alignment with cluster-wise discrepancy analysis to enable precise 3D anomaly localization. Specifically, each test sample is first registered to multiple normal prototypes to enable direct structural comparison. To evaluate anomalies at a local level, clustering is performed over the point cloud, and similarity is computed between features from the test sample and the prototypes within each cluster. Rather than selecting cluster centroids randomly, a keypoint-guided strategy is employed, where geometrically informative points are chosen as centroids. This ensures that clusters are centered on feature-rich regions, enabling more meaningful and stable distance-based comparisons. Extensive experiments on the Real3D-AD benchmark demonstrate that the proposed method achieves state-of-the-art performance in both object-level and point-level anomaly detection, even using only raw features.

Heavy-flavour physics is an essential component of the particle-physics programme, offering critical tests of the Standard Model and far-reaching sensitivity to physics beyond it. Experiments such as LHCb, Belle II, and BESIII drive progress in the field, along with contributions from ATLAS and CMS. The LHCb Upgrade II and upgraded Belle II experiments will provide unique and highly sensitive measurements for decades, playing a key role in the searches for new physics. Future facilities with significant heavy-flavour capabilities will further expand these opportunities. We advocate for a European Strategy that fully supports Upgrade II of LHCb and an upgrade of Belle II, along with their subsequent exploitation. Additionally, we support a long-term plan that fully integrates flavour physics in an $e^+e^-$ collider to run as a $Z$ factory.

In spectral theory, the multiplicity of nearly degenerate eigenvalues presents significant challenges. This paper introduces a new difference quotient formula to capture the behavior of nearly degenerate Laplacian eigenvalues resulting from domain perturbations. Additionally, we propose a novel numerical algorithm for rigorously estimating the difference quotient of these multiple eigenvalues in response to domain deformation, using a recently developed guaranteed computation method for eigenvalue problems. As an application, we solve the open problem of the simplicity of the second Dirichlet eigenvalue for nearly equilateral triangles, offering a partial solution to Conjecture 6.47 in A. Henrot's book ``Shape Optimization and Spectral Theory."