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In this study, we investigate the spin and orbital densities induced by magnetization dynamics in a planar bilayer heterostructure. To do this, we employed a theory of adiabatic pumping using the Keldysh formalism and Wigner expansion. We first conduct simulations on a model system to determine the parameters that control the spin and orbital pumping into an adjacent non-magnetic metal. We conclude that, in principle, the orbital pumping can be as significant as spin pumping when the spin-orbit coupling is present in the ferromagnet. We extend the study to realistic heterostructures involving heavy metals (W, Pt, Au) and light metals (Ti, Cu) by using first-principles calculations. We demonstrate that orbital pumping is favored in metals with $d$ states close to the Fermi level, such as Ti, Pt, and W, but is quenched in materials lacking such states, such as Cu and Au. Orbital injection is also favored in materials with strong spin-orbit coupling, leading to large orbital pumping in Ni/(Pt, W) bilayers.

The necessary time required to control a many-body quantum system is a critically important issue for the future development of quantum technologies. However, it is generally quite difficult to analyze directly, since the time evolution operator acting on a quantum system is in the form of time-ordered exponential. In this work, we examine the Baker-Campbell-Hausdorff (BCH) formula in detail and show that a distance between unitaries can be introduced, allowing us to obtain a lower bound on the control time. We find that, as far as we can compare, this lower bound on control time is tighter (better) than the standard quantum speed limits. This is because this distance takes into account the algebraic structure induced by Hamiltonians through the BCH formula, reflecting the curved nature of operator space. Consequently, we can avoid estimates based on shortcuts through algebraically impossible paths, in contrast to geometric methods that estimate the control time solely by looking at the target state or unitary operator., Comment: 27 pages, 2 figures. In order to make the paper as accessible as possible, we have added a bit lengthy appendices

The integration of Retrieval-Augmented Generation (RAG) with Multimodal Large Language Models (MLLMs) has expanded the scope of multimodal query resolution. However, current systems struggle with intent understanding, information retrieval, and safety filtering, limiting their effectiveness. This paper introduces Contextual Understanding and Enhanced Search with MLLM (CUE-M), a novel multimodal search pipeline that addresses these challenges through a multi-stage framework comprising image context enrichment, intent refinement, contextual query generation, external API integration, and relevance-based filtering. CUE-M incorporates a robust safety framework combining image-based, text-based, and multimodal classifiers, dynamically adapting to instance- and category-specific risks. Evaluations on a multimodal Q&A dataset and a public safety benchmark demonstrate that CUE-M outperforms baselines in accuracy, knowledge integration, and safety, advancing the capabilities of multimodal retrieval systems., Comment: Preprint. Under review

High-energy large-scale particle colliders generate data at extraordinary rates. Developing real-time high-throughput data compression algorithms to reduce data volume and meet the bandwidth requirement for storage has become increasingly critical. Deep learning is a promising technology that can address this challenging topic. At the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider, a Time Projection Chamber (TPC) serves as the main tracking detector, which records three-dimensional particle trajectories in a volume of a gas-filled cylinder. In terms of occupancy, the resulting data flow can be very sparse reaching $10^{-3}$ for proton-proton collisions. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. In contrast, emerging deep learning-based models, particularly those utilizing convolutional neural networks for compression, have outperformed these conventional methods in terms of compression ratios and reconstruction accuracy. However, research on the efficacy of these deep learning models in handling sparse datasets, like those produced in particle colliders, remains limited. Furthermore, most deep learning models do not adapt their processing speeds to data sparsity, which affects efficiency. To address this issue, we propose a novel approach for TPC data compression via key-point identification facilitated by sparse convolution. Our proposed algorithm, BCAE-VS, achieves a $75\%$ improvement in reconstruction accuracy with a $10\%$ increase in compression ratio over the previous state-of-the-art model. Additionally, BCAE-VS manages to achieve these results with a model size over two orders of magnitude smaller. Lastly, we have experimentally verified that as sparsity increases, so does the model's throughput., Comment: 37 pages, 12 figures, submitted to Journal of Computational Physics

In magnetic systems, angular momentum is carried by the spin and orbital degrees of freedom. Non-local devices can be used to study angular momentum transport. They consist of parallel heavy-metal nanowires placed on top of magnetic insulators like yttrium iron garnet (YIG), facilitating the transmission of information by magnons, generated by the accumulation of spin at the interface, created via the Spin Hall Effect (SHE) and detected via the inverse SHE (iSHE). It has been demonstrated that these processes have comparable efficiencies when the role of the detector and injector is reversed, which points to reciprocity of the processes. However, we show that by adding Ru as a source of direct and inverse orbital Hall effect (OHE), the system no longer exhibits this reciprocity. Specifically, the generation of magnons via the combination of SHE and OHE and detection via the iSHE is found to be about 35% more efficient than the inverse process for our system.

This paper explores the problem of 3D human pose estimation from only low-level acoustic signals. The existing active acoustic sensing-based approach for 3D human pose estimation implicitly assumes that the target user is positioned along a line between loudspeakers and a microphone. Because reflection and diffraction of sound by the human body cause subtle acoustic signal changes compared to sound obstruction, the existing model degrades its accuracy significantly when subjects deviate from this line, limiting its practicality in real-world scenarios. To overcome this limitation, we propose a novel method composed of a position discriminator and reverberation-resistant model. The former predicts the standing positions of subjects and applies adversarial learning to extract subject position-invariant features. The latter utilizes acoustic signals before the estimation target time as references to enhance robustness against the variations in sound arrival times due to diffraction and reflection. We construct an acoustic pose estimation dataset that covers diverse human locations and demonstrate through experiments that our proposed method outperforms existing approaches., Comment: Accepted at BMVC2024

We address the orbital distribution of stars in merging nuclear star clusters (NSCs) and the subsequent effects on supermassive black hole binary (SMBHB) evolution. We ran direct-summation $N$-body simulations with different initial conditions to do a detailed study of the resulting NSC after their progenitors had merged. Our findings reveal that prograde stars form a flattened structure, while retrograde stars have a more spherical distribution. The axial ratios of the prograde component vary based on the presence and mass ratio of the SMBHs. The fraction of prograde and retrograde stars depends on the merger orbital properties and the SMBH mass ratio. The interactions of retrograde stars with the SMBHB affect the eccentricity and separation evolution of the binary. Our analysis reveals a strong correlation between the angular momentum and eccentricity of the SMBH binary. This relationship could serve as a means to infer information about the stellar dynamics surrounding the binary. We find that prograde orbits are particularly close to the binary of SMBHs, a promising fact regarding extreme mass ratio inspiral (EMRI) production. Moreover, prograde and retrograde stars have different kinematic structures, with the prograde stars typically rotating faster than the retrograde ones. The line-of-sight velocity and velocity dispersion, as well as the velocity anisotropy of each NSC, depend on the initial merger orbital properties and SMBH mass ratios. The prograde and retrograde stars always show different behaviours. The distribution of stellar orbits and the dynamical properties of each kinematic population can potentially be used as a way to tell the properties of the parent nuclei apart, and has an important impact on expected rates of EMRIs, which will be detected by future gravitational wave observatories such as the Laser Interferometer Space Antenna (LISA). [abridged], Comment: 14 pages, 8 figures, 2 tables. Two appendices on zenodo (https://zenodo.org/records/13986686). Accepted for publication in A&A

Cooperatively Localizing robots should seek optimal control strategies to maximize precision of position estimation and ensure safety in flight. Observability-Aware Trajectory Optimization has strong potential to address this issue, but no concrete link between observability and precision has been proven yet. In this paper, we prove that improvement in positioning precision inherently follows from optimizing observability. Based on this finding, we develop an Observability-Aware Control principle to generate observability-optimal control strategies. We implement this principle in a Model Predictive Control framework, and we verify it on a team of quadrotor Unmanned Aerial Vehicles comprising a follower vehicle localizing itself by tracking a leader vehicle in both simulations and real-world flight tests. Our results demonstrate that maximizing observability contributed to improving global positioning precision for the quadrotor team., Comment: 12 pages, 5 figures

Convergence of a matrix decomposition technique, the multi-field singular value decomposition (MFSVD) which efficiently analyzes nonlinear correlations by simultaneously decomposing multiple fields, is investigated. Toward applications in turbulence studies, we demonstrate that SVD for an artificial matrix with multi-scale structures reproduces the power-law-like distribution in the singular value spectrum with several orthogonal modes. Then, MFSVD is applied to practical turbulence field data produced by numerical simulations. It is clarified that relative errors in the reproduction of quadratic nonlinear quantities in multi-field turbulence converge remarkably faster than the single-field case, which requires thousands of modes to converge., Comment: 11 pages, 4 figures

The scalar spin chirality, which characterizes the fundamental unit of noncoplanar spin structures, plays an important role in rich chiral physics of magnetic materials. In particular, the intensive research efforts over the past two decades have demonstrated that the scalar spin chirality is the source of various novel Hall transports in solid-state systems, offering a primary route to bring about chiral phenomena in condensed matter physics. However, in all of the previous studies, the scalar spin chirality has been given as a stationary background, serving only a passive role in the transport properties of materials. It remains an open question whether or not the scalar spin chirality itself can exhibit a Hall-type transport. In this work, we show that the answer is yes: The scalar spin chirality is Hall-transported in Kagome ferromagnets and antiferromagnets under an external bias, engendering a phenomenon which we dub the scalar spin chirality Hall effect. Notably, this effect is present even in the absence of any spin-orbit coupling. The analytical theory for the scalar spin chirality Hall effect is corroborated by atomistic spin simulations. Our findings call for the need to lift the conventional assumption that the scalar spin chirality is a passive background in order to discover the active roles of the scalar spin chirality in transport properties., Comment: 9 pages, 4 figures

Adam is one of the most popular optimization algorithms in deep learning. However, it is known that Adam does not converge in theory unless choosing a hyperparameter, i.e., $\beta_2$, in a problem-dependent manner. There have been many attempts to fix the non-convergence (e.g., AMSGrad), but they require an impractical assumption that the gradient noise is uniformly bounded. In this paper, we propose a new adaptive gradient method named ADOPT, which achieves the optimal convergence rate of $\mathcal{O} ( 1 / \sqrt{T} )$ with any choice of $\beta_2$ without depending on the bounded noise assumption. ADOPT addresses the non-convergence issue of Adam by removing the current gradient from the second moment estimate and changing the order of the momentum update and the normalization by the second moment estimate. We also conduct intensive numerical experiments, and verify that our ADOPT achieves superior results compared to Adam and its variants across a wide range of tasks, including image classification, generative modeling, natural language processing, and deep reinforcement learning. The implementation is available at https://github.com/iShohei220/adopt., Comment: Accepted at Neural Information Processing Systems (NeurIPS 2024)

We further develop the BPS/CFT correspondence between quiver W-algebras/$qq$-characters and partition functions of gauge origami. We introduce $qq$-characters associated with multi-dimensional partitions with nontrivial boundary conditions which we call Donaldson--Thomas (DT) $qq$-characters. They are operator versions of the equivariant DT vertices of toric Calabi--Yau three and four-folds. Moreover, we revisit the construction of the D8 $qq$-characters with no boundary conditions and give a quantum algebraic derivation of the sign rules of the magnificent four partition function. We also show that under the proper sign rules, the D6 and D8 $qq$-characters with no boundary conditions all commute with each other and discuss its physical interpretation., Comment: 66+13 pages

This study examines the educational effect of the Academic Support Center at Kogakuin University. Following the initial assessment, it was suggested that group bias had led to an underestimation of the Center's true impact. To address this issue, the authors applied the theory of causal inference. By using T-learner, the conditional average treatment effect (CATE) of the Center's face-to-face (F2F) personal assistance program was evaluated. Extending T-learner, the authors produced a new CATE function that depends on the number of treatments (F2F sessions) and used the estimated function to predict the CATE performance of F2F assistance., Comment: 12 pages, 7 figures

Large-scale pre-trained models (PTMs) provide remarkable zero-shot classification capability covering a wide variety of object classes. However, practical applications do not always require the classification of all kinds of objects, and leaving the model capable of recognizing unnecessary classes not only degrades overall accuracy but also leads to operational disadvantages. To mitigate this issue, we explore the selective forgetting problem for PTMs, where the task is to make the model unable to recognize only the specified classes while maintaining accuracy for the rest. All the existing methods assume "white-box" settings, where model information such as architectures, parameters, and gradients is available for training. However, PTMs are often "black-box," where information on such models is unavailable for commercial reasons or social responsibilities. In this paper, we address a novel problem of selective forgetting for black-box models, named Black-Box Forgetting, and propose an approach to the problem. Given that information on the model is unavailable, we optimize the input prompt to decrease the accuracy of specified classes through derivative-free optimization. To avoid difficult high-dimensional optimization while ensuring high forgetting performance, we propose Latent Context Sharing, which introduces common low-dimensional latent components among multiple tokens for the prompt. Experiments on four standard benchmark datasets demonstrate the superiority of our method with reasonable baselines. The code is available at https://github.com/yusukekwn/Black-Box-Forgetting., Comment: NeurIPS 2024

Chirality is ubiquitous in nature and manifests in a wide range of phenomena including chemical reactions, biological processes, and quantum transport of electrons. In quantum materials, the chirality of fermions, given by the relative directions between the electron spin and momentum, is connected to the band topology of electronic states. Here, we show that in structurally chiral materials like CoSi, the orbital angular momentum (OAM) serves as the main driver of a nontrivial band topology in this new class of unconventional topological semimetals, even when spin-orbit coupling is negligible. A nontrivial orbital-momentum locking of multifold chiral fermions in the bulk leads to a pronounced OAM texture of the helicoid Fermi arcs at the surface. Our findings highlight the pivotal role of the orbital degree of freedom for the chirality and topology of electron states, in general, and pave the way towards the application of topological chiral semimetals in orbitronic devices.

Altermagnets are magnetic materials with antiferromagnetic spin ordering but exhibit ferromagnetic properties. Understanding the microscopic origin of the latter is a central problem. Ferromagnet-like properties such as the anomalous Hall effect are linked with weak ferromagnetism, whose microscopic origin in altermagnets remains unclear however. We show theoretically that the alternating $g$-tensor anisotropy in altermagnets can induce weak ferromagnetism even when the Dzyaloshinskii-Moriya interaction is forbidden. We demonstrate this mechanism for both collinear and noncollinear spin altermagnets and explain various properties of the weak ferromagnetism in altermagnets. Our findings provide insights for characterizing and manipulating magnetic configurations in altermagnets.

A measure of asymmetry is a quantification method that allows for the comparison of categorical evaluations before and after treatment effects or among different target populations, irrespective of sample size. We focus on square contingency tables that summarize survey results between two time points or cohorts, represented by the same categorical variables. We propose a measure to evaluate the degree of departure from a symmetry model using cosine similarity. This proposal is based on the Fisher-Rao distance, allowing asymmetry to be interpreted as a geodesic distance between two distributions. Various measures of asymmetry have been proposed, but visualizing the relationship of these quantification methods on a two-dimensional plane demonstrates that the proposed measure provides the geometrically simplest and most natural quantification. Moreover, the visualized figure indicates that the proposed method for measuring departures from symmetry is less affected by very few cells with extreme asymmetry. A simulation study shows that for square contingency tables with an underlying asymmetry model, our method can directly extract and quantify only the asymmetric structure of the model, and can more sensitively detect departures from symmetry than divergence-type measures.

Large Language Models (LLMs) have demonstrated exceptional capabilities in understanding and generating natural language. However, their high deployment costs often pose a barrier to practical applications, especially. Cascading local and server models offers a promising solution to this challenge. While existing studies on LLM cascades have primarily focused on the performance-cost trade-off, real-world scenarios often involve more complex requirements. This paper introduces a novel LLM Cascade strategy with Multi-Objective Optimization, enabling LLM cascades to consider additional objectives (e.g., privacy) and better align with the specific demands of real-world applications while maintaining their original cascading abilities. Extensive experiments on three benchmarks validate the effectiveness and superiority of our approach.

Molecular dynamics (MD) simulations were conducted using the generalized replica exchange method (gREM) on the 4-cyano-4$^{\prime}$-$n$-alkylbiphenyl ($n$CB) system with $n=5$, 6, 7, and 8, which exhibits a nematic-isotropic (NI) phase transition. Sampling near the phase transition temperature in systems undergoing first-order phase transitions, such as the NI phase transition, is demanding due to the substantial energy gap between the two phases. To address this, gREM, specifically designed for first-order phase transitions, was utilized to enhance sampling near the NI phase transition temperature. Free-energy calculations based on the energy representation (ER) theory were employed to characterize the NI phase transition. ER evaluates the insertion free energy of $n$CB molecule for both nematic and isotropic phases, revealing a change in the temperature dependence across the NI phase transition. Further decomposition into intermolecular interaction energetic and entropic terms shows quantitatively the balance between these contributions at the NI phase transition temperature., Comment: 9 pages, 5 figures for main text, 5 pages for Supplementary material

The thermal Hall effect has emerged as an ideal probe for investigating topological phenomena of charge-neutral excitation. Notably, it reveals crucial aspects of spin-lattice couplings that have been difficult to access for decades. However, the exchange striction mechanism from a lattice-induced change in exchange interaction has often been ignored in thermal Hall experiments. MnPS3 can offer a platform to study exchange striction on the thermal Hall effect due to its significant spin-lattice coupling and field-induced non-collinear spin configuration. Our thermal transport data show distinct temperature and field dependence of longitudinal thermal conductivity ($\kappa_{xx}$) and thermal Hall effect ($\kappa_{xy}$). By using detailed theoretical calculation, we found that the inclusion of the exchange striction is essential for a better description of both $\kappa_{xx}$ and $\kappa_{xy}$. Our result demonstrates the importance of the exchange striction mechanism for a complete understanding of the magnon-phonon-driven thermal Hall effect., Comment: 24 pages, 6 figures, accepted for publication in Phys. Rev. B

Recent discovery of orbital currents in several material platforms including light element metals has opened new possibilities for exploring novel transport phenomena and applications to spin-orbitronic devices. These orbital currents, similar to spin currents, have the ability to generate torque on adjacent magnetic layers, opening a new avenue for efficient spintronic devices. However, separating spin and orbital currents has been one of the major challenges. Here, we show evidence for large conventional as well as unconventional spin and orbital currents in IrO$_2$ and disentangle them by crystal symmetry. We study the anisotropic spin and orbital Hall effects in IrO$_2$ (001), (100), and (111) orientations and find unconventional z-polarized orbital torques using angular spin torque ferromagnetic resonance of IrO$_2$/Ni heterostructures, which are in agreement regarding the relative signs with theoretical calculations of spin and orbital Hall conductivity. This work provides a promising route towards highly efficient low power spintronic and orbitronic devices in oxide heterostructures.

Circumstellar material (CSM) produced by mass loss from massive stars ($\gtrsim10 M_{\odot}$) through strong stellar winds or binary stripping provides rich information for understanding progenitors of core-collapse supernova remnants. In this paper we present a grating spectroscopy of a Galactic SNR G292.0+1.8, which is claimed to be a Type Ib/c remnant in a binary system according to recent studies. If G292.0+1.8 was experienced a strong mass-loss via binary interactions before its explosion, an oxygen-rich material produced in the He-burning layer is expected to be observed in the central belt-like structure formed by shock-heated CSM. Using the Reflection Grating Spectrometer onboard XMM-Newton, we detect N VII Ly$\alpha$ line (0.50 keV) for the first time in G292.0+1.8 and find that the abundance ratio of nitrogen to oxygen is significantly lower (N/O$=0.5\pm0.1$) than the solar value. This low N/O suggests that the progenitor of experienced strong mass-loss and ended up to a Wolf-Rayet (WR) star exposing the He-burning layer at the pre-supernova. Comparing our result and the evolution models of single stars and binaries, we conclude that the progenitor of G292.0+1.8 experienced strong mass-loss enough to occur a Type Ib/c supernova. Our finding is another crucial piece of evidence for a stripped-envelope supernova such as Type Ib/c as the origin of G292.0+1.8., Comment: Accepted for publication in ApJ, 9 pages, 4 figures

We develop a new computational framework to solve sequential Bayesian optimal experimental design (SBOED) problems constrained by large-scale partial differential equations with infinite-dimensional random parameters. We propose an adaptive terminal formulation of the optimality criteria for SBOED to achieve adaptive global optimality. We also establish an equivalent optimization formulation to achieve computational simplicity enabled by Laplace and low-rank approximations of the posterior. To accelerate the solution of the SBOED problem, we develop a derivative-informed latent attention neural operator (LANO), a new neural network surrogate model that leverages (1) derivative-informed dimension reduction for latent encoding, (2) an attention mechanism to capture the dynamics in the latent space, (3) an efficient training in the latent space augmented by projected Jacobian, which collectively leads to an efficient, accurate, and scalable surrogate in computing not only the parameter-to-observable (PtO) maps but also their Jacobians. We further develop the formulation for the computation of the MAP points, the eigenpairs, and the sampling from posterior by LANO in the reduced spaces and use these computations to solve the SBOED problem. We demonstrate the superior accuracy of LANO compared to two other neural architectures and the high accuracy of LANO compared to the finite element method (FEM) for the computation of MAP points and eigenvalues in solving the SBOED problem with application to the experimental design of the time to take MRI images in monitoring tumor growth. We show that the proposed computational framework achieves an amortized $180\times$ speedup.

Measuring 3D geometric structures of indoor scenes requires dedicated depth sensors, which are not always available. Echo-based depth estimation has recently been studied as a promising alternative solution. All previous studies have assumed the use of echoes in the audible range. However, one major problem is that audible echoes cannot be used in quiet spaces or other situations where producing audible sounds is prohibited. In this paper, we consider echo-based depth estimation using inaudible ultrasonic echoes. While ultrasonic waves provide high measurement accuracy in theory, the actual depth estimation accuracy when ultrasonic echoes are used has remained unclear, due to its disadvantage of being sensitive to noise and susceptible to attenuation. We first investigate the depth estimation accuracy when the frequency of the sound source is restricted to the high-frequency band, and found that the accuracy decreased when the frequency was limited to ultrasonic ranges. Based on this observation, we propose a novel deep learning method to improve the accuracy of ultrasonic echo-based depth estimation by using audible echoes as auxiliary data only during training. Experimental results with a public dataset demonstrate that our method improves the estimation accuracy., Comment: ICIP 2024

In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on common training datasets such as DIV2K and DF2K. However, we investigate and rethink the training data from the perspectives of diversity and quality, {thereby addressing the question of ``How important is SR training for SR models?''}. To this end, we propose an automated image evaluation pipeline. With this, we stratify existing high-resolution image datasets and larger-scale image datasets such as ImageNet and PASS to compare their performances. We find that datasets with (i) low compression artifacts, (ii) high within-image diversity as judged by the number of different objects, and (iii) a large number of images from ImageNet or PASS all positively affect SR performance. We hope that the proposed simple-yet-effective dataset curation pipeline will inform the construction of SR datasets in the future and yield overall better models., Comment: Accepted to ECCV2024

A compact energy-recovery linac (cERL) has been un-der construction at KEK since 2009 to develop key technologies for the energy-recovery linac. The cERL began operating in 2013 to create a high-current beam with a low-emittance beam with stable continuous wave (CW) superconducting cavities. Owing to the development of critical components, such as the DC gun, superconducting cavities, and the design of ideal beam transport optics, we have successfully established approximately 1 mA stable CW operation with a small beam emittance and extremely small beam loss. This study presents the details of our key technologies and experimental results for achieving 100% energy recovery operation with extremely small beam loss during a stable, approximately 1 mA CW beam operation., Comment: 11 pages, 16 figures

In this work, we discuss evaluating video foundation models in a fair and robust manner. Unlike language or image foundation models, many video foundation models are evaluated with differing parameters (such as sampling rate, number of frames, pretraining steps, etc.), making fair and robust comparisons challenging. Therefore, we present a carefully designed evaluation framework for measuring two core capabilities of video comprehension: appearance and motion understanding. Our findings reveal that existing video foundation models, whether text-supervised like UMT or InternVideo2, or self-supervised like V-JEPA, exhibit limitations in at least one of these capabilities. As an alternative, we introduce TWLV-I, a new video foundation model that constructs robust visual representations for both motion- and appearance-based videos. Based on the average top-1 accuracy of linear probing on five action recognition benchmarks, pretrained only on publicly accessible datasets, our model shows a 4.6%p improvement compared to V-JEPA (ViT-L) and a 7.7%p improvement compared to UMT (ViT-L). Even when compared to much larger models, our model demonstrates a 7.2%p improvement compared to DFN (ViT-H), a 2.7%p improvement compared to V-JEPA (ViT-H) and a 2.8%p improvement compared to InternVideo2 (ViT-g). We provide embedding vectors obtained by TWLV-I from videos of several commonly used video benchmarks, along with evaluation source code that can directly utilize these embeddings. The code is available at https://github.com/twelvelabs-io/video-embeddings-evaluation-framework., Comment: 17 pages; Twelve Labs Technical Report

Water-window X-rays are crucial in medical and biological applications, enabling natural contrast imaging of biological cells in their near-native states without external staining. However, water-window X-ray sources whose output photon energy can be arbitrarily specified - a crucial feature in many high-contrast imaging applications - are still challenging to obtain except at large synchrotron facilities. Here, we present a solution to this challenge by demonstrating table-top, water-window X-ray generation from free electron-driven van der Waals materials, resulting in output photon energies that can be continuously tuned across the entire water window regime. In addition, we present a truly predictive theoretical framework that combines first-principles electromagnetism with Monte Carlo simulations to accurately predict the photon flux and brightness in absolute numbers. Using this framework, we theoretically obtain fundamental scaling laws for the tunable photon flux, showing good agreement with experimental results and providing a path to the design of powerful emitters based on free electron-driven quantum materials. We show that we can achieve photon fluxes needed for imaging and spectroscopy applications (over 1E8 photons per second on sample) where compactness is important, and the ultrahigh fluxes of synchrotron sources are not needed. Importantly, our theory highlights the critical role played by the large mean free paths and interlayer atomic spacings unique to van der Waals structures, showing the latter's advantages over other materials in generating water window X-rays. Our results should pave the way to advanced techniques and new modalities in water-window X-ray generation and high-resolution biological imaging., Comment: 22 pages, 3 figures

Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine learning systems and has shaped the field of OOD detection. Meanwhile, several other problems are closely related to OOD detection, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD). To unify these problems, a generalized OOD detection framework was proposed, taxonomically categorizing these five problems. However, Vision Language Models (VLMs) such as CLIP have significantly changed the paradigm and blurred the boundaries between these fields, again confusing researchers. In this survey, we first present a generalized OOD detection v2, encapsulating the evolution of AD, ND, OSR, OOD detection, and OD in the VLM era. Our framework reveals that, with some field inactivity and integration, the demanding challenges have become OOD detection and AD. In addition, we also highlight the significant shift in the definition, problem settings, and benchmarks; we thus feature a comprehensive review of the methodology for OOD detection, including the discussion over other related tasks to clarify their relationship to OOD detection. Finally, we explore the advancements in the emerging Large Vision Language Model (LVLM) era, such as GPT-4V. We conclude this survey with open challenges and future directions., Comment: survey paper. We welcome questions, issues, and paper requests via https://github.com/AtsuMiyai/Awesome-OOD-VLM

Employing the TNG100 run of the IllustrisTNG project, we characterize the environment of Low Surface Brightness Galaxies (LSBGs) across varying scales, from their associated dark matter halos to their distribution within the broader cosmic structure. We find no significant differences in the halo concentration index $c_{200}$ between LSBGs and their High Surface Brightness (HSBGs) counterparts, with LSBGs residing in halos with higher spin parameter $\lambda$ and slightly more spherical shapes than HSBGs. LSBGs show a stronger alignment between the dark and stellar angular momentum vectors than their high surface brightness counterparts. The relative abundance of LSBGs within groups and clusters displays a central deficit, hinting at potential destruction upon reaching these core regions. Studying the density field, we find a preference for rotation-dominated LSBGs to reside in low-density environments, while dispersion-dominated LSBGs thrive in high-density regions where galaxy interactions govern their evolution, an observation corroborated by our analysis of the two-point correlation function $\xi (r)$. Our examination of the cosmic web reveals no significant differences in the distance to the closest large-scale structure, barring a few exceptions. This suggests a limited impact of large-scale spatial distribution on mechanisms driving LSBG evolution. All together, we conclude that the halo vicinity and local environment at the scale of galaxy clusters, where mechanisms such as galaxy mergers and tidal stripping, as well as stellar and gas accretion take place, is the most likely environment that favour the emergence of LSBGs with different morphologies, mostly driven by the presence or absence of important local interaction phenomena., Comment: 18 pages, 8 figures, accepted for publication in Monthly Notices of the Royal Astronomical Society

The neutron electric dipole moment (EDM) is a sensitive probe for currently undiscovered sources of charge-parity symmetry violation. As part of the TRIUMF Ultracold Advanced Neutron (TUCAN) collaboration, we are developing spin analyzers for ultracold neutrons (UCNs) to be used for a next-generation experiment to measure the neutron EDM with unprecedented precision. Spin-state analysis of UCNs constitutes an essential part of the neutron EDM measurement sequence. Magnetized iron films used as spin filters of UCNs are crucial experimental components, whose performance directly influences the statistical sensitivity of the measurement. To test such iron film spin filters, we propose the use of polarized cold-neutron reflectometry, in addition to conventional UCN transmission experiments. The new method provides information on iron film samples complementary to the UCN tests and accelerates the development cycles. We developed a collaborative effort to produce iron film spin filters and test them with cold and ultracold neutrons available at JRR-3/MINE2 and J-PARC/MLF BL05. In this article, we review the methods of neutron EDM measurements, discuss the complementarity of this new approach to test UCN spin filters, provide an overview of our related activities, and present the first results of polarized cold-neutron reflectometry recently conducted at the MINE2 beamline.

A novel information entropy of turbulence systems with multiple field quantities is formulated. Inspired by quantum mechanics, the von Neumann entropy (vNE) and the entanglement entropy(EE) are derived from a density matrix for the turbulence state in terms of the multi-field singular value decomposition (MFSVD). Applying the information-theoretic entropy analyses to spatio-temporal dynamics in turbulent plasmas with phase-transition-like behavior, we discover a new nontrivial transition threshold regarding the vNE, which significantly deviates from the transition threshold of the field energy considered in the conventional approaches. These findings provide us with new classifications of the turbulence state in terms of combined energy and information. It is also shown that the EE for nonlinear interactions in turbulence simultaneously describes not only the information for the strength of nonlinear mode couplings but also the direction of net energy transfer., Comment: 13 page, 5 figures

The topological semimetal CrPt$_3$ has potential for generating unconventional spin torques due to its ferrimagnetic ordering, topological band structure, and high anomalous Hall effect. CrPt$_3$ exhibits ferrimagnetic behavior only in its chemically ordered phase and is paramagnetic in its chemically disordered phase. By controlling the growth and annealing temperatures, epitaxial films of both chemically ordered and disordered phases of CrPt$_3$ are prepared allowing us to investigate the role of magnetic ordering on unconventional torque generation. We use angle dependent spin-torque ferromagnetic resonance and second harmonic Hall measurements to probe the spin torques generated from epitaxial CrPt$_3$ in CrPt$_3$/Cu/Ni$_{81}$Fe$_{19}$ heterostructures. With current applied along specific directions with respect to the crystal order we reveal unconventional spin torques in both ordered and disordered films. When current flows parallel to the $[1\overline{1}1]$ and $[\overline{1}11]$ directions we observe an unconventional field-like torque that is opposite in sign for the two directions. Our calculations reveal that this unconventional torque originates from an indirect nonlocal spin-orbit torque due to spin scattering at the CrPt$_3$/Cu interface, in addition to symmetry breaking at this interface.

We calculate the two-loop heavy quarkonium Hamiltonian within potential-NRQCD effective field theory in the non-annihilation channel. This calculation represents the first non-trivial step towards determining the N$^4$LO Hamiltonian in the weak coupling regime. The large amount of computation is systematically handled by employing the $\beta$ expansion, differential equations for master integrals, and adopting a single-step matching procedure, in contrast to the conventional two-step approach., Comment: 5 pages, No figures; 2-loop Hamiltonian before expansion in eps added in Supplementary Material; minor modifications in the text; Version to appear in Phys$.$Rev$.$Lett

The coupled transport of the charge and orbital angular momentum of electrons is at the heart of orbitronics. Here, we discuss the reciprocal relation between the direct and inverse orbital Hall effects (OHEs) in thin films. We argue that the conventional orbital current is ill-defined as it does not satisfy the reciprocal relation owing to non-conservation of the orbital angular momentum. We resolve the problem by adopting the definition of the so-called \emph{proper} orbital current, which is directly related to orbital accumulation. We prove the reciprocal relation between the \emph{global} response of orbital and charge currents. However, we show that their \emph{local} distributions are generally different, especially due to gigantic contributions at surfaces, which may lead to unintuitive results when charge and orbital currents are locally measured. We demonstrate our predictions by first-principles calculations on W(110) and Pt(111) thin films. In W(110), the direct and inverse OHEs are severely non-reciprocal locally in each layer although the total responses are exactly reciprocal. Interestingly, the SHEs are almost reciprocal locally in each layer. On the other hand, in Pt(111), both OHEs and SHEs are locally non-reciprocal, which we attribute to the pronounced spin-orbit interaction. We propose that the locally distinct responses may be used to distinguish the spin and orbital currents in experiments.

As pretrained text-to-image diffusion models have become a useful tool for image synthesis, people want to specify the results in various ways. In this paper, we introduce a method to produce results with the same structure of a target image but painted with colors from a reference image, i.e., appearance transfer, especially following the semantic correspondence between the result and the reference. E.g., the result wing takes color from the reference wing, not the reference head. Existing methods rely on the query-key similarity within self-attention layer, usually producing defective results. To this end, we propose to find semantic correspondences and explicitly rearrange the features according to the semantic correspondences. Extensive experiments show the superiority of our method in various aspects: preserving the structure of the target and reflecting the color from the reference according to the semantic correspondences, even when the two images are not aligned., Comment: project page : https://sooyeon-go.github.io/eye_for_an_eye/

Monitor of All-sky X-ray Image (MAXI) detected a superflare, releasing $5\times 10^{37}$ erg in 2$-$10 keV, of the RS CVn-type star IM Peg at 10:41 UT on 2023 July 23 with its Gas Slit Camera (GSC; 2$-$30 keV). We conducted X-ray follow-up observations of the superflare with Neutron Star Interior Composition ExploreR (NICER; 0.2$-$12 keV) starting at 16:52 UT on July 23 until 06:00 UT on August 2. NICER X-ray spectra clearly showed emission lines of the Fe XXV He$\alpha$ and Fe XXVI Ly$\alpha$ for $\sim 1.5$ days since the MAXI detection. The Fe XXV He$\alpha$ line was blue-shifted with its maximum Doppler velocity reaching $-2200 \pm 600$ $\mathrm{km \: s^{-1}}$, suggesting an upward-moving plasma during the flare, such as a coronal mass ejection (CME) and/or chromospheric evaporation. This is the first case that the Fe XXV He$\alpha$ line is blue-shifted during a stellar flare and its velocity overwhelmingly exceeds the escape velocity of the star ($-230$ $\mathrm{km \: s^{-1}}$). One hour before the most pronounced blueshift detection, a signature of reheating the flare plasma was observed. We discuss the origin of the blueshift, a CME or high-velocity chromospheric evaporation., Comment: 14 pages, 9 figures. Accepted for publication in The Astrophysical Journal Letters

Language models (LMs) encode world knowledge in their internal parameters through training. However, LMs may learn personal and confidential information from the training data, leading to privacy concerns such as data leakage. Therefore, research on knowledge deletion from LMs is essential. This study focuses on the knowledge stored in LMs and analyzes the relationship between the side effects of knowledge deletion and the entities related to the knowledge. Our findings reveal that deleting knowledge related to popular entities can have catastrophic side effects. Furthermore, this research is the first to analyze knowledge deletion in models trained on synthetic knowledge graphs, indicating a new direction for controlled experiments.

The widespread absence of diacritical marks in Arabic text poses a significant challenge for Arabic natural language processing (NLP). This paper explores instances of naturally occurring diacritics, referred to as "diacritics in the wild," to unveil patterns and latent information across six diverse genres: news articles, novels, children's books, poetry, political documents, and ChatGPT outputs. We present a new annotated dataset that maps real-world partially diacritized words to their maximal full diacritization in context. Additionally, we propose extensions to the analyze-and-disambiguate approach in Arabic NLP to leverage these diacritics, resulting in notable improvements. Our contributions encompass a thorough analysis, valuable datasets, and an extended diacritization algorithm. We release our code and datasets as open source., Comment: Accepted to ACL 2024

It is generally hard to put robust constraints on progenitor masses of supernovae (SNe) and remnants (SNRs) observationally, while they offer tantalizing clues to understanding explosion mechanisms and mass distribution. Our recent study suggests that ``shell merger'', which is theoretically expected for stellar evolution, can appreciably affect final yields of inter-mediate mass elements (IMEs; such as Ne, Mg, and Si). In light of this, here we report results of X-ray spectral analysis of a Galactic SNR G359.0-0.9, whose abundance pattern may possibly be anomalous according to a previous study. Our spectroscopy using all the available data taken with XMM-Newton reveals that this remnant is classified as Mg-rich SNRs because of its high Mg-to-Ne ratio (Z_Mg/Z_Ne=1.90+0.27-0.19; mass ratio 0.66+0.09-0.07) and conclude that the result cannot be explained without the shell merger. By comparing the observation with theoretical calculations, we prefer the so-called Ne-burning shell intrusion and in this case the progenitor mass M_ZAMS is likely <15M_sun. We confirm the result also by our new molecular line observations with the NRO-45 m telescope: G359.0-0.9 is located in the Scutum-Centaurus arm (2.66--2.94 kpc) and in this case the resultant total ejecta mass ~6.8M_sun is indeed consistent with the above estimate. Our method using mass ratios of IMEs presented in this paper will become useful to distinguish the type of the shell merger, the Ne-burning shell intrusion and the O-burning shell merger, for future SNR studies.