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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. To cope with the large amount of calculations, we make use of the $\beta$ expansion and differential equation for master integrals and systematize the procedure., Comment: 5 pages, No figures

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.

Reducing serving cost and latency is a fundamental concern for the deployment of language models (LMs) in business applications. To address this, cascades of LMs offer an effective solution that conditionally employ smaller models for simpler queries. Cascaded systems are typically built with independently trained models, neglecting the advantages of considering inference-time interactions of the cascaded LMs during training. In this paper, we present cascade-aware training(CAT), an approach to optimizing the overall quality-cost performance tradeoff of a cascade of LMs. We achieve inference-time benefits by training the small LM with awareness of its place in a cascade and downstream capabilities. We demonstrate the value of the proposed method with over 60 LM tasks of the SuperGLUE, WMT22, and FLAN2021 datasets., Comment: 22 pages, 13 figures

Towards performing a realistic autonomous minimally invasive spinal fixation procedure, in this paper, we introduce a unique robotic drilling system utilizing a concentric tube steerable drilling robot (CT-SDR) integrated with a seven degree-of-freedom robotic manipulator. The CT-SDR in integration with the robotic arm enables creating precise J-shape trajectories enabling access to the areas within the vertebral body that currently are not accessible utilizing existing rigid instruments. To ensure safety and accuracy of the autonomous drilling procedure, we also performed required calibration procedures. The performance of the proposed robotic system and the calibration steps were thoroughly evaluated by performing various drilling experiments on simulated Sawbone samples., Comment: 6 pages, 3 figures, Accepted for publication at the 2024 International Symposium on Medical Robotics

We present Diffusion Model Patching (DMP), a simple method to boost the performance of pre-trained diffusion models that have already reached convergence, with a negligible increase in parameters. DMP inserts a small, learnable set of prompts into the model's input space while keeping the original model frozen. The effectiveness of DMP is not merely due to the addition of parameters but stems from its dynamic gating mechanism, which selects and combines a subset of learnable prompts at every step of the generative process (e.g., reverse denoising steps). This strategy, which we term "mixture-of-prompts", enables the model to draw on the distinct expertise of each prompt, essentially "patching" the model's functionality at every step with minimal yet specialized parameters. Uniquely, DMP enhances the model by further training on the same dataset on which it was originally trained, even in a scenario where significant improvements are typically not expected due to model convergence. Experiments show that DMP significantly enhances the converged FID of DiT-L/2 on FFHQ 256x256 by 10.38%, achieved with only a 1.43% parameter increase and 50K additional training iterations., Comment: Project page: https://sangminwoo.github.io/DMP/

In this paper, with the goal of enhancing the minimally invasive spinal fixation procedure in osteoporotic patients, we propose a first-of-its-kind image-guided robotic framework for performing an autonomous and patient-specific procedure using a unique concentric tube steerable drilling robot (CT-SDR). Particularly, leveraging a CT-SDR, we introduce the concept of J-shape drilling based on a pre-operative trajectory planned in CT scan of a patient followed by appropriate calibration, registration, and navigation steps to safely execute this trajectory in real-time using our unique robotic setup. To thoroughly evaluate the performance of our framework, we performed several experiments on two different vertebral phantoms designed based on CT scan of real patients., Comment: 10 pages, 3 figures. This paper has been accepted for publication at the 2024 International Conference on Medical Image Computing and Computer Assisted Interventions

Spinal fixation procedures are currently limited by the rigidity of the existing instruments and pedicle screws leading to fixation failures and rigid pedicle screw pull out. Leveraging our recently developed Concentric Tube Steerable Drilling Robot (CT-SDR) in integration with a robotic manipulator, to address the aforementioned issue, here we introduce the transformative concept of Spatial Spinal Fixation (SSF) using a unique Flexible Pedicle Screw (FPS). The proposed SSF procedure enables planar and out-of-plane placement of the FPS throughout the full volume of the vertebral body. In other words, not only does our fixation system provide the option of drilling in-plane and out-of-plane trajectories, it also enables implanting the FPS inside linear (represented by an I-shape) and/or non-linear (represented by J-shape) trajectories. To thoroughly evaluate the functionality of our proposed robotic system and the SSF procedure, we have performed various experiments by drilling different I-J and J-J drilling trajectory pairs into our custom-designed L3 vertebral phantoms and analyzed the accuracy of the procedure using various metrics., Comment: 6 pages, 4 figures. This paper has been accepted for publication at the 2024 International Conference on Intelligent Robots and Systems

Large language models (LLMs) have been effectively used for many computer vision tasks, including image classification. In this paper, we present a simple yet effective approach for zero-shot image classification using multimodal LLMs. By employing multimodal LLMs, we generate comprehensive textual representations from input images. These textual representations are then utilized to generate fixed-dimensional features in a cross-modal embedding space. Subsequently, these features are fused together to perform zero-shot classification using a linear classifier. Our method does not require prompt engineering for each dataset; instead, we use a single, straightforward, set of prompts across all datasets. We evaluated our method on several datasets, and our results demonstrate its remarkable effectiveness, surpassing benchmark accuracy on multiple datasets. On average, our method achieved an accuracy gain of 4.1 percentage points, with an increase of 6.8 percentage points on the ImageNet dataset, compared to prior methods. Our findings highlight the potential of multimodal LLMs to enhance computer vision tasks such as zero-shot image classification, offering a significant improvement over traditional methods.

The manipulation of magnetization via Magnetic torques is one of the most important phenomena in spintronics. In thin films, conventionally, a charge current flowing in a heavy metal is used to generate transverse spin currents and to exert torques on the magnetization of an adjacent ferromagnetic thin film layer. Here, in contrast to the typically employed heavy metals, we study spin-to-charge conversion in ferromagnetic heterostructures with large spin-orbit interaction that function as the torque-generating layers. In particular, we chose perpendicular magnetic anisotropy (PMA) multilayers [Co/Ni] and [Co/Pt] as the torque-generating layers and drive magnetization dynamics in metallic ferromagnetic thin film $\mathrm{Co_{20}Fe_{60}B_{20}}$ (CoFeB) layers with in-plane magnetic anisotropy (IMA). We investigate the spin dynamics driven by spin-orbit torque (SOT) and the concomitant charge current generation by the inverse SOT process using an inductive technique based on a vector network analyzer. In our experimental findings, we find that the SOTs generated by our multilayers are of a magnitude comparable to those produced by Pt, consistent with first-principles calculations. Furthermore, we noted a significant correlation between the SOT and the thickness of the CoFeB layer.

Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations. However, they have several drawbacks, including training instability and inability to cover the entire data distribution, especially for regions where data are rare. This is particularly challenging for whole-event, full-detector simulations in high-energy heavy-ion experiments, such as sPHENIX at the Relativistic Heavy Ion Collider and Large Hadron Collider experiments, where thousands of particles are produced per event and interact with the detector. This work investigates the effectiveness of Denoising Diffusion Probabilistic Models (DDPMs) as an AI-based generative surrogate model for the sPHENIX experiment that includes the heavy-ion event generation and response of the entire calorimeter stack. DDPM performance in sPHENIX simulation data is compared with a popular rival, GANs. Results show that both DDPMs and GANs can reproduce the data distribution where the examples are abundant (low-to-medium calorimeter energies). Nonetheless, DDPMs significantly outperform GANs, especially in high-energy regions where data are rare. Additionally, DDPMs exhibit superior stability compared to GANs. The results are consistent between both central and peripheral centrality heavy-ion collision events. Moreover, DDPMs offer a substantial speedup of approximately a factor of 100 compared to the traditional Geant4 simulation method., Comment: 11 pages, 7 figures

Imaging polarimetry plays an essential role in various fields since it imparts rich information that cannot be obtained through mere intensity and spectral measurements. To retrieve full Stokes parameters, at least four sensor pixels are required, each of which projects incident light to a different polarization state in the Stokes space. Conventional full-Stokes division-of-focal-plane (DoFP) cameras realize this function by integrating angled polarizers and retarders on top of image sensors. Due to the inevitable absorption at the polarizers, however, the maximum efficiency of these schemes is limited to 50% in theory. Instead of polarizers, three sets of lossless polarization beam splitters can be used to achieve higher-efficiency polarimetry, however, at the cost of reduced spatial resolution due to the need for six redundant sensor pixels. In this paper, we reveal, for the first time to our knowledge, that low-loss four-output polarization splitting (without filtering) is possible using a single-layer dielectric metasurface. Although these four states are not orthogonal to each other, our metasurface enables simultaneous sorting and focusing onto four sensor pixels with an efficiency exceeding 50\%, which is not feasible by a simple combination of space-optic components. The designed metasurface composed of silicon nanoposts is fabricated to experimentally demonstrate complete retrieval of full Stokes parameters at the near-infrared wavelength range from 1500 to 1600 nm with $-$2.28-dB efficiency. Finally, simple imaging polarimetry is demonstrated using a 3$\times$4 superpixel array.

XRPIX is the monolithic X-ray SOI (silicon-on-insulator) pixel detector, which has a time resolution better than 10 $\rm{\mu}$s as well as a high detection efficiency for X-rays above 10 keV. XRPIX is planned to be installed on future X-ray satellites. To mount on satellites, it is essential that the ADC (analog-to-digital converter) be implemented on the detector because such peripheral circuits must be as compact as possible to achieve a large imaging area in the limited space in satellites. Thus, we developed a new XRPIX device with the on-chip ADC, and evaluated its performances. As the results, the integral non-linearity was evaluated to be 6 LSB (least significant bit), equivalent to 36 eV. The differential non-linearity was less than 0.7 LSB, and input noise from the on-chip ADC was 5~$\rm{e^{-}}$. Also, we evaluated end-to-end performance including the sensor part as well as the on-chip ADC. As the results, energy resolution at 5.9 keV was 294 $\rm{\pm}$ 4 eV in full-width at half maximum for the best pixel., Comment: 17 pages, 9 figures, NIMA, accepted

Boson sampling, a computational task believed to be classically hard to simulate, is expected to hold promise for demonstrating quantum computational advantage using near-term quantum devices. However, noise in experimental implementations poses a significant challenge, potentially rendering boson sampling classically simulable and compromising its classical intractability. Numerous studies have proposed classical algorithms under various noise models that can efficiently simulate boson sampling as noise rates increase with circuit depth. To address this issue particularly related to circuit depth, we explore the viability of achieving quantum computational advantage through boson sampling with shallow-depth linear optical circuits. Specifically, as the average-case hardness of estimating output probabilities of boson sampling is a crucial ingredient in demonstrating its classical intractability, we make progress on establishing the average-case hardness confined to logarithmic-depth regimes. We also obtain the average-case hardness for logarithmic-depth Fock-state boson sampling subject to lossy environments and for the logarithmic-depth Gaussian boson sampling. By providing complexity-theoretical backgrounds for the classical simulation hardness of logarithmic-depth boson sampling, we expect that our findings will mark a crucial step towards a more noise-tolerant demonstration of quantum advantage with shallow-depth boson sampling.

Presently, with the assistance of advanced LLM application development frameworks, more and more LLM-powered applications can effortlessly augment the LLMs' knowledge with external content using the retrieval augmented generation (RAG) technique. However, these frameworks' designs do not have sufficient consideration of the risk of external content, thereby allowing attackers to undermine the applications developed with these frameworks. In this paper, we reveal a new threat to LLM-powered applications, termed retrieval poisoning, where attackers can guide the application to yield malicious responses during the RAG process. Specifically, through the analysis of LLM application frameworks, attackers can craft documents visually indistinguishable from benign ones. Despite the documents providing correct information, once they are used as reference sources for RAG, the application is misled into generating incorrect responses. Our preliminary experiments indicate that attackers can mislead LLMs with an 88.33\% success rate, and achieve a 66.67\% success rate in the real-world application, demonstrating the potential impact of retrieval poisoning.

We continue the study of generalized gauge theory called gauge origami, based on the quantum algebraic approach initiated in [arXiv:2310.08545]. In this article, we in particular explore the D2 brane system realized by the screened vertex operators of the corresponding W-algebra. The partition function of this system given by the corresponding conformal block is identified with the vertex function associated with quasimaps to Nakajima quiver varieties and generalizations, that plays a central role in the quantum $q$-Langlands correspondence. Based on the quantum algebraic perspective, we address three new aspects of the correspondence: (i) Direct equivalence between the electric and magnetic blocks by constructing stable envelopes from the chamber structure of the vertex operators, (ii) Double affine generalization of quantum $q$-Langlands correspondence, and (iii) Conformal block realization of the origami vertex function associated with intersection of quasimaps, that realizes the higher-rank multi-leg Pandharipande-Thomas vertices of 3-fold and 4-fold., Comment: 30 pages

We bring forward a logical system of transition algebras that enhances many-sorted first-order logic using features from dynamic logics. The sentences we consider include compositions, unions, and transitive closures of transition relations, which are treated similarly to the actions used in dynamic logics in order to define necessity and possibility operators. This leads to a higher degree of expressivity than that of many-sorted first-order logic. For example, one can finitely axiomatize both the finiteness and the reachability of models, neither of which are ordinarily possible in many-sorted first-order logic. We introduce syntactic entailment and study basic properties such as compactness and completeness, showing that the latter does not hold when standard finitary proof rules are used. Consequently, we define proof rules having both finite and countably infinite premises, and we provide conditions under which completeness can be proved. To that end, we generalize the forcing method introduced in model theory by Robinson from a single signature to a category of signatures, and we apply it to obtain a completeness result for signatures that are at most countable.