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High-performance DUV spectroscopy drives advancements in biomedical research, clinical diagnosis, and material science. Existing DUV resonant nanostructures face instability and photoluminescent noise challenges. We propose robust Si metasurfaces leveraging polaritonic resonances, a unique property driven by interband transitions, for enhanced nanophotonic sensing. Our polaritonic Kerker-type void metasurface enables double-resonance Raman scattering to analyze 2D semiconductors, improves biomolecule autofluorescence, and offers superior stability. This scalable platform unlocks versatile applications in interdisciplinary DUV spectroscopy and emerging nanomaterials research., Comment: in press, the DOI will be DOI: 10.1002/adfm.202420439

This Letter reports the precise measurement of reactor antineutrino spectrum and flux based on the full data set of 4.7 million inverse-beta-decay (IBD) candidates collected at Daya Bay near detectors. Expressed in terms of the IBD yield per fission, the antineutrino spectra from all reactor fissile isotopes and the specific $\mathrm{^{235}U}$ and $\mathrm{^{239}Pu}$ isotopes are measured with 1.3$\%$, 3$\%$ and 8$\%$ uncertainties respectively near the 3 MeV spectrum peak in reconstructed energy, reaching the best precision in the world. The total antineutrino flux and isotopic $\mathrm{^{235}U}$ and $\mathrm{^{239}Pu}$ fluxes are precisely measured to be $5.84\pm0.07$, $6.16\pm0.12$ and $4.16\pm0.21$ in units of $10^{-43} \mathrm{cm^2/fission}$. These measurements are compared with the Huber-Mueller (HM) model, the reevaluated conversion model based on the Kurchatov Institute (KI) measurement and the latest Summation Model (SM2023). The Daya Bay flux shows good consistency with KI and SM2023 models, but disagrees with HM model. The Daya Bay spectrum, however, disagrees with all model predictions.

Despite recent advances in Vision-Language Models (VLMs), many still over-rely on visual language priors present in their training data rather than true visual reasoning. To examine the situation, we introduce ViLP, a visual question answering (VQA) benchmark that pairs each question with three potential answers and three corresponding images: one image whose answer can be inferred from text alone, and two images that demand visual reasoning. By leveraging image generative models, we ensure significant variation in texture, shape, conceptual combinations, hallucinated elements, and proverb-based contexts, making our benchmark images distinctly out-of-distribution. While humans achieve near-perfect accuracy, modern VLMs falter; for instance, GPT-4 achieves only 66.17% on ViLP. To alleviate this, we propose a self-improving framework in which models generate new VQA pairs and images, then apply pixel-level and semantic corruptions to form "good-bad" image pairs for self-training. Our training objectives compel VLMs to focus more on actual visual inputs and have demonstrated their effectiveness in enhancing the performance of open-source VLMs, including LLaVA-v1.5 and Cambrian.

We report recent progress on $\varepsilon_K$ evaluated directly from the standard model (SM) with lattice QCD inputs such as $\hat{B}_K$, exclusive $|V_{cb}|$, $|V_{us}|$, $|V_{ud}|$, $\xi_0$, $\xi_2$, $\xi_\text{LD}$, $f_K$, and $m_c$. We find that the standard model with exclusive $|V_{cb}|$ and lattice QCD inputs describes only $2/3 \cong 65\%$ of the experimental value of $|\varepsilon_K|$ and does not explain its remaining 35\%, which represents a strong tension in $|\varepsilon_K|$ at the $5.1\sigma \sim 4.1\sigma$ level between the SM theory and experiment. We also find that this tension disappears when we use the inclusive value of $|V_{cb}|$ obtained using the heavy quark expansion based on the QCD sum rule approach. We also report results for $|\varepsilon_K|$ obtained using the Brod-Gorbahn-Stamou (BGS) method for $\eta_i$ of $u-t$ unitarity, which leads to even a stronger tension of $5.7\sigma \sim 4.2\sigma$ with lattice QCD inputs., Comment: 11 pages, 4 figures, Lattice 2024 proceeding. arXiv admin note: substantial text overlap with arXiv:2312.02986, arXiv:2301.12375

With the rise of AI, NVIDIA GPUs have become the de facto standard for AI system design. This paper presents a comprehensive evaluation of Intel Gaudi NPUs as an alternative to NVIDIA GPUs for AI model serving. First, we create a suite of microbenchmarks to compare Intel Gaudi-2 with NVIDIA A100, showing that Gaudi-2 achieves competitive performance not only in primitive AI compute, memory, and communication operations but also in executing several important AI workloads end-to-end. We then assess Gaudi NPU's programmability by discussing several software-level optimization strategies to employ for implementing critical FBGEMM operators and vLLM, evaluating their efficiency against GPU-optimized counterparts. Results indicate that Gaudi-2 achieves energy efficiency comparable to A100, though there are notable areas for improvement in terms of software maturity. Overall, we conclude that, with effective integration into high-level AI frameworks, Gaudi NPUs could challenge NVIDIA GPU's dominance in the AI server market, though further improvements are necessary to fully compete with NVIDIA's robust software ecosystem., Comment: Under Review

In this work, we propose a new method for ordering nets during the process of layer assignment in global routing problems. The global routing problems that we focus on in this work are based on routing problems that occur in the design of substrates in multilayered semiconductor packages. The proposed new method is based on machine learning techniques and we show that the proposed method supersedes conventional net ordering techniques based on heuristic score functions. We perform global routing experiments in multilayered semiconductor package environments in order to illustrate that the routing order based on our new proposed technique outperforms previous methods based on heuristics. Our approach of using machine learning for global routing targets specifically the net ordering step which we show in this work can be significantly improved by deep learning., Comment: 18 pages, 13 figures, 6 tables; published in Scientific Reports

Dramatic advances in the quality of the latent diffusion models (LDMs) also led to the malicious use of AI-generated images. While current AI-generated image detection methods assume the availability of real/AI-generated images for training, this is practically limited given the vast expressibility of LDMs. This motivates the training-free detection setup where no related data are available in advance. The existing LDM-generated image detection method assumes that images generated by LDM are easier to reconstruct using an autoencoder than real images. However, we observe that this reconstruction distance is overfitted to background information, leading the current method to underperform in detecting images with simple backgrounds. To address this, we propose a novel method called HFI. Specifically, by viewing the autoencoder of LDM as a downsampling-upsampling kernel, HFI measures the extent of aliasing, a distortion of high-frequency information that appears in the reconstructed image. HFI is training-free, efficient, and consistently outperforms other training-free methods in detecting challenging images generated by various generative models. We also show that HFI can successfully detect the images generated from the specified LDM as a means of implicit watermarking. HFI outperforms the best baseline method while achieving magnitudes of

The expansion of large language models (LLMs) with hundreds of billions of parameters presents significant challenges to computational resources, particularly data movement and memory bandwidth. Long-context LLMs, which process sequences of tens of thousands of tokens, further increase the demand on the memory system as the complexity in attention layers and key-value cache sizes is proportional to the context length. Processing-in-Memory (PIM) maximizes memory bandwidth by moving compute to the data and can address the memory bandwidth challenges; however, PIM is not necessarily scalable to accelerate long-context LLM because of limited per-module memory capacity and the inflexibility of fixed-functional unit PIM architecture and static memory management. In this work, we propose LoL-PIM which is a multi-node PIM architecture that accelerates long context LLM through hardware-software co-design. In particular, we propose how pipeline parallelism can be exploited across a multi-PIM module while a direct PIM access (DPA) controller (or DMA for PIM) is proposed that enables dynamic PIM memory management and results in efficient PIM utilization across a diverse range of context length. We developed an MLIR-based compiler for LoL-PIM extending a commercial PIM-based compiler where the software modifications were implemented and evaluated, while the hardware changes were modeled in the simulator. Our evaluations demonstrate that LoL-PIM significantly improves throughput and reduces latency for long-context LLM inference, outperforming both multi-GPU and GPU-PIM systems (up to 8.54x and 16.0x speedup, respectively), thereby enabling more efficient deployment of LLMs in real-world applications., Comment: 15 pages, 12 figures

The frameworks that explore scientific and technological evolution suggest that discoveries and inventions are intrinsic processes, while the wealth of knowledge accumulated over time enables researchers to make further advancements, echoing Newton's sentiment of "standing on the shoulders of giants." Despite the exponential growth in new scientific and technical knowledge, the consolidation-disruption (D) index suggests a concerning decline in the disruptiveness of papers and patents. "Exaptation" a concept borrowed from biological evolution, is now recognized as a pivotal yet often neglected mechanism in technological evolution. Significant technologies often do not emerge out of thin air but rather result from the application of existing technologies in other domains. For instance, bird feathers initially served as waterproofing and insulation before enabling flight, and microwave ovens originated from radar magnetrons. Exaptation, acknowledged as the catalyst for "innovation beyond intention" signifies a cross-field evolutionary process that is driven by functional shifts in pre-existing knowledge, technology, or artifacts. In this study, we introduce the concept of exaptation value, deliberately excluding serendipity. Our analysis reveals that, despite a declining trend in the disruptiveness of innovation, there is an increasing trend in the application of cross-domain knowledge within the innovation process over time. We also explore the impact of technology exaptation on innovation disruptiveness and discuss how leveraging technology adaptability enhances innovation's disruptive potential.

We report an updated neutrino mixing angle of $\theta_{13}$ obtained from a complete data sample of the RENO experiment. The experiment has measured the amplitude and frequency of reactor anti-electron-neutrinos ($\bar{\nu}_{e}$) oscillations at the Hanbit nuclear power plant, Younggwang, Korea, since August 2011. As of March 2023, the data acquisition was completed after a total of 3800 live days of detector operation. The observed candidates via inverse beta decay (IBD) are 1,211,995 (144,667) in the near (far) detector. Based on an observed energy-dependent reactor neutrino disappearance, neutrino oscillation parameters of $\theta_{13}$ and $\lvert\Delta m_{ee}^2\rvert$ are precisely determined as $\sin^{2}2\theta_{13}=0.0920_{-0.0042}^{+0.0044}(\text{stat.})_{-0.0041}^{+0.0041}(\text{syst.})$ and $\lvert\Delta m_{ee}^2\rvert=\left[2.57_{-0.11}^{+0.10}(\text{stat.})_{-0.05}^{+0.05}(\text{syst.})\right]\times10^{-3}~\text{eV}^{2}$. Compared to the previous RENO results published in Ref.~\cite{PhysRevLett.121.201801}, the precision is improved from 7.5\% to 6.4\% for $\sin^{2}2\theta_{13}$ and from 5.2\% to 4.5\% for $\lvert\Delta m_{ee}^2\rvert$. The statistical error of the measurement has reached our goal and is hardly improved with additional data-taking., Comment: 13 pages, 11 figures

We study dark gauge-mediated supersymmetry breaking (dark GMSB) in a theory with a new unbroken $U(1)_{D}$ local symmetry and massless dark photon. Messenger fields charged under both Standard Model and dark gauge symmetries produce new soft supersymmetry-breaking terms due to gauge kinetic mixing between $U(1)_Y$ hypercharge and $U(1)_D$. We show that large kinetic mixing induces significant distortions to the superpartner spectra relative to conventional GMSB. Notably, shifts in the Higgs soft masses impact the conditions for electroweak symmetry breaking, lowering the $\mu$ parameter and yielding a relatively light Higgsino that may be accessible at the LHC. Furthermore, for very simple messenger representations, a very light bino-dark photino mixed state is present in the spectrum, which may be probed through exotic Higgs boson decays at future Higgs factories. We also examine the cosmological and phenomenological consequences of the messengers, the lightest of which is absolutely stable and carries fractional electric charge., Comment: 45 pages, 20 figures

As part of the BISTRO survey, we present JCMT 850 $\mu$m polarimetric observations towards the Orion Integral-Shaped Filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multi-scale analysis shows that the magnetic field structure in OMC-3 is very consistent at all the scales, whereas the field structure in OMC-2 shows no correlation across different scales. In OMC-1, the field retains its mean orientation from large to small scales, but shows some deviations at small scales. Histograms of relative orientations between the magnetic field and filaments reveal a bimodal distribution for OMC-1, a relatively random distribution for OMC-2, and a distribution with a predominant peak at 90$^\circ$ for OMC-3. Furthermore, the magnetic fields in OMC-1 and OMC-3 both appear to be aligned perpendicular to the fibers, which are denser structures within the filament, but the field in OMC-2 is aligned along with the fibers. All these suggest that gravity, turbulence, and magnetic field are each playing a leading role in OMC-1, 2, and 3, respectively. While OMC-2 and 3 have almost the same gas mass, density, and non-thermal velocity dispersion, there are on average younger and fewer young stellar objects in OMC-3, providing evidence that a stronger magnetic field will induce slower and less efficient star formation in molecular clouds., Comment: published in the ApJ Letters

This paper presents a fast inverse design framework for complex multilayered, multiport pixelated surfaces - a class of structures largely unexplored in current research. Leveraging a method-of-moments (MoM) electromagnetic (EM) solver, the framework enables the rapid synthesis of pixelated device designs. A novel matrix reconstruction technique, based on pre-labeling matrix entries as "inter-pixel" or "inner-pixel," accelerates simulations for each variation of the pixelated structure. To mitigate the cubic increase in computation time associated with additional layers, GPU acceleration is employed. Further enhancing convergence speed, a stochastic multi-pixel flipping search algorithm is integrated into the framework. The effectiveness of this approach is demonstrated through the design of a diplexer achieving a -3-dB bandwidth for one channel spanning 5.23-5.94 GHz and another covering 6.17-7.15 GHz, validated by a full-wave solver., Comment: 4 pages, 6 figures

In Redirected Walking (RDW), resets are an overt method that explicitly interrupts users, and they should be avoided to provide a quality user experience. The number of resets depends on the configuration of the physical environment; thus, inappropriate object placement can lead to frequent resets, causing motion sickness and degrading presence. However, estimating the number of resets based on the physical layout is challenging. It is difficult to measure reset frequency with real users repeatedly testing different layouts, and virtual simulations offer limited real-time verification. As a result, while rearranging objects can reduce resets, users have not been able to fully take advantage of this opportunity, highlighting the need for rapid assessment of object placement. To address this, in Study 1, we collected simulation data and analyzed the average number of resets for various object placements. In study 2, we developed a system that allows users to evaluate reset frequency using a real-time placement interface powered by the first learning-based reset prediction model. Our model predicts resets from top-down views of the physical space, leveraging a Vision Transformer architecture. The model achieved a root mean square error (RMSE) of $23.88$. We visualized the model's attention scores using heatmaps to analyze the regions of focus during prediction. Through the interface, users can reorganize furniture while instantly observing the change in the predicted number of resets, thus improving their interior for a better RDW experience with fewer resets.

Earthquakes are rare. Hence there is a fundamental call for reliable methods to generate realistic ground motion data for data-driven approaches in seismology. Recent GAN-based methods fall short of the call, as the methods either require special information such as geological traits or generate subpar waveforms that fail to satisfy seismological constraints such as phase arrival times. We propose a specialized Latent Diffusion Model (LDM) that reliably generates realistic waveforms after learning from real earthquake data with minimal conditions: location and magnitude. We also design a domain-specific training method that exploits the traits of earthquake dataset: multiple observed waveforms time-aligned and paired to each earthquake source that are tagged with seismological metadata comprised of earthquake magnitude, depth of focus, and the locations of epicenter and seismometers. We construct the time-aligned earthquake dataset using Southern California Earthquake Data Center (SCEDC) API, and train our model with the dataset and our proposed training method for performance evaluation. Our model surpasses all comparable data-driven methods in various test criteria not only from waveform generation domain but also from seismology such as phase arrival time, GMPE analysis, and spectrum analysis. Our result opens new future research directions for deep learning applications in seismology.

Recent advances in flexible keyword spotting (KWS) with text enrollment allow users to personalize keywords without uttering them during enrollment. However, there is still room for improvement in target keyword performance. In this work, we propose a novel few-shot transfer learning method, called text-aware adapter (TA-adapter), designed to enhance a pre-trained flexible KWS model for specific keywords with limited speech samples. To adapt the acoustic encoder, we leverage a jointly pre-trained text encoder to generate a text embedding that acts as a representative vector for the keyword. By fine-tuning only a small portion of the network while keeping the core components' weights intact, the TA-adapter proves highly efficient for few-shot KWS, enabling a seamless return to the original pre-trained model. In our experiments, the TA-adapter demonstrated significant performance improvements across 35 distinct keywords from the Google Speech Commands V2 dataset, with only a 0.14% increase in the total number of parameters., Comment: 5 pages, 3 figures, Accepted by ICASSP 2025

Feed-forward 3D Gaussian Splatting (3DGS) models have gained significant popularity due to their ability to generate scenes immediately without needing per-scene optimization. Although omnidirectional images are getting more popular since they reduce the computation for image stitching to composite a holistic scene, existing feed-forward models are only designed for perspective images. The unique optical properties of omnidirectional images make it difficult for feature encoders to correctly understand the context of the image and make the Gaussian non-uniform in space, which hinders the image quality synthesized from novel views. We propose OmniSplat, a pioneering work for fast feed-forward 3DGS generation from a few omnidirectional images. We introduce Yin-Yang grid and decompose images based on it to reduce the domain gap between omnidirectional and perspective images. The Yin-Yang grid can use the existing CNN structure as it is, but its quasi-uniform characteristic allows the decomposed image to be similar to a perspective image, so it can exploit the strong prior knowledge of the learned feed-forward network. OmniSplat demonstrates higher reconstruction accuracy than existing feed-forward networks trained on perspective images. Furthermore, we enhance the segmentation consistency between omnidirectional images by leveraging attention from the encoder of OmniSplat, providing fast and clean 3DGS editing results.

We investigate the electrical transport characteristics of boron-doped diamond (BDD) across frequencies ranging from direct current to 3 GHz to explore the potential of BDD circuits as microwave waveguides. Three homoepitaxial BDD films with varying boron concentrations, exhibiting insulating to metallic properties, are fabricated on a single-crystalline diamond substrate containing nitrogen-vacancy (NV) centers. The $\Omega$-shaped BDD circuit demonstrates an approximately 30 $\Omega$ impedance at the resonance frequency of the NV center, facilitating the propagation of microwaves over the circuit, even with a standard 50 $\Omega$ reference impedance. We successfully perform optically detected magnetic resonance (ODMR) on NV centers within diamonds using BDD circuits and observed continuous-wave ODMR spectra across all circuits. Additionally, we record Rabi oscillations in pulsed ODMR measurements using the metallic BDD circuit. The integration of NV centers with BDDs presents a compact, robust, and adaptable platform for quantum sensing in challenging environments and for quantum information processing.

3D Gaussian Splatting (3DGS) has attracted significant attention for its high-quality novel view rendering, inspiring research to address real-world challenges. While conventional methods depend on sharp images for accurate scene reconstruction, real-world scenarios are often affected by defocus blur due to finite depth of field, making it essential to account for realistic 3D scene representation. In this study, we propose CoCoGaussian, a Circle of Confusion-aware Gaussian Splatting that enables precise 3D scene representation using only defocused images. CoCoGaussian addresses the challenge of defocus blur by modeling the Circle of Confusion (CoC) through a physically grounded approach based on the principles of photographic defocus. Exploiting 3D Gaussians, we compute the CoC diameter from depth and learnable aperture information, generating multiple Gaussians to precisely capture the CoC shape. Furthermore, we introduce a learnable scaling factor to enhance robustness and provide more flexibility in handling unreliable depth in scenes with reflective or refractive surfaces. Experiments on both synthetic and real-world datasets demonstrate that CoCoGaussian achieves state-of-the-art performance across multiple benchmarks., Comment: Project Page: https://Jho-Yonsei.github.io/CoCoGaussian/

Gallium oxide is a novel advanced material gaining increasing attention for its unique combination of functional properties. It forms in several phases or polymorphs - {\alpha}, {\beta}, {\gamma}, and {\kappa} - having variable properties because of different lattice symmetries. Optical properties are of particular importance as they determine specific device applications and can also be used for phase identification. However, a direct comparison of optical polymorph signatures, including such critical parameters as bandgaps, is challenging due to the scattered, limited, or even absent data for certain phases in the literature. To address this issue, in the present work we systematically cross-correlate optical emission and absorption features of {\alpha}, {\beta}, {\gamma}, and {\kappa} thin films, as well as differently oriented {\beta}-phase bulk crystals and {\gamma}/{\beta} double polymorph structures. We demonstrate that the optical bandgap and emission features scale consistently across these polymorphs upon minimization of the methodological uncertainties. As a result, this work provides a comparative library of near- and far-field optical signatures of the polymorphs for use by the multidisciplinary research community working with gallium oxide., Comment: 17 pages, 14 figures, 4 tables

Recent medical vision-language models (VLMs) have shown promise in 2D medical image interpretation. However extending them to 3D medical imaging has been challenging due to computational complexities and data scarcity. Although a few recent VLMs specified for 3D medical imaging have emerged, all are limited to learning volumetric representation of a 3D medical image as a set of sub-volumetric features. Such process introduces overly correlated representations along the z-axis that neglect slice-specific clinical details, particularly for 3D medical images where adjacent slices have low redundancy. To address this limitation, we introduce MS-VLM that mimic radiologists' workflow in 3D medical image interpretation. Specifically, radiologists analyze 3D medical images by examining individual slices sequentially and synthesizing information across slices and views. Likewise, MS-VLM leverages self-supervised 2D transformer encoders to learn a volumetric representation that capture inter-slice dependencies from a sequence of slice-specific features. Unbound by sub-volumetric patchification, MS-VLM is capable of obtaining useful volumetric representations from 3D medical images with any slice length and from multiple images acquired from different planes and phases. We evaluate MS-VLM on publicly available chest CT dataset CT-RATE and in-house rectal MRI dataset. In both scenarios, MS-VLM surpasses existing methods in radiology report generation, producing more coherent and clinically relevant reports. These findings highlight the potential of MS-VLM to advance 3D medical image interpretation and improve the robustness of medical VLMs.

Inductive reasoning - the process of inferring general rules from a small number of observations - is a fundamental aspect of human intelligence. Recent works suggest that large language models (LLMs) can engage in inductive reasoning by sampling multiple hypotheses about the rules and selecting the one that best explains the observations. However, due to the IID sampling, semantically redundant hypotheses are frequently generated, leading to significant wastage of compute. In this paper, we 1) demonstrate that increasing the temperature to enhance the diversity is limited due to text degeneration issue, and 2) propose a novel method to improve the diversity while maintaining text quality. We first analyze the effect of increasing the temperature parameter, which is regarded as the LLM's diversity control, on IID hypotheses. Our analysis shows that as temperature rises, diversity and accuracy of hypotheses increase up to a certain point, but this trend saturates due to text degeneration. To generate hypotheses that are more semantically diverse and of higher quality, we propose a novel approach inspired by human inductive reasoning, which we call Mixture of Concepts (MoC). When applied to several inductive reasoning benchmarks, MoC demonstrated significant performance improvements compared to standard IID sampling and other approaches., Comment: 14 pages

Physical insight into a material can be first gained by its color since the reflectance spectrum from an object reflects its microstructure and complex reflective indices. We here present a comprehensive overview of electrodynamics and optics related to reflectance spectra and color and provide an open-source Python code for simulating reflectance spectra and extracting color values. The validity and applicability of the code are demonstrated through comparative analysis with both literature and experimental data., Comment: 20 pages, 4 figures, and 3 codes

Pediatric Emergency Department (PED) overcrowding presents a significant global challenge, prompting the need for efficient solutions. This paper introduces the BioBridge framework, a novel approach that applies Natural Language Processing (NLP) to Electronic Medical Records (EMRs) in written free-text form to enhance decision-making in PED. In non-English speaking countries, such as South Korea, EMR data is often written in a Code-Switching (CS) format that mixes the native language with English, with most code-switched English words having clinical significance. The BioBridge framework consists of two core modules: "bridging modality in context" and "unified bio-embedding." The "bridging modality in context" module improves the contextual understanding of bilingual and code-switched EMRs. In the "unified bio-embedding" module, the knowledge of the model trained in the medical domain is injected into the encoder-based model to bridge the gap between the medical and general domains. Experimental results demonstrate that the proposed BioBridge significantly performance traditional machine learning and pre-trained encoder-based models on several metrics, including F1 score, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and Brier score. Specifically, BioBridge-XLM achieved enhancements of 0.85% in F1 score, 0.75% in AUROC, and 0.76% in AUPRC, along with a notable 3.04% decrease in the Brier score, demonstrating marked improvements in accuracy, reliability, and prediction calibration over the baseline XLM model. The source code will be made publicly available., Comment: Accepted at IEEE Access 2024

We consider Bayesian inference on the spiked eigenstructures of high-dimensional covariance matrices; specifically, we focus on estimating the eigenvalues and corresponding eigenvectors of high-dimensional covariance matrices in which a few eigenvalues are significantly larger than the rest. We impose an inverse-Wishart prior distribution on the unknown covariance matrix and derive the posterior distributions of the eigenvalues and eigenvectors by transforming the posterior distribution of the covariance matrix. We prove that the posterior distribution of the spiked eigenvalues and corresponding eigenvectors converges to the true parameters under the spiked high-dimensional covariance assumption, and also that the posterior distribution of the spiked eigenvector attains the minimax optimality under the single spiked covariance model. Simulation studies and real data analysis demonstrate that our proposed method outperforms all existing methods in quantifying uncertainty., Comment: 54 pages, 2 figures

We implemented a high-performance optical character recognition model for classical handwritten documents using data augmentation with highly variable cropping within the document region. Optical character recognition in handwritten documents, especially classical documents, has been a challenging topic in many countries and research organizations due to its difficulty. Although many researchers have conducted research on this topic, the quality of classical texts over time and the unique stylistic characteristics of various authors have made it difficult, and it is clear that the recognition of hanja handwritten documents is a meaningful and special challenge, especially since hanja, which has been developed by reflecting the vocabulary, semantic, and syntactic features of the Joseon Dynasty, is different from classical Chinese characters. To study this challenge, we used 1100 cursive documents, which are small in size, and augmented 100 documents per document by cropping a randomly sized region within each document for training, and trained them using a two-stage object detection model, High resolution neural network (HRNet), and applied the resulting model to achieve a high inference recognition rate of 90% for cursive documents. Through this study, we also confirmed that the performance of OCR is affected by the simplified characters, variants, variant characters, common characters, and alternators of Chinese characters that are difficult to see in other studies, and we propose that the results of this study can be applied to optical character recognition of modern documents in multiple languages as well as other typefaces in classical documents.

This study develops a systematic approach for evaluating the effect of a treatment on a time-to-event outcome in a matched-pair study. While most methods for paired right-censored outcomes allow determining an overall treatment effect over the course of follow-up, they generally lack in providing detailed insights into how the effect changes over time. To address this gap, we propose novel tests for paired right-censored outcomes using randomization inference. We further extend our tests to matched observational studies by developing corresponding sensitivity analysis methods to take into account departures from randomization. Simulations demonstrate the robustness of our approach against various non-proportional hazards alternatives, including a crossing survival curves scenario. We demonstrate the application of our methods using a matched observational study from the Korean Longitudinal Study of Aging (KLoSA) data, focusing on the effect of social engagement on survival.

Multimodal incremental learning needs to digest the information from multiple modalities while concurrently learning new knowledge without forgetting the previously learned information. There are numerous challenges for this task, mainly including the larger storage size of multimodal data in exemplar-based methods and the computational requirement of finetuning on huge multimodal models. In this paper, we leverage the parameter-efficient tuning scheme to reduce the burden of fine-tuning and propose the exemplar masking framework to efficiently replay old knowledge. Specifically, the non-important tokens are masked based on the attention weights and the correlation across different modalities, significantly reducing the storage size of an exemplar and consequently saving more exemplars under the same memory buffer. Moreover, we design a multimodal data augmentation technique to diversify exemplars for replaying prior knowledge. In experiments, we not only evaluate our method in existing multimodal datasets but also extend the ImageNet-R dataset to a multimodal dataset as a real-world application, where captions are generated by querying multimodal large language models (e.g., InstructBLIP). Extensive experiments show that our exemplar masking framework is more efficient and robust to catastrophic forgetting under the same limited memory buffer. Code is available at https://github.com/YiLunLee/Exemplar_Masking_MCIL., Comment: Project page: https://github.com/YiLunLee/Exemplar_Masking_MCIL

Cooperative effects such as super(sub)radiance in quantum systems arise from the interplay among quantum emitters. While bright superradiant states have been extensively studied and yielded significant insights into cooperative phenomena, subradiant states have remained less explored due to their inherently dark state nature. However, subradiance holds significant potential as valuable quantum resources that exploit long-lived and large-scale entanglement, which is a key for advancing quantum information technologies. Here, we demonstrate a long-lived subradiant state among multiple quantum emitters coupled to a directional low Q cavity. In a tailored photonic environment with balanced cavity dissipation, emitter-field coupling strength, and incoherent pumping, two coupled quantum dots exhibit a steady-state population in a subradiant state with highly negative cooperativity. As an important hallmark of a subradiant state, the system shows large photon bunching (g^((2))(0)>>2) and suppressed single-photon decay. In addition, controlling the excitation wavelength provides a useful tool for manipulating dephasing and the number of coupled emitters, which leads to significant changes in photon statistics. Our approach to inducing cavity-mediated subradiance paves the way for creating and harnessing quantum correlations in quantum emitters via a long-lived entangled quantum state, essential for quantum storage and metrology., Comment: In the manuscript, 16 pages and 4 figures. In supplementary, 6 pages and 7 figures

Self-supervised learning (SSL) methods based on the instance discrimination tasks with InfoNCE have achieved remarkable success. Despite their success, SSL models often struggle to generate effective representations for unseen-domain data. To address this issue, research on unsupervised domain generalization (UDG), which aims to develop SSL models that can generate domain-irrelevant features, has been conducted. Most UDG approaches utilize contrastive learning with InfoNCE to generate representations, and perform feature alignment based on strong assumptions to generalize domain-irrelevant common features from multi-source domains. However, existing methods that rely on instance discrimination tasks are not effective at extracting domain-irrelevant common features. This leads to the suppression of domain-irrelevant common features and the amplification of domain-relevant features, thereby hindering domain generalization. Furthermore, strong assumptions underlying feature alignment can lead to biased feature learning, reducing the diversity of common features. In this paper, we propose a novel approach, DomCLP, Domain-wise Contrastive Learning with Prototype Mixup. We explore how InfoNCE suppresses domain-irrelevant common features and amplifies domain-relevant features. Based on this analysis, we propose Domain-wise Contrastive Learning (DCon) to enhance domain-irrelevant common features. We also propose Prototype Mixup Learning (PMix) to generalize domain-irrelevant common features across multiple domains without relying on strong assumptions. The proposed method consistently outperforms state-of-the-art methods on the PACS and DomainNet datasets across various label fractions, showing significant improvements. Our code will be released. Our project page is available at https://github.com/jinsuby/DomCLP., Comment: Code page: https://github.com/jinsuby/DomCLP

Video inpainting (VI) is a challenging task that requires effective propagation of observable content across frames while simultaneously generating new content not present in the original video. In this study, we propose a robust and practical VI framework that leverages a large generative model for reference generation in combination with an advanced pixel propagation algorithm. Powered by a strong generative model, our method not only significantly enhances frame-level quality for object removal but also synthesizes new content in the missing areas based on user-provided text prompts. For pixel propagation, we introduce a one-shot pixel pulling method that effectively avoids error accumulation from repeated sampling while maintaining sub-pixel precision. To evaluate various VI methods in realistic scenarios, we also propose a high-quality VI benchmark, HQVI, comprising carefully generated videos using alpha matte composition. On public benchmarks and the HQVI dataset, our method demonstrates significantly higher visual quality and metric scores compared to existing solutions. Furthermore, it can process high-resolution videos exceeding 2K resolution with ease, underscoring its superiority for real-world applications., Comment: AAAI 2025

We construct infinitely many new $\frac{1}{16}$-BPS cohomologies of the 4d maximal super-Yang-Mills theory and interpret them as a black hole wrapped by dual giant graviton hairs. Since the black hole inside a dual giant feels the RR 5-form flux reduced by one unit, its microstate should essentially be an $SU(N-1)$ cohomology. However, due to the fortuitous nature of the black hole microstates, promoting an $SU(N-1)$ black hole state to $SU(N)$ generally fails to yield a cohomology. We show at $N=3$ that suitable fusion products with the dual giants yield cohomologies. The core black hole size is probed by the minimal size of the dual giant which can wrap it. We also discuss two types of large black hole hairs: large conformal descendants of gravitons and large dual giants. We prove that any $SU(N)$ black hole cohomology admits infinitely many hairs of the first type., Comment: 42 pages

Understanding the semiconductor-electrolyte interface in photoelectrochemical (PEC) systems is crucial for optimizing stability and reactivity. Despite the challenges in establishing reliable surface structure models during PEC cycles, this study explores the complex surface reconstructions of BiVO$_{4}$(010) by employing a computational workflow integrated with a state-of-the-art active learning protocol for a machine-learning interatomic potential and global optimization techniques. Within this workflow, we identified 494 unique reconstructed surface structures that surpass conventional chemical intuition-driven, bulk-truncated models. After constructing the surface Pourbaix diagram under Bi- and V-rich electrolyte conditions using density functional theory and hybrid functional calculations, we proposed structural models for the experimentally observed Bi-rich BiVO$_{4}$ surfaces. By performing hybrid functional molecular dynamics simulations with explicit treatment of water molecules on selected reconstructed BiVO$_{4}$(010) surfaces, we observed spontaneous water dissociation, marking the first theoretical report of this phenomenon. Our findings demonstrate significant water dissociation on reconstructed Bi-rich surfaces, highlighting the critical role of bare and under-coordinated Bi sites (only observable in reconstructed surfaces) in driving hydration processes. Our work establishes a foundation for understanding the role of complex, reconstructed Bi surfaces in surface hydration and reactivity. Additionally, our theoretical framework for exploring surface structures and predicting reactivity in multicomponent oxides offers a precise approach to describing complex surface and interface processes in PEC systems.

We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training is based primarily on organic data sources such as web content or code, phi-4 strategically incorporates synthetic data throughout the training process. While previous models in the Phi family largely distill the capabilities of a teacher model (specifically GPT-4), phi-4 substantially surpasses its teacher model on STEM-focused QA capabilities, giving evidence that our data-generation and post-training techniques go beyond distillation. Despite minimal changes to the phi-3 architecture, phi-4 achieves strong performance relative to its size -- especially on reasoning-focused benchmarks -- due to improved data, training curriculum, and innovations in the post-training scheme.

To alleviate the training burden in federated learning while enhancing convergence speed, Split Federated Learning (SFL) has emerged as a promising approach by combining the advantages of federated and split learning. However, recent studies have largely overlooked competitive situations. In this framework, the SFL model owner can choose the cut layer to balance the training load between the server and clients, ensuring the necessary level of privacy for the clients. Additionally, the SFL model owner sets incentives to encourage client participation in the SFL process. The optimization strategies employed by the SFL model owner influence clients' decisions regarding the amount of data they contribute, taking into account the shared incentives over clients and anticipated energy consumption during SFL. To address this framework, we model the problem using a hierarchical decision-making approach, formulated as a single-leader multi-follower Stackelberg game. We demonstrate the existence and uniqueness of the Nash equilibrium among clients and analyze the Stackelberg equilibrium by examining the leader's game. Furthermore, we discuss privacy concerns related to differential privacy and the criteria for selecting the minimum required cut layer. Our findings show that the Stackelberg equilibrium solution maximizes the utility for both the clients and the SFL model owner., Comment: 12 pages, 10 figures

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements (3$\times$2pt) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining $\Lambda$CDM parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure $\Omega_\mathrm{m}=0.300\pm0.017$ and $\sigma_8=0.797\pm0.026$. Compared to constraints from Planck primary CMB anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ($1.2\sigma$) for the two-parameter difference. We further obtain $S_8\equiv\sigma_8(\Omega_\mathrm{m}/0.3)^{0.5}=0.796\pm0.013$ which is lower than the Planck measurement at the $1.6\sigma$ level. The combined SPT cluster, DES 3$\times$2pt, and Planck datasets mildly prefer a non-zero positive neutrino mass, with a 95% upper limit $\sum m_\nu<0.25~\mathrm{eV}$ on the sum of neutrino masses. Assuming a $w$CDM model, we constrain the dark energy equation of state parameter $w=-1.15^{+0.23}_{-0.17}$ and when combining with Planck primary CMB anisotropies, we recover $w=-1.20^{+0.15}_{-0.09}$, a $1.7\sigma$ difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology., Comment: Submitted to Phys. Rev. D

Applying language models (LMs) to tables is challenging due to the inherent structural differences between two-dimensional tables and one-dimensional text for which the LMs were originally designed. Furthermore, when applying linearized tables to LMs, the maximum token lengths often imposed in self-attention calculations make it difficult to comprehensively understand the context spread across large tables. To address these challenges, we present PieTa (Piece of Table), a new framework for sub-table-based question answering (QA). PieTa operates through an iterative process of dividing tables into smaller windows, using LMs to select relevant cells within each window, and merging these cells into a sub-table. This multi-resolution approach captures dependencies across multiple rows and columns while avoiding the limitations caused by long context inputs. Instantiated as a simple iterative sub-table union algorithm, PieTa demonstrates improved performance over previous sub-table-based QA approaches.

X-ray magnetic circular dichroism (XMCD) and resonant inelastic X-ray scattering with magnetic circular dichroism (RIXS-MCD) provide unparalleled insights into the electronic and magnetic dynamics of complex materials. Yet, their spectra remain challenging to interpret due to intricate many-body interactions. Here, we introduce a theoretical framework based on the Anderson impurity model, fully incorporating charge transfer (CT) and core-valence exchange correlation (CVEC) effects. Using epitaxial ferromagnetic La0.7Sr0.3MnO3 film as a model system, we capture elusive spectral features, demonstrating the necessity of CT inclusion for resolving XMCD subpeaks and revealing the profound impact of CVEC on RIXS-MCD spectra. Our approach not only successfully mirrors experimental results but also opens new avenues for exploring spin, orbital, and charge excitations in 3d transition metals and other correlated materials.

Timely detection and treatment are essential for maintaining eye health. Visual acuity (VA), which measures the clarity of vision at a distance, is a crucial metric for managing eye health. Machine learning (ML) techniques have been introduced to assist in VA measurement, potentially alleviating clinicians' workloads. However, the inherent uncertainties in ML models make relying solely on them for VA prediction less than ideal. The VA prediction task involves multiple sources of uncertainty, requiring more robust approaches. A promising method is to build prediction sets or intervals rather than point estimates, offering coverage guarantees through techniques like conformal prediction and Probably Approximately Correct (PAC) prediction sets. Despite the potential, to date, these approaches have not been applied to the VA prediction task.To address this, we propose a method for deriving prediction intervals for estimating visual acuity from fundus images with a PAC guarantee. Our experimental results demonstrate that the PAC guarantees are upheld, with performance comparable to or better than that of two prior works that do not provide such guarantees., Comment: To be published in ML4H 2024

Recent advancements in deep learning for tabular data have shown promise, but challenges remain in achieving interpretable and lightweight models. This paper introduces Table2Image, a novel framework that transforms tabular data into realistic and diverse image representations, enabling deep learning methods to achieve competitive classification performance. To address multicollinearity in tabular data, we propose a variance inflation factor (VIF) initialization, which enhances model stability and robustness by incorporating statistical feature relationships. Additionally, we present an interpretability framework that integrates insights from both the original tabular data and its transformed image representations, by leveraging Shapley additive explanations (SHAP) and methods to minimize distributional discrepancies. Experiments on benchmark datasets demonstrate the efficacy of our approach, achieving superior accuracy, area under the curve, and interpretability compared to recent leading deep learning models. Our lightweight method provides a scalable and reliable solution for tabular data classification.

We present a novel framework for computing differential cross-sections in quantum field theory using the optical theorem and loop amplitudes, circumventing the traditional method of squaring scattering amplitudes. This approach addresses two major computational challenges in high-multiplicity processes: complexity from amplitude squaring and the extensive summations over color and helicity. Our method employs quantum off-shell recursion, a loop-level generalization of Berends--Giele recursion, combined with Veltman's largest time equation (LTE) through a doubling prescription of fields. By deriving Dyson--Schwinger equations within this doubled framework and constructing quantum perturbiner expansions, we develop recursive relations for generating LTEs. We validate our method by successfully reproducing the differential cross-section for tree-level $2 \to 2$ and $2 \to 4$ scalar scattering for $\phi^{4}$ theory through one-loop and three-loop amplitude calculation respectively. This framework offers an efficient alternative to conventional methods and can be broadly applied to theories with color charges, such as QCD and the Standard Model., Comment: 28 pages, 6 figures

Beam Drift Chamber (BDC) is designed to reconstruct the trajectories of incident rare isotope beams provided by RAON (Rare isotope Accelerator complex for ON-line experiments) into the experimental target of LAMPS (Large Acceptance Multi-Purpose Spectrometer). To conduct the performance test of the BDC, the prototype BDC (pBDC) is manufactured and evaluated with the high energy ion beams from HIMAC (Heavy Ion Medical Accelerator in Chiba) facility in Japan. Two kinds of ion beams, 100 MeV proton, and 200 MeV/u $^{12}$C, have been utilized for this evaluation, and the track reconstruction efficiency and position resolution have been measured as the function of applied high voltage. This paper introduces the construction details and presents the track reconstruction efficiency and position resolution of pBDC., Comment: 13 pages, 15 figures

This technical report introduces the EXAONE 3.5 instruction-tuned language models, developed and released by LG AI Research. The EXAONE 3.5 language models are offered in three configurations: 32B, 7.8B, and 2.4B. These models feature several standout capabilities: 1) exceptional instruction following capabilities in real-world scenarios, achieving the highest scores across seven benchmarks, 2) outstanding long-context comprehension, attaining the top performance in four benchmarks, and 3) competitive results compared to state-of-the-art open models of similar sizes across nine general benchmarks. The EXAONE 3.5 language models are open to anyone for research purposes and can be downloaded from https://huggingface.co/LGAI-EXAONE. For commercial use, please reach out to the official contact point of LG AI Research: contact_us@lgresearch.ai., Comment: arXiv admin note: text overlap with arXiv:2408.03541

Transformers, a groundbreaking architecture proposed for Natural Language Processing (NLP), have also achieved remarkable success in Computer Vision. A cornerstone of their success lies in the attention mechanism, which models relationships among tokens. While the tokenization process in NLP inherently ensures that a single token does not contain multiple semantics, the tokenization of Vision Transformer (ViT) utilizes tokens from uniformly partitioned square image patches, which may result in an arbitrary mixing of visual concepts in a token. In this work, we propose to substitute the grid-based tokenization in ViT with superpixel tokenization, which employs superpixels to generate a token that encapsulates a sole visual concept. Unfortunately, the diverse shapes, sizes, and locations of superpixels make integrating superpixels into ViT tokenization rather challenging. Our tokenization pipeline, comprised of pre-aggregate extraction and superpixel-aware aggregation, overcomes the challenges that arise in superpixel tokenization. Extensive experiments demonstrate that our approach, which exhibits strong compatibility with existing frameworks, enhances the accuracy and robustness of ViT on various downstream tasks.

We present the development of a decay energy spectroscopy (DES) method for the analysis of radioactive impurities using magnetic microcalorimeters (MMCs). The DES system was designed to analyze radionuclides, such as Ra-226, Th-228, and their daughter nuclides, in materials like copper, commonly used in rare-event search experiments. We tested the DES system with a gold foil absorber measuring 20x20x0.05 mm^3, large enough to accommodate a significant drop of source solution. Using this large absorber and an MMC sensor, we conducted a long-term measurement over ten days of live time, requiring 11 ADR cooling cycles. The combined spectrum achieved an energy resolution of 45 keV FWHM, sufficient to identify most alpha and DES peaks of interest. Specific decay events from radionuclide contaminants in the absorber were identified. This experiment confirms the capability of the DES system to measure alpha decay chains of Ra-226 and Th-228, offering a promising method for radio-impurity evaluation in ultra-low background experiments., Comment: 5 pages, 5 figures

We present the first circularly polarized Floquet engineering time-resolved Resonant Inelastic X-ray Scattering (tr-RIXS) experiment in H$_3$LiIr$_2$O$_6$, an iridium-based Kitaev system. Our calculations and experimental results are consistent with the modification of the low energy magnetic excitations in H$_3$LiIr$_2$O$_6$ only during illumination by the laser pulse, consistent with the Floquet engineering of the exchange interactions. However, the penetration length mismatch between the X-ray probe and laser pump and the intrinsic complexity of Kitaev magnets prevented us from unequivocally extracting towards which ground H$_3$LiIr$_2$O$_6$ was driven. We outline possible solutions to these challenges for Floquet stabilization and observation of the Kitaev Quantum Spin Liquid limit by RIXS.

Low-rank adaptation (LoRA) has become the dominant method for parameter-efficient LLM fine-tuning, with LoRA-based quantization error compensation (LQEC) emerging as a powerful tool for recovering accuracy in compressed LLMs. However, LQEC has underperformed in sub-4-bit scenarios, with no prior investigation into understanding this limitation. We propose RILQ (Rank-Insensitive LoRA-based Quantization Error Compensation) to understand fundamental limitation and boost 2-bit LLM accuracy. Based on rank analysis revealing model-wise activation discrepancy loss's rank-insensitive nature, RILQ employs this loss to adjust adapters cooperatively across layers, enabling robust error compensation with low-rank adapters. Evaluations on LLaMA-2 and LLaMA-3 demonstrate RILQ's consistent improvements in 2-bit quantized inference across various state-of-the-art quantizers and enhanced accuracy in task-specific fine-tuning. RILQ maintains computational efficiency comparable to existing LoRA methods, enabling adapter-merged weight-quantized LLM inference with significantly enhanced accuracy, making it a promising approach for boosting 2-bit LLM performance., Comment: The typo in Table 4 has been corrected

In this work, we address the challenges posed by the high nonlinearity of the Butler-Volmer (BV) equation in forward and inverse simulations of the pseudo-two-dimensional (P2D) model using the physics-informed neural network (PINN) framework. The BV equation presents significant challenges for PINNs, primarily due to the hyperbolic sine term, which renders the Hessian of the PINN loss function highly ill-conditioned. To address this issue, we introduce a bypassing term that improves numerical stability by substantially reducing the condition number of the Hessian matrix. Furthermore, the small magnitude of the ionic flux \( j \) often leads to a common failure mode where PINNs converge to incorrect solutions. We demonstrate that incorporating a secondary conservation law for the solid-phase potential \( \psi \) effectively prevents such convergence issues and ensures solution accuracy. The proposed methods prove effective for solving both forward and inverse problems involving the BV equation. Specifically, we achieve precise parameter estimation in inverse scenarios and reliable solution predictions for forward simulations., Comment: 26 pages, 10 figures, 3 tables

Video monocular depth estimation is essential for applications such as autonomous driving, AR/VR, and robotics. Recent transformer-based single-image monocular depth estimation models perform well on single images but struggle with depth consistency across video frames. Traditional methods aim to improve temporal consistency using multi-frame temporal modules or prior information like optical flow and camera parameters. However, these approaches face issues such as high memory use, reduced performance with dynamic or irregular motion, and limited motion understanding. We propose STATIC, a novel model that independently learns temporal consistency in static and dynamic area without additional information. A difference mask from surface normals identifies static and dynamic area by measuring directional variance. For static area, the Masked Static (MS) module enhances temporal consistency by focusing on stable regions. For dynamic area, the Surface Normal Similarity (SNS) module aligns areas and enhances temporal consistency by measuring feature similarity between frames. A final refinement integrates the independently learned static and dynamic area, enabling STATIC to achieve temporal consistency across the entire sequence. Our method achieves state-of-the-art video depth estimation on the KITTI and NYUv2 datasets without additional information.