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We present the integral field spectroscopic observations of ionized gas (H$\alpha$ and [{\ion{N}{II}}]) using the PCWI, along with deep CO(2-1) observations by the $^\backprime\bar{\rm U}^\backprime\bar{\rm u}$ receiver on JCMT for AGC 102004. The velocity field of H$\alpha$ shows an anomalous distribution in the North-Western (NW) disk. The H$\alpha$ spectrum is well-fitted by two Gaussian components, and the weak Gaussian component is dominated by the anomalous H$\alpha$ in the NW disk. The Gaussian fit center of H$\alpha$ emission is offset by +24.2 km s$^{-1}$ from the systemic velocity obtained from the HI emission. We derive the gas-phase metallicity, 12+log(O/H), using [{\ion{N}{II}}]$\lambda$6583/H$\alpha$ ratio as a proxy. The mean value of 12+log(O/H) is 8.30 $\pm$ 0.19 over the whole galaxy. The metallicity in the outer disk is lower than the detection limit of 7.72, indicating the metallicity gradient exists in AGC 102004. We speculate a minor/mini-merger event could have happened to the NW disk. CO(2-1) emission has non-detection in AGC 102004, reaching a noise level of 0.33 mK smoothed to 30 km s$^{-1}$. The upper limit of molecular gas mass in AGC 102004 is 2.1 $\times$ 10$^7$ M$\odot$ with X$_{\rm CO}$ = 3.02$\times$10$^{20}$ cm$^{-2}$ (K km s$^{-1}$)$^{-1}$. The M$_{\rm H_2}$/M$^{\rm corr}_{\rm HI}$ of AGC 102004 is lower than 0.0037 and lower than that of normal galaxies., Comment: 10 pages, 9 figures, Accepted for publication in ApJ

Real-time lightweight time series anomaly detection has become increasingly crucial in cybersecurity and many other domains. Its ability to adapt to unforeseen pattern changes and swiftly identify anomalies enables prompt responses and critical decision-making. While several such anomaly detection approaches have been introduced in recent years, they primarily utilize a single type of recurrent neural networks (RNNs) and have been implemented in only one deep learning framework. It is unclear how the use of different types of RNNs available in various deep learning frameworks affects the performance of these anomaly detection approaches due to the absence of comprehensive evaluations. Arbitrarily choosing a RNN variant and a deep learning framework to implement an anomaly detection approach may not reflect its true performance and could potentially mislead users into favoring one approach over another. In this paper, we aim to study the influence of various types of RNNs available in popular deep learning frameworks on real-time lightweight time series anomaly detection. We reviewed several state-of-the-art approaches and implemented a representative anomaly detection approach using well-known RNN variants supported by three widely recognized deep learning frameworks. A comprehensive evaluation is then conducted to analyze the performance of each implementation across real-world, open-source time series datasets. The evaluation results provide valuable guidance for selecting the appropriate RNN variant and deep learning framework for real-time, lightweight time series anomaly detection., Comment: 20 pages, 4 figures, 7 tables, The 26th International Conference on Information and Communications Security, 26-28 August, 2024, Mytilene, Lesvos, Greece (ICICS2024)

Robot vacuum cleaners have become increasingly popular and are widely used in various smart environments. To improve user convenience, manufacturers also introduced smartphone applications that enable users to customize cleaning settings or access information about their robot vacuum cleaners. While this integration enhances the interaction between users and their robot vacuum cleaners, it results in potential privacy concerns because users' personal information may be exposed. To address these concerns, end-to-end encryption is implemented between the application, cloud service, and robot vacuum cleaners to secure the exchanged information. Nevertheless, network header metadata remains unencrypted and it is still vulnerable to network eavesdropping. In this paper, we investigate the potential risk of private information exposure through such metadata. A popular robot vacuum cleaner was deployed in a real smart environment where passive network eavesdropping was conducted during several selected cleaning events. Our extensive analysis, based on Association Rule Learning, demonstrates that it is feasible to identify certain events using only the captured Internet traffic metadata, thereby potentially exposing private user information and raising privacy concerns., Comment: 18 pages, 11 figures, 4 tables, The 26th International Conference on Information and Communications Security, 26-28 August, 2024, Mytilene, Lesvos, Greece (ICICS2024)

Magnetic fields play an important role in shaping and regulating star formation in molecular clouds. Here, we present one of the first studies examining the relative orientations between magnetic ($B$) fields and the dust emission, gas column density, and velocity centroid gradients on the 0.02 pc (core) scales, using the BISTRO and VLA+GBT observations of the NGC 1333 star-forming clump. We quantified these relative orientations using the Project Rayleigh Statistic (PRS) and found preferential global parallel alignment between the $B$ field and dust emission gradients, consistent with large-scale studies with Planck. No preferential global alignments, however, are found between the $B$ field and velocity gradients. Local PRS calculated for subregions defined by either dust emission or velocity coherence further revealed that the $B$ field does not preferentially align with dust emission gradients in most emission-defined subregions, except in the warmest ones. The velocity-coherent structures, on the other hand, also showed no preferred $B$ field alignments with velocity gradients, except for one potentially bubble-compressed region. Interestingly, the velocity gradient magnitude in NGC 1333 ubiquitously features prominent ripple-like structures that are indicative of magnetohydrodynamic (MHD) waves. Finally, we found $B$ field alignments with the emission gradients to correlate with dust temperature and anticorrelate with column density, velocity dispersion, and velocity gradient magnitude. The latter two anticorrelations suggest that alignments between gas structures and $B$ fields can be perturbed by physical processes that elevate velocity dispersion and velocity gradients, such as infall, accretions, and MHD waves., Comment: Accepted to the Monthly Notices of the Royal Astronomical Society Main Journal

We present Stable Video 4D (SV4D), a latent video diffusion model for multi-frame and multi-view consistent dynamic 3D content generation. Unlike previous methods that rely on separately trained generative models for video generation and novel view synthesis, we design a unified diffusion model to generate novel view videos of dynamic 3D objects. Specifically, given a monocular reference video, SV4D generates novel views for each video frame that are temporally consistent. We then use the generated novel view videos to optimize an implicit 4D representation (dynamic NeRF) efficiently, without the need for cumbersome SDS-based optimization used in most prior works. To train our unified novel view video generation model, we curated a dynamic 3D object dataset from the existing Objaverse dataset. Extensive experimental results on multiple datasets and user studies demonstrate SV4D's state-of-the-art performance on novel-view video synthesis as well as 4D generation compared to prior works., Comment: Project page: https://sv4d.github.io/

This research addresses the issue of missing structured data in dental records by extracting diagnostic information from unstructured text. The updated periodontology classification system's complexity has increased incomplete or missing structured diagnoses. To tackle this, we use advanced AI and NLP methods, leveraging GPT-4 to generate synthetic notes for fine-tuning a RoBERTa model. This significantly enhances the model's ability to understand medical and dental language. We evaluated the model using 120 randomly selected clinical notes from two datasets, demonstrating its improved diagnostic extraction accuracy. The results showed high accuracy in diagnosing periodontal status, stage, and grade, with Site 1 scoring 0.99 and Site 2 scoring 0.98. In the subtype category, Site 2 achieved perfect scores, outperforming Site 1. This method enhances extraction accuracy and broadens its use across dental contexts. The study underscores AI and NLP's transformative impact on healthcare delivery and management. Integrating AI and NLP technologies enhances documentation and simplifies administrative tasks by precisely extracting complex clinical information. This approach effectively addresses challenges in dental diagnostics. Using synthetic training data from LLMs optimizes the training process, improving accuracy and efficiency in identifying periodontal diagnoses from clinical notes. This innovative method holds promise for broader healthcare applications, potentially improving patient care quality., Comment: 11 pages, 2 tables, 3 figures, under review

The Kitaev chain can be engineered within a quantum dot-superconductor array, hosting Majorana zero modes at fine-tuned sweet spots. In this work, we propose and simulate the occurrence of Rabi and Ramsey oscillations to feasibly construct a minimal Majorana qubit in the quantum dot setup. Our real-time results incorporate realistic effects, e.g., charge noise and leakage, reflecting the latest experimental progress. We demonstrate that Majorana qubits with larger energy gaps exhibit significantly enhanced performance -- longer dephasing times, higher quality factors, reduced leakage probabilities, and improved visibilities -- compared to those with smaller gaps and with conventional quantum dot-based charge qubits. We introduce a method for reading out Majorana qubits via quantum capacitance measurements. Our work paves the way for future experiments on realizing Majorana qubits in quantum dot-superconductor arrays., Comment: 7 Pages, 4 figures

We analyze the frequencies of three known roAp stars, TIC 96315731, TIC 72392575, and TIC 318007796, using the light curves from the Transiting Exoplanet Survey Satellite (TESS). For TIC 96315731, the rotational and pulsational frequencies are $0.1498360\,\mathrm{d}^{-1}$ and $165.2609\,\mathrm{d}^{-1}$, respectively. In the case of TIC 72392575, the rotational frequency is $0.25551\,\mathrm{d}^{-1}$. We detect a quintuplet of pulsation frequencies with a center frequency of $135.9233\,\mathrm{d}^{-1}$, along with two signals within the second pair of rotational sidelobes of the quintuplet separated by the rotation frequency. These two signals may correspond to the frequencies of a dipole mode. In TIC 318007796, the rotational and pulsational frequencies are $0.2475021\,\mathrm{d}^{-1}$, $192.73995\,\mathrm{d}^{-1}$, and $196.33065\,\mathrm{d}^{-1}$, respectively. Based on the oblique pulsator model, we calculate the rotation inclination $\left( i \right)$ and magnetic obliquity angle $\left( \beta \right)$ for the stars, which provides the geometry of the pulsation modes. Combining the phases of the frequency quintuplets, the pulsation amplitude and phase modulation curves, and the results of spherical harmonic decomposition, we conclude that the pulsation modes of frequency quintuplets in TIC 96315731, TIC 72392575, and TIC 318007796 correspond to distorted dipole mode, distorted quadrupole mode, and distorted dipole mode, respectively.

Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling complex interactions between nodes. A fundamental and crucial requirement for representation learning in CTDGs is the appropriate estimation and preservation of proximity. However, due to the sparse and evolving characteristics of CTDGs, the spatial-temporal properties inherent in high-order proximity remain largely unexplored. Despite its importance, this property presents significant challenges due to the computationally intensive nature of personalized interaction intensity estimation and the dynamic attributes of CTDGs. To this end, we propose a novel Correlated Spatial-Temporal Positional encoding that incorporates a parameter-free personalized interaction intensity estimation under the weak assumption of the Poisson Point Process. Building on this, we introduce the Dynamic Graph Transformer with \Correlated Spatial-Temporal Positional Encoding (CorDGT), which efficiently retains the evolving spatial-temporal high-order proximity for effective node representation learning in CTDGs. Extensive experiments on seven small and two large-scale datasets demonstrate the superior performance and scalability of the proposed CorDGT.

This paper characterizes a realistic feed for cylindrical metasurfaces, allowing it to be included in metasurface design. Specifically, it investigates a coaxial feed which is displaced from the center (off-center) of concentrically-cascaded cylindrical metasurfaces. Formulas are reported to quickly compute the multimodal S-matrix (scattering properties) of a displaced feed from that of the central feed. The theory is rigorously derived based on the addition theorem of Hankel functions for all azimuthal modes. Moreover, the resulting multimodal S-matrix is combined with the multimodal wave matrix theory used to model cylindrical metasurfaces, allowing devices to be designed that realize arbitrary field transformations from a displaced coaxial feed. A design example is reported, which opens new opportunities in the realization of realistic, high-performance cylindrical-metasurface-based devices., Comment: 11 pages, 8 figures

MoXI is a new intermediate verification language introduced in 2024 to promote the standardization and open-source implementations for symbolic model checking by extending the SMT-LIB 2 language with constructs to define state-transition systems. The tool suite of MoXI provides a translator from MoXI to Btor2, which is a lower-level intermediate language for hardware verification, and a translation-based model checker, which invokes mature hardware model checkers for Btor2 to analyze the translated verification tasks. The extensibility of such a translation-based model checker is restricted because more complex theories, such as integer or real arithmetics, cannot be precisely expressed with bit-vectors of fixed lengths in Btor2. We present MoXIchecker, the first model checker that solves MoXI verification tasks directly. Instead of translating MoXI to lower-level languages, MoXIchecker uses the solver-agnostic library PySMT for SMT solvers as backend for its verification algorithms. MoXIchecker is extensible because it accommodates verification tasks involving more complex theories, not limited by lower-level languages, facilitates the implementation of new algorithms, and is solver-agnostic by using the API of PySMT. In our evaluation, MoXIchecker uniquely solved tasks that use integer or real arithmetics, and achieved a comparable performance against the translation-based model checker from the MoXI tool suite., Comment: 13 pages, 6 figures, 2 tables

Advanced digital assistants can significantly enhance task performance, reduce user burden, and provide personalized guidance to improve users' abilities. However, the development of such intelligent digital assistants presents a formidable challenge. To address this, we introduce TOM, a conceptual architecture and software platform (https://github.com/TOM-Platform) designed to support the development of intelligent wearable assistants that are contextually aware of both the user and the environment. This system was developed collaboratively with AR/MR researchers, HCI researchers, AI/Robotic researchers, and software developers, and it continues to evolve to meet the diverse requirements of these stakeholders. TOM facilitates the creation of intelligent assistive AR applications for daily activities and supports the recording and analysis of user interactions, integration of new devices, and the provision of assistance for various activities. Additionally, we showcase several proof-of-concept assistive services and discuss the challenges involved in developing such services., Comment: 14 pages, 6 figures, 2 tables

The neural codec model reduces speech data transmission delay and serves as the foundational tokenizer for speech language models (speech LMs). Preserving emotional information in codecs is crucial for effective communication and context understanding. However, there is a lack of studies on emotion loss in existing codecs. This paper evaluates neural and legacy codecs using subjective and objective methods on emotion datasets like IEMOCAP. Our study identifies which codecs best preserve emotional information under various bitrate scenarios. We found that training codec models with both English and Chinese data had limited success in retaining emotional information in Chinese. Additionally, resynthesizing speech through these codecs degrades the performance of speech emotion recognition (SER), particularly for emotions like sadness, depression, fear, and disgust. Human listening tests confirmed these findings. This work guides future speech technology developments to ensure new codecs maintain the integrity of emotional information in speech.

Literature review requires researchers to synthesize a large amount of information and is increasingly challenging as the scientific literature expands. In this work, we investigate the potential of LLMs for producing hierarchical organizations of scientific studies to assist researchers with literature review. We define hierarchical organizations as tree structures where nodes refer to topical categories and every node is linked to the studies assigned to that category. Our naive LLM-based pipeline for hierarchy generation from a set of studies produces promising yet imperfect hierarchies, motivating us to collect CHIME, an expert-curated dataset for this task focused on biomedicine. Given the challenging and time-consuming nature of building hierarchies from scratch, we use a human-in-the-loop process in which experts correct errors (both links between categories and study assignment) in LLM-generated hierarchies. CHIME contains 2,174 LLM-generated hierarchies covering 472 topics, and expert-corrected hierarchies for a subset of 100 topics. Expert corrections allow us to quantify LLM performance, and we find that while they are quite good at generating and organizing categories, their assignment of studies to categories could be improved. We attempt to train a corrector model with human feedback which improves study assignment by 12.6 F1 points. We release our dataset and models to encourage research on developing better assistive tools for literature review., Comment: 2024 ACL Findings

Accurate thermometry of laser-cooled ions is crucial for the performance of the trapped-ions quantum computing platform. However, most existing methods face a computational exponential bottleneck. Recently, a thermometry method based on bichromatic driving was theoretically proposed by Ivan Vybornyi et al. to overcome this obstacle, which allows the computational complexity to remain constant with the increase of ion numbers. In this paper, we provide a detailed statistical analysis of this method and prove its robustness to several imperfect experimental conditions using Floquet theory. We then experimentally verify its good performance on a linear segmented surface-electrode ion trap platform for the first time. This method is proven to be effective from near the motional ground state to a few mean phonon numbers. Our theoretical analysis and experimental verification demonstrate that the scheme can accurately and efficiently measure the temperature in ion crystals.

This paper introduces Golden-Retriever, designed to efficiently navigate vast industrial knowledge bases, overcoming challenges in traditional LLM fine-tuning and RAG frameworks with domain-specific jargon and context interpretation. Golden-Retriever incorporates a reflection-based question augmentation step before document retrieval, which involves identifying jargon, clarifying its meaning based on context, and augmenting the question accordingly. Specifically, our method extracts and lists all jargon and abbreviations in the input question, determines the context against a pre-defined list, and queries a jargon dictionary for extended definitions and descriptions. This comprehensive augmentation ensures the RAG framework retrieves the most relevant documents by providing clear context and resolving ambiguities, significantly improving retrieval accuracy. Evaluations using three open-source LLMs on a domain-specific question-answer dataset demonstrate Golden-Retriever's superior performance, providing a robust solution for efficiently integrating and querying industrial knowledge bases.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising auxiliary technology for radio frequency imaging. However, existing works face challenges of faint and intricate back-scattered waves and the restricted field-of-view (FoV), both resulting from complex target structures and a limited number of antennas. The synergistic benefits of multi-RIS-aided imaging hold promise for addressing these challenges. Here, we propose a dual-RIS-aided imaging system, Dreamer, which operates collaboratively in complementary modes (reflection-mode and transmission-mode). Dreamer significantly expands the FoV and enhances perception by deploying dual-RIS across various spatial and measurement patterns. Specifically, we perform a fine-grained analysis of how radio-frequency (RF) signals encode scene information in the scattered object modeling. Based on this modeling, we design illumination strategies to balance spatial resolution and observation scale, and implement a prototype system in a typical indoor environment. Moreover, we design a novel artificial neural network with a CNN-external-attention mechanism to translate RF signals into high-resolution images of human contours. Our approach achieves an impressive SSIM score exceeding 0.83, validating its effectiveness in broadening perception modes and enhancing imaging capabilities. The code to reproduce our results is available at https://github.com/fuhaiwang/Dreamer., Comment: 15 pages

We report on the first measurement of the elemental fragmentation cross sections (EFCSs) of $^{29-33}\mathrm{Si}$ on a carbon target at $\sim$230~MeV/nucleon. The experimental data covering charge changes of $\Delta Z$ = 1-4 are reproduced well by the isospin-dependent quantum molecular dynamics (IQMD) coupled with the evaporation GEMINI (IQMD+GEMINI) model. We further explore the mechanisms underlying the single-proton removal reaction in this model framework. We conclude that the cross sections from direct proton knockout exhibit a overall weak dependence on the mass number of $\mathrm{Si}$ projectiles. The proton evaporation induced after the projectile excitation significantly affects the cross sections for neutron-deficient $\mathrm{Si}$ isotopes, while neutron evaporation plays a crucial role in the reactions of neutron-rich $\mathrm{Si}$ isotopes. It is presented that the relative magnitude of one-proton and one-neutron separation energies is an essential factor that influences evaporation processes., Comment: 7 pages, 4 figures

Direct imaging of exoplanets allows us to measure positions and chemical signatures of exoplanets. Given the limited resources for space observations where the atmosphere is absent, we want to make these measurements from the ground. However, it is difficult from the ground because it requires an adaptive optics system to provide an extremely well corrected wavefront to enable coronographic techniques. Currently only natural guide star AO systems have demonstrated the necessary wavefront correction for direct imaging of exoplanets. However, using a stellar source as the guide star for wavefront sensing limits the number of exoplanet systems we can directly image because it requires a relatively bright V~10 mag star. To increase the number of observable targets, we need to push the limit of natural guide stars to fainter magnitudes with high Strehl ratio correction. We propose to combine laser guide star (LGS) and natural guide star (NGS) wavefront sensing to achieve the high Strehl correction with fainter natural guide stars. We call this approach Hybrid Atmospheric Phase Analysis (HAPA); 'hapa' in Hawaiian means 'half' or 'of mixed ethnic heritage'. The relatively bright LGS is used for higher order correction, whereas the NGS is used for high accuracy lower order correction. We focus on demonstrating this approach using Robo-AO-2 at the UH 2.2m telescope on Maunakea with a UV Rayleigh laser at 355 nm. The laser focuses at 10 km altitude and has an equivalent magnitude of m_U~8. In this report specifically, we present simulated results of HAPA employed at Robo-AO-2, with the LGS system having a single configuration of 16x16 subaperture Shack-Hartmann wavefront sensor and the NGS system having 6 different configurations -- 16x16, 8x8, 5x5, 4x4, 2x2 and 1x1. We also discuss the on-sky experiments we plan to carry out with HAPA at the UH 2.2m telescope., Comment: 11 pages, 4 figures, 8 tables, SPIE Astronomical Telescopes + Instrumentation 2024

Recent studies customizing Multimodal Large Language Models (MLLMs) for domain-specific tasks have yielded promising results, especially in the field of scientific chart comprehension. These studies generally utilize visual instruction tuning with specialized datasets to enhance question and answer (QA) accuracy within the chart domain. However, they often neglect the fundamental discrepancy between natural image-caption pre-training data and digital chart image-QA data, particularly in the models' capacity to extract underlying numeric values from charts. This paper tackles this oversight by exploring the training processes necessary to improve MLLMs' comprehension of charts. We present three key findings: (1) Incorporating raw data values in alignment pre-training markedly improves comprehension of chart data. (2) Replacing images with their textual representation randomly during end-to-end fine-tuning transfer the language reasoning capability to chart interpretation skills. (3) Requiring the model to first extract the underlying chart data and then answer the question in the fine-tuning can further improve the accuracy. Consequently, we introduce CHOPINLLM, an MLLM tailored for in-depth chart comprehension. CHOPINLLM effectively interprets various types of charts, including unannotated ones, while maintaining robust reasoning abilities. Furthermore, we establish a new benchmark to evaluate MLLMs' understanding of different chart types across various comprehension levels. Experimental results show that CHOPINLLM exhibits strong performance in understanding both annotated and unannotated charts across a wide range of types.

An increasing number of Chinese people are troubled by different degrees of visual impairment, which has made the modal conversion between a single image or video frame in the visual field and the audio expressing the same information a research hotspot. Deep learning technologies such as OCR+Vocoder and Im2Wav enable English audio synthesis or image-to-sound matching in a self-supervised manner. However, the audio data used for training is limited and English is not universal for visually impaired people with different educational levels. Therefore, for the sake of solving the problems of data volume and language applicability to improve the reading efficiency of visually impaired people, a set of image-to-speech framework CLIP-KNN-Fastspeech2 based on the Chinese context was constructed. The framework integrates multiple basic models and adopts the strategy of independent pre-training and joint fine-tuning. First, the Chinese CLIP and Fastspeech2 text-to-speech models were pre-trained on two public datasets, MUGE and Baker, respectively, and their convergence was verified. Subsequently, joint fine-tuning was performed using a self-built Braille image dataset. Experimental results on multiple public datasets such as VGGSound, Flickr8k, ImageHear, and the self-built Braille dataset BIT-DP show that the model has improved objective indicators such as BLEU4,FAD(Fr\'echet Audio Distance), WER(Word Error Ratio), and even inference speed. This verifies that the constructed model still has the ability to synthesize high-quality speech under limited data, and also proves the effectiveness of the joint training strategy that integrates multiple basic models.

This study addresses the nuanced challenge of estimating head translations within the context of six-degrees-of-freedom (6DoF) head pose estimation, placing emphasis on this aspect over the more commonly studied head rotations. Identifying a gap in existing methodologies, we recognized the underutilized potential synergy between facial geometry and head translation. To bridge this gap, we propose a novel approach called the head Translation, Rotation, and face Geometry network (TRG), which stands out for its explicit bidirectional interaction structure. This structure has been carefully designed to leverage the complementary relationship between face geometry and head translation, marking a significant advancement in the field of head pose estimation. Our contributions also include the development of a strategy for estimating bounding box correction parameters and a technique for aligning landmarks to image. Both of these innovations demonstrate superior performance in 6DoF head pose estimation tasks. Extensive experiments conducted on ARKitFace and BIWI datasets confirm that the proposed method outperforms current state-of-the-art techniques. Codes are released at https://github.com/asw91666/TRG-Release.

Remarkable advancements in the recolorization of Neural Radiance Fields (NeRF) have simplified the process of modifying NeRF's color attributes. Yet, with the potential of NeRF to serve as shareable digital assets, there's a concern that malicious users might alter the color of NeRF models and falsely claim the recolorized version as their own. To safeguard against such breaches of ownership, enabling original NeRF creators to establish rights over recolorized NeRF is crucial. While approaches like CopyRNeRF have been introduced to embed binary messages into NeRF models as digital signatures for copyright protection, the process of recolorization can remove these binary messages. In our paper, we present GeometrySticker, a method for seamlessly integrating binary messages into the geometry components of radiance fields, akin to applying a sticker. GeometrySticker can embed binary messages into NeRF models while preserving the effectiveness of these messages against recolorization. Our comprehensive studies demonstrate that GeometrySticker is adaptable to prevalent NeRF architectures and maintains a commendable level of robustness against various distortions. Project page: https://kevinhuangxf.github.io/GeometrySticker/.

In a multi-valley electronic band structure, lifting of the valley degeneracy is associated with rotational symmetry breaking in the electronic fluid, and may emerge through spontaneous symmetry breaking order, or through a large response to a small external perturbation such as strain. In this work we use scanning tunneling microscopy to investigate an unexpected rotational symmetry breaking in Landau levels formed in the unusual floating surface band of ZrSiS. We visualize a ubiquitous splitting of Landau levels into valley-polarized sub-levels. We demonstrate methods to measure valley-selective Landau level spectroscopy, to infer unknown Landau level indices, and to precisely measure each valley's Berry phase in a way that is agnostic to the band structure and topology of the system. These techniques allow us to obtain each valley's dispersion curve and infer a rigid valley-dependent contribution to the band energies. Ruling out spontaneous symmetry breaking by establishing the sample-dependence of this valley splitting, we explain the effect in terms of residual strain. A quantitative estimate indicates that uniaxial strain can be measured to a precision of $ \lt 0.025 \% $. The extreme valley-polarization of the Landau levels results from as little as $ \sim 0.1 \% $ strain, and this suggests avenues for manipulation using deliberate strain engineering., Comment: 13 pages, 9 figures (plus 13 pages, 9 figures)

Large language models (LLMs) have demonstrated great success in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still suffer from several key issues, such as hallucination problems, knowledge update issues, and lacking domain-specific expertise. The appearance of retrieval-augmented generation (RAG), which leverages an external knowledge database to augment LLMs, makes up those drawbacks of LLMs. This paper reviews all significant techniques of RAG, especially in the retriever and the retrieval fusions. Besides, tutorial codes are provided for implementing the representative techniques in RAG. This paper further discusses the RAG training, including RAG with/without datastore update. Then, we introduce the application of RAG in representative natural language processing tasks and industrial scenarios. Finally, this paper discusses the future directions and challenges of RAG for promoting its development.

Let $K$ be a totally real field and $\{\rho_{\pi,\lambda}:\mathrm{Gal}_K\to\mathrm{GL}_n(\overline E_\lambda)\}_\lambda$ the strictly compatible system of $K$ defined over $E$ attached to a regular algebraic polarized cuspidal automorphic representation $\pi$ of $\mathrm{GL}_n(\mathbb A_K)$. Let $\mathbf G_\lambda$ be the algebraic monodromy group of $\rho_{\pi,\lambda}$. If there exists $\lambda_0$ such that (a) $\rho_{\pi,\lambda_0}$ is irreducible, (b) $\mathbf G_{\lambda_0}$ is connected and of type $A_1$, and (c) either at most one basic factor in the exterior tensor decomposition of the tautological representation of $\mathbf G_{\lambda_0}^{\mathrm{der}}$ is $(\mathrm{SL}_2,\mathrm{std})$ or the tautological representation of $\mathbf G_{\lambda_0}^{\mathrm{der}}$ is $(\mathrm{SO}_4,\mathrm{std})$, we prove that $\mathbf G_{\lambda,\mathbb C}\subset\mathrm{GL}_{n, \mathbb C}$ is independent of $\lambda$, $\rho_{\pi,\lambda}$ is irreducible for all $\lambda$ and residually irreducible for almost all $\lambda$. If moreover $K=\mathbb Q$, we prove that the compatible system $\{\rho_{\pi,\lambda}\}_\lambda$ is up to twist constructed from some two-dimensional modular compatible systems., Comment: 14 pages

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has obtained more than 23 million spectra, opening an unprecedented opportunity to study stellar physics, as well as the formation and evolution of our Milky Way. In order to obtain the accurate stellar parameters, we develop a LAMOST Medium-Resolution Spectral Analysis Pipeline (LAMA), which estimates the stellar parameters from the LAMOST medium-resolution spectra, including the effective temperature (Teff), surface gravity (logg), metallicity ([Fe/H]), radial velocity, and rotational velocity (vsini). LAMA estimates these parameters utilizing the template-matching method. The comparison between our results and those from the high-resolution ones, including APOGEE, GALAH, and PASTEL, shows no obvious bias, indicating the reliability of our results. The accuracy of Teff and [Fe/H] can reach 75 K and 0.12 dex, respectively, for the LAMOST Medium-Resolution Spectroscopic Survey (MRS) spectra with a signal-to-noise ratio higher than 10. For dwarfs, the uncertainty of logg is around 0.17 dex, while, for giants, it ranges from 0.18 to 0.30 dex, with the errors decreasing as logg increases. Using LAMA for the LAMOST-MRS spectra, we estimate the stellar parameters of 497,412 stars. This sample will be very helpful for investigating the formation and evolution of our Galaxy., Comment: 18 pages, 21 figures, 4 tables

Fine-grained recognition involves the classification of images from subordinate macro-categories, and it is challenging due to small inter-class differences. To overcome this, most methods perform discriminative feature selection enabled by a feature extraction backbone followed by a high-level feature refinement step. Recently, many studies have shown the potential behind vision transformers as a backbone for fine-grained recognition, but their usage of its attention mechanism to select discriminative tokens can be computationally expensive. In this work, we propose a novel and computationally inexpensive metric to identify discriminative regions in an image. We compare the similarity between the global representation of an image given by the CLS token, a learnable token used by transformers for classification, and the local representation of individual patches. We select the regions with the highest similarity to obtain crops, which are forwarded through the same transformer encoder. Finally, high-level features of the original and cropped representations are further refined together in order to make more robust predictions. Through extensive experimental evaluation we demonstrate the effectiveness of our proposed method, obtaining favorable results in terms of accuracy across a variety of datasets. Furthermore, our method achieves these results at a much lower computational cost compared to the alternatives. Code and checkpoints are available at: \url{https://github.com/arkel23/GLSim}., Comment: Main: 12 pages, 5 figures, 5 tables. Appendix: 9 pages, 9 figures, 10 tables. Total: 21 pages, 14 figures, 15 tables

Rydberg tweezer arrays provide a versatile platform to explore quantum magnets with dipolar XY or van-der-Waals Ising ZZ interactions. Here, we propose a scheme combining dipolar and van-der-Waals interactions between Rydberg atoms, where the amplitude of the latter can be greater than that of the former, realizing an extended Hubbard model with long-range tunnelings in optical tweezer arrays. For repulsive interactions, we predict the existence of a robust supersolid phase accessible in current Rydberg tweezer experiments on the triangular lattice supported by large-scale quantum Monte Carlo simulations based on explicitly calculated pair interactions for ${}^{87}$Rb and with a critical entropy per particle $S/N \approx 0.19$. Such a lattice supersolid is long-lived, found over a wide parameter range in an isotropic and flat two-dimensional geometry, and can be realized for 100s of particles. Its thermodynamical and dynamical properties can hence be studied at a far larger scale than hitherto possible., Comment: 5+2 pages, 4+2 figures

Highly r-process-enhanced stars are rare and usually metal-poor ([Fe/H] < - 1.0), and mainly populate the Milky Way halo and dwarf galaxies. This study presents the discovery of a relatively bright (V = 12.72), highly r-process-enhanced (r-II) star ([Eu/Fe] = +1.32, [Ba/Eu] = - 0.95), LAMOST J020623.21 + 494127.9. This star was selected from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) medium-resolution (R ~ 7500) spectroscopic survey; follow-up high-resolution (R ~ 25,000) observations were conducted with the High Optical Resolution Spectrograph (HORuS) installed on the Gran Telescopio Canarias (GTC). The stellar parameters (${T_{\rm eff}}$ = 4130 K, $\rm log\,g $ = 1.52, $ \rm[Fe/H] $ = $ - $0.54, $\xi$ = 1.80 $ \rm{km\,{s^{-1}}} $) have been inferred taking into account non-local thermodynamic equilibrium (NLTE) effects. The abundances of [Ce/Fe], [Pr/Fe], and [Nd/Fe] are +0.19, +0.65 and +0.64, respectively, relatively low compared to the Solar r-process pattern normalized to Eu. This star has a high metallicity ([Fe/H] = - 0.54) compared to most other highly r-process-enhanced stars, and has the highest measured abundance ratio of Eu to H ([Eu/H] = +0.78). It is classified as a thin-disk star based on its kinematics, and does not appear to belong to any known stream or dwarf galaxy., Comment: 5 figures, 3 tables

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset.

Adversarial attacks are a potential threat to machine learning models by causing incorrect predictions through imperceptible perturbations to the input data. While these attacks have been extensively studied in unstructured data like images, applying them to tabular data, poses new challenges. These challenges arise from the inherent heterogeneity and complex feature interdependencies in tabular data, which differ from the image data. To account for this distinction, it is necessary to establish tailored imperceptibility criteria specific to tabular data. However, there is currently a lack of standardised metrics for assessing the imperceptibility of adversarial attacks on tabular data. To address this gap, we propose a set of key properties and corresponding metrics designed to comprehensively characterise imperceptible adversarial attacks on tabular data. These are: proximity to the original input, sparsity of altered features, deviation from the original data distribution, sensitivity in perturbing features with narrow distribution, immutability of certain features that should remain unchanged, feasibility of specific feature values that should not go beyond valid practical ranges, and feature interdependencies capturing complex relationships between data attributes. We evaluate the imperceptibility of five adversarial attacks, including both bounded attacks and unbounded attacks, on tabular data using the proposed imperceptibility metrics. The results reveal a trade-off between the imperceptibility and effectiveness of these attacks. The study also identifies limitations in current attack algorithms, offering insights that can guide future research in the area. The findings gained from this empirical analysis provide valuable direction for enhancing the design of adversarial attack algorithms, thereby advancing adversarial machine learning on tabular data., Comment: 33 pages

Macro placement is a vital step in digital circuit design that defines the physical location of large collections of components, known as macros, on a 2-dimensional chip. The physical layout obtained during placement determines key performance metrics of the chip, such as power consumption, area, and performance. Existing learning-based methods typically fall short because of their reliance on reinforcement learning, which is slow and limits the flexibility of the agent by casting placement as a sequential process. Instead, we use a powerful diffusion model to place all components simultaneously. To enable such models to train at scale, we propose a novel architecture for the denoising model, as well as an algorithm to generate large synthetic datasets for pre-training. We empirically show that our model can tackle the placement task, and achieve competitive performance on placement benchmarks compared to state-of-the-art methods.

We prove the $L_1$ in time and $B^{s+1}_{q,1}\times B^s_{q,1}$ in space maximal regularity for the Stokes equations in the viscous compressible fluid flows in domains in the $N$ dimensional Euclidean space $R^N$ whose boundary is $C^3$ compact hypersurface. As an application, the local well-posedness is proved for the compressible Navier-Stokes equations with Dirichlet condition. Danchin and Tolksdorf have studied the same problem in a bounded domains. Since they used Da-Prato and Grisvard theory directly, they need the assumption that the domain is compact. We imvestigate a new method to obtain $L_1$ maximal regularity which is based on real interpolation theories and thanks to this method, we can remove the compactness assumptions in the study due to Danchin and Torksdorf. Our method can be applied to obtain $L_1$ in time maximal regularity theorem for the initial boundary value problem of the system of parabolic or hyperbolic-parabolic equations with non-homogeneous boundary conditions.

Large Language Models (LLMs) have demonstrated remarkable proficiency in a wide range of NLP tasks. However, when it comes to authorship verification (AV) tasks, which involve determining whether two given texts share the same authorship, even advanced models like ChatGPT exhibit notable limitations. This paper introduces a novel approach, termed InstructAV, for authorship verification. This approach utilizes LLMs in conjunction with a parameter-efficient fine-tuning (PEFT) method to simultaneously improve accuracy and explainability. The distinctiveness of InstructAV lies in its ability to align classification decisions with transparent and understandable explanations, representing a significant progression in the field of authorship verification. Through comprehensive experiments conducted across various datasets, InstructAV demonstrates its state-of-the-art performance on the AV task, offering high classification accuracy coupled with enhanced explanation reliability.

Dynamic quantum phase transitions (DQPT) following quantum quenches exhibit singular behavior of the overlap between the initial and evolved states. Here we present a formalism to incorporate a geometric phase into quench dynamics of mixed quantum states, a process named the Uhlmann quench, based on the Uhlmann parallel transport. To overcome the incompatibility between the Uhlmann parallel-transport condition and Hamiltonian dynamics, we formulate the evolution of purification of the density matrix in a form which not only respects the dynamics according to the density matrix but also follows the Uhlmann parallel-transport condition to generate a geometric phase after a quantum quench. For cyclic processes exemplified by a spin-1/2 system, geometric DQPTs (GDQPTs) can emerge with both singular behavior in the dynamic analogue of the free energy and jumps of the geometric phase. Moreover, the Uhlmann phase reflecting the holonomy is generated at the end of each cycle. The Uhlmann quench thus paves the way for investigating the interplay between quantum dynamics and geometric processes in mixed states., Comment: 10 pages, 1 figure

We count integral quaternion zeros of $\gamma_1^2 \pm \dots \pm \gamma_n^2$, giving an asymptotic when $n\ge 9$, and a likely near-optimal bound when $n=8$. To do so, we introduce a new, nonabelian delta symbol method, which is of independent interest. Our asymptotic at height $X$ takes the form $cX^{4n-8} + O(X^{3n+\varepsilon})$ for suitable $c \in \mathbb{C}$ and any $\varepsilon>0.$ We construct special subvarieties implying that, in general, $3n+\varepsilon$ can be at best improved to $3n-2.$, Comment: 66 pages, 0 figures. Added supplementary material by Arala, Hou, Hsu, Li, and Wang

The interplay between information-scrambling Hamiltonians and local continuous measurements hosts platforms for exotic measurement-induced phase transition in out-of-equilibrium steady states. Here, we consider such transitions under the addition of local random white noise and measurement inefficiency in a XX spin chain. We identify a non-monotonic dependence on the local noise strength in both the averaged entanglement and operator correlations, specifically the subsystem parity variance. While the non-monotonicity persists at any finite efficiency for the operator correlations, it disappears at finite inefficiency for the entanglement. The analysis of scaling with the system size in a finite length chain indicates that, at finite efficiency, this effect leads to distinct MiPTs for operator correlations and entanglement. Our result hints at a difference between area-law entanglement scaling and Zeno-localized phases for inefficient monitoring., Comment: 11 pages, 7 figures

Video-language alignment is a crucial multi-modal task that benefits various downstream applications, e.g., video-text retrieval and video question answering. Existing methods either utilize multi-modal information in video-text pairs or apply global and local alignment techniques to promote alignment precision. However, these methods often fail to fully explore the spatio-temporal relationships among vision tokens within video and across different video-text pairs. In this paper, we propose a novel Spatio-Temporal Graph Transformer module to uniformly learn spatial and temporal contexts for video-language alignment pre-training (dubbed STGT). Specifically, our STGT combines spatio-temporal graph structure information with attention in transformer block, effectively utilizing the spatio-temporal contexts. In this way, we can model the relationships between vision tokens, promoting video-text alignment precision for benefiting downstream tasks. In addition, we propose a self-similarity alignment loss to explore the inherent self-similarity in the video and text. With the initial optimization achieved by contrastive learning, it can further promote the alignment accuracy between video and text. Experimental results on challenging downstream tasks, including video-text retrieval and video question answering, verify the superior performance of our method., Comment: under review

Traditional Chinese medicine (TCM) relies on specific combinations of herbs in prescriptions to treat symptoms and signs, a practice that spans thousands of years. Predicting TCM prescriptions presents a fascinating technical challenge with practical implications. However, this task faces limitations due to the scarcity of high-quality clinical datasets and the intricate relationship between symptoms and herbs. To address these issues, we introduce DigestDS, a new dataset containing practical medical records from experienced experts in digestive system diseases. We also propose a method, TCM-FTP (TCM Fine-Tuning Pre-trained), to leverage pre-trained large language models (LLMs) through supervised fine-tuning on DigestDS. Additionally, we enhance computational efficiency using a low-rank adaptation technique. TCM-FTP also incorporates data augmentation by permuting herbs within prescriptions, capitalizing on their order-agnostic properties. Impressively, TCM-FTP achieves an F1-score of 0.8031, surpassing previous methods significantly. Furthermore, it demonstrates remarkable accuracy in dosage prediction, achieving a normalized mean square error of 0.0604. In contrast, LLMs without fine-tuning perform poorly. Although LLMs have shown capabilities on a wide range of tasks, this work illustrates the importance of fine-tuning for TCM prescription prediction, and we have proposed an effective way to do that.

We performed closed-loop lab testing of large-format deformable mirrors (DMs) with hybrid variable reluctance actuators. TNO has been developing the hybrid variable reluctance actuators in support for a new generation of adaptive secondary mirrors (ASMs), which aim to be more robust and reliable. Compared to the voice coil actuators, this new actuator technology has a higher current to force efficiency, and thus can support DMs with thicker facesheets. Before putting this new technology on-sky, it is necessary to understand how to control it and how it behaves in closed-loop. We performed closed-loop tests with the Shack-Hartmann wavefront sensor with three large-format deformable mirrors that use the TNO actuators: DM3, FLASH, and IRTF-ASM-1 ASM. The wavefront sensor and the real-time control systems were developed for the NASA Infrared Telescope Facility (IRTF) and the UH 2.2-meter telescope ASMs. We tested IRTF-ASM-1 on-sky and proved that it meets all of our performance requirements. This work presents our lab setup for the experiments, the techniques we have employed to drive these new ASMs, the results of our closed-loop lab tests for FLASH and IRTF-ASM-1, and the on-sky closed-loop results of IRTF-ASM-1 ASM., Comment: 15 pages, 12 figures, SPIE Astronomical Telescopes + Instrumentation 2024

Semi-supervised learning (SSL) has achieved remarkable performance with a small fraction of labeled data by leveraging vast amounts of unlabeled data from the Internet. However, this large pool of untrusted data is extremely vulnerable to data poisoning, leading to potential backdoor attacks. Current backdoor defenses are not yet effective against such a vulnerability in SSL. In this study, we propose a novel method, Unlabeled Data Purification (UPure), to disrupt the association between trigger patterns and target classes by introducing perturbations in the frequency domain. By leveraging the Rate- Distortion-Perception (RDP) trade-off, we further identify the frequency band, where the perturbations are added, and justify this selection. Notably, UPure purifies poisoned unlabeled data without the need of extra clean labeled data. Extensive experiments on four benchmark datasets and five SSL algorithms demonstrate that UPure effectively reduces the attack success rate from 99.78% to 0% while maintaining model accuracy, Comment: under review

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

We calculate exclusive vector meson photoproduction within the Color Glass Condensate framework in high-energy photon-nucleus scattering probed experimentally in ultra peripheral heavy ion collisions at RHIC and at the LHC. When the free parameters are constrained by the $\gamma+p$ data from HERA, we predict significant nuclear suppression for both the coherent and incoherent photoproduction cross section in the TeV range. Our results indicate that the LHC data prefers even stronger saturation effects at the highest collision energies. Furthermore, we demonstrate how the linear polarization of photons in ulra peripheral collisions generates azimuthal modulations in the decay products of the exclusively produced vector meson. We show how these measurements can probe details of the nuclear geometry, specifically the deformed structure of the uranium nuclei., Comment: 5 pages, 6 figures. Talk by H.M at DIS2024

In this work, we introduce Speech-Copilot, a modular framework for instruction-oriented speech-processing tasks that minimizes human effort in toolset construction. Unlike end-to-end methods using large audio-language models, Speech-Copilot builds speech processing-specific toolsets by analyzing pre-collected task instructions and breaking tasks into manageable sub-tasks. It features a flexible agent based on large language models that performs tasks through program generation. Our approach achieves state-of-the-art performance on the Dynamic-SUPERB benchmark, demonstrating its effectiveness across diverse speech-processing tasks. Key contributions include: 1) developing an innovative framework for speech processing-specific toolset construction, 2) establishing a high-performing agent based on large language models, and 3) offering a new perspective on addressing challenging instruction-oriented speech-processing tasks. Without additional training processes required by end-to-end approaches, our method provides a flexible and extendable solution for a wide range of speech-processing applications., Comment: 8 pages, 2 figures

Large Language Models (LLMs) are frequently updated due to data or architecture changes to improve their performance. When updating models, developers often focus on increasing overall performance metrics with less emphasis on being compatible with previous model versions. However, users often build a mental model of the functionality and capabilities of a particular machine learning model they are interacting with. They have to adapt their mental model with every update -- a draining task that can lead to user dissatisfaction. In practice, fine-tuned downstream task adapters rely on pretrained LLM base models. When these base models are updated, these user-facing downstream task models experience instance regression or negative flips -- previously correct instances are now predicted incorrectly. This happens even when the downstream task training procedures remain identical. Our work aims to provide seamless model updates to a user in two ways. First, we provide evaluation metrics for a notion of compatibility to prior model versions, specifically for generative tasks but also applicable for discriminative tasks. We observe regression and inconsistencies between different model versions on a diverse set of tasks and model updates. Second, we propose a training strategy to minimize the number of inconsistencies in model updates, involving training of a compatibility model that can enhance task fine-tuned language models. We reduce negative flips -- instances where a prior model version was correct, but a new model incorrect -- by up to 40% from Llama 1 to Llama 2.

In this paper, we consider the problem of prototype-based vision-language reasoning problem. We observe that existing methods encounter three major challenges: 1) escalating resource demands and prolonging training times, 2) contending with excessive learnable parameters, and 3) fine-tuning based only on a single modality. These challenges will hinder their capability to adapt Vision-Language Models (VLMs) to downstream tasks. Motivated by this critical observation, we propose a novel method called NODE-Adapter, which utilizes Neural Ordinary Differential Equations for better vision-language reasoning. To fully leverage both visual and textual modalities and estimate class prototypes more effectively and accurately, we divide our method into two stages: cross-modal prototype construction and cross-modal prototype optimization using neural ordinary differential equations. Specifically, we exploit VLM to encode hand-crafted prompts into textual features and few-shot support images into visual features. Then, we estimate the textual prototype and visual prototype by averaging the textual features and visual features, respectively, and adaptively combine the textual prototype and visual prototype to construct the cross-modal prototype. To alleviate the prototype bias, we then model the prototype optimization process as an initial value problem with Neural ODEs to estimate the continuous gradient flow. Our extensive experimental results, which cover few-shot classification, domain generalization, and visual reasoning on human-object interaction, demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches.

Nonlinear optics plays important roles in the research of fundamental physics and the applications of high-performance optoelectronic devices. The bulk nonlinear optical responses arise from the uniform light absorption in noncentrosymmetric crystals, and hence are usually considered to be the collective phenomena of all atoms. Here we show, in contrast to this common expectation, the nonlinear optical responses in antiferromagnets can be selectively accumulated near the surfaces, representing a skin effect. This is because the inversion symmetry, despite being broken globally by magnetism, is barely violated locally deeply inside these antiferromagnets. Using A-type layered antiferromagnets as the representatives, we predict that the spatial-dependent nonlinear optical responses, such as bulk photovoltaic effect (BPVE) and second harmonic generation (SHG), are notable in the top- and bottom-most layers and decay rapidly when moving away from the surfaces. Such a phenomenon is strongly associated with the antiferromagnetism and exists in a broad range of antiferromagnets composed of centrosymmetric sublattices, offering promising device applications using these antiferromagnets. Our work uncovers a previously overlooked property of nonlinear optical responses and opens new opportunities for high-performance antiferromagnetic optospintronics.

X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases, necessitating comprehensive clinical features and data integration. We present a novel technique to enhance the clinical context through augmentation techniques with clinical tabular data, thereby improving its applicability and reliability in AI medical diagnostics. To address this, we introduce a pioneering approach to clinical data augmentation that employs large language models (LLMs) to generate patient contextual synthetic data. This methodology is crucial for training more robust deep learning models in healthcare. It preserves the integrity of real patient data while enriching the dataset with contextually relevant synthetic features, significantly enhancing model performance. DALL-M uses a three-phase feature generation process: (i) clinical context storage, (ii) expert query generation, and (iii) context-aware feature augmentation. DALL-M generates new, clinically relevant features by synthesizing chest X-ray images and reports. Applied to 799 cases using nine features from the MIMIC-IV dataset, it created an augmented set of 91 features. This is the first work to generate contextual values for existing and new features based on patients' X-ray reports, gender, and age and to produce new contextual knowledge during data augmentation. Empirical validation with machine learning models, including Decision Trees, Random Forests, XGBoost, and TabNET, showed significant performance improvements. Incorporating augmented features increased the F1 score by 16.5% and Precision and Recall by approximately 25%. DALL-M addresses a critical gap in clinical data augmentation, offering a robust framework for generating contextually enriched datasets., Comment: we introduce a pioneering approach to clinical data augmentation that employs large language models (LLMs) to generate patient contextual synthetic data. It preserves the integrity of real patient data while enriching the dataset with contextually relevant synthetic features, significantly enhancing model performance

Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. In this paper, we propose \textbf{NeRFProtector}, which adopts a plug-and-play strategy to protect NeRF's copyright during its creation. NeRFProtector utilizes a pre-trained watermarking base model, enabling NeRF creators to embed binary messages directly while creating their NeRF. Our plug-and-play property ensures NeRF creators can flexibly choose NeRF variants without excessive modifications. Leveraging our newly designed progressive distillation, we demonstrate performance on par with several leading-edge neural rendering methods. Our project is available at: \url{https://qsong2001.github.io/NeRFProtector}., Comment: Accepted by ECCV2024