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For any given submersion $\pi:X\to B$ with closed, oriented and spin$^c$ fibers of even dimension, equipped with a Riemannian and differential spin$^c$ structure $\boldsymbol{\pi}$, we construct an analytic index $\textrm{ind}^a_k$ in odd $\mathbb{Z}/k\mathbb{Z}$ $K$-theory on the cocycle level by associating to every cocycle $(\mathbf{E}, \mathbf{F}, \alpha)$ of the odd $\mathbb{Z}/k\mathbb{Z}$ $K$-theory group of $X$ a cocycle $\textrm{ind}^a_k(\mathbf{E}, \mathbf{F}, \alpha)$ of the odd $\mathbb{Z}/k\mathbb{Z}$ $K$-theory group of $B$. The cocycle $\textrm{ind}^a_k(\mathbf{E}, \mathbf{F}, \alpha)$ is defined in terms of the twisted spin$^c$ Dirac operators associated to $(\mathbf{E}, \boldsymbol{\pi})$ and $(\mathbf{F}, \boldsymbol{\pi})$, which are not assumed to satisfy the kernel bundle assumption. We prove a Riemann-Roch-Grothendieck type formula in odd $\mathbb{Z}/k\mathbb{Z}$ $K$-theory, which expresses the Cheeger-Chern-Simons form of $\textrm{ind}^a_k(\mathbf{E}, \mathbf{F}, \alpha)$ in terms of that of $(\mathbf{E}, \mathbf{F}, \alpha)$. Furthermore, we show that the analytic index $\textrm{ind}^a_k$ and the Riemann-Roch-Grothendieck type formula in odd $\mathbb{Z}/k\mathbb{Z}$ $K$-theory refine the geometric bundle part of the analytic index and the Riemann-Roch-Grothendieck theorem in $\mathbb{R}/\mathbb{Z}$ $K$-theory, respectively. An as intermediate result, we give a proof that the analytic indexes in differential $K$-theory defined without the kernel bundle assumption via the Atiyah-Singer-Gorokhovsky-Lott approach and the Miscenko-Fomenko-Freed-Lott approach, respectively, are equal., Comment: 60 pages. Submitted for publication. Comments are very welcome

Magnetic fields (B-fields) are ubiquitous in the interstellar medium (ISM), and they play an essential role in the formation of molecular clouds and subsequent star formation. However, B-fields in interstellar environments remain challenging to measure, and their properties typically need to be inferred from dust polarization observations over multiple physical scales. In this work, we seek to use a recently proposed approach called the Velocity Gradient Technique (VGT) to study B-fields in star-forming regions and compare the results with dust polarization observations in different wavelengths. The VGT is based on the anisotropic properties of eddies in magnetized turbulence to derive B-field properties in the ISM. We investigate that this technique is synergistic with dust polarimetry when applied to a turbulent diffused medium for the purpose of measuring its magnetization. Specifically, we use the VGT on molecular line data toward the NGC~1333 star-forming region ($\rm ^{12}CO$, $\rm ^{13}CO$, $\rm C^{18}O$, and $\rm N_{2}H^{+}$), and we compare the derived B-field properties with those inferred from 214 and 850~$\mu$m dust polarization observations of the region using SOFIA/HAWC+ and JCMT/POL-2, respectively. We estimate both the inclination angle and the 3D Alfv\'enic Mach Number $M_A$ from the molecular line gradients. Crucially, testing this technique on gravitationally bound, dynamic, and turbulent regions, and comparing the results with those obtained from polarization observations at different wavelength, such as the plane-of-the-sky field orientation, is an important test on the applicability of the VGT in various density regimes of the ISM., Comment: 16 pages, 6 figures, Accepted for publication in ApJ

We present results from a high-resolution interstellar turbulence simulation and show that it closely reproduces recent $Planck$ measurements. Our model captures the scaling of $EE$ and $BB$ spectra, and the $EE/BB$ ratio in the inertial range. The PDF of the dust polarization fraction is also consistent with observations. The $TE$ cross-correlation is in broad agreement with observations. This simulation provides new insights into the physical origins of the observed $E/B$ asymmetry and positive $TE$ signal, facilitating the development of advanced Galactic dust emission models for current and future CMB experiments., Comment: 6 pages, 3 figures, 1 table

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

Bayesian analysis has begun to be more widely adopted in X-ray spectroscopy, but it has largely been constrained to relatively simple physical models due to limitations in X-ray modelling software and computation time. As a result, Bayesian analysis of numerical models with high physics complexity have remained out of reach. This is a challenge, for example when modelling the X-ray emission of accreting black hole X-ray binaries, where the slow model computations severely limit explorations of parameter space and may bias the inference of astrophysical parameters. Here, we present RTFAST-Spectra: a neural network emulator that acts as a drop in replacement for the spectral portion of the black hole X-ray reverberation model RTDIST. This is the first emulator for the reltrans model suite and the first emulator for a state-of-the-art x-ray reflection model incorporating relativistic effects with 17 physically meaningful model parameters. We use Principal Component Analysis to create a light-weight neural network that is able to preserve correlations between complex atomic lines and simple continuum, enabling consistent modelling of key parameters of scientific interest. We achieve a $\mathcal{O}(10^2)$ times speed up over the original model in the most conservative conditions with $\mathcal{O}(1\%)$ precision over all 17 free parameters in the original numerical model, taking full posterior fits from months to hours. We employ Markov Chain Monte Carlo sampling to show how we can better explore the posteriors of model parameters in simulated data and discuss the complexities in interpreting the model when fitting real data., Comment: 22 pages, 35 figures. Submitted to MNRAS

We present the first seconds-long general relativistic neutrino-radiation simulations of accretion-induced collapse (AIC) of white dwarfs using hadron-quark matter equations of state with different onset densities and latent heat of the first-order quantum chromodynamics phase transition (PT). The collapse forms a proto-neutron star that continues contracting due to neutrino cooling and the softening effect of mixed nuclear-quark matter, followed by a second bounce triggered by the PT to deconfined quark matter, leading to a stable remnant of proto-hybrid star. The resultant distinct neutrino bursts and narrow mass range of AIC events offer critical insights into the existence and properties of hybrid stars, including constraints on the onset density of PT., Comment: 9 pages, 5 figures

Audio Description (AD) plays a pivotal role as an application system aimed at guaranteeing accessibility in multimedia content, which provides additional narrations at suitable intervals to describe visual elements, catering specifically to the needs of visually impaired audiences. In this paper, we introduce $\mathrm{CA^3D}$, the pioneering unified Context-Aware Automatic Audio Description system that provides AD event scripts with precise locations in the long cinematic content. Specifically, $\mathrm{CA^3D}$ system consists of: 1) a Temporal Feature Enhancement Module to efficiently capture longer term dependencies, 2) an anchor-based AD event detector with feature suppression module that localizes the AD events and extracts discriminative feature for AD generation, and 3) a self-refinement module that leverages the generated output to tweak AD event boundaries from coarse to fine. Unlike conventional methods which rely on metadata and ground truth AD timestamp for AD detection and generation tasks, the proposed $\mathrm{CA^3D}$ is the first end-to-end trainable system that only uses visual cue. Extensive experiments demonstrate that the proposed $\mathrm{CA^3D}$ improves existing architectures for both AD event detection and script generation metrics, establishing the new state-of-the-art performances in the AD automation., Comment: 10 pages

The rising use of deepfakes in criminal activities presents a significant issue, inciting widespread controversy. While numerous studies have tackled this problem, most primarily focus on deepfake detection. These reactive solutions are insufficient as a fundamental approach for crimes where authenticity verification is not critical. Existing proactive defenses also have limitations, as they are effective only for deepfake models based on specific Generative Adversarial Networks (GANs), making them less applicable in light of recent advancements in diffusion-based models. In this paper, we propose a proactive defense method named FaceShield, which introduces novel attack strategies targeting deepfakes generated by Diffusion Models (DMs) and facilitates attacks on various existing GAN-based deepfake models through facial feature extractor manipulations. Our approach consists of three main components: (i) manipulating the attention mechanism of DMs to exclude protected facial features during the denoising process, (ii) targeting prominent facial feature extraction models to enhance the robustness of our adversarial perturbation, and (iii) employing Gaussian blur and low-pass filtering techniques to improve imperceptibility while enhancing robustness against JPEG distortion. Experimental results on the CelebA-HQ and VGGFace2-HQ datasets demonstrate that our method achieves state-of-the-art performance against the latest deepfake models based on DMs, while also exhibiting applicability to GANs and showcasing greater imperceptibility of noise along with enhanced robustness.

End-to-end optimization, which simultaneously optimizes optics and algorithms, has emerged as a powerful data-driven method for computational imaging system design. This method achieves joint optimization through backpropagation by incorporating differentiable optics simulators to generate measurements and algorithms to extract information from measurements. However, due to high computational costs, it is challenging to model both aberration and diffraction in light transport for end-to-end optimization of compound optics. Therefore, most existing methods compromise physical accuracy by neglecting wave optics effects or off-axis aberrations, which raises concerns about the robustness of the resulting designs. In this paper, we propose a differentiable optics simulator that efficiently models both aberration and diffraction for compound optics. Using the simulator, we conduct end-to-end optimization on scene reconstruction and classification. Experimental results demonstrate that both lenses and algorithms adopt different configurations depending on whether wave optics is modeled. We also show that systems optimized without wave optics suffer from performance degradation when wave optics effects are introduced during testing. These findings underscore the importance of accurate wave optics modeling in optimizing imaging systems for robust, high-performance applications.

Models of active galactic nuclei often invoke a close physical association between the broad-line region and the accretion disk. We evaluate this theoretical expectation by investigating the relationship between the inclination angle of the BLR ($\theta_\mathrm{BLR}$) and the inclination angle of the inner accretion disk ($\theta_\mathrm{disk}$). For a sample of eight active galactic nuclei that have published values of $\theta_\mathrm{BLR}$ estimated from dynamical modeling of the BLR based on velocity-resolved reverberation mapping experiments, we analyze high-quality, joint XMM-Newton and NuSTAR X-ray observations to derive new, robust measurements of $\theta_\mathrm{disk}$ through broadband (0.3--78\,keV) reflection spectroscopy. We find a strong, positive correlation between $\theta_\mathrm{BLR}$ and $\theta_\mathrm{disk}$ (Pearson correlation coefficient 0.856, $p$-value 0.007), although Monte Carlo simulations indicate that the level of significance is only marginal ($<3\,\sigma$). Nevertheless, the nearly linear relation between $\theta_\mathrm{BLR}$ and $\theta_\mathrm{disk}$ suggests of a possible physical alignment between the accretion disk and the BLR. Future studies with a larger and more homogeneous sample are needed to confirm the correlation and refine our understanding of the structure and dynamics of the central regions of active galaxies., Comment: 24 pages, 12 figures, 6 tables; submitted to the Astrophysical Journal

We have performed magnetization, specific heat, and muon spin relaxation ($\mu$SR) measurements on single crystals of the noncentrosymmetric superconductor Ir$_{2}$Ga$_{9}$. The isothermal magnetization measurements show that there is a crossover from Type-I to Type-II superconductivity with decreasing temperature. Potential multi-band superconductivity of Ir$_{2}$Ga$_{9}$~is observed in the specific heat data. $\mu$SR~measurement is performed to map the phase diagram of Ir$_{2}$Ga$_{9}$, and both Type-I and Type-II superconductivity characteristics are obtained. Most importantly, a more unique region with the coexistence of Type-I and Type-II $\mu$SR signals is observed. In addition, time reversal symmetry is found to be preserved in Ir$_{2}$Ga$_{9}$ by zero field $\mu$SR measurement.

Recently, the James Webb Space Telescope (JWST) has revealed a new class of high redshift (high-$z$, $z>4$) compact galaxies which are red in the rest-frame optical and blue in the rest-frame UV as V-shaped spectral energy distributions (SEDs), referred to as "Little Red Dots" (LRDs). It is very likely that LRDs host obscured broad-line active galactic nuclei (AGNs). In the meanwhile, Green pea galaxies (GPs), which are compact dwarf galaxies at low redshift, share various similar properties with high redshift star-forming galaxies. Here we aim to find the connection between the LRDs and GPs hosting broad-line AGNs (BLGPs). With a sample of 19 BLGPs obtained from our previous work, we further identify 7 GPs with V-shaped rest-frame UV-to-optical SEDs that are likely local analogs to LRDs. These V-shaped BLGPs exhibit faint UV absolute magnitudes and sub-Eddington rates similar to those of LRDs. Three of them occupy a similar region as LRDs in the BPT diagram, suggesting they have similar ionization conditions and gas-phase metallicities to LRDs. These similarities suggest that V-shaped BLGPs can be taken as local analogs of high-redshift LRDs. In addition, most (16/19) BLGPs, including 6 V-shaped BLGPs, host over-massive black holes above the local $M_{\rm BH}$-$M_{*}$ relation, making it the first sample of galaxies hosting over-massive black holes at $z<0.4$. These findings will help us learn more about the formation and co-evolution of early galaxies and black holes., Comment: 11 pages, 4 figures, 2 tables, submitted to the Astrophysical Journal Letters. Comments welcome!

We study the galaxies hosting ultra-strong MgII (USMgII) absorbers at small impact parameters of $\sim$2" (5 - 20 kpc), spanning a redshift range of $0.4 \le z \le 1.7$, using deep, high-resolution images from Hyper Suprime-Cam Subaru Strategic Survey and spectra from SDSS survey. From a total of 418 USMgII absorbers with $W_{2796}\ \ge 3 \mathring{A}$, along 412 quasar sightlines, we detect 50 galaxies based on [O II] $\lambda\lambda$3727,3729 nebular emission detected at $\ge 2\sigma$ level. Utilizing the [O II] emission from the stacked spectrum and employing the best-fit galaxy SED template, we further identify 86 galaxies, leading to a total of 136 bona fide USMgII galaxies. With a prerequisite of having a minimum of four HSC passbands available, we find a detection rate of $\sim$38% at an average impact parameter of 11.4 kpc. We find that galaxies hosting USMgII systems are typically star-forming main sequence galaxies, with 21% exhibiting a starburst nature. The non-zero [O II] emission along the `clear' sightlines, with no stellar counterpart, hints that the USMgII absorbers may likely emanate from the unseen faint galaxies near the quasar. The USMgII absorbers preferentially align along the major and minor axes of the galaxy, which suggests that they originate in the disk or large-scale wind. We show that the distribution of $W_{2796}$ as a function of impact parameter indicates a discernible radial dependence for the `disk' and `wind' subsets, with the observed large scatter in $W_{2796}$ potentially attributed to large-scale outflows. The quasar sightline hosting USMgII systems show a factor three higher galaxy surface density at impact parameters of $\lesssim 50$kpc, highlights the multiple pathways giving rise to USMgII absorption., Comment: 12 Pages, 9 Figures, A&A (accepted for the publication)

Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse scenarios. While learned methods using hand labels or self-supervised data improve generalizability, they often require a tremendous amount of data and can be vulnerable to domain shifts. To improve generalization in novel environments, recent works have incorporated adaptation and self-supervision to develop autonomous systems that can learn from their own experiences online. However, current works often rely on significant prior data, for example minutes of human teleoperation data for each terrain type, which is difficult to scale with more environments and robots. To address these limitations, we propose SALON, a perception-action framework for fast adaptation of traversability estimates with minimal human input. SALON rapidly learns online from experience while avoiding out of distribution terrains to produce adaptive and risk-aware cost and speed maps. Within seconds of collected experience, our results demonstrate comparable navigation performance over kilometer-scale courses in diverse off-road terrain as methods trained on 100-1000x more data. We additionally show promising results on significantly different robots in different environments. Our code is available at https://theairlab.org/SALON.

This study investigates the reattachment of flow on a stalled NACA 0025 airfoil with an array of circular synthetic jet actuators. Experimental flow visualizations are used to assess the spanwise control authority of the array and the three-dimensionality of the flow. Numerical simulations provide insights into the flow structures created by the actuation, and how they evolve with different parameters., Comment: 5 pages, 7 figures, conference

Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultural biases that do not accurately assess other languages and cultures. To address this research gap, we introduce KULTURE Bench, an evaluation framework specifically designed for Korean culture that features datasets of cultural news, idioms, and poetry. It is designed to assess language models' cultural comprehension and reasoning capabilities at the word, sentence, and paragraph levels. Using the KULTURE Bench, we assessed the capabilities of models trained with different language corpora and analyzed the results comprehensively. The results show that there is still significant room for improvement in the models' understanding of texts related to the deeper aspects of Korean culture., Comment: Accepted by the 38th Pacific Asia Conference on Language, Information and Computation

Multi-year observations from the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project have significantly increased the number of quasars with reliable reverberation-mapping lag measurements. We statistically analyze target properties, light-curve characteristics, and survey design choices to identify factors crucial for successful and efficient RM surveys. Analyzing 172 high-confidence ("gold") lag measurements from SDSS-RM for the H$\beta$, MgII, and CIV emission lines, we find that the Durbin-Watson statistic (a statistical test for residual correlation) is the most significant predictor of light curves suitable for lag detection. Variability signal-to-noise ratio and emission-line placement on the detector also correlate with successful lag measurements. We further investigate the impact of observing cadence on survey design by analyzing the effect of reducing observations in the first year of SDSS-RM. Our results demonstrate that a modest reduction in observing cadence to $\sim$1.5 weeks between observations can retain approximately 90% of the lag measurements compared to twice-weekly observations in the initial year. Provided similar and uniform sampling in subsequent years, this adjustment has a minimal effect on the overall recovery of lags across all emission lines. These results provide valuable inputs for optimizing future RM surveys., Comment: 22 pages, 9 figures, and 3 tables. Submitted to the Astrophysical Journal

The rise of "bedroom producers" has democratized music creation, while challenging producers to objectively evaluate their work. To address this, we present AI TrackMate, an LLM-based music chatbot designed to provide constructive feedback on music productions. By combining LLMs' inherent musical knowledge with direct audio track analysis, AI TrackMate offers production-specific insights, distinguishing it from text-only approaches. Our framework integrates a Music Analysis Module, an LLM-Readable Music Report, and Music Production-Oriented Feedback Instruction, creating a plug-and-play, training-free system compatible with various LLMs and adaptable to future advancements. We demonstrate AI TrackMate's capabilities through an interactive web interface and present findings from a pilot study with a music producer. By bridging AI capabilities with the needs of independent producers, AI TrackMate offers on-demand analytical feedback, potentially supporting the creative process and skill development in music production. This system addresses the growing demand for objective self-assessment tools in the evolving landscape of independent music production., Comment: Accepted for the NeurIPS 2024 Creative AI Track

We articulate fundamental mismatches between technical methods for machine unlearning in Generative AI, and documented aspirations for broader impact that these methods could have for law and policy. These aspirations are both numerous and varied, motivated by issues that pertain to privacy, copyright, safety, and more. For example, unlearning is often invoked as a solution for removing the effects of targeted information from a generative-AI model's parameters, e.g., a particular individual's personal data or in-copyright expression of Spiderman that was included in the model's training data. Unlearning is also proposed as a way to prevent a model from generating targeted types of information in its outputs, e.g., generations that closely resemble a particular individual's data or reflect the concept of "Spiderman." Both of these goals--the targeted removal of information from a model and the targeted suppression of information from a model's outputs--present various technical and substantive challenges. We provide a framework for thinking rigorously about these challenges, which enables us to be clear about why unlearning is not a general-purpose solution for circumscribing generative-AI model behavior in service of broader positive impact. We aim for conceptual clarity and to encourage more thoughtful communication among machine learning (ML), law, and policy experts who seek to develop and apply technical methods for compliance with policy objectives., Comment: Presented at the 2nd Workshop on Generative AI and Law at ICML (July 2024)

As Graph Neural Networks (GNNs) become increasingly popular for learning from large-scale graph data across various domains, their susceptibility to adversarial attacks when using graph reduction techniques for scalability remains underexplored. In this paper, we present an extensive empirical study to investigate the impact of graph reduction techniques, specifically graph coarsening and sparsification, on the robustness of GNNs against adversarial attacks. Through extensive experiments involving multiple datasets and GNN architectures, we examine the effects of four sparsification and six coarsening methods on the poisoning attacks. Our results indicate that, while graph sparsification can mitigate the effectiveness of certain poisoning attacks, such as Mettack, it has limited impact on others, like PGD. Conversely, graph coarsening tends to amplify the adversarial impact, significantly reducing classification accuracy as the reduction ratio decreases. Additionally, we provide a novel analysis of the causes driving these effects and examine how defensive GNN models perform under graph reduction, offering practical insights for designing robust GNNs within graph acceleration systems.

We introduce a radiology-focused visual language model designed to generate radiology reports from chest X-rays. Building on previous findings that large language models (LLMs) can acquire multimodal capabilities when aligned with pretrained vision encoders, we demonstrate similar potential with chest X-ray images. This integration enhances the ability of model to understand and describe chest X-ray images. Our model combines an image encoder with a fine-tuned LLM based on the Vicuna-7B architecture, enabling it to generate different sections of a radiology report with notable accuracy. The training process involves a two-stage approach: (i) initial alignment of chest X-ray features with the LLM (ii) followed by fine-tuning for radiology report generation., Comment: Accepted by BioNLP@ACL 2024

We present experimental results on tuning biphoton frequency by introducing a detuned coupling field in spontaneous four-wave mixing (SFWM), and examine its impact on the pairing ratio. This tunability is achieved by manipulating the inherent electromagnetically induced transparency (EIT) effect in the double-$\Lambda$ scheme. Introducing a detuned coupling field degrades the efficiency of EIT-based stimulated four-wave mixing, which in turn reduces the biphoton pairing ratio. However, this reduction can be mitigated by increasing the optical power of the coupling field. Additionally, we observe that blue- and red-detuning the biphoton frequency results in distinct temporal profiles of biphoton wavepackets due to phase mismatch. These findings provide insights into the mechanisms of frequency-tunable biphoton generation via SFWM, and suggest potential optimizations for applications in quantum communication and information processing.

Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual signals for various low-vision tasks including image super-resolution (ISR). INR-based ISR methods typically learn continuous representations, providing flexibility for generating high-resolution images at any desired scale from their low-resolution counterparts. However, existing INR-based ISR methods utilize multi-layer perceptrons for parameterization in the network; this does not take account of the hierarchical structure existing in local sampling points and hence constrains the representation capability. In this paper, we propose a new \textbf{H}ierarchical encoding based \textbf{I}mplicit \textbf{I}mage \textbf{F}unction for continuous image super-resolution, \textbf{HIIF}, which leverages a novel hierarchical positional encoding that enhances the local implicit representation, enabling it to capture fine details at multiple scales. Our approach also embeds a multi-head linear attention mechanism within the implicit attention network by taking additional non-local information into account. Our experiments show that, when integrated with different backbone encoders, HIIF outperforms the state-of-the-art continuous image super-resolution methods by up to 0.17dB in PSNR. The source code of HIIF will be made publicly available at \url{www.github.com}.

We study the rest-frame ultraviolet-optical color gradients of 669 galaxies at $4

Glycated hemoglobin (HbA1c) is the most important factor in diabetes control. Since HbA1c reflects the average blood glucose level over the preceding three months, it is unaffected by the patient's activity level or diet before the test. Noninvasive HbA1c measurement reduces both the pain and complications associated with fingertip piercing to collect blood. Photoplethysmography is helpful for measuring HbA1c without blood samples. Herein, only two wavelengths (615 and 525 nm) were used to estimate HbA1c noninvasively, where two different ratio calibrations were applied and performances were compared to a work that uses three wavelengths. For the fingertip type, the Pearson r values for HbA1c estimates are 0.896 and 0.905 considering ratio calibrations for blood vessel and whole finger models, respectively. Using another value (HbA1c) calibration in addition to ratio calibrations, we can improve this performance, such that the Pearson r values of HbA1c levels are 0.929 and 0.930 for blood vessel and whole finger models, respectively. In the previous study using three wavelengths, the Pearson r values were 0.916 and 0.959 for the blood-vessel and whole-finger models, respectively. Here, the RCF of SpO2 estimation is 0.986 when SpO2 ratio calibration is applied, while in the previous study, the RCF values of SpO2 estimation were 0.983 and 0.986 for the blood-vessel and whole finger models, respectively. Thus, we show that HbA1c estimation using only two wavelengths has comparable performance to previous studies., Comment: 17 pages, 8 figures

Retrieval-augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge to reduce hallucinations and incorporate up-to-date information without retraining. As an essential part of RAG, external knowledge bases are commonly built by extracting structured data from unstructured PDF documents using Optical Character Recognition (OCR). However, given the imperfect prediction of OCR and the inherent non-uniform representation of structured data, knowledge bases inevitably contain various OCR noises. In this paper, we introduce OHRBench, the first benchmark for understanding the cascading impact of OCR on RAG systems. OHRBench includes 350 carefully selected unstructured PDF documents from six real-world RAG application domains, along with Q&As derived from multimodal elements in documents, challenging existing OCR solutions used for RAG To better understand OCR's impact on RAG systems, we identify two primary types of OCR noise: Semantic Noise and Formatting Noise and apply perturbation to generate a set of structured data with varying degrees of each OCR noise. Using OHRBench, we first conduct a comprehensive evaluation of current OCR solutions and reveal that none is competent for constructing high-quality knowledge bases for RAG systems. We then systematically evaluate the impact of these two noise types and demonstrate the vulnerability of RAG systems. Furthermore, we discuss the potential of employing Vision-Language Models (VLMs) without OCR in RAG systems. Code: https://github.com/opendatalab/OHR-Bench

String field theories exhibit exponential suppression of interactions among component fields at high energies due to infinite-derivative factors such as $e^{\ell^2 \, \Box / 2}$ in their vertices. This nonlocality has hindered the development of a consistent Hamiltonian formalism, leading some to question whether such a formalism is even viable. To address this challenge, we introduce a toy model inspired by string field theory, and construct its Hamiltonian formalism by demanding that all correlation functions derived from the path-integral formalism are reproduced. Within this framework, we demonstrate that negative-norm states are eliminated by physical-state conditions, while zero-norm states decouple from the physical state space. This approach provides a novel perspective on the nonlocality inherent in string field theories., Comment: 66 pages, 1 figure

We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MULTIMODAL UNIVERSE contains hundreds of millions of astronomical observations, constituting 100\,TB of multi-channel and hyper-spectral images, spectra, multivariate time series, as well as a wide variety of associated scientific measurements and "metadata". In addition, we include a range of benchmark tasks representative of standard practices for machine learning methods in astrophysics. This massive dataset will enable the development of large multi-modal models specifically targeted towards scientific applications. All codes used to compile the MULTIMODAL UNIVERSE and a description of how to access the data is available at https://github.com/MultimodalUniverse/MultimodalUniverse, Comment: Accepted at NeurIPS Datasets and Benchmarks track

Space missions, particularly complex, large-scale exploration campaigns, can often involve many discrete decisions or events in their concepts of operations. Whilst a variety of methods exist for the optimisation of continuous variables in mission design, the inherent presence of discrete events in mission ConOps disrupts the possibility of using methods that are dependent on having well-defined, continuous mathematical expressions to define the systems. Typically, mission architects will circumvent this problem by solving the system optimisation for every permutation of the categorical decisions if practical, or use metaheuristic solvers if not. However, this can be prohibitively expensive in terms of computation time. Alternatively, categorical decisions in optimisation problems can be expressed using binary variables. If implemented naively, commercially available MILP solvers are still slow to solve such a problem. Problems of this class can be solved more efficiently using column generation methods. Here, restricted problems are created by removing significant numbers of variables. The restricted problem is solved, and the unused variables are priced to test which, if any, could improve the objective of the restricted problem if they were to be added. Column generation methods are problem-specific, and so there is no guaranteed solution to these categorical problems. As such, the following paper proposes guidelines for defining restricted problems representing space exploration mission concepts of operations. The column generation process is described and then applied to two case studies: a ConOps for a crewed Mars mission, in which the design, assembly, and staging of the trans-Martian spacecraft is modelled using discrete decisions; and the payload delivery scheduling of translunar logistics in the context of an extended Artemis surface exploration campaign., Comment: 21 pages, 10 figures, presented at 75th International Astronautical Congress (IAC), Milan, Italy, 2024

Conversational scenarios are very common in real-world settings, yet existing co-speech motion synthesis approaches often fall short in these contexts, where one person's audio and gestures will influence the other's responses. Additionally, most existing methods rely on offline sequence-to-sequence frameworks, which are unsuitable for online applications. In this work, we introduce an audio-driven, auto-regressive system designed to synthesize dynamic movements for two characters during a conversation. At the core of our approach is a diffusion-based full-body motion synthesis model, which is conditioned on the past states of both characters, speech audio, and a task-oriented motion trajectory input, allowing for flexible spatial control. To enhance the model's ability to learn diverse interactions, we have enriched existing two-person conversational motion datasets with more dynamic and interactive motions. We evaluate our system through multiple experiments to show it outperforms across a variety of tasks, including single and two-person co-speech motion generation, as well as interactive motion generation. To the best of our knowledge, this is the first system capable of generating interactive full-body motions for two characters from speech in an online manner., Comment: 15 pages, 10 figures

Transformer-based architectures have demonstrated remarkable success across various domains, but their deployment on edge devices remains challenging due to high memory and computational demands. In this paper, we introduce a novel Reuse Attention mechanism, tailored for efficient memory access and computational optimization, enabling seamless operation on resource-constrained platforms without compromising performance. Unlike traditional multi-head attention (MHA), which redundantly computes separate attention matrices for each head, Reuse Attention consolidates these computations into a shared attention matrix, significantly reducing memory overhead and computational complexity. Comprehensive experiments on ImageNet-1K and downstream tasks show that the proposed UniForm models leveraging Reuse Attention achieve state-of-the-art imagenet classification accuracy while outperforming existing attention mechanisms, such as Linear Attention and Flash Attention, in inference speed and memory scalability. Notably, UniForm-l achieves a 76.7% Top-1 accuracy on ImageNet-1K with 21.8ms inference time on edge devices like the Jetson AGX Orin, representing up to a 5x speedup over competing benchmark methods. These results demonstrate the versatility of Reuse Attention across high-performance GPUs and edge platforms, paving the way for broader real-time applications, Comment: 13 Pages, 8 Tables, 7 Figures

Traditional global positioning systems often underperform indoors, whereas Wi-Fi has become an effective medium for various radio sensing services. Specifically, utilizing channel state information (CSI) from Wi-Fi networks provides a non-contact method for precise indoor positioning; yet, accurately interpreting the complex CSI matrix to develop a reliable strategy for physical similarity measurement remains challenging. This paper presents BiCSI, which merges binary encoding with fingerprint-based techniques to improve position matching for detecting semi-stationary targets. Inspired by gene sequencing processes, BiCSI initially converts CSI matrices into binary sequences and employs Hamming distances to evaluate signal similarity. The results show that BiCSI achieves an average accuracy above 98% and a mean absolute error (MAE) of less than three centimeters, outperforming algorithms directly dependent on physical measurements by at least two-fold. Moreover, the proposed method for extracting feature vectors from CSI matrices as fingerprints significantly reduces data storage requirements to the kilobyte range, far below the megabytes typically required by conventional machine learning models. Additionally, the results demonstrate that the proposed algorithm adapts well to multiple physical similarity metrics, and remains robust over different time periods, enhancing its utility and versatility in various scenarios., Comment: 10 pages, 14 figures, this article was submitted to IEEE for possible publication

Works on the energy extraction from a rotating black hole via magnetic reconnection attract more attentions in recent years. Discussions on this topic, however, are often based on many simplifications, such as assuming a circularly flowing bulk plasma and a fixed orientation angle. A significant gap remains between theoretical models and the magnetic reconnection occurring in real astrophysical scenarios. In our previous work, we investigated the influence of orientation angle on energy extraction and figured out the differences between the plunging and circularly flowing bulk plasma. We introduced the concept of covering factor to quantify the capability of an accretion system in extracting energy via magnetic reconnection from a rotating black hole. In this work, as an improvement, we extend our discussions by treating the parameters in reconnection models as free parameters, bringing the theoretical model closer to the situations in real astrophysical systems. We employ two reconnection models, in which the geometric index and the guide field fraction are respectively induced. We separately present the dependences of energy extraction on the geometric index and the guide field fraction. More importantly, we propose to define the averaged covering factor weighted by reconnection rate to quantify the overall capability of an accretion system in extracting energy via magnetic reconnection from a rotating black hole, under the assumption that the magnetic reconnection process occurs stochastically and randomly within the ergosphere., Comment: Accepted for publication in PRD

Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images. This review highlights the substantial benefits of integrating geometric data into AI models, addressing challenges such as high inter-class variations, domain gaps, and the separation of style from content by incorporating geometric information. Models not only improve AI-generated graphics synthesis quality, but also effectively distinguish between style and content, utilizing inherent model biases and shared data traits. We explore methods like geometric data extraction from artistic images, the impact on human perception, and its use in discriminative tasks. The review also discusses the potential for improving data quality through innovative annotation techniques and the use of geometric data to enhance model adaptability and output refinement. Overall, incorporating geometric guidance boosts model performance in classification and synthesis tasks, providing crucial insights for future AI applications in the visual arts domain., Comment: 56 pages, 8 tables, 1 figure (35 embedded images), Artificial Intelligence Review (AIR) 2024

Early detection of skin abnormalities plays a crucial role in diagnosing and treating skin cancer. Segmentation of affected skin regions using AI-powered devices is relatively common and supports the diagnostic process. However, achieving high performance remains a significant challenge due to the need for high-resolution images and the often unclear boundaries of individual lesions. At the same time, medical devices require segmentation models to have a small memory foot-print and low computational cost. Based on these requirements, we introduce a novel lightweight model called MambaU-Lite, which combines the strengths of Mamba and CNN architectures, featuring just over 400K parameters and a computational cost of more than 1G flops. To enhance both global context and local feature extraction, we propose the P-Mamba block, a novel component that incorporates VSS blocks along-side multiple pooling layers, enabling the model to effectively learn multiscale features and enhance segmentation performance. We evaluate the model's performance on two skin datasets, ISIC2018 and PH2, yielding promising results. Our source code will be made publicly available at: https://github.com/nqnguyen812/MambaU-Lite., Comment: 10 pages, 3 figures, 2 tables

Super-Alfvenic turbulence is important for many astrophysical objects, particularly galaxy clusters. In this paper, we explore the accuracy of Synchrotron Intensity Gradients (SIGs) and X-ray intensity gradients to map magnetic fields in super-Alfvenic turbulence for a set of astrophysically relevant parameters of turbulent driving. Analyzing our synthetic observations, we report a good accuracy for both techniques. Our results are suggestive that other types of Gradient Technique (GT) can be successfully employed to trace magnetic fields within super-Alfvenic sub-sonic turbulence., Comment: Submitted to ApJ

We report the results of an extensive numerical study of the $Sp(4)$ lattice gauge theory with three (Dirac) flavors of fermion in the two-index antisymmetric representation. In the presence of (degenerate) fermion masses, the theory has an enhanced global $SU(6)$ symmetry, broken explicitly and spontaneously to its $SO(6)$ subgroup. This symmetry breaking pattern makes the theory interesting for applications in the context of composite Higgs models, as well as for the implementation of top partial compositeness. It can also provide a dynamical realisation of the strongly interacting massive particle paradigm for the origin of dark matter. We adopt the standard plaquette gauge action with the Wilson-Dirac formulation for the fermions and apply the (rational) hybrid Monte Carlo algorithm in our ensemble generation process. We monitor the autocorrelation and topology of the ensembles. We explore the bare parameter space, and identify the weak and strong coupling regimes separated by a line of first-order bulk phase transitions. We measure two-point correlation functions between meson operators that transform as non-trivial representations of $SO(6)$, and extract the ground-state masses and the decay constants, in all accessible spin and parity channels. In addition, we measure the mass of the first excited state for the vector meson by solving a generalised eigenvalue problem. Spectral quantities show a mass dependence that is compatible with the expectation that, at long distances, the theory undergoes confinement, accompanied by the spontaneous breaking of the approximate global symmetries acting on the matter fields. Finally, we discuss the continuum and massless extrapolations, after setting the physical scale using the gradient flow method, and compare the results to those of existing studies in the quenched approximation, as well as to the literature on closely related theories., Comment: 43 pages, 20 figures, 11 tables

We propose a framework to edit real-world Lidar scans with novel object layouts while preserving a realistic background environment. Compared to the synthetic data generation frameworks where Lidar point clouds are generated from scratch, our framework focuses on new scenario generation in a given background environment, and our method also provides labels for the generated data. This approach ensures the generated data remains relevant to the specific environment, aiding both the development and the evaluation of algorithms in real-world scenarios. Compared with novel view synthesis, our framework allows the creation of counterfactual scenarios with significant changes in the object layout and does not rely on multi-frame optimization. In our framework, the object removal and insertion are supported by generative background inpainting and object point cloud completion, and the entire pipeline is built upon spherical voxelization, which realizes the correct Lidar projective geometry by construction. Experiments show that our framework generates realistic Lidar scans with object layout changes and benefits the development of Lidar-based self-driving systems., Comment: Submitted to IEEE International Conference on Robotics and Automation (ICRA). 6 pages, 7 figures

Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain experts and numerical software developers to provide 15TB of data across 16 datasets covering diverse domains such as biological systems, fluid dynamics, acoustic scattering, as well as magneto-hydrodynamic simulations of extra-galactic fluids or supernova explosions. These datasets can be used individually or as part of a broader benchmark suite. To facilitate usage of the Well, we provide a unified PyTorch interface for training and evaluating models. We demonstrate the function of this library by introducing example baselines that highlight the new challenges posed by the complex dynamics of the Well. The code and data is available at https://github.com/PolymathicAI/the_well., Comment: 38th Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmarks

We study the collapse of an attractive Bose-Einstein condensate, where an unstable system evolves towards a singularity, by numerically solving the underlying cubic-quintic nonlinear Schr\"odinger equation. We find good agreement between our simulations and the atom-loss measurements with a $^{39}$K condensate. Our simulations reveal an interplay of weak collapse and the propensity of the system to form a hotspot, and we uncover new scaling laws that govern this behavior. We also identify promising signatures of the theoretically predicted, but so far experimentally elusive, elastic three-body interactions., Comment: Main text (6 pages, 4 figures), Supplemental Material (2 pages, 4 figures)

The abundances resulting from $r$-process nucleosynthesis as predicted by simulations of binary neutron-star (BNS) mergers remain an open question as the current state-of-the-art is still restricted to three-species neutrino transport. We present the first BNS merger simulations employing a moment-based general-relativistic neutrino transport with five neutrino species, thus including (anti)muons and advanced muonic $\beta$-processes, and contrast them with traditional three neutrino-species simulations. Our results show that a muonic trapped-neutrino equilibrium is established, forming a different trapped-neutrino hierarchy akin to the electronic equilibrium. The formation of (anti)muons and the muonization via muonic $\beta$-processes enhance the neutrino luminosity, leading to rapid cooling in the early post-merger phase. Since muonic processes redirect part of the energy otherwise used for protonization by electronic processes, they yield a cooler remnant and disk, together with neutrino-driven winds that are more neutron-rich. Importantly, the unbound ejected mass is smaller than three-species simulations and, because of its comparatively smaller temperature and proton fraction, it can enhance lanthanide production and reduce the overproduction of light $r$-process elements for softer equations of state. This finding underlines the importance of muonic interactions and five neutrino species in long-lived BNS remnants.

The topological properties of gapped graphene have been explored for valleytronics applications. Prior transport experiments indicated their topological nature through large nonlocal resistance in Hall-bar devices, but the origin of this resistance was unclear. This study focused on dual-gate bilayer graphene (BLG) devices with naturally cleaved edges, examining how edge-etching with an oxygen plasma process affects electron transport. Before etching, local resistance at the charge neutral point increased exponentially with the displacement field and nonlocal resistance was well explained by ohmic contribution, which is typical of gapped BLG. After-etching, however, local resistance saturated with increasing displacement field, and nonlocal resistance deviated by three orders of magnitude from ohmic contribution. We suggest that these significant changes in local and nonlocal resistance arise from the formation of edge conducting pathways after the edge-etching, rather than from a topological property of gapped BLG that has been claimed in previous literatures., Comment: 22 pages, 6 figures

This study proposes a qualitative analysis of self replies in Wikipedia talk pages, more precisely when the first two messages of a discussion are written by the same user. This specific pattern occurs in more than 10% of threads with two messages or more and can be explained by a number of reasons. After a first examination of the lexical specificities of second messages, we propose a seven categories typology and use it to annotate two reference samples (English and French) of 100 threads each. Finally, we analyse and compare the performance of human annotators (who reach a reasonable global efficiency) and instruction-tuned LLMs (which encounter important difficulties with several categories).

Changing-look active galactic nuclei (CL-AGNs), characterized by emerging or disappearing of broad lines accompanied with extreme continuum flux variability, have drawn much attention for their potential of revealing physical processes underlying AGN evolution. We perform seven-season spectroscopic monitoring on Mrk~1018, one of the earliest identified CL-AGN. Around 2020, we detect a full-cycle changing-look transition of Mrk~1018 within one year, associated with a nucleus outburst, which likely arise from the disk instability in the transition region between the outer standard rotation-dominated disk and inner advection-dominated accretion flow. Over the past forty-five years, the accretion rate of Mrk~1018 changed 1000 times and the maximum Eddington ratio reached 0.02. By investigating the relation between broad-line properties and Eddington ratio ($L_{\rm bol}/L_{\rm Edd}$), we find strong evidence that the full-cycle type transition is regulated by accretion. There exists a turnover point in the Balmer decrement, which is observed for the first time. The broad Balmer lines change from a single peak in Type 1.0-1.2 to double peaks in Type 1.5-1.8 and the double-peak separation decreases with increasing accretion rate. We also find that the full width at half maximum (FWHM) of the broad Balmer lines obeys FWHM$\propto (L_{\rm bol}/L_{\rm Edd})^{-0.27}$, as expected for a virialized BLR. The velocity dispersion $\sigma_{\rm line}$ follows a similar trend in Type 1.5-1.8, but displays a sharp increases in Type 1.0-1.2, resulting in a dramatic drop of FWHM/$\sigma_{\rm line}$. These findings suggest that a virialized BLR together with accretion-dependent turbulent motions might be responsible for the diversity of BLR phenomena across AGN population., Comment: 24pages, 12figures, accepted by ApJS

In this paper, we introduce the Actions and Objects Pathways (AOPath) for out-of-domain generalization in video question answering tasks. AOPath leverages features from a large pretrained model to enhance generalizability without the need for explicit training on the unseen domains. Inspired by human brain, AOPath dissociates the pretrained features into action and object features, and subsequently processes them through separate reasoning pathways. It utilizes a novel module which converts out-of-domain features into domain-agnostic features without introducing any trainable weights. We validate the proposed approach on the TVQA dataset, which is partitioned into multiple subsets based on genre to facilitate the assessment of generalizability. The proposed approach demonstrates 5% and 4% superior performance over conventional classifiers on out-of-domain and in-domain datasets, respectively. It also outperforms prior methods that involve training millions of parameters, whereas the proposed approach trains very few parameters.

Radiology report generation (RRG) is a challenging task, as it requires a thorough understanding of medical images, integration of multiple temporal inputs, and accurate report generation. Effective interpretation of medical images, such as chest X-rays (CXRs), demands sophisticated visual-language reasoning to map visual findings to structured reports. Recent studies have shown that multimodal large language models (MLLMs) can acquire multimodal capabilities by aligning with pre-trained vision encoders. However, current approaches predominantly focus on single-image analysis or utilise rule-based symbolic processing to handle multiple images, thereby overlooking the essential temporal information derived from comparing current images with prior ones. To overcome this critical limitation, we introduce Libra, a temporal-aware MLLM tailored for CXR report generation using temporal images. Libra integrates a radiology-specific image encoder with a MLLM and utilises a novel Temporal Alignment Connector to capture and synthesise temporal information of images across different time points with unprecedented precision. Extensive experiments show that Libra achieves new state-of-the-art performance among the same parameter scale MLLMs for RRG tasks on the MIMIC-CXR. Specifically, Libra improves the RadCliQ metric by 12.9% and makes substantial gains across all lexical metrics compared to previous models.

Identifying defects and anomalies in industrial products is a critical quality control task. Traditional manual inspection methods are slow, subjective, and error-prone. In this work, we propose a novel zero-shot training-free approach for automated industrial image anomaly detection using a multimodal machine learning pipeline, consisting of three foundation models. Our method first uses a large language model, i.e., GPT-3. generate text prompts describing the expected appearances of normal and abnormal products. We then use a grounding object detection model, called Grounding DINO, to locate the product in the image. Finally, we compare the cropped product image patches to the generated prompts using a zero-shot image-text matching model, called CLIP, to identify any anomalies. Our experiments on two datasets of industrial product images, namely MVTec-AD and VisA, demonstrate the effectiveness of this method, achieving high accuracy in detecting various types of defects and anomalies without the need for model training. Our proposed model enables efficient, scalable, and objective quality control in industrial manufacturing settings., Comment: Accepted to APSIPA ASC 2024

In the advanced technology nodes, the integrated design rule checker (DRC) is often utilized in place and route tools for fast optimization loops for power-performance-area. Implementing integrated DRC checkers to meet the standard of commercial DRC tools demands extensive human expertise to interpret foundry specifications, analyze layouts, and debug code iteratively. However, this labor-intensive process, requiring to be repeated by every update of technology nodes, prolongs the turnaround time of designing circuits. In this paper, we present DRC-Coder, a multi-agent framework with vision capabilities for automated DRC code generation. By incorporating vision language models and large language models (LLM), DRC-Coder can effectively process textual, visual, and layout information to perform rule interpretation and coding by two specialized LLMs. We also design an auto-evaluation function for LLMs to enable DRC code debugging. Experimental results show that targeting on a sub-3nm technology node for a state-of-the-art standard cell layout tool, DRC-Coder achieves perfect F1 score 1.000 in generating DRC codes for meeting the standard of a commercial DRC tool, highly outperforming standard prompting techniques (F1=0.631). DRC-Coder can generate code for each design rule within four minutes on average, which significantly accelerates technology advancement and reduces engineering costs., Comment: Proceedings of the 2025 International Symposium on Physical Design (ISPD '25), March 16--19, 2025, Austin, TX, USA

Convolutional Neural Networks (CNNs) achieve high performance in image classification tasks but are challenging to deploy on resource-limited hardware due to their large model sizes. To address this issue, we leverage Mutual Information, a metric that provides valuable insights into how deep learning models retain and process information through measuring the shared information between input features or output labels and network layers. In this study, we propose a structured filter-pruning approach for CNNs that identifies and selectively retains the most informative features in each layer. Our approach successively evaluates each layer by ranking the importance of its feature maps based on Conditional Mutual Information (CMI) values, computed using a matrix-based Renyi {\alpha}-order entropy numerical method. We propose several formulations of CMI to capture correlation among features across different layers. We then develop various strategies to determine the cutoff point for CMI values to prune unimportant features. This approach allows parallel pruning in both forward and backward directions and significantly reduces model size while preserving accuracy. Tested on the VGG16 architecture with the CIFAR-10 dataset, the proposed method reduces the number of filters by more than a third, with only a 0.32% drop in test accuracy.

We report progress on our lattice calculations for the mass spectra of low-lying composite states in the Sp(4) gauge theory coupled to two and three flavors of Dirac fermions transforming in the fundamental and the two-index antisymmetric representations, respectively. This theory provides an ultraviolet completion to the composite Higgs model with Goldstone modes in the SU(4)/Sp(4) coset and with partial compositeness for generating the top-quark mass. We measure the meson and chimera baryon masses. These masses are crucial for constructing the composite Higgs model. In particular, the chimera baryon masses are important inputs for implementing top partial compositeness. We employ Wilson fermions and the Wilson plaquette action in our simulations. Techniques such as APE and Wuppertal smearing, as well as the procedure of generalised eigenvalue problem, are implemented in our analysis., Comment: 12 pages, 8 figures, 1 tables, Proceedings of the 41st International Symposium on Lattice Field Theory (Lattice 2024), July 28th - August 3rd, 2024, University of Liverpool, UK