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Full Waveform Inversion (FWI) is a vital technique for reconstructing high-resolution subsurface velocity maps from seismic waveform data, governed by partial differential equations (PDEs) that model wave propagation. Traditional machine learning approaches typically map seismic data to velocity maps by encoding seismic waveforms into latent embeddings and decoding them into velocity maps. In this paper, we introduce a novel framework that reframes FWI as a joint diffusion process in a shared latent space, bridging seismic waveform data and velocity maps. Our approach has two key components: first, we merge the bottlenecks of two separate autoencoders-one for seismic data and one for velocity maps-into a unified latent space using vector quantization to establish a shared codebook. Second, we train a diffusion model in this latent space, enabling the simultaneous generation of seismic and velocity map pairs by sampling and denoising the latent representations, followed by decoding each modality with its respective decoder. Remarkably, our jointly generated seismic-velocity pairs approximately satisfy the governing PDE without any additional constraint, offering a new geometric interpretation of FWI. The diffusion process learns to score the latent space according to its deviation from the PDE, with higher scores representing smaller deviations from the true solutions. By following this diffusion process, the model traces a path from random initialization to a valid solution of the governing PDE. Our experiments on the OpenFWI dataset demonstrate that the generated seismic and velocity map pairs not only exhibit high fidelity and diversity but also adhere to the physical constraints imposed by the governing PDE.

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

Employing grazing-incidence small-angle X-ray scattering (GISAXS) and X-ray reflectivity (XRR), we demonstrate that films composed of polyethylene glycol (PEG)-grafted silver nanoparticles (AgNP) and gold nanoparticles (AuNP), as well as their binary mixtures, form highly stable hexagonal structures at the vapor-liquid interface. These nanoparticles exhibit remarkable stability under varying environmental conditions, including changes in pH, mixing concentration, and PEG chain length. Short-chain PEG grafting produces dense, well-ordered films, while longer chains produce more complex, less dense quasi-bilayer structures. AuNPs exhibit higher grafting densities than AgNPs, leading to more ordered in-plane arrangements. In binary mixtures, AuNPs dominate the population at the surface, while AgNPs integrate into the system, expanding the lattice without forming a distinct binary superstructure. These results offer valuable insights into the structural behavior of PEG-grafted nanoparticles and provide a foundation for optimizing binary nanoparticle assemblies for advanced nanotechnology applications., Comment: 17 pages, 8 figures, 2 tables

In this paper, we propose a new method to enhance compositional understanding in pre-trained vision and language models (VLMs) without sacrificing performance in zero-shot multi-modal tasks. Traditional fine-tuning approaches often improve compositional reasoning at the cost of degrading multi-modal capabilities, primarily due to the use of global hard negative (HN) loss, which contrasts global representations of images and texts. This global HN loss pushes HN texts that are highly similar to the original ones, damaging the model's multi-modal representations. To overcome this limitation, we propose Fine-grained Selective Calibrated CLIP (FSC-CLIP), which integrates local hard negative loss and selective calibrated regularization. These innovations provide fine-grained negative supervision while preserving the model's representational integrity. Our extensive evaluations across diverse benchmarks for both compositionality and multi-modal tasks show that FSC-CLIP not only achieves compositionality on par with state-of-the-art models but also retains strong multi-modal capabilities. Code is available at: https://github.com/ytaek-oh/fsc-clip., Comment: EMNLP 2024 (Long, Main). Project page: https://ytaek-oh.github.io/fsc-clip

Object removal refers to the process of erasing designated objects from an image while preserving the overall appearance. Existing works on object removal erase removal targets using image inpainting networks. However, image inpainting networks often generate unsatisfactory removal results. In this work, we find that the current training approach which encourages a single image inpainting model to handle both object removal and restoration tasks is one of the reasons behind such unsatisfactory result. Based on this finding, we propose a task-decoupled image inpainting framework which generates two separate inpainting models: an object restorer for object restoration tasks and an object remover for object removal tasks. We train the object restorer with the masks that partially cover the removal targets. Then, the proposed framework makes an object restorer to generate a guidance for training the object remover. Using the proposed framework, we obtain a class-specific object remover which focuses on removing objects of a target class, aiming to better erase target class objects than general object removers. We also introduce a data curation method that encompasses the image selection and mask generation approaches used to produce training data for the proposed class-specific object remover. Using the proposed curation method, we can simulate the scenarios where an object remover is trained on the data with object removal ground truth images. Experiments on multiple datasets show that the proposed class-specific object remover can better remove target class objects than object removers based on image inpainting networks.

Fast radio bursts (FRBs) are millisecond-duration radio waves from the Universe. Even though more than 50 physical models have been proposed, the origin and physical mechanism of FRB emissions are still unknown. The classification of FRBs is one of the primary approaches to understanding their mechanisms, but previous studies classified conventionally using only a few observational parameters, such as fluence and duration, which might be incomplete. To overcome this problem, we use an unsupervised machine-learning model, the Uniform Manifold Approximation and Projection (UMAP) to handle seven parameters simultaneously, including amplitude, linear temporal drift, time duration, central frequency, bandwidth, scaled energy, and fluence. We test the method for homogeneous 977 sub-bursts of FRB 20121102A detected by the Arecibo telescope. Our machine-learning analysis identified five distinct clusters, suggesting the possible existence of multiple different physical mechanisms responsible for the observed FRBs from the FRB 20121102A source. The geometry of the emission region and the propagation effect of FRB signals could also make such distinct clusters. This research will be a benchmark for future FRB classifications when dedicated radio telescopes such as the Square Kilometer Array (SKA) or Bustling Universe Radio Survey Telescope in Taiwan (BURSTT) discover more FRBs than before., Comment: Accepted for publication for PASA

Constraint admissible positively invariant (CAPI) sets play a pivotal role in ensuring safety in control and planning applications, such as the recursive feasibility guarantee of explicit reference governor and model predictive control. However, existing methods for finding CAPI sets for nonlinear systems are often limited to single equilibria or specific system dynamics. This limitation underscores the necessity for a method to construct a CAPI set for general reference tracking control and a broader range of systems. In this work, we leverage recent advancements in learning-based methods to derive Lyapunov functions, particularly focusing on those with piecewise-affine activation functions. Previous attempts to find an invariant set with the piecewise-affine neural Lyapunov function have focused on the estimation of the region of attraction with mixed integer programs. We propose a methodology to determine the maximal CAPI set for any reference with the neural Lyapunov function by transforming the problem into multiple linear programs. Additionally, to enhance applicability in real-time control scenarios, we introduce a learning-based approach to train the estimator, which infers the CAPI set from a given reference. The proposed approach is validated with multiple simulations to show that it can generate a valid CAPI set with the given neural Lyapunov functions for any reference. We also employ the proposed CAPI set estimation method in the explicit reference governor and demonstrate its effectiveness for constrained control., Comment: 8 pages, 6 figures, Accepted to 63nd IEEE Conference on Decision and Control (CDC 2024)

As the prevalence of wearable devices, learning egocentric motions becomes essential to develop contextual AI. In this work, we present EgoLM, a versatile framework that tracks and understands egocentric motions from multi-modal inputs, e.g., egocentric videos and motion sensors. EgoLM exploits rich contexts for the disambiguation of egomotion tracking and understanding, which are ill-posed under single modality conditions. To facilitate the versatile and multi-modal framework, our key insight is to model the joint distribution of egocentric motions and natural languages using large language models (LLM). Multi-modal sensor inputs are encoded and projected to the joint latent space of language models, and used to prompt motion generation or text generation for egomotion tracking or understanding, respectively. Extensive experiments on large-scale multi-modal human motion dataset validate the effectiveness of EgoLM as a generalist model for universal egocentric learning., Comment: Project Page: https://hongfz16.github.io/projects/EgoLM

Recent advancements in image captioning have explored text-only training methods to overcome the limitations of paired image-text data. However, existing text-only training methods often overlook the modality gap between using text data during training and employing images during inference. To address this issue, we propose a novel approach called Image-like Retrieval, which aligns text features with visually relevant features to mitigate the modality gap. Our method further enhances the accuracy of generated captions by designing a Fusion Module that integrates retrieved captions with input features. Additionally, we introduce a Frequency-based Entity Filtering technique that significantly improves caption quality. We integrate these methods into a unified framework, which we refer to as IFCap ($\textbf{I}$mage-like Retrieval and $\textbf{F}$requency-based Entity Filtering for Zero-shot $\textbf{Cap}$tioning). Through extensive experimentation, our straightforward yet powerful approach has demonstrated its efficacy, outperforming the state-of-the-art methods by a significant margin in both image captioning and video captioning compared to zero-shot captioning based on text-only training., Comment: Accepted to EMNLP 2024

Mixture-of-Experts (MoE) models scale more effectively than dense models due to sparse computation through expert routing, selectively activating only a small subset of expert modules. However, sparse computation challenges traditional training practices, as discrete expert routing hinders standard backpropagation and thus gradient-based optimization, which are the cornerstone of deep learning. To better pursue the scaling power of MoE, we introduce GRIN (GRadient-INformed MoE training), which incorporates sparse gradient estimation for expert routing and configures model parallelism to avoid token dropping. Applying GRIN to autoregressive language modeling, we develop a top-2 16$\times$3.8B MoE model. Our model, with only 6.6B activated parameters, outperforms a 7B dense model and matches the performance of a 14B dense model trained on the same data. Extensive evaluations across diverse tasks demonstrate the potential of GRIN to significantly enhance MoE efficacy, achieving 79.4 on MMLU, 83.7 on HellaSwag, 74.4 on HumanEval, and 58.9 on MATH., Comment: 58 pages

The layered cobaltate CaCoO$_2$ exhibits a unique herringbone-like structure. Serving as a potential prototype for a new class of complex lattice patterns, we study the properties of CaCoO$_2$ using X-ray absorption spectroscopy (XAS) and resonant inelastic X-ray scattering (RIXS). Our results reveal a significant inter-plane hybridization between the Ca $4s-$ and Co $3d-$orbitals, leading to an inversion of the textbook orbital occupation of a square planar geometry. Further, our RIXS data reveal a strong low energy mode, with anomalous intensity modulations as a function of momentum transfer close to a quasi-static response suggestive of electronic and/or orbital ordering. These findings indicate that the newly discovered herringbone structure exhibited in CaCoO$_2$ may serve as a promising laboratory for the design of materials having strong electronic, orbital and lattice correlations.

Joint modeling of different data sources in decision-making processes is crucial for understanding decision dynamics in consumer behavior models. Sequential Sampling Models (SSMs), grounded in neuro-cognitive principles, provide a systematic approach to combining information from multi-source data, such as those based on response times and choice outcomes. However, parameter estimation of SSMs is challenging due to the complexity of joint likelihood functions. Likelihood-Free inference (LFI) approaches enable Bayesian inference in complex models with intractable likelihoods, like SSMs, and only require the ability to simulate synthetic data from the model. Extending a popular approach to simulation efficient LFI for single-source data, we propose Multi-objective Bayesian Optimization for Likelihood-Free Inference (MOBOLFI) to estimate the parameters of SSMs calibrated using multi-source data. MOBOLFI models a multi-dimensional discrepancy between observed and simulated data, using a discrepancy for each data source. Multi-objective Bayesian Optimization is then used to ensure simulation efficient approximation of the SSM likelihood. The use of a multivariate discrepancy allows for approximations to individual data source likelihoods in addition to the joint likelihood, enabling both the detection of conflicting information and a deeper understanding of the importance of different data sources in estimating individual SSM parameters. We illustrate the advantages of our approach in comparison with the use of a single discrepancy in a simple synthetic data example and an SSM example with real-world data assessing preferences of ride-hailing drivers in Singapore to rent electric vehicles. Although we focus on applications to SSMs, our approach applies to the Likelihood-Free calibration of other models using multi-source data., Comment: 42 pages, 17 figures

Polycyclic aromatic hydrocarbon (PAH) dust emission has been proposed as an effective extinction-independent star formation rate (SFR) indicator in the mid-infrared (MIR), but this may depend on conditions in the interstellar medium. The coverage of the AKARI/Infrared Camera (IRC) allows us to study the effects of metallicity, starburst intensity, and active galactic nuclei on PAH emission in galaxies with $f_{\nu}(L18W)\lesssim 19$ AB mag. Observations include follow-up, rest-frame optical spectra of 443 galaxies within the AKARI North Ecliptic Pole survey that have IRC detections from 7-24 $\mu$m. We use optical emission line diagnostics to infer SFR based on H$\alpha$ and [O II]$\lambda\lambda 3726,3729$ emission line luminosities. The PAH 6.2 $\mu$m and PAH 7.7 $\mu$m luminosities ($L(PAH\ 6.2\ \mu m)$ and $L(PAH\ 7.7\ \mu m)$, respectively) derived using multi-wavelength model fits are consistent with those derived from slitless spectroscopy within 0.2 dex. $L(PAH\ 6.2\ \mu m)$ and $L(PAH\ 7.7\ \mu m)$ correlate linearly with the 24 $\mu$m-dust corrected H$\alpha$ luminosity only for normal, star-forming ``main-sequence" galaxies. Assuming multi-linear correlations, we quantify the additional dependencies on metallicity and starburst intensity, which we use to correct our PAH SFR calibrations at $0

Building on previous works to construct a homogeneous sample of brown dwarfs in binary systems, we investigate microlensing events detected by the Korea Microlensing Telescope Network (KMTNet) survey during the 2022 and 2023 seasons. Given the difficulty in distinguishing brown-dwarf events from those produced by binary lenses with nearly equal-mass components, we analyze all lensing events detected during the seasons that exhibit anomalies characteristic of binary-lens systems. Using the same criteria consistently applied in previous studies, we identify six additional brown dwarf candidates through the analysis of lensing events KMT-2022-BLG-0412, KMT-2022-BLG-2286, KMT-2023-BLG-0201, KMT-2023-BLG-0601, KMT-2023-BLG-1684, and KMT-2023-BLG-1743. An examination of the mass posteriors shows that the median mass of the lens companions ranges from 0.02 $M_\odot$ to 0.05 $M_\odot$, indicating that these companions fall within the brown-dwarf mass range. The mass of the primary lenses ranges from 0.11 $M_\odot$ to 0.68 $M_\odot$, indicating that they are low-mass stars with substantially lower masses compared to the Sun., Comment: 13 pages, 17 figures, 12 tables

The purpose of this paper is two-fold: firstly, we give a characterization on the level of non-unital operator systems for when the zero map is a boundary representation. As a consequence, we show that a non-unital operator system arising from the direct limit of C*-algebras under positive maps is a C*-algebra if and only if its unitization is a C*-algebra. Secondly, we show that the completely positive approximation property and the completely contractive approximation property of a non-unital operator system is equivalent to its bidual being an injective von Neumann algebra. This implies in particular that all non-unital operator systems with the completely contractive approximation property must necessarily admit an abundance of positive elements.

This letter presents a versatile trajectory planning pipeline for aerial tracking. The proposed tracker is capable of handling various chasing settings such as complex unstructured environments, crowded dynamic obstacles and multiple-target following. Among the entire pipeline, we focus on developing a predictor for future target motion and a chasing trajectory planner. For rapid computation, we employ the sample-check-select strategy: modules sample a set of candidate movements, check multiple constraints, and then select the best trajectory. Also, we leverage the properties of Bernstein polynomials for quick calculations. The prediction module predicts the trajectories of the targets, which do not overlap with static and dynamic obstacles. Then the trajectory planner outputs a trajectory, ensuring various conditions such as occlusion and collision avoidance, the visibility of all targets within a camera image and dynamical limits. We fully test the proposed tracker in simulations and hardware experiments under challenging scenarios, including dual-target following, environments with dozens of dynamic obstacles and complex indoor and outdoor spaces., Comment: 8 pages, 9 figures

Building an echocardiography view classifier that maintains performance in real-life cases requires diverse multi-site data, and frequent updates with newly available data to mitigate model drift. Simply fine-tuning on new datasets results in "catastrophic forgetting", and cannot adapt to variations of view labels between sites. Alternatively, collecting all data on a single server and re-training may not be feasible as data sharing agreements may restrict image transfer, or datasets may only become available at different times. Furthermore, time and cost associated with re-training grows with every new dataset. We propose a class-incremental learning method which learns an expert network for each dataset, and combines all expert networks with a score fusion model. The influence of ``unqualified experts'' is minimised by weighting each contribution with a learnt in-distribution score. These weights promote transparency as the contribution of each expert is known during inference. Instead of using the original images, we use learned features from each dataset, which are easier to share and raise fewer licensing and privacy concerns. We validate our work on six datasets from multiple sites, demonstrating significant reductions in training time while improving view classification performance., Comment: Accepted for Oral at MICCAI workshop ASMUS-2024

We investigate microlensing data collected by the Korea Microlensing Telescope Network (KMTNet) survey. Our investigation reveals that the light curves of two lensing events, KMT-2021-BLG-2609 and KMT-2022-BLG-0303, exhibit a similar anomaly, in which short-term positive deviations appear on the sides of the low-magnification lensing light curves. To unravel the nature of these anomalies, we meticulously analyze each of the lensing events. Our investigations reveal that these anomalies stem from a shared channel, wherein the source passed near the planetary caustic induced by a planet with projected separations from the host star exceeding the Einstein radius. We find that interpreting the anomaly of KMT-2021-BLG-2609 is complicated by the "inner--outer" degeneracy, whereas for KMT-2022-BLG-0303, there is no such issue despite similar lens-system configurations. In addition to this degeneracy, interpreting the anomaly in KMT-2021-BLG-2609 involves an additional degeneracy between a pair of solutions, in which the source partially envelops the caustic and the other three solutions in which the source fully envelopes the caustic. As in an earlier case of this so-called von Schlieffen--Cannae degeneracy, the former solutions have substantially higher mass ratio. Through Bayesian analyses conducted based on the measured lensing observables of the event time scale and angular Einstein radius, the host of KMT-2021-BLG-2609L is determined to be a low-mass star with a mass $\sim 0.2~M_\odot$ in terms of a median posterior value, while the planet's mass ranges from approximately 0.032 to 0.112 times that of Jupiter, depending on the solutions. For the planetary system KMT-2022-BLG-0303L, it features a planet with a mass of approximately $0.51~M_{\rm J}$ and a host star with a mass of about $0.37~M_\odot$. In both cases, the lenses are most likely situated in the bulge., Comment: 10 pages, 11 figures, 6 tables

Modern graphics processing units (GPUs) provide an unprecedented level of computing power. In this study, we present a high-performance, multi-GPU implementation of the analytical nuclear gradient for Kohn-Sham time-dependent density functional theory (TDDFT), employing the Tamm-Dancoff approximation (TDA) and Gaussian-type atomic orbitals as basis functions. We discuss GPU-efficient algorithms for the derivatives of electron repulsion integrals and exchange-correlation functionals within the range-separated scheme. As an illustrative example, we calculated the TDA-TDDFT gradient of the S1 state of a full-scale green fluorescent protein with explicit water solvent molecules, totaling 4353 atoms, at the wB97X/def2-SVP level of theory. Our algorithm demonstrates favorable parallel efficiencies on a high-speed distributed system equipped with 256 Nvidia A100 GPUs, achieving >70% with up to 64 GPUs and 31% with 256 GPUs, effectively leveraging the capabilities of modern high-performance computing systems., Comment: 13 pages, 9 figures

In the pursuit of understanding the population of stellar remnants within the Milky Way, we analyze the sample of $\sim 950$ microlensing events observed by the Spitzer Space Telescope between 2014 and 2019. In this study we focus on a sub-sample of nine microlensing events, selected based on their long timescales, small microlensing parallaxes and joint observations by the Gaia mission, to increase the probability that the chosen lenses are massive and the mass is measurable. Among the selected events we identify lensing black holes and neutron star candidates, with potential confirmation through forthcoming release of the Gaia time-series astrometry in 2026. Utilizing Bayesian analysis and Galactic models, along with the Gaia Data Release 3 proper motion data, four good candidates for dark remnants were identified: OGLE-2016-BLG-0293, OGLE-2018-BLG-0483, OGLE-2018-BLG-0662, and OGLE-2015-BLG-0149, with lens masses of $2.98^{+1.75}_{-1.28}~M_{\odot}$, $4.65^{+3.12}_{-2.08}~M_{\odot}$, $3.15^{+0.66}_{-0.64}~M_{\odot}$ and $1.4^{+0.75}_{-0.55}~M_{\odot}$, respectively. Notably, the first two candidates are expected to exhibit astrometric microlensing signals detectable by Gaia, offering the prospect of validating the lens masses. The methodologies developed in this work will be applied to the full Spitzer microlensing sample, populating and analyzing the time-scale ($t_{\rm E}$) vs. parallax ($\pi_{\rm E}$) diagram to derive constraints on the population of lenses in general and massive remnants in particular., Comment: submitted to ApJ

Self-supervised multi-frame monocular depth estimation relies on the geometric consistency between successive frames under the assumption of a static scene. However, the presence of moving objects in dynamic scenes introduces inevitable inconsistencies, causing misaligned multi-frame feature matching and misleading self-supervision during training. In this paper, we propose a novel framework called ProDepth, which effectively addresses the mismatch problem caused by dynamic objects using a probabilistic approach. We initially deduce the uncertainty associated with static scene assumption by adopting an auxiliary decoder. This decoder analyzes inconsistencies embedded in the cost volume, inferring the probability of areas being dynamic. We then directly rectify the erroneous cost volume for dynamic areas through a Probabilistic Cost Volume Modulation (PCVM) module. Specifically, we derive probability distributions of depth candidates from both single-frame and multi-frame cues, modulating the cost volume by adaptively fusing those distributions based on the inferred uncertainty. Additionally, we present a self-supervision loss reweighting strategy that not only masks out incorrect supervision with high uncertainty but also mitigates the risks in remaining possible dynamic areas in accordance with the probability. Our proposed method excels over state-of-the-art approaches in all metrics on both Cityscapes and KITTI datasets, and demonstrates superior generalization ability on the Waymo Open dataset., Comment: Accepted by ECCV 2024. Project Page: https://sungmin-woo.github.io/prodepth/

In unsupervised anomaly detection (UAD) research, while state-of-the-art models have reached a saturation point with extensive studies on public benchmark datasets, they adopt large-scale tailor-made neural networks (NN) for detection performance or pursued unified models for various tasks. Towards edge computing, it is necessary to develop a computationally efficient and scalable solution that avoids large-scale complex NNs. Motivated by this, we aim to optimize the UAD performance with minimal changes to NN settings. Thus, we revisit the reconstruction-by-inpainting approach and rethink to improve it by analyzing strengths and weaknesses. The strength of the SOTA methods is a single deterministic masking approach that addresses the challenges of random multiple masking that is inference latency and output inconsistency. Nevertheless, the issue of failure to provide a mask to completely cover anomalous regions is a remaining weakness. To mitigate this issue, we propose Feature Attenuation of Defective Representation (FADeR) that only employs two MLP layers which attenuates feature information of anomaly reconstruction during decoding. By leveraging FADeR, features of unseen anomaly patterns are reconstructed into seen normal patterns, reducing false alarms. Experimental results demonstrate that FADeR achieves enhanced performance compared to similar-scale NNs. Furthermore, our approach exhibits scalability in performance enhancement when integrated with other single deterministic masking methods in a plug-and-play manner., Comment: 11 pages, 6 figures, 5 tables

The proliferation of edge devices necessitates efficient computational architectures for lightweight tasks, particularly deep neural network (DNN) inference. Traditional NPUs, though effective for such operations, face challenges in power, cost, and area when integrated into lightweight edge devices. The RISC-V architecture, known for its modularity and open-source nature, offers a viable alternative. This paper introduces the RISC-V R-extension, a novel approach to enhancing DNN process efficiency on edge devices. The extension features rented-pipeline stages and architectural pipeline registers (APR), which optimize critical operation execution, thereby reducing latency and memory access frequency. Furthermore, this extension includes new custom instructions to support these architectural improvements. Through comprehensive analysis, this study demonstrates the boost of R-extension in edge device processing, setting the stage for more responsive and intelligent edge applications., Comment: 6 pages, 6 figures, ICAIIC 2024

Neural networks can learn spurious correlations that lead to the correct prediction in a validation set, but generalise poorly because the predictions are right for the wrong reason. This undesired learning of naive shortcuts (Clever Hans effect) can happen for example in echocardiogram view classification when background cues (e.g. metadata) are biased towards a class and the model learns to focus on those background features instead of on the image content. We propose a simple, yet effective random background augmentation method called BackMix, which samples random backgrounds from other examples in the training set. By enforcing the background to be uncorrelated with the outcome, the model learns to focus on the data within the ultrasound sector and becomes invariant to the regions outside this. We extend our method in a semi-supervised setting, finding that the positive effects of BackMix are maintained with as few as 5% of segmentation labels. A loss weighting mechanism, wBackMix, is also proposed to increase the contribution of the augmented examples. We validate our method on both in-distribution and out-of-distribution datasets, demonstrating significant improvements in classification accuracy, region focus and generalisability. Our source code is available at: https://github.com/kitbransby/BackMix, Comment: Accepted at MICCAI 2024 (Pre-print)

The nature of the interaction between active galactic nuclei (AGNs) and their host galaxies remains an unsolved question. Therefore, conducting an AGN census is valuable to AGN research. Nevertheless, a significant fraction of AGNs are obscured by their environment, which blocks UV and optical emissions due to the dusty torus surrounding the central supermassive black hole (SMBH). To overcome this challenge, mid-infrared (IR) surveys have emerged as a valuable tool for identifying obscured AGNs, as the obscured light is re-emitted in this range. With its high sensitivity, the James Webb Space Telescope (JWST) uncovered more fainter objects than previous telescopes. By applying the SED fitting, this work investigates AGN candidates in JWST Cosmic Evolution Early Release Science (CEERS) fields. We identified 42 candidates, 30 of them are classified as composites ($0.2\leq f_{\rm AGN, IR}< 0.5$), and 12 of them are AGNs ($f_{\rm AGN, IR}\geq 0.5$). We report the AGN luminosity contributions and AGN number fractions as a function of redshift and total infrared luminosity, showing that previously reported increasing relations are not apparent in our sample due to the sample size. We also extend the previous results on ultra-luminous infrared galaxies (ULIRGs, $L_{\rm TIR}\geq 10^{12} L_{\odot}$) to less luminous AGNs, highlighting the power of JWST., Comment: 15 pages, 20 figures, 4 tables. Accepted for publication in MNRAS. The 3 min summary: https://www.youtube.com/watch?v=mWUebbgUOh8

We investigated the nature of the anomalies appearing in four microlensing events KMT-2020-BLG-0757, KMT-2022-BLG-0732, KMT-2022-BLG-1787, and KMT-2022-BLG-1852. The light curves of these events commonly exhibit initial bumps followed by subsequent troughs that extend across a substantial portion of the light curves. We performed thorough modeling of the anomalies to elucidate their characteristics. Despite their prolonged durations, which differ from the usual brief anomalies observed in typical planetary events, our analysis revealed that each anomaly in these events originated from a planetary companion located within the Einstein ring of the primary star. It was found that the initial bump arouse when the source star crossed one of the planetary caustics, while the subsequent trough feature occurred as the source traversed the region of minor image perturbations lying between the pair of planetary caustics. The estimated masses of the host and planet, their mass ratios, and the distance to the discovered planetary systems are $(M_{\rm host}/M_\odot, M_{\rm planet}/M_{\rm J}, q/10^{-3}, \dl/{\rm kpc}) = (0.58^{+0.33}_{-0.30}, 10.71^{+6.17}_{-5.61}, 17.61\pm 2.25,6.67^{+0.93}_{-1.30})$ for KMT-2020-BLG-0757, $(0.53^{+0.31}_{-0.31}, 1.12^{+0.65}_{-0.65}, 2.01 \pm 0.07, 6.66^{+1.19}_{-1.84})$ for KMT-2022-BLG-0732, $(0.42^{+0.32}_{-0.23}, 6.64^{+4.98}_{-3.64}, 15.07\pm 0.86, 7.55^{+0.89}_{-1.30})$ for KMT-2022-BLG-1787, and $(0.32^{+0.34}_{-0.19}, 4.98^{+5.42}_{-2.94}, 8.74\pm 0.49, 6.27^{+0.90}_{-1.15})$ for KMT-2022-BLG-1852. These parameters indicate that all the planets are giants with masses exceeding the mass of Jupiter in our solar system and the hosts are low-mass stars with masses substantially less massive than the Sun., Comment: 10 pages, 12 figures, 7 tables

We introduce Nymeria - a large-scale, diverse, richly annotated human motion dataset collected in the wild with multiple multimodal egocentric devices. The dataset comes with a) full-body ground-truth motion; b) multiple multimodal egocentric data from Project Aria devices with videos, eye tracking, IMUs and etc; and c) a third-person perspective by an additional observer. All devices are precisely synchronized and localized in on metric 3D world. We derive hierarchical protocol to add in-context language descriptions of human motion, from fine-grain motion narration, to simplified atomic action and high-level activity summarization. To the best of our knowledge, Nymeria dataset is the world's largest collection of human motion in the wild; first of its kind to provide synchronized and localized multi-device multimodal egocentric data; and the world's largest motion-language dataset. It provides 300 hours of daily activities from 264 participants across 50 locations, total travelling distance over 399Km. The language descriptions contain 301.5K sentences in 8.64M words from a vocabulary size of 6545. To demonstrate the potential of the dataset, we evaluate several SOTA algorithms for egocentric body tracking, motion synthesis, and action recognition. Data and code are open-sourced for research (c.f. https://www.projectaria.com/datasets/nymeria).

Large policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rather than training new behaviors from scratch, we can fine-tune such vision-language-action (VLA) models to obtain robust, generalizable policies for visuomotor control. Yet, widespread adoption of VLAs for robotics has been challenging as 1) existing VLAs are largely closed and inaccessible to the public, and 2) prior work fails to explore methods for efficiently fine-tuning VLAs for new tasks, a key component for adoption. Addressing these challenges, we introduce OpenVLA, a 7B-parameter open-source VLA trained on a diverse collection of 970k real-world robot demonstrations. OpenVLA builds on a Llama 2 language model combined with a visual encoder that fuses pretrained features from DINOv2 and SigLIP. As a product of the added data diversity and new model components, OpenVLA demonstrates strong results for generalist manipulation, outperforming closed models such as RT-2-X (55B) by 16.5% in absolute task success rate across 29 tasks and multiple robot embodiments, with 7x fewer parameters. We further show that we can effectively fine-tune OpenVLA for new settings, with especially strong generalization results in multi-task environments involving multiple objects and strong language grounding abilities, and outperform expressive from-scratch imitation learning methods such as Diffusion Policy by 20.4%. We also explore compute efficiency; as a separate contribution, we show that OpenVLA can be fine-tuned on consumer GPUs via modern low-rank adaptation methods and served efficiently via quantization without a hit to downstream success rate. Finally, we release model checkpoints, fine-tuning notebooks, and our PyTorch codebase with built-in support for training VLAs at scale on Open X-Embodiment datasets., Comment: Website: https://openvla.github.io/

Neural implicit representation has attracted attention in 3D reconstruction through various success cases. For further applications such as scene understanding or editing, several works have shown progress towards object compositional reconstruction. Despite their superior performance in observed regions, their performance is still limited in reconstructing objects that are partially observed. To better treat this problem, we introduce category-level neural fields that learn meaningful common 3D information among objects belonging to the same category present in the scene. Our key idea is to subcategorize objects based on their observed shape for better training of the category-level model. Then we take advantage of the neural field to conduct the challenging task of registering partially observed objects by selecting and aligning against representative objects selected by ray-based uncertainty. Experiments on both simulation and real-world datasets demonstrate that our method improves the reconstruction of unobserved parts for several categories., Comment: RA-L. 8 pages, 8 figures, 4 tables

We propose a novel method for fast and accurate training of physics-informed neural networks (PINNs) to find solutions to boundary value problems (BVPs) and initial boundary value problems (IBVPs). By combining the methods of training deep neural networks (DNNs) and Extreme Learning Machines (ELMs), we develop a model which has the expressivity of DNNs with the fine-tuning ability of ELMs. We showcase the superiority of our proposed method by solving several BVPs and IBVPs which include linear and non-linear ordinary differential equations (ODEs), partial differential equations (PDEs) and coupled PDEs. The examples we consider include a stiff coupled ODE system where traditional numerical methods fail, a 3+1D non-linear PDE, Kovasznay flow and Taylor-Green vortex solutions to incompressible Navier-Stokes equations and pure advection solution of 1+1 D compressible Euler equation. The Theory of Functional Connections (TFC) is used to exactly impose initial and boundary conditions (IBCs) of (I)BVPs on PINNs. We propose a modification to the TFC framework named Reduced TFC and show a significant improvement in the training and inference time of PINNs compared to IBCs imposed using TFC. Furthermore, Reduced TFC is shown to be able to generalize to more complex boundary geometries which is not possible with TFC. We also introduce a method of applying boundary conditions at infinity for BVPs and numerically solve the pure advection in 1+1 D Euler equations using these boundary conditions., Comment: Accepted for publication in CNSNS

Artificial neural networks (ANNs) are powerful tools capable of approximating any arbitrary mathematical function, but their interpretability remains limited, rendering them as black box models. To address this issue, numerous methods have been proposed to enhance the explainability and interpretability of ANNs. In this study, we introduce the application of information geometric framework to investigate phase transition-like behavior during the training of ANNs and relate these transitions to overfitting in certain models. The evolution of ANNs during training is studied by looking at the probability distribution of its parameters. Information geometry utilizing the principles of differential geometry, offers a unique perspective on probability and statistics by considering probability density functions as points on a Riemannian manifold. We create this manifold using a metric based on Fisher information to define a distance and a velocity. By parameterizing this distance and velocity with training steps, we study how the ANN evolves as training progresses. Utilizing standard datasets like MNIST, FMNIST and CIFAR-10, we observe a transition in the motion on the manifold while training the ANN and this transition is identified with over-fitting in the ANN models considered. The information geometric transitions observed is shown to be mathematically similar to the phase transitions in physics. Preliminary results showing finite-size scaling behavior is also provided. This work contributes to the development of robust tools for improving the explainability and interpretability of ANNs, aiding in our understanding of the variability of the parameters these complex models exhibit during training., Comment: Accepted for publication in Neurocomputing journal

A countable discrete group is called Choquet-Deny if for any non-degenerate probability measure on the group, the corresponding space of bounded harmonic functions is trivial. Building on the previous work of Jaworski, a complete characterization of Choquet-Deny groups was recently achieved by Frisch, Hartman, Tamuz, and Ferdowski. In this article, we extend the study of the Choquet-Deny property to the framework of discrete measured groupoids. Our primary result offers a complete characterization of this property in terms of the isotropy groups and the equivalence relation associated with the given groupoid. Additionally, we use the implications derived from our main theorem to classify the Choquet-Deny property of transformation groupoids., Comment: 37 pages, v2: Subsection 2.4.1 on the Choquet-Deny property and icc quotients was added

We report a free-floating planet (FFP) candidate identified from the analysis of the microlensing event KMT-2023-BLG-2669. The lensing light curve is characterized by a short duration $(\lesssim 3\,{\rm days})$ and a small amplitude $(\lesssim 0.7\,{\rm mag})$. From the analysis, we find the Einstein timescale of $t_{\rm E} \backsimeq 0.33\,{\rm days}$ and the Einstein radius of $\theta_{\rm E} \backsimeq 4.41\,{\mu}{\rm as}$. These measurements enable us to infer the lens mass as $M = 8\,M_{\oplus} (\pi_{\rm rel} / 0.1\,{\rm mas})^{-1}$, where $\pi_{\rm rel}$ is the relative lens-source parallax. The inference implies that the lens is a sub-Neptune- to Saturn-mass object depending on its unknown distance. This is the ninth isolated planetary-mass microlens with $\theta_{\rm E} < 10\,{\mu}{\rm as}$, which (as shown by \citealt{gould22}) is a useful threshold for a FFP candidate. We conduct extensive searches for possible signals of a host star in the light curve, but find no strong evidence for the host. We investigate the possibility of using late-time high-resolution imaging to probe for possible hosts. In particular, we discuss that for the case of finite-source point-lens FFP candidates, it would be possible to search for very wide separation hosts immediately, although such searches are "high-risk, high-reward"., Comment: 26 pages, 6 figures, accepted in publication in AJ

We investigate the nature of the short-term anomaly that appears in the lensing light curve of KMT-2023-BLG-1866. The anomaly was only partly covered due to its short duration, less than a day, coupled with cloudy weather conditions and restricted nighttime duration. Considering intricacy of interpreting partially covered signals, we thoroughly explore all potential degenerate solutions. Through this process, we identify three planetary scenarios that equally well account for the observed anomaly. These scenarios are characterized by the specific planetary parameters: $(s, q)_{\rm inner} = [0.9740 \pm 0.0083, (2.46 \pm 1.07) \times 10^{-5}]$, $(s, q)_{\rm intermediate} = [0.9779 \pm 0.0017, (1.56 \pm 0.25)\times 10^{-5}]$, and $(s, q)_{\rm outer} = [0.9894 \pm 0.0107, (2.31 \pm 1.29)\times 10^{-5}]$, where $s$ and $q$ denote the projected separation (scaled to the Einstein radius) and mass ratio between the planet and its host, respectively. We identify that the ambiguity between the inner and outer solutions stems from the inner-outer degeneracy, while the similarity between the intermediate solution and the others is due to an accidental degeneracy caused by incomplete anomaly coverage. Through Bayesian analysis utilizing the constraints derived from measured lensing observables and blending flux, our estimation indicates that the lens system comprises a very low-mass planet orbiting an early M-type star situated approximately (6.2 -- 6.5)~kpc from Earth in terms of median posterior values for the different solutions. The median mass of the planet host is in the range of (0.48 -- 0.51)~$M_\odot$, and that of the planet's mass spans a range of (2.6 -- 4.0)~$M_{\rm E}$, varying across different solutions. The detection of KMT-2023-BLG-1866Lb signifies the extension of the lensing surveys to very low-mass planets that have been difficult to be detected from earlier surveys., Comment: 9 pages, 8 figures, 4 tables

Bilateral teleoperation of an aerial manipulator facilitates the execution of industrial missions thanks to the combination of the aerial platform's maneuverability and the ability to conduct complex tasks with human supervision. Heretofore, research on such operations has focused on flying without any physical interaction or exerting a pushing force on a contact surface that does not involve abrupt changes in the interaction force. In this paper, we propose a human reaction time compensating haptic-based bilateral teleoperation strategy for an aerial manipulator extracting a wedged object from a static structure (i.e., plug-pulling), which incurs an abrupt decrease in the interaction force and causes additional difficulty for an aerial platform. A haptic device composed of a 4-degree-of-freedom robotic arm and a gripper is made for the teleoperation of aerial wedged object-extracting tasks, and a haptic-based teleoperation method to execute the aerial manipulator by the haptic device is introduced. We detect the extraction of the object by the estimation of the external force exerted on the aerial manipulator and generate reference trajectories for both the aerial manipulator and the haptic device after the extraction. As an example of the extraction of a wedged object, we conduct comparative plug-pulling experiments with a quadrotor-based aerial manipulator. The results validate that the proposed bilateral teleoperation method reduces the overshoot in the aerial manipulator's position and ensures fast recovery to its initial position after extracting the wedged object., Comment: to be presented in 2024 IEEE International Conference on Unmanned Aircraft Systems (ICUAS), Chania, Crete, Greece, 2024

We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. Our training dataset is a scaled-up version of the one used for phi-2, composed of heavily filtered publicly available web data and synthetic data. The model is also further aligned for robustness, safety, and chat format. We also provide parameter-scaling results with a 7B, 14B models trained for 4.8T tokens, called phi-3-small, phi-3-medium, both significantly more capable than phi-3-mini (e.g., respectively 75%, 78% on MMLU, and 8.7, 8.9 on MT-bench). To enhance multilingual, multimodal, and long-context capabilities, we introduce three models in the phi-3.5 series: phi-3.5-mini, phi-3.5-MoE, and phi-3.5-Vision. The phi-3.5-MoE, a 16 x 3.8B MoE model with 6.6 billion active parameters, achieves superior performance in language reasoning, math, and code tasks compared to other open-source models of similar scale, such as Llama 3.1 and the Mixtral series, and on par with Gemini-1.5-Flash and GPT-4o-mini. Meanwhile, phi-3.5-Vision, a 4.2 billion parameter model derived from phi-3.5-mini, excels in reasoning tasks and is adept at handling both single-image and text prompts, as well as multi-image and text prompts., Comment: 24 pages

This work proposes a saturated robust controller for a fully actuated multirotor that takes disturbance rejection and rotor thrust saturation into account. A disturbance rejection controller is required to prevent performance degradation in the presence of parametric uncertainty and external disturbance. Furthermore, rotor saturation should be properly addressed in a controller to avoid performance degradation or even instability due to a gap between the commanded input and the actual input during saturation. To address these issues, we present a modified saturated RISE (Robust Integral of the Sign of the Error) control method. The proposed modified saturated RISE controller is developed for expansion to a system with a non-diagonal, state-dependent input matrix. Next, we present reformulation of the system dynamics of a fully actuated multirotor, and apply the control law to the system. The proposed method is validated in simulation where the proposed controller outperforms the existing one thanks to the capability of handling the input matrix., Comment: 6 pages, 5 figures, 2024 International Conference on Unmanned Aircraft Systems (ICUAS) accepted

Aerial unperching of multirotors has received little attention as opposed to perching that has been investigated to elongate operation time. This study presents a new aerial robot capable of both perching and unperching autonomously on/from a ferromagnetic surface during flight, and a switching controller to avoid rotor saturation and mitigate overshoot during transition between free-flight and perching. To enable stable perching and unperching maneuvers on/from a vertical surface, a lightweight ($\approx$ $1$ \si{kg}), fully actuated tiltrotor that can hover at $90^\circ$ pitch angle is first developed. We design a perching/unperching module composed of a single servomotor and a magnet, which is then mounted on the tiltrotor. A switching controller including exclusive control modes for transitions between free-flight and perching is proposed. Lastly, we propose a simple yet effective strategy to ensure robust perching in the presence of measurement and control errors and avoid collisions with the perching site immediately after unperching. We validate the proposed framework in experiments where the tiltrotor successfully performs perching and unperching on/from a vertical surface during flight. We further show effectiveness of the proposed transition mode in the switching controller by ablation studies where large overshoot and even collision with a perching site occur. To the best of the authors' knowledge, this work presents the first autonomous aerial unperching framework using a fully actuated tiltrotor., Comment: 7 pages, 10 figures, 2024 IEEE International Conference on Robotics and Automation (ICRA) accepted

Object removal refers to the process of erasing designated objects from an image while preserving the overall appearance, and it is one area where image inpainting is widely used in real-world applications. The performance of an object remover is quantitatively evaluated by measuring the quality of object removal results, similar to how the performance of an image inpainter is gauged. Current works reporting quantitative performance evaluations utilize original images as references. In this letter, to validate the current evaluation methods cannot properly evaluate the performance of an object remover, we create a dataset with object removal ground truth and compare the evaluations made by the current methods using original images to those utilizing object removal ground truth images. The disparities between two evaluation sets validate that the current methods are not suitable for measuring the performance of an object remover. Additionally, we propose new evaluation methods tailored to gauge the performance of an object remover. The proposed methods evaluate the performance through class-wise object removal results and utilize images without the target class objects as a comparison set. We confirm that the proposed methods can make judgments consistent with human evaluators in the COCO dataset, and that they can produce measurements aligning with those using object removal ground truth in the self-acquired dataset.

We introduce ECLAIR (Extended Classification of Lidar for AI Recognition), a new outdoor large-scale aerial LiDAR dataset designed specifically for advancing research in point cloud semantic segmentation. As the most extensive and diverse collection of its kind to date, the dataset covers a total area of 10$km^2$ with close to 600 million points and features eleven distinct object categories. To guarantee the dataset's quality and utility, we have thoroughly curated the point labels through an internal team of experts, ensuring accuracy and consistency in semantic labeling. The dataset is engineered to move forward the fields of 3D urban modeling, scene understanding, and utility infrastructure management by presenting new challenges and potential applications. As a benchmark, we report qualitative and quantitative analysis of a voxel-based point cloud segmentation approach based on the Minkowski Engine., Comment: 11 pages, 7 figures

We undertake a project to reexamine microlensing data gathered from high-cadence surveys. The aim of the project is to reinvestigate lensing events with light curves exhibiting intricate anomaly features associated with caustics, yet lacking prior proposed models to explain these features. Through detailed reanalyses considering higher-order effects, we identify that accounting for orbital motions of lenses is vital in accurately explaining the anomaly features observed in the light curves of the lensing events OGLE-2018-BLG-0971, MOA-2023-BLG-065, and OGLE-2023-BLG-0136. We estimate the masses and distances to the lenses by conducting Bayesian analyses using the lensing parameters of the newly found lensing solutions. From these analyses, we identify that the lenses of the events OGLE-2018-BLG-0971 and MOA-2023-BLG-065 are binaries composed of M dwarfs, while the lens of OGLE-2023-BLG-0136 is likely to be a binary composed of an early K-dwarf primary and a late M-dwarf companion. For all lensing events, the probability of the lens residing in the bulge is considerably higher than that of it being located in the disk., Comment: 11 pages, 13 figures, 6 tables

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs., Comment: 44 pages; updated authors list and fixed author names

Generative modeling of complex behaviors from labeled datasets has been a longstanding problem in decision making. Unlike language or image generation, decision making requires modeling actions - continuous-valued vectors that are multimodal in their distribution, potentially drawn from uncurated sources, where generation errors can compound in sequential prediction. A recent class of models called Behavior Transformers (BeT) addresses this by discretizing actions using k-means clustering to capture different modes. However, k-means struggles to scale for high-dimensional action spaces or long sequences, and lacks gradient information, and thus BeT suffers in modeling long-range actions. In this work, we present Vector-Quantized Behavior Transformer (VQ-BeT), a versatile model for behavior generation that handles multimodal action prediction, conditional generation, and partial observations. VQ-BeT augments BeT by tokenizing continuous actions with a hierarchical vector quantization module. Across seven environments including simulated manipulation, autonomous driving, and robotics, VQ-BeT improves on state-of-the-art models such as BeT and Diffusion Policies. Importantly, we demonstrate VQ-BeT's improved ability to capture behavior modes while accelerating inference speed 5x over Diffusion Policies. Videos and code can be found https://sjlee.cc/vq-bet, Comment: Github repo: https://github.com/jayLEE0301/vq_bet_official

Distribution system (DS) communication failures following extreme events often degrade monitoring and control functions, thus preventing the acquisition of complete global DS component state information, on which existing post-disaster DS restoration methods are based. This letter proposes methods of inferring the states of DS components in the case of incomplete component state information. By using the known DS information, the operating states of unobservable DS branches and buses can be inferred, providing complete information for DS performance restoration before full communication recovery

Light curves of microlensing events occasionally deviate from the smooth and symmetric form of a single-lens single-source event. While most of these anomalous events can be accounted for by employing a binary-lens single-source (2L1S) or a single-lens binary-source (1L2S) framework, it is established that a small fraction of events remain unexplained by either of these interpretations. We carry out a project in which data collected by high-cadence microlensing surveys were reinvestigated with the aim of uncovering the nature of anomalous lensing events with no proposed 2L1S or 1L2S models. From the project, we find that the anomaly appearing in the lensing event OGLE-2023-BLG-0836 cannot be explained by the usual interpretations and conduct a comprehensive analysis of the event. From thorough modeling of the light curve under sophisticated lens-system configurations, we have arrived at the conclusion that a triple-mass lens system is imperative to account for the anomaly features observed in the lensing light curve. From the Bayesian analysis using the measured observables of the event time scale and angular Einstein radius, we determine that the least massive component of the lens has a planetary mass of $4.36^{+2.35}_{-2.18}~M_{\rm J}$. This planet orbits within a stellar binary system composed of two stars with masses $0.71^{+0.38}_{-0.36}~M_\odot$ and $0.56^{+0.30}_{-0.28}~M_\odot$. This lensing event signifies the sixth occurrence of a planetary microlensing system in which a planet belongs to a stellar binary system., Comment: 11 pages, 12 figures