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Existing structured pruning typically involves multi-stage training procedures that often demand heavy computation. Pruning at initialization, which aims to address this limitation, reduces training costs but struggles with performance. To address these challenges, we propose an efficient framework for one-cycle structured pruning without compromising model performance. In this approach, we integrate pre-training, pruning, and fine-tuning into a single training cycle, referred to as the `one cycle approach'. The core idea is to search for the optimal sub-network during the early stages of network training, guided by norm-based group saliency criteria and structured sparsity regularization. We introduce a novel pruning indicator that determines the stable pruning epoch by assessing the similarity between evolving pruning sub-networks across consecutive training epochs. Also, group sparsity regularization helps to accelerate the pruning process and results in speeding up the entire process. Extensive experiments on datasets, including CIFAR-10/100, and ImageNet, using VGGNet, ResNet, MobileNet, and ViT architectures, demonstrate that our method achieves state-of-the-art accuracy while being one of the most efficient pruning frameworks in terms of training time. The source code will be made publicly available., Comment: 12 pages, 6 figures

This paper investigates fast diffusion equations with a divergence type of drift term. We establish the existence of nonnegative $L^q$-weak solutions which satisfies energy estimates or even further with speed estimates in Wasserstein spaces under assuming certain classes for the drift that are differ from those in porous medium equations. When the drift has divergence-free structure, we construct weak solutions under weaker conditions on the drift. Moreover, the same techniques partially extend to porous medium equations. As an application, we also discuss a viscous Boussinesq system of fast diffusion type.

We consider nonlinear drift-diffusion equations (both porous medium equations and fast diffusion equations) with a measure-valued external force. We establish existence of nonnegative weak solutions satisfying gradient estimates, provided that the drift term belongs to a sub-scaling class relevant to $L^1$-space. If the drift is divergence-free, such a class is, however, relaxed so that drift suffices to be included in a certain supercritical scaling class, and the nonlinear diffusion can be less restrictive as well. By handling both the measure data and the drift, we obtain a new type of energy estimates. As an application, we construct weak solutions for a specific type of nonlinear diffusion equation with measure data coupled to the incompressible Navier-Stokes equations., Comment: 20 pages

Traditional microlensing event vetting methods require highly trained human experts, and the process is both complex and time-consuming. This reliance on manual inspection often leads to inefficiencies and constrains the ability to scale for widespread exoplanet detection, ultimately hindering discovery rates. To address the limits of traditional microlensing event vetting, we have developed LensNet, a machine learning pipeline specifically designed to distinguish legitimate microlensing events from false positives caused by instrumental artifacts, such as pixel bleed trails and diffraction spikes. Our system operates in conjunction with a preliminary algorithm that detects increasing trends in flux. These flagged instances are then passed to LensNet for further classification, allowing for timely alerts and follow-up observations. Tailored for the multi-observatory setup of the Korea Microlensing Telescope Network (KMTNet) and trained on a rich dataset of manually classified events, LensNet is optimized for early detection and warning of microlensing occurrences, enabling astronomers to organize follow-up observations promptly. The internal model of the pipeline employs a multi-branch Recurrent Neural Network (RNN) architecture that evaluates time-series flux data with contextual information, including sky background, the full width at half maximum of the target star, flux errors, PSF quality flags, and air mass for each observation. We demonstrate a classification accuracy above 87.5%, and anticipate further improvements as we expand our training set and continue to refine the algorithm., Comment: 23 pages, 13 figures, Accepted for publication in the The Astronomical Journal

We examined the anomalies in the light curves of the lensing events MOA-2022-BLG-033, KMT-2023-BLG-0119, and KMT-2023-BLG-1896. We conducted detailed modeling of the light curves to uncover the nature of the anomalies. This modeling revealed that all signals originated from planetary companions to the primary lens. The planet-to-host mass ratios are very low: $q\sim 7.5\times 10^{-5}$ for MOA-2022-BLG-033, $q\sim 3.6\times 10^{-4}$ for KMT-2023-BLG-0119, and $q\sim 6.9\times 10^{-5}$ for KMT-2023-BLG-1896. The anomalies occurred as the source passed through the negative deviation region behind the central caustic along the planet-host axis. The solutions are subject to a common inner-outer degeneracy, resulting in variations in estimating the projected planet-host separation. For KMT-2023-BLG-1896, although the planetary scenario provides the best explanation of the anomaly, the binary companion scenario is marginally possible. We estimate the physical parameters of the planetary systems through Bayesian analyses based on the lensing observables. The analysis identifies MOA-2022-BLG-033L as a planetary system with an ice giant, approximately 12 times the mass of Earth, orbiting an early M dwarf star. The companion of KMT-2023-BLG-1896L is also an ice giant, with a mass around 16 Earth masses, orbiting a mid-K-type main-sequence star. The companion of KMT-2023-BLG-0119L, which has a mass about the mass of Saturn, orbits a mid-K-type dwarf star. The lens for MOA-2022-BLG-033 is highly likely to be located in the disk, whereas for the other events, the probabilities of the lens being in the disk or the bulge are roughly comparable., Comment: 9 pages, 10 figures, 6 tables

Recent vision-language foundation models still frequently produce outputs misaligned with their inputs, evidenced by object hallucination in captioning and prompt misalignment in the text-to-image generation model. Recent studies have explored methods for identifying misaligned elements, aiming not only to enhance interpretability but also to improve model performance. However, current approaches primarily rely on large foundation models in a zero-shot manner or fine-tuned models with human annotations, which limits scalability due to significant computational costs. This work proposes a novel approach, dubbed CLIP4DM, for detecting dense misalignments from pre-trained CLIP, specifically focusing on pinpointing misaligned words between image and text. We carefully revamp the gradient-based attribution computation method, enabling negative gradient of individual text tokens to indicate misalignment. We also propose F-CLIPScore, which aggregates misaligned attributions with a global alignment score. We evaluate our method on various dense misalignment detection benchmarks, covering various image and text domains and misalignment types. Our method demonstrates state-of-the-art performance among zero-shot models and competitive performance with fine-tuned models while maintaining superior efficiency. Our qualitative examples show that our method has a unique strength to detect entity-level objects, intangible objects, and attributes that can not be easily detected for existing works. We conduct ablation studies and analyses to highlight the strengths and limitations of our approach. Our code is publicly available at https://github.com/naver-ai/CLIP4DM., Comment: 16 pages, 14 figures, AAAI 2025

We carried out a project involving the systematic analysis of microlensing data from the Korea Microlensing Telescope Network survey. The aim of this project is to identify lensing events with complex anomaly features that are difficult to explain using standard binary-lens or binary-source models. Our investigation reveals that the light curves of microlensing events KMT-2021-BLG-0284, KMT-2022-BLG-2480, and KMT-2024-BLG-0412 display highly complex patterns with three or more anomaly features. These features cannot be adequately explained by a binary-lens (2L1S) model alone. However, the 2L1S model can effectively describe certain segments of the light curve. By incorporating an additional source into the modeling, we identified a comprehensive model that accounts for all the observed anomaly features. Bayesian analysis, based on constraints provided by lensing observables, indicates that the lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are binary systems composed of M dwarfs. For KMT-2022-BLG-2480, the primary lens is an early K-type main-sequence star with an M dwarf companion. The lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are likely located in the bulge, whereas the lens of KMT-2022-BLG-2480 is more likely situated in the disk. In all events, the binary stars of the sources have similar magnitudes due to a detection bias favoring binary source events with a relatively bright secondary source star, which increases detection efficiency., Comment: 9 pages, 9 figures

We analysed microlensing data to uncover the nature of the anomaly that appeared near the peak of the short-timescale microlensing event KMT-2024-BLG-1044. Despite the anomaly's brief duration of less than a day, it was densely observed through high-cadence monitoring conducted by the KMTNet survey. Detailed modelling of the light curve confirmed the planetary origin of the anomaly and revealed two possible solutions, due to an inner--outer degeneracy. The two solutions provide different measured planet parameters: $(s, q)_{\rm inner} = [1.0883 \pm 0.0027, (3.125 \pm 0.248)\times 10^{-4}]$ for the inner solutions and $(s, q)_{\rm outer} = [1.0327 \pm 0.0054, (3.350 \pm 0.316)\times 10^{-4}]$ for the outer solutions. Using Bayesian analysis with constraints provided by the short event timescale ($t_{\rm E} \sim 9.1$~day) and the small angular Einstein radius ($\theta_{\rm E}\sim 0.16$~mas for the inner solution and $\sim 0.10$~mas for the outer solutio), we determined that the lens is a planetary system consisting of a host near the boundary between a star and a brown dwarf and a planet with a mass lower than that of Uranus. The discovery of the planetary system highlights the crucial role of the microlensing technique in detecting planets that orbit substellar brown dwarfs or very low-mass stars., Comment: 8 pages, 10 figures

Model execution allows us to prototype and analyse software engineering models by stepping through their possible behaviours, using techniques like animation and simulation. On the other hand, deductive verification allows us to construct formal proofs demonstrating satisfaction of certain critical properties in support of high-assurance software engineering. To ensure coherent results between execution and proof, we need unifying semantics and automation. In this paper, we mechanise Interaction Trees (ITrees) in Isabelle/HOL to produce an execution and verification framework. ITrees are coinductive structures that allow us to encode infinite labelled transition systems, yet they are inherently executable. We use ITrees to create verification tools for stateful imperative programs, concurrent programs with message passing in the form of the CSP and \Circus languages, and abstract system models in the style of the Z and B methods. We demonstrate how ITrees can account for diverse semantic presentations, such as structural operational semantics, a relational program model, and CSP's failures-divergences trace model. Finally, we demonstrate how ITrees can be executed using the Isabelle code generator to support the animation of models., Comment: 41 pages, accepted for publication in ACM TOSEM. Journal extension of arXiv:2105.05133

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

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

The ability to compare objects, scenes, or situations is crucial for effective decision-making and problem-solving in everyday life. For instance, comparing the freshness of apples enables better choices during grocery shopping while comparing sofa designs helps optimize the aesthetics of our living space. Despite its significance, the comparative capability is largely unexplored in artificial general intelligence (AGI). In this paper, we introduce MLLM-CompBench, a benchmark designed to evaluate the comparative reasoning capability of multimodal large language models (MLLMs). MLLM-CompBench mines and pairs images through visually oriented questions covering eight dimensions of relative comparison: visual attribute, existence, state, emotion, temporality, spatiality, quantity, and quality. We curate a collection of around 40K image pairs using metadata from diverse vision datasets and CLIP similarity scores. These image pairs span a broad array of visual domains, including animals, fashion, sports, and both outdoor and indoor scenes. The questions are carefully crafted to discern relative characteristics between two images and are labeled by human annotators for accuracy and relevance. We use MLLM-CompBench to evaluate recent MLLMs, including GPT-4V(ision), Gemini-Pro, and LLaVA-1.6. Our results reveal notable shortcomings in their comparative abilities. We believe MLLM-COMPBENCH not only sheds light on these limitations but also establishes a solid foundation for future enhancements in the comparative capability of MLLMs., Comment: This paper has been accepted to NeurIPS 2024. The first two authors contributed equally to this work

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

Large Multimodal Models (LMMs) excel at comprehending human instructions and demonstrate remarkable results across a broad spectrum of tasks. Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF) further refine LLMs by aligning them with specific preferences. These methods primarily use ranking-based feedback for entire generations. With advanced AI models (Teacher), such as GPT-4 and Claude 3 Opus, we can request various types of detailed feedback that are expensive for humans to provide. We propose a two-stage algorithm ARES that Alternates REinforcement Learning (RL) and Supervised Fine-Tuning (SFT). First, we request the Teacher to score how much each sentence contributes to solving the problem in a Chain-of-Thought (CoT). This sentence-level feedback allows us to consider individual valuable segments, providing more granular rewards for the RL procedure. Second, we ask the Teacher to correct the wrong reasoning after the RL stage. The RL procedure requires massive efforts for hyperparameter tuning and often generates errors like repetitive words and incomplete sentences. With the correction feedback, we stabilize the RL fine-tuned model through SFT. We conduct experiments on multi-model dataset ScienceQA and A-OKVQA to demonstrate the effectiveness of our proposal. ARES rationale reasoning achieves around 70% win rate against baseline models judged by GPT-4o. Additionally, we observe that the improved rationale reasoning leads to a 2.5% increase in inference answer accuracy on average for the multi-modal datasets., Comment: Accepted to EMNLP 2024

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 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

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

Recent advances in named entity recognition (NER) have pushed the boundary of the task to incorporate visual signals, leading to many variants, including multi-modal NER (MNER) or grounded MNER (GMNER). A key challenge to these tasks is that the model should be able to generalize to the entities unseen during the training, and should be able to handle the training samples with noisy annotations. To address this obstacle, we propose SCANNER (Span CANdidate detection and recognition for NER), a model capable of effectively handling all three NER variants. SCANNER is a two-stage structure; we extract entity candidates in the first stage and use it as a query to get knowledge, effectively pulling knowledge from various sources. We can boost our performance by utilizing this entity-centric extracted knowledge to address unseen entities. Furthermore, to tackle the challenges arising from noisy annotations in NER datasets, we introduce a novel self-distillation method, enhancing the robustness and accuracy of our model in processing training data with inherent uncertainties. Our approach demonstrates competitive performance on the NER benchmark and surpasses existing methods on both MNER and GMNER benchmarks. Further analysis shows that the proposed distillation and knowledge utilization methods improve the performance of our model on various benchmarks., Comment: 13 pages, 7 figures, NAACL 2024

Visual Question Answering (VQA) often involves diverse reasoning scenarios across Vision and Language (V&L). Most prior VQA studies, however, have merely focused on assessing the model's overall accuracy without evaluating it on different reasoning cases. Furthermore, some recent works observe that conventional Chain-of-Thought (CoT) prompting fails to generate effective reasoning for VQA, especially for complex scenarios requiring multi-hop reasoning. In this paper, we propose II-MMR, a novel idea to identify and improve multi-modal multi-hop reasoning in VQA. In specific, II-MMR takes a VQA question with an image and finds a reasoning path to reach its answer using two novel language promptings: (i) answer prediction-guided CoT prompt, or (ii) knowledge triplet-guided prompt. II-MMR then analyzes this path to identify different reasoning cases in current VQA benchmarks by estimating how many hops and what types (i.e., visual or beyond-visual) of reasoning are required to answer the question. On popular benchmarks including GQA and A-OKVQA, II-MMR observes that most of their VQA questions are easy to answer, simply demanding "single-hop" reasoning, whereas only a few questions require "multi-hop" reasoning. Moreover, while the recent V&L model struggles with such complex multi-hop reasoning questions even using the traditional CoT method, II-MMR shows its effectiveness across all reasoning cases in both zero-shot and fine-tuning settings., Comment: Accepted to ACL 2024 Findings

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

Automatic web navigation aims to build a web agent that can follow language instructions to execute complex and diverse tasks on real-world websites. Existing work primarily takes HTML documents as input, which define the contents and action spaces (i.e., actionable elements and operations) of webpages. Nevertheless, HTML documents may not provide a clear task-related context for each element, making it hard to select the right (sequence of) actions. In this paper, we propose to contextualize HTML elements through their "dual views" in webpage screenshots: each HTML element has its corresponding bounding box and visual content in the screenshot. We build upon the insight -- web developers tend to arrange task-related elements nearby on webpages to enhance user experiences -- and propose to contextualize each element with its neighbor elements, using both textual and visual features. The resulting representations of HTML elements are more informative for the agent to take action. We validate our method on the recently released Mind2Web dataset, which features diverse navigation domains and tasks on real-world websites. Our method consistently outperforms the baseline in all the scenarios, including cross-task, cross-website, and cross-domain ones., Comment: Accepted to CVPR 2024

We analyze the anomalies appearing in the light curves of the three microlensing events MOA-2022-BLG-563, KMT-2023-BLG-0469, and KMT-2023-BLG-0735. The anomalies exhibit common short-term dip features that appear near the peak. From the detailed analyses of the light curves, we find that the anomalies were produced by planets accompanied by the lenses of the events. For all three events, the estimated mass ratios between the planet and host are on the order of $10^{-4}$: $q\sim 8 \times 10^{-4}$ for MOA-2022-BLG-563L, $q\sim 2.5\times 10^{-4}$ for KMT-2023-BLG-0469L, and $q\sim 1.9\times 10^{-4}$ for KMT-2023-BLG-0735L. The interpretations of the anomalies are subject to a common inner-outer degeneracy, which causes ambiguity when estimating the projected planet-host separation. We estimated the planet mass, $M_{\rm p}$, host mass, $M_{\rm h}$, and distance, $D_{\rm L}$, to the planetary system by conducting Bayesian analyses using the observables of the events. The estimated physical parameters of the planetary systems are $(M_{\rm h}/M_\odot, M_{\rm p}/M_{\rm J}, D_{\rm L}/{\rm kpc}) = (0.48^{+0.36}_{-0.30}, 0.40^{+0.31}_{-0.25}, 6.53^{+1.12}_{-1.57})$ for MOA-2022-BLG-563L, $(0.47^{+0.35}_{-0.26}, 0.124^{+0.092}_{-0.067}, 7.07^{+1.03}_{-1.19})$ for KMT-2023-BLG-0469L, and $(0.62^{+0.34}_{-0.35}, 0.125^{+0.068}_{-0.070}, 6.26^{+1.27}_{-1.67})$ for KMT-2023-BLG-0735L. According to the estimated parameters, all planets are cold planets with projected separations that are greater than the snow lines of the planetary systems, they have masses that lie between the masses of Uranus and Jupiter of the Solar System, and the hosts of the planets are main-sequence stars that are less massive than the Sun., Comment: 11 pages, 7 tables, 10 figures

We consider a parabolic-elliptic type of Keller-Segel equations with generalized diffusion and logistic source under homogeneous Neumann-Neumann boundary conditions. We construct bounded weak solutions globally in time in an unbalanced optimal transport framework, provided that the magnitude of the chemotactic sensitivity can be restricted depending on parameters. In the case of subquadratic degradation of the logistic source, we quantify the chemotactic sensitivity, in particular, in terms of the power of degradation and the pointwise bound of the initial density., Comment: 29 pages

Following Shin et al. (2023b), which is a part of the Systematic KMTNet Planetary Anomaly Search series (i.e., a search for planets in the 2016 KMTNet prime fields), we conduct a systematic search of the 2016 KMTNet sub-prime fields using a semi-machine-based algorithm to identify hidden anomalous events missed by the conventional by-eye search. We find four new planets and seven planet candidates that were buried in the KMTNet archive. The new planets are OGLE-2016-BLG-1598Lb, OGLE-2016-BLG-1800Lb, MOA-2016-BLG-526Lb, and KMT-2016-BLG-2321Lb, which show typical properties of microlensing planets, i.e., giant planets orbit M dwarf host stars beyond their snow lines. For the planet candidates, we find planet/binary or 2L1S/1L2S degeneracies, which are an obstacle to firmly claiming planet detections. By combining the results of Shin et al. (2023b) and this work, we find a total of nine hidden planets, which is about half the number of planets discovered by eye in 2016. With this work, we have met the goal of the systematic search series for 2016, which is to build a complete microlensing planet sample. We also show that our systematic searches significantly contribute to completing the planet sample, especially for planet/host mass ratios smaller than $10^{-3}$, which were incomplete in previous by-eye searches of the KMTNet archive., Comment: 63 pages, 16 Tables, 19 Figures, Submitted in the AAS journal

The recent development on large multimodal models (LMMs), especially GPT-4V(ision) and Gemini, has been quickly expanding the capability boundaries of multimodal models beyond traditional tasks like image captioning and visual question answering. In this work, we explore the potential of LMMs like GPT-4V as a generalist web agent that can follow natural language instructions to complete tasks on any given website. We propose SEEACT, a generalist web agent that harnesses the power of LMMs for integrated visual understanding and acting on the web. We evaluate on the recent MIND2WEB benchmark. In addition to standard offline evaluation on cached websites, we enable a new online evaluation setting by developing a tool that allows running web agents on live websites. We show that GPT-4V presents a great potential for web agents -- it can successfully complete 51.1 of the tasks on live websites if we manually ground its textual plans into actions on the websites. This substantially outperforms text-only LLMs like GPT-4 or smaller models (FLAN-T5 and BLIP-2) specifically fine-tuned for web agents. However, grounding still remains a major challenge. Existing LMM grounding strategies like set-of-mark prompting turns out to be not effective for web agents, and the best grounding strategy we develop in this paper leverages both the HTML structure and visuals. Yet, there is still a substantial gap with oracle grounding, leaving ample room for further improvement. All code, data, and evaluation tools are available at https://github.com/OSU-NLP-Group/SeeAct.

The gravitational microlensing technique is most sensitive to planets in a Jupiter-like orbit and has detected more than 200 planets. However, only a few wide-orbit ($s > 2$) microlensing planets have been discovered, where $s$ is the planet-to-host separation normalized to the angular Einstein ring radius, $\theta_{\rm E}$. Here we present the discovery and analysis of a strong candidate wide-orbit microlensing planet in the event, OGLE-2017-BLG-0448. The whole light curve exhibits long-term residuals to the static binary-lens single-source model, so we investigate the residuals by adding the microlensing parallax, microlensing xallarap, an additional lens, or an additional source. For the first time, we observe a complex degeneracy between all four effects. The wide-orbit models with $s \sim 2.5$ and a planet-to-host mass-ratio of $q \sim 10^{-4}$ are significantly preferred, but we cannot rule out the close models with $s \sim 0.35$ and $q \sim 10^{-3}$. A Bayesian analysis based on a Galactic model indicates that, despite the complicated degeneracy, the surviving wide-orbit models all contain a super-Earth-mass to Neptune-mass planet at a projected planet-host separation of $\sim 6$ au and the surviving close-orbit models all consist of a Jovian-mass planet at $\sim 1$ au. The host star is probably an M or K dwarf. We discuss the implications of this dimension-degeneracy disaster on microlensing light-curve analysis and its potential impact on statistical studies., Comment: submitted to AJ

We report on the discovery and analysis of the planetary microlensing event OGLE-2019-BLG-1180 with a planet-to-star mass ratio $q \sim 0.003$. The event OGLE-2019-BLG-1180 has unambiguous cusp-passing and caustic-crossing anomalies, which were caused by a wide planetary caustic with $s \simeq 2$, where $s$ is the star-planet separation in units of the angular Einstein radius $\theta_{E}$. Thanks to well-covered anomalies by the Korea Micorolensing Telescope Network (KMTNet), we measure both the angular Einstein radius and the microlens parallax in spite of a relatively short event timescale of $t_{E} = 28$ days. However, because of a weak constraint on the parallax, we conduct a Bayesian analysis to estimate the physical lens parameters. We find that the lens system is a super-Jupiter-mass planet of $M_{p} = 1.75^{+0.54}_{-0.51} M_{J}$ orbiting a late-type star of $M_{h}=0.55^{+0.27}_{-0.26} M_\odot$ at a distance of $D_{L} = 6.1^{+0.9}_{-1.3}$ kpc. The projected star-planet separation is $a_{\perp} = 5.19^{+0.90}_{-1.23}$ au, which means that the planet orbits at about four times the snow line of the host star. Considering the relative lens-source proper motion of $\mu_{rel} = 6$ mas/yr, the lens will be separated from the source by 60 mas in 2029. At that time one can measure the lens flux from adaptive optics imaging of Kec or a next-generation 30 m class telescope. OGLE-2019-BLG-1180Lb represents a growing population of wide-orbit planets detected by KMTNet, so we also present a general investigation into prospects for further expanding the sample of such planets., Comment: 12 pages, 12 figures, 5 tables, published in AJ