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LiDAR place recognition is a crucial module in localization that matches the current location with previously observed environments. Most existing approaches in LiDAR place recognition dominantly focus on the spinning type LiDAR to exploit its large FOV for matching. However, with the recent emergence of various LiDAR types, the importance of matching data across different LiDAR types has grown significantly-a challenge that has been largely overlooked for many years. To address these challenges, we introduce HeLiOS, a deep network tailored for heterogeneous LiDAR place recognition, which utilizes small local windows with spherical transformers and optimal transport-based cluster assignment for robust global descriptors. Our overlap-based data mining and guided-triplet loss overcome the limitations of traditional distance-based mining and discrete class constraints. HeLiOS is validated on public datasets, demonstrating performance in heterogeneous LiDAR place recognition while including an evaluation for long-term recognition, showcasing its ability to handle unseen LiDAR types. We release the HeLiOS code as an open source for the robotics community at https://github.com/minwoo0611/HeLiOS., Comment: 8 pages, 7 figures, 5 table

Recent successes of artificial intelligence and deep learning often depend on the well-collected training dataset which is assumed to have an identical distribution with the test dataset. However, this assumption, which is called closed-set learning, is hard to meet in realistic scenarios for deploying deep learning models. As one of the solutions to mitigate this assumption, research on out-of-distribution (OOD) detection has been actively explored in various domains. In OOD detection, we assume that we are given the data of a new class that was not seen in the training phase, i.e., outlier, at the evaluation phase. The ultimate goal of OOD detection is to detect and classify such unseen outlier data as a novel "unknown" class. Among various research branches for OOD detection, generating a virtual outlier during the training phase has been proposed. However, conventional generation-based methodologies utilize in-distribution training dataset to imitate outlier instances, which limits the quality of the synthesized virtual outlier instance itself. In this paper, we propose a novel methodology for OOD detection named Auxiliary Range Expansion for Outlier Synthesis, or ARES. ARES models the region for generating out-of-distribution instances by escaping from the given in-distribution region; instead of remaining near the boundary of in-distribution region. Various stages consists ARES to ultimately generate valuable OOD-like virtual instances. The energy score-based discriminator is then trained to effectively separate in-distribution data and outlier data. Quantitative experiments on broad settings show the improvement of performance by our method, and qualitative results provide logical explanations of the mechanism behind it.

We present VoiceDiT, a multi-modal generative model for producing environment-aware speech and audio from text and visual prompts. While aligning speech with text is crucial for intelligible speech, achieving this alignment in noisy conditions remains a significant and underexplored challenge in the field. To address this, we present a novel audio generation pipeline named VoiceDiT. This pipeline includes three key components: (1) the creation of a large-scale synthetic speech dataset for pre-training and a refined real-world speech dataset for fine-tuning, (2) the Dual-DiT, a model designed to efficiently preserve aligned speech information while accurately reflecting environmental conditions, and (3) a diffusion-based Image-to-Audio Translator that allows the model to bridge the gap between audio and image, facilitating the generation of environmental sound that aligns with the multi-modal prompts. Extensive experimental results demonstrate that VoiceDiT outperforms previous models on real-world datasets, showcasing significant improvements in both audio quality and modality integration., Comment: Accepted to ICASSP 2025

Understanding the geometric properties of quantum states and their implications in fundamental physical phenomena is at the core of modern physics. The Quantum Geometric Tensor (QGT) is a central physical object in this regard, encoding complete information about the geometry of the quantum state. The imaginary part of the QGT is the well-known Berry curvature, which plays a fundamental role in the topological magnetoelectric and optoelectronic phenomena. The real part of the QGT is the quantum metric, whose importance has come to prominence very recently, giving rise to a new set of quantum geometric phenomena, such as anomalous Landau levels, flat band superfluidity, excitonic Lamb shifts, and nonlinear Hall effect. Despite the central importance of the QGT, its experimental measurements have been restricted only to artificial two-level systems. In this work, we develop a framework to measure the QGT (both quantum metric and Berry curvature) in crystalline solids using polarization-, spin-, and angle-resolved photoemission spectroscopy. Using this framework, we demonstrate the effective reconstruction of the QGT in solids in the archetype kagome metal CoSn, which hosts topological flat bands. The key idea is to introduce another geometrical tensor, the quasi-QGT, whose components, the band Drude weight and orbital angular momentum, are experimentally accessible and can be used for extracting the QGT. Establishing such a momentum- and energy-resolved spectroscopic probe of the QGT is poised to significantly advance our understanding of quantum geometric responses in a wide range of crystalline systems.

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

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

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

Motion artifacts caused by prolonged acquisition time are a significant challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue segmentation. These artifacts appear as blurred images that mimic tissue-like appearances, making segmentation difficult. This study proposes a novel deep learning framework that demonstrates superior performance in both motion correction and robust brain tissue segmentation in the presence of artifacts. The core concept lies in a complementary process: a disentanglement learning network progressively removes artifacts, leading to cleaner images and consequently, more accurate segmentation by a jointly trained motion estimation and segmentation network. This network generates three outputs: a motioncorrected image, a motion deformation map that identifies artifact-affected regions, and a brain tissue segmentation mask. This deformation serves as a guidance mechanism for the disentanglement process, aiding the model in recovering lost information or removing artificial structures introduced by the artifacts. Extensive in-vivo experiments on pediatric motion data demonstrate that our proposed framework outperforms state-of-the-art methods in segmenting motion-corrupted MRI scans., Comment: Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

Maritime environmental sensing requires overcoming challenges from complex conditions such as harsh weather, platform perturbations, large dynamic objects, and the requirement for long detection ranges. While cameras and LiDAR are commonly used in ground vehicle navigation, their applicability in maritime settings is limited by range constraints and hardware maintenance issues. Radar sensors, however, offer robust long-range detection capabilities and resilience to physical contamination from weather and saline conditions, making it a powerful sensor for maritime navigation. Among various radar types, X-band radar (e.g., marine radar) is widely employed for maritime vessel navigation, providing effective long-range detection essential for situational awareness and collision avoidance. Nevertheless, it exhibits limitations during berthing operations where close-range object detection is critical. To address this shortcoming, we incorporate W-band radar (e.g., Navtech imaging radar), which excels in detecting nearby objects with a higher update rate. We present a comprehensive maritime sensor dataset featuring multi-range detection capabilities. This dataset integrates short-range LiDAR data, medium-range W-band radar data, and long-range X-band radar data into a unified framework. Additionally, it includes object labels for oceanic object detection usage, derived from radar and stereo camera images. The dataset comprises seven sequences collected from diverse regions with varying levels of estimation difficulty, ranging from easy to challenging, and includes common locations suitable for global localization tasks. This dataset serves as a valuable resource for advancing research in place recognition, odometry estimation, SLAM, object detection, and dynamic object elimination within maritime environments. Dataset can be found in following link: https://sites.google.com/view/rpmmoana, Comment: 9 pages, 8 figures, 3 tables

A later school start time policy has been recommended as a solution to adolescents' sleep deprivation. We estimated the impacts of later school start times on adolescents' sleep and substance use by leveraging a quasi-experiment in which school start time was delayed in some regions in South Korea. A later school start time policy was implemented in 2014 and 2015, which delayed school start times approximately 30-90 minutes. We applied difference-in-differences and event-study designs to longitudinal data on a nationally-representative cohort of adolescents from 2010 to 2015, which annually tracked sleep and substance use of 1,133 adolescents from grade 7 through grade 12. The adoption of a later school start time policy was initially associated with a 19-minute increase in sleep duration (95% CI, 5.52 to 32.04), driven by a delayed wake time and consistent bedtime. The policy was also associated with statistically significant reductions in monthly smoking and drinking frequencies. However, approximately a year after implementation, the observed increase in sleep duration shrank to 7-minute (95% CI, -12.60 to 25.86) and became statistically nonsignificant. Similarly, the observed reduction in smoking and drinking was attenuated a year after. Our findings suggest that policies that increase sleep in adolescents may have positive effects on health behaviors, but additional efforts may be required to sustain positive impacts over time. Physicians and education and health policymakers should consider the long-term effects of later school start times on adolescent health and well-being.

Nanomechanical resonators with exceptionally low dissipation are advancing mechanics-based sensors and quantum technologies. The key for these advances is the engineering of localized phononic modes that are well-isolated from the environment, i.e., that exhibit a high mechanical quality factor, Qm. Membrane phononic crystals fabricated from strained thin films can realize high-Qm single or multiple localized phononic defect modes. These defect modes can be efficiently interfaced with out-of-plane light or capacitively coupled to a microwave quantum circuit, enabling readout and control of their motion. When membrane phononic crystals are fabricated from a crystalline film, they could offer built-in functionality. We demonstrate a membrane phononic crystal realized in a strained 90 nm-thin film of aluminum nitride (AlN), which is a crystalline piezoelectric material. We engineer a high-Qm localized phononic defect mode at 1.8 MHz with a Qxf-product of 1.5 10^13 Hz at room temperature. In future devices, the built-in piezoelectricity of AlN can be utilized for in-situ tuning of mechanical mode frequencies, defect mode couplings, or acoustic bandgaps, which can be used as building blocks of tunable phononic circuits.

Full-stack autonomous driving system spans diverse technological domains-including perception, planning, and control-that each require in-depth research. Moreover, validating such technologies of the system necessitates extensive supporting infrastructure, from simulators and sensors to high-definition maps. These complexities with barrier to entry pose substantial limitations for individual developers and research groups. Recently, open-source autonomous driving software platforms have emerged to address this challenge by providing autonomous driving technologies and practical supporting infrastructure for implementing and evaluating autonomous driving functionalities. Among the prominent open-source platforms, Autoware and Apollo are frequently adopted in both academia and industry. While previous studies have assessed each platform independently, few have offered a quantitative and detailed head-to-head comparison of their capabilities. In this paper, we systematically examine the core modules of Autoware and Apollo and evaluate their middleware performance to highlight key differences. These insights serve as a practical reference for researchers and engineers, guiding them in selecting the most suitable platform for their specific development environments and advancing the field of full-stack autonomous driving system., Comment: arxiv preprint

Finding and properly segmenting cracks in images of concrete is a challenging task. Cracks are thin and rough and being air filled do yield a very weak contrast in 3D images obtained by computed tomography. Enhancing and segmenting dark lower-dimensional structures is already demanding. The heterogeneous concrete matrix and the size of the images further increase the complexity. ML methods have proven to solve difficult segmentation problems when trained on enough and well annotated data. However, so far, there is not much 3D image data of cracks available at all, let alone annotated. Interactive annotation is error-prone as humans can easily tell cats from dogs or roads without from roads with cars but have a hard time deciding whether a thin and dark structure seen in a 2D slice continues in the next one. Training networks by synthetic, simulated images is an elegant way out, bears however its own challenges. In this contribution, we describe how to generate semi-synthetic image data to train CNN like the well known 3D U-Net or random forests for segmenting cracks in 3D images of concrete. The thickness of real cracks varies widely, both, within one crack as well as from crack to crack in the same sample. The segmentation method should therefore be invariant with respect to scale changes. We introduce the so-called RieszNet, designed for exactly this purpose. Finally, we discuss how to generalize the ML crack segmentation methods to other concrete types., Comment: This is a preprint of the chapter: T. Barisin, C. Jung, A. Nowacka, C. Redenbach, K. Schladitz: Cracks in concrete, published in Statistical Machine Learning for Engineering with Applications (LNCS), edited by J. Franke, A. Sch\"obel, reproduced with permission of Springer Nature Switzerland AG 2024. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-66253-9

Machine learning offers attractive solutions to challenging image processing tasks. Tedious development and parametrization of algorithmic solutions can be replaced by training a convolutional neural network or a random forest with a high potential to generalize. However, machine learning methods rely on huge amounts of representative image data along with a ground truth, usually obtained by manual annotation. Thus, limited availability of training data is a critical bottleneck. We discuss two use cases: optical quality control in industrial production and segmenting crack structures in 3D images of concrete. For optical quality control, all defect types have to be trained but are typically not evenly represented in the training data. Additionally, manual annotation is costly and often inconsistent. It is nearly impossible in the second case: segmentation of crack systems in 3D images of concrete. Synthetic images, generated based on realizations of stochastic geometry models, offer an elegant way out. A wide variety of structure types can be generated. The within structure variation is naturally captured by the stochastic nature of the models and the ground truth is for free. Many new questions arise. In particular, which characteristics of the real image data have to be met to which degree of fidelity., Comment: Preprint of: K. Schladitz, C. Redenbach, T. Barisin, C. Jung, N. Jeziorski, L. Bosnar, J. Fulir, P. Gospodneti\'c: Simulation of Microstructures and Machine Learning, published in Continuum Models and Discrete Systems by F. Willot, J. Dirrenberger, S. Forest, D. Jeulin, A.V. Cherkaev (eds), 2024, Springer Cham. The final version is https://doi.org/10.1007/978-3-031-58665-1

Higher-order topological insulators (HOTIs) represent a novel class of topological materials, characterised by the emergence of topological boundary modes at dimensions two or more lower than those of bulk materials. Recent experimental studies have identified conducting channels at the hinges of HOTIs, although their topological nature remains unexplored. In this study, we investigated Shapiro steps in Al-WTe$_2$-Al proximity Josephson junctions (JJs) under microwave irradiation and examined the topological properties of the hinge states in WTe$_2$. Specifically, we analysed the microwave frequency dependence of the absence of the first Shapiro step in hinge-dominated JJs, attributing this phenomenon to the 4$\pi$-periodic current-phase relationship characteristic of topological JJs. These findings may encourage further research into topological superconductivity with topological hinge states in superconducting hybrid devices based on HOTIs. Such advances could lead to the realisation of Majorana zero modes for topological quantum physics and pave the way for applications in spintronic devices.

Text-to-image diffusion models show remarkable generation performance following text prompts, but risk generating Not Safe For Work (NSFW) contents from unsafe prompts. Existing approaches, such as prompt filtering or concept unlearning, fail to defend against adversarial attacks while maintaining benign image quality. In this paper, we propose a novel approach called Distorting Embedding Space (DES), a text encoder-based defense mechanism that effectively tackles these issues through innovative embedding space control. DES transforms unsafe embeddings, extracted from a text encoder using unsafe prompts, toward carefully calculated safe embedding regions to prevent unsafe contents generation, while reproducing the original safe embeddings. DES also neutralizes the nudity embedding, extracted using prompt ``nudity", by aligning it with neutral embedding to enhance robustness against adversarial attacks. These methods ensure both robust defense and high-quality image generation. Additionally, DES can be adopted in a plug-and-play manner and requires zero inference overhead, facilitating its deployment. Extensive experiments on diverse attack types, including black-box and white-box scenarios, demonstrate DES's state-of-the-art performance in both defense capability and benign image generation quality. Our model is available at https://github.com/aei13/DES.

The gravitational wave (GW) event S230518h is a potential binary neutron star-black hole merger (NSBH) event that was detected during engineering run 15 (ER15), which served as the commissioning period before the LIGO-Virgo-KAGRA (LVK) O4a observing run. Despite its low probability of producing detectable electromagnetic emissions, we performed extensive follow-up observations of this event using the GECKO telescopes in the southern hemisphere. Our observation covered 61.7\% of the 90\% credible region, a $\rm 284\:deg^2$ area accessible from the southern hemisphere, reaching a median limiting magnitude of $R=21.6$ mag. In these images, we conducted a systematic search for an optical counterpart of this event by combining a CNN-based classifier and human verification. We identified 128 transient candidates, but no significant optical counterpart was found that could have caused the GW signal. Furthermore, we provide feasible KN properties that are consistent with the upper limits of observation. Although no optical counterpart was found, our result demonstrates both GECKO's efficient wide-field follow-up capabilities and usefulness for constraining properties of kilonovae from NSBH mergers at distances of $\sim 200$ Mpc., Comment: 25 pages, 14 figures, Accepted for publication in ApJ

We establish sufficient conditions for the density of shallow neural networks \cite{C89} on the family of continuous real functions defined on a compact metric space, taking into account variations in the function domains. For this we use the Gromov-Hausdorff distance defined in \cite{5G}., Comment: 10 pages

The emergence of the Next Generation Sequencing increases drastically the volume of transcriptomic data. Although many standalone algorithms and workflows for novel microRNA (miRNA) prediction have been proposed, few are designed for processing large volume of sequence data from large genomes, and even fewer further annotate functional miRNAs by analyzing multiple libraries. We propose an improved pipeline for a high volume data facility by implementing mirLibSpark based on the Apache Spark framework. This pipeline is the fastest actual method, and provides an accuracy improvement compared to the standard. In this paper, we deliver the first distributed functional miRNA predictor as a standalone and fully automated package. It is an efficient and accurate miRNA predictor with functional insight. Furthermore, it compiles with the gold-standard requirement on plant miRNA predictions., Comment: 13 pages, 4 figures, 2 tables, published in conference proceedings

Multi-agent systems commonly distribute tasks among specialized, autonomous agents, yet they often lack mechanisms to replace or reassign underperforming agents in real time. Inspired by the free-agency model of Major League Baseball, the Reinforcement Learning Free Agent (RLFA) algorithm introduces a reward-based mechanism to detect and remove agents exhibiting persistent underperformance and seamlessly insert more capable ones. Each agent internally uses a mixture-of-experts (MoE) approach, delegating incoming tasks to specialized sub-models under the guidance of a gating function. A primary use case is fraud detection, where RLFA promptly swaps out an agent whose detection accuracy dips below a preset threshold. A new agent is tested in a probationary mode, and upon demonstrating superior performance, fully replaces the underperformer. This dynamic, free-agency cycle ensures sustained accuracy, quicker adaptation to emerging threats, and minimal disruption to ongoing operations. By continually refreshing its roster of agents, the system fosters ongoing improvements and more resilient collaboration in multi-agent Generative AI environments.

Methods to ensure factual accuracy of text generated by large language models (LLM) in clinical medicine are lacking. VeriFact is an artificial intelligence system that combines retrieval-augmented generation and LLM-as-a-Judge to verify whether LLM-generated text is factually supported by a patient's medical history based on their electronic health record (EHR). To evaluate this system, we introduce VeriFact-BHC, a new dataset that decomposes Brief Hospital Course narratives from discharge summaries into a set of simple statements with clinician annotations for whether each statement is supported by the patient's EHR clinical notes. Whereas highest agreement between clinicians was 88.5%, VeriFact achieves up to 92.7% agreement when compared to a denoised and adjudicated average human clinican ground truth, suggesting that VeriFact exceeds the average clinician's ability to fact-check text against a patient's medical record. VeriFact may accelerate the development of LLM-based EHR applications by removing current evaluation bottlenecks., Comment: 62 pages, 5 figures, 1 table, pre-print manuscript

Image acquisition techniques such as micro-computed tomography are nowadays widely available. Quantitative analysis of the resulting 3D image data enables geometric characterization of the micro-structure of materials. Stochastic geometry models can be fit to the observed micro-structures. By alteration of the model parameters, virtual micro-structures with modified geometry can be generated. Numerical simulation of elastic properties in realizations of these models yields deeper insight on the influence of particular micro-structural features. Ultimately, this allows for an optimization of the micro-structure geometry for particular applications. Here, we present this workflow at the example of open cell foams. Applicability is demonstrated using an aluminum alloy foam sample. The structure observed in a micro-computed tomography image is modeled by the edge system of a random Laguerre tessellation generated by a system of closely packed spheres. Elastic moduli are computed in the binarized micro-CT image of the foam as well as in realizations of the model. They agree well with the results of a compression test on the real material., Comment: Preprint of a chapter published in Research in Mathematics of Materials Science. Association for Women in Mathematics Series 31, reproduced with permission of Springer Nature Switzerland AG 2022. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-04496-0_11

We demonstrate volumetric fluorescence lifetime microscopy (FLIM) using the electro-optic FLIM technique. Images acquired in a selective plane illumination microscope are gated using a Pockels cell driven at 80 MHz, enabling light sheet FLIM acquisition with up to 800 {\mu}m field of view. Volume acquisitions are demonstrated on fluorescent bead mixtures and in live Arabidopsis thaliana root samples using both genetically encoded fluorescent proteins and endogenous autofluorescence.

We develop a novel way to probe subgalactic-scale matter distribution with diffractive lensing on gravitational waves. Five-year observations from Einstein Telescope and DECIGO are expected to probe $k= 10^5\sim 10^8 \,{\rm Mpc}^{-1}$ down to $P(k) = 10^{-16} \sim 10^{-14} \,{\rm Mpc}^3$ level. These results can be interpreted in terms of primordial black holes in the range $M_{\rm PBH} \gtrsim 10^{-3}M_\odot$ down to $f_{\rm PBH} = 10^{-6}$ level, or QCD axion minihalos in the range $m_a = 10^{-3} \sim 10^{-12} \,{\rm eV}$. A key result of the paper is the approximate relation between the scale $k$ and the gravitational wave frequency $f$, derived in an ensemble of `multi-lensing' events. This relation enables direct measurement of the power spectrum at specific scales, with sensitivities characterized by model-independent kernels $\delta P(k)$. Additionally, we delineate the statistical properties of `multi-lensing' based on the `Fresnel number' $N_F$. When $N_F \gtrsim {\cal O}(1)$, the statistical significance can be approximately calculated by Variance of lensing effects, which is directly related to the power spectrum among other moments of matter distribution., Comment: 32 pages, 11 figures

Chronic rhinosinusitis (CRS) is a common and persistent sinus imflammation that affects 5 - 12\% of the general population. It significantly impacts quality of life and is often difficult to assess due to its subjective nature in clinical evaluation. We introduce PARASIDE, an automatic tool for segmenting air and soft tissue volumes of the structures of the sinus maxillaris, frontalis, sphenodalis and ethmoidalis in T1 MRI. By utilizing that segmentation, we can quantify feature relations that have been observed only manually and subjectively before. We performed an exemplary study and showed both volume and intensity relations between structures and radiology reports. While the soft tissue segmentation is good, the automated annotations of the air volumes are excellent. The average intensity over air structures are consistently below those of the soft tissues, close to perfect separability. Healthy subjects exhibit lower soft tissue volumes and lower intensities. Our developed system is the first automated whole nasal segmentation of 16 structures, and capable of calculating medical relevant features such as the Lund-Mackay score.

To address the issue of powerful row hammer (RH) attacks, our study involved an extensive analysis of the prevalent attack patterns in the field. We discovered a strong correlation between the timing and density of the active-to-active command period, ${tRC}$, and the likelihood of RH attacks. In this paper, we introduce MARC, an innovative ARFM-driven RH mitigation IP that significantly reinforces existing RH mitigation IPs. MARC dynamically adjusts the frequency of RFM in response to the severity of the RH attack environment, offering a tailored security solution that not only detects the threats but also adapts to varying threat levels. MARC's detection mechanism has demonstrated remarkable efficiency, identifying over 99\% of attack patterns. Moreover, MARC is designed as a compact hardware module, facilitating tight integration either on the memory controller-side or DRAM-side within the memory system. It only occupies a negligible hardware area of 3363~\textit{$\mu m^2$}. By activating ARFM based on MARC's detection, the additional energy overhead is also negligible in normal workloads. We conduct experiments to compare the highest row count throughout the patterns, defined as max exposure, between the vanilla RH mitigation IPs and the MARC-enhanced versions of the same IPs, focusing on both DRAM-side and memory controller-side. On the DRAM-side, MARC + probabilistic scheme and MARC + counter-based tracking scheme achieve 8.1$\times$ and 1.5$\times$ improvement in max exposure ratio compared to the vanilla IPs, respectively. On the memory controller-side, the MARC + PARA and MARC + Graphene achieve 50$\times$ and 5.7$\times$ improvement in max exposure ratio compared to the vanilla IPs, respectively. MARC ensures optimal security without sacrificing system performance, making MARC a pioneering solution in the realm of RH attack mitigation., Comment: 12 pages, 19 figures

The KOTO II experiment is proposed to measure the branching ratio of the decay $K_L\to\pi^0\nu\bar{\nu}$ at J-PARC. With a beamline to extract long-lived neutral kaons at 5 degrees from a production target, the single event sensitivity of the decay is $8.5\times 10^{-13}$, which is much smaller than the Standard Model prediction $3\times 10^{-11}$. This allows searches for new physics beyond the Standard Model and the first discovery of the decay with a significance exceeding $5\sigma$. As the only experiment proposed in the world dedicated to rare kaon decays, KOTO II will be indispensable in the quest for a complete understanding of flavor dynamics in the quark sector. Moreover, by combining efforts from the kaon community worldwide, we plan to develop the KOTO II detector further and expand the physics reach of the experiment to include measurements of the branching ratio of the $K_L\to\pi^0\ell^+\ell^-$ decays, studies of other $K_L$ decays, and searches for dark photons, axions, and axion-like particles. KOTO II will therefore obtain a comprehensive understanding of $K_L$ decays, providing further constraints on new physics scenarios with existing $K^+$ results., Comment: Submitted to the J-PARC PAC. arXiv admin note: substantial text overlap with arXiv:2110.04462

Computed tomography (CT) and clinical numeric data are essential modalities for cancer evaluation, but building large-scale multimodal training datasets for developing medical foundation models remains challenging due to the structural complexity of multi-slice CT data and high cost of expert annotation. In this study, we propose MEDFORM, a multimodal pre-training strategy that guides CT image representation learning using complementary information from clinical data for medical foundation model development. MEDFORM efficiently processes CT slice through multiple instance learning (MIL) and adopts a dual pre-training strategy: first pretraining the CT slice feature extractor using SimCLR-based self-supervised learning, then aligning CT and clinical modalities through cross-modal contrastive learning. Our model was pre-trained on three different cancer types: lung cancer (141,171 slices), breast cancer (8,100 slices), colorectal cancer (10,393 slices). The experimental results demonstrated that this dual pre-training strategy improves cancer classification performance and maintains robust performance in few-shot learning scenarios. Code available at https://github.com/DigitalHealthcareLab/25MultiModalFoundationModel.git, Comment: 8 pages, 1 figure

Compositional Zero-Shot Learning (CZSL) aims to recognize subtle differences in meaning or the combination of states and objects through the use of known and unknown concepts during training. Existing methods either focused on prompt configuration or on using prompts to tune the pre-trained Vision-Language model. However, these methods faced challenges in accurately identifying subtle differences in meaning or combining states with objects. To jointly eradicate the above issues and construct an efficient and effective CZSL technique, we suggest a method to improve attribute recognition performance by utilizing diverse Prompt Learning with an Inter/Intra-Modality Fusion Synthesizer in scene understanding involving subtle semantic differences and multiple objects., Comment: AIAP 2025

The colliding-wind binary system $\eta$ Carinae has been identified as a source of high-energy (HE, below $\sim$100\,GeV) and very-high-energy (VHE, above $\sim$100\,GeV) gamma rays in the last decade, making it unique among these systems. With its eccentric 5.5-year-long orbit, the periastron passage, during which the stars are separated by only $1-2$\,au, is an intriguing time interval to probe particle acceleration processes within the system. In this work, we report on an extensive VHE observation campaign that for the first time covers the full periastron passage carried out with the High Energy Stereoscopic System (H.E.S.S.) in its 5-telescope configuration with upgraded cameras. VHE gamma-ray emission from $\eta$ Carinae was detected during the periastron passage with a steep spectrum with spectral index $\Gamma= 3.3 \pm 0.2_{\mathrm{stat}} \, \pm 0.1_{\mathrm{syst}}$. Together with previous and follow-up observations, we derive a long-term light curve sampling one full orbit, showing hints of an increase of the VHE flux towards periastron, but no hint of variability during the passage itself. An analysis of contemporaneous Fermi-LAT data shows that the VHE spectrum represents a smooth continuation of the HE spectrum. From modelling the combined spectrum we conclude that the gamma-ray emission region is located at distances of ${\sim}10 - 20$\,au from the centre of mass of the system and that protons are accelerated up to energies of at least several TeV inside the system in this phase., Comment: Accepted at A&A; 11 pages, 9 figures

Point tracking in videos is a fundamental task with applications in robotics, video editing, and more. While many vision tasks benefit from pre-trained feature backbones to improve generalizability, point tracking has primarily relied on simpler backbones trained from scratch on synthetic data, which may limit robustness in real-world scenarios. Additionally, point tracking requires temporal awareness to ensure coherence across frames, but using temporally-aware features is still underexplored. Most current methods often employ a two-stage process: an initial coarse prediction followed by a refinement stage to inject temporal information and correct errors from the coarse stage. These approach, however, is computationally expensive and potentially redundant if the feature backbone itself captures sufficient temporal information. In this work, we introduce Chrono, a feature backbone specifically designed for point tracking with built-in temporal awareness. Leveraging pre-trained representations from self-supervised learner DINOv2 and enhanced with a temporal adapter, Chrono effectively captures long-term temporal context, enabling precise prediction even without the refinement stage. Experimental results demonstrate that Chrono achieves state-of-the-art performance in a refiner-free setting on the TAP-Vid-DAVIS and TAP-Vid-Kinetics datasets, among common feature backbones used in point tracking as well as DINOv2, with exceptional efficiency. Project page: https://cvlab-kaist.github.io/Chrono/

In the era of precision measurements of neutrino oscillation parameters, it is necessary for experiments to disentangle discrepancies that may indicate physics beyond the Standard Model in the neutrino sector. KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study the oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on the initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photosensors. A high-purity neutrino sample was extracted during 2020 and 2021, corresponding to an exposure of 433 kton-years. This sample is analysed following a binned log-likelihood approach to search for invisible neutrino decay, in a three-flavour neutrino oscillation scenario, where the third neutrino mass state $\nu_3$ decays into an invisible state, e.g. a sterile neutrino. The resulting best fit of the invisible neutrino decay parameter is $\alpha_3 = 0.92^{+1.08}_{-0.57}\times 10^{-4}~\mathrm{eV^2}$, corresponding to a scenario with $\theta_{23}$ in the second octant and normal neutrino mass ordering. The results are consistent with the Standard Model, within a $2.1\,\sigma$ interval., Comment: 20 pages, 8 figures

Two-photon interference, a quantum phenomenon arising from the principle of indistinguishability, is a powerful tool for quantum state engineering and plays a fundamental role in various quantum technologies. These technologies demand robust and efficient sources of quantum light, as well as scalable, integrable and multifunctional platforms. In this regard, quantum optical metasurfaces (QOMs) are emerging as promising platforms for quantum light generation, namely biphotons via spontaneous parametric down-conversion (SPDC), and its engineering. Due to the relaxation of phase matching, SPDC in QOMs allows different channels of biphoton generation, such as those supported by overlapping resonances, to occur simultaneously. In previously reported QOMs, however, SPDC was too weak to observe such effects. Here we develop QOMs based on [110]-oriented GaAs that provide more than an order of magnitude enhancement in SPDC rate, after accounting for the spectral bandwidth, compared to any other QOMs studied to date. This boosted efficiency allows the QOMs support the simultaneous generation of SPDC from several spectrally overlapping optical modes. Using polarization components in the interferometer analyzer, we intentionally erase the distinguishability between the biphotons from a high-$Q$ quasi-bound-state-in-the-continuum resonance and a low-$Q$ Mie resonance, which results in the first-time observation of two-photon interference in the spectral domain in these types of devices. This quantum interference can considerably enrich the generation of entangled photons in metasurfaces. Their advanced multifunctionality, improved nonlinear response, ease of fabrication and compact footprint of [110]-GaAs QOMs position them as promising platforms to fulfill the requirements for photonic quantum technologies.

The Event Horizon Telescope (EHT) has successfully revealed the shadow of the supermassive black hole, M87*, with an unprecedented angular resolution of approximately 20 uas at 230 GHz. However, because of limited short baseline lengths, the EHT has been constrained in its ability to recover larger scale jet structures. The extended Korean VLBI Network (eKVN) is committed to joining the EHT from 2024 that can improve short baseline coverage. This study evaluates the impact of the participation of eKVN in the EHT on the recovery of the M87* jet. Synthetic data, derived from a simulated M87* model, were observed using both the EHT and the combined EHT+eKVN arrays, followed by image reconstructions from both configurations. The results indicate that the inclusion of eKVN significantly improves the recovery of jet structures by reducing residual noise. Furthermore, jackknife tests, in which one or two EHT telescopes were omitted - simulating potential data loss due to poor weather - demonstrate that eKVN effectively compensates for these missing telescopes, particularly in short baseline coverage. Multi-frequency synthesis imaging at 86-230 GHz shows that the EHT+eKVN array enhances the recovered spectral index distribution compared to the EHT alone and improves image reconstruction at each frequency over single-frequency imaging. As the EHT continues to expand its array configuration and observing capabilities to probe black hole physics more in depth, the integration of eKVN into the EHT will significantly enhance the stability of observational results and improve image fidelity. This advancement will be particularly valuable for future regular monitoring observations, where consistent data quality is essential., Comment: 13 pages, 13 figures, 2 tables

We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star and Fanar Prime, two highly capable Arabic Large Language Models (LLMs) that are best in the class on well established benchmarks for similar sized models. Fanar Star is a 7B (billion) parameter model that was trained from scratch on nearly 1 trillion clean and deduplicated Arabic, English and Code tokens. Fanar Prime is a 9B parameter model continually trained on the Gemma-2 9B base model on the same 1 trillion token set. Both models are concurrently deployed and designed to address different types of prompts transparently routed through a custom-built orchestrator. The Fanar platform provides many other capabilities including a customized Islamic Retrieval Augmented Generation (RAG) system for handling religious prompts, a Recency RAG for summarizing information about current or recent events that have occurred after the pre-training data cut-off date. The platform provides additional cognitive capabilities including in-house bilingual speech recognition that supports multiple Arabic dialects, voice and image generation that is fine-tuned to better reflect regional characteristics. Finally, Fanar provides an attribution service that can be used to verify the authenticity of fact based generated content. The design, development, and implementation of Fanar was entirely undertaken at Hamad Bin Khalifa University's Qatar Computing Research Institute (QCRI) and was sponsored by Qatar's Ministry of Communications and Information Technology to enable sovereign AI technology development.

While CLIP has significantly advanced multimodal understanding by bridging vision and language, the inability to grasp negation - such as failing to differentiate concepts like "parking" from "no parking" - poses substantial challenges. By analyzing the data used in the public CLIP model's pre-training, we posit this limitation stems from a lack of negation-inclusive data. To address this, we introduce data generation pipelines that employ a large language model (LLM) and a multimodal LLM to produce negation-inclusive captions. Fine-tuning CLIP with data generated from our pipelines, we develop NegationCLIP, which enhances negation awareness while preserving the generality. Moreover, to enable a comprehensive evaluation of negation understanding, we propose NegRefCOCOg-a benchmark tailored to test VLMs' ability to interpret negation across diverse expressions and positions within a sentence. Experiments on various CLIP architectures validate the effectiveness of our data generation pipelines in enhancing CLIP's ability to perceive negation accurately. Additionally, NegationCLIP's enhanced negation awareness has practical applications across various multimodal tasks, demonstrated by performance gains in text-to-image generation and referring image segmentation.

This paper proposes a Theme-Explanation Structure-based Table Summarization (Tabular-TX) pipeline designed to efficiently process table data. Tabular-TX preprocesses table data by focusing on highlighted cells and then generates summary sentences structured with a Theme Part in the form of adverbial phrases followed by an Explanation Part in the form of clauses. In this process, customized analysis is performed by considering the structural characteristics and comparability of the table. Additionally, by utilizing In-Context Learning, Tabular-TX optimizes the analytical capabilities of large language models (LLMs) without the need for fine-tuning, effectively handling the structural complexity of table data. Results from applying the proposed Tabular-TX to generate table-based summaries demonstrated superior performance compared to existing fine-tuning-based methods, despite limitations in dataset size. Experimental results confirmed that Tabular-TX can process complex table data more effectively and established it as a new alternative for table-based question answering and summarization tasks, particularly in resource-constrained environments., Comment: 6 pages, in Korean language. The 2024 Joint Conference on Human and Cognitive Language Technology, Korean Association for Corpus Linguistics (HCLT-KACL 2024)

Researchers have long debated which spatial arrangements and swimming synchronizations are beneficial for the hydrodynamic performance of fish in schools. In our previous work (Seo and Mittal, Bioinsp. Biomim., Vol. 17, 066020, 2022), we demonstrated using direct numerical simulations that hydrodynamic interactions with the wake of a leading body-caudal fin carangiform swimmer could significantly enhance the swimming performance of a trailing swimmer by augmenting the leading-edge vortex (LEV) on its caudal fin. In this study, we develop a model based on the phenomenology of LEV enhancement, which utilizes wake velocity data from direct numerical simulations of a leading fish to predict the trailing swimmer's hydrodynamic performance without additional simulations. This approach enables a comprehensive analysis of the effects of relative positioning, phase difference, flapping amplitude, Reynolds number, and the number of swimmers in the school on thrust enhancement. The results offer several insights regarding the effect of these parameters that have implications for fish schools as well as for bio-inspired underwater vehicle applications., Comment: This paper is considering submission to Journal of Fluid Mechanics

Data augmentation (DA) is ubiquitously used in training of Automatic Speech Recognition (ASR) models. DA offers increased data variability, robustness and generalization against different acoustic distortions. Recently, personalization of ASR models on mobile devices has been shown to improve Word Error Rate (WER). This paper evaluates data augmentation in this context and proposes persoDA; a DA method driven by user's data utilized to personalize ASR. persoDA aims to augment training with data specifically tuned towards acoustic characteristics of the end-user, as opposed to standard augmentation based on Multi-Condition Training (MCT) that applies random reverberation and noises. Our evaluation with an ASR conformer-based baseline trained on Librispeech and personalized for VOICES shows that persoDA achieves a 13.9% relative WER reduction over using standard data augmentation (using random noise & reverberation). Furthermore, persoDA shows 16% to 20% faster convergence over MCT., Comment: ICASSP'25-Copyright 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

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

We propose GOTLoc, a robust localization method capable of operating even in outdoor environments where GPS signals are unavailable. The method achieves this robust localization by leveraging comparisons between scene graphs generated from text descriptions and maps. Existing text-based localization studies typically represent maps as point clouds and identify the most similar scenes by comparing embeddings of text and point cloud data. However, point cloud maps have limited scalability as it is impractical to pre-generate maps for all outdoor spaces. Furthermore, their large data size makes it challenging to store and utilize them directly on actual robots. To address these issues, GOTLoc leverages compact data structures, such as scene graphs, to store spatial information, enabling individual robots to carry and utilize large amounts of map data. Additionally, by utilizing publicly available map data, such as OpenStreetMap, which provides global information on outdoor spaces, we eliminate the need for additional effort to create custom map data. For performance evaluation, we utilized the KITTI360Pose dataset in conjunction with corresponding OpenStreetMap data to compare the proposed method with existing approaches. Our results demonstrate that the proposed method achieves accuracy comparable to algorithms relying on point cloud maps. Moreover, in city-scale tests, GOTLoc required significantly less storage compared to point cloud-based methods and completed overall processing within a few seconds, validating its applicability to real-world robotics. Our code is available at https://github.com/donghwijung/GOTLoc.

Compact, star-forming galaxies with high star formation rate surface densities ($\Sigma_{\text{SFR}}$) are often efficient Lyman continuum (LyC) emitters at $z\leq 4.5$, likely as intense stellar feedback creates low-density channels that allow photons to escape. Irregular or disturbed morphologies, such as those resulting from mergers, can also facilitate LyC escape by creating anisotropic gas distributions. We investigate the influence of galaxy morphology on LyC production and escape at redshifts $5 \leq z \leq 7$ using observations from various \textit{James Webb Space Telescope} (JWST) surveys. Our sample consists of 436 sources, which are predominantly low-mass ($\sim 10^{8.15} M_\odot$), star-forming galaxies with ionizing photon efficiency ($\xi_{\rm ion}$) values consistent with canonical expectations. Since direct measurements of $f_{\rm esc}$ are not possible during the Epoch of Reionization (EoR), we predict $f_{\rm esc}$ for high-redshift galaxies by applying survival analysis to a subsample of LyC emitters from the Low-Redshift Lyman Continuum Survey (LzLCS), selected to be direct analogs of reionization-era galaxies. We find that these galaxies exhibit on average modest predicted escape fractions ($\sim 0.04$). Additionally, we assess the correlation between morphological features and LyC emission. Our findings indicate that neither $\xi_{\rm ion}$ nor the predicted $f_{\rm esc}$ values show a significant correlation with the presence of merger signatures. This suggests that in low-mass galaxies at $z \geq 5$, strong morphological disturbances are not the primary mechanism driving LyC emission and leakage. Instead, compactness and star formation activity likely play a more pivotal role in regulating LyC escape., Comment: Submitted to A&A

TTS (Text-to-Speech) document reader from Microsoft, Adobe, Apple, and OpenAI have been serviced worldwide. They provide relatively good TTS results for general plain text, but sometimes skip contents or provide unsatisfactory results for mathematical expressions. This is because most modern academic papers are written in LaTeX, and when LaTeX formulas are compiled, they are rendered as distinctive text forms within the document. However, traditional TTS document readers output only the text as it is recognized, without considering the mathematical meaning of the formulas. To address this issue, we propose MathReader, which effectively integrates OCR, a fine-tuned T5 model, and TTS. MathReader demonstrated a lower Word Error Rate (WER) than existing TTS document readers, such as Microsoft Edge and Adobe Acrobat, when processing documents containing mathematical formulas. MathReader reduced the WER from 0.510 to 0.281 compared to Microsoft Edge, and from 0.617 to 0.281 compared to Adobe Acrobat. This will significantly contribute to alleviating the inconvenience faced by users who want to listen to documents, especially those who are visually impaired. The code is available at https://github.com/hyeonsieun/MathReader., Comment: Accepted at ICASSP 2025

Indoor localization in challenging non-line-of-sight (NLOS) environments often leads to mediocre accuracy with traditional approaches. Deep learning (DL) has been applied to tackle these challenges; however, many DL approaches overlook computational complexity, especially for floating-point operations (FLOPs), making them unsuitable for resource-limited devices. Transformer-based models have achieved remarkable success in natural language processing (NLP) and computer vision (CV) tasks, motivating their use in wireless applications. However, their use in indoor localization remains nascent, and directly applying Transformers for indoor localization can be both computationally intensive and exhibit limitations in accuracy. To address these challenges, in this work, we introduce a novel tokenization approach, referred to as Sensor Snapshot Tokenization (SST), which preserves variable-specific representations of power delay profile (PDP) and enhances attention mechanisms by effectively capturing multi-variate correlation. Complementing this, we propose a lightweight Swish-Gated Linear Unit-based Transformer (L-SwiGLU Transformer) model, designed to reduce computational complexity without compromising localization accuracy. Together, these contributions mitigate the computational burden and dependency on large datasets, making Transformer models more efficient and suitable for resource-constrained scenarios. The proposed tokenization method enables the Vanilla Transformer to achieve a 90th percentile positioning error of 0.388 m in a highly NLOS indoor factory, surpassing conventional tokenization methods. The L-SwiGLU ViT further reduces the error to 0.355 m, achieving an 8.51% improvement. Additionally, the proposed model outperforms a 14.1 times larger model with a 46.13% improvement, underscoring its computational efficiency., Comment: The paper has been submitted to IEEE Transactions on Machine Learning in Communications and Networking

The transition to a carbon-neutral energy system requires massive installation of renewable energy sources and economically feasible energy storage solutions. This study addresses these challenges by optimizing the design and control strategies of an energy system that meets the heat and electricity demands of a community. The proposed system integrates solar and wind power with energy storage, including seasonal thermal energy storage (STES) and battery, coupled via a heat pump. This approach enhances self-sufficiency and effectively mitigates seasonal mismatches. To model heat transfer between the storage and the ground in the STES system, we employ a multi-node lumped-parameter method. The optimization problem is formulated as a periodic optimal control problem, which is then transcribed into a nonlinear programming problem. To reduce computational complexity, we apply the averaging method, which significantly lowers the effort required to solve the problem. We apply this approach to a case study, where the economically optimized configuration results in a projected total energy cost per household of approximately 75 EUR/month over 30 years for both heat and electricity. This study demonstrates the feasibility of designing economically viable, autonomous energy communities in real-world scenarios, and provides a comprehensive optimization framework for designing system components and control strategies., Comment: 35 pages, 12 figures, submitted to Renewable Energy. Editor-in-chief: Nidia Caetano

A recent proposal reinterprets the eikonal as the scattering generator, which computes scattering observables through an action as a symmetry generator. The aim of this study is to incorporate dissipative effects from radiation into this framework, where the eikonal is generalised to the radiation eikonal by including mediator field degrees of freedom. The proposed generalisation is tested through several post-Minkowskian scattering observables; scattering waveform, radiated momentum, and (time asymmetric) radiation loss in the impulse., Comment: 7 pages, 4 figures

Figures and their captions play a key role in scientific publications. However, despite their importance, many captions in published papers are poorly crafted, largely due to a lack of attention by paper authors. While prior AI research has explored caption generation, it has mainly focused on reader-centered use cases, where users evaluate generated captions rather than actively integrating them into their writing. This paper addresses this gap by investigating how paper authors incorporate AI-generated captions into their writing process through a user study involving 18 participants. Each participant rewrote captions for two figures from their own recently published work, using captions generated by state-of-the-art AI models as a resource. By analyzing video recordings of the writing process through interaction analysis, we observed that participants often began by copying and refining AI-generated captions. Paper writers favored longer, detail-rich captions that integrated textual and visual elements but found current AI models less effective for complex figures. These findings highlight the nuanced and diverse nature of figure caption composition, revealing design opportunities for AI systems to better support the challenges of academic writing., Comment: This paper will appear at AAAI 2025 Workshop (2nd AI4Research Workshop: Towards a Knowledge-grounded Scientific Research Lifecycle)

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