Search

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

We report an updated neutrino mixing angle of $\theta_{13}$ obtained from a complete data sample of the RENO experiment. The experiment has measured the amplitude and frequency of reactor anti-electron-neutrinos ($\bar{\nu}_{e}$) oscillations at the Hanbit nuclear power plant, Younggwang, Korea, since August 2011. As of March 2023, the data acquisition was completed after a total of 3800 live days of detector operation. The observed candidates via inverse beta decay (IBD) are 1,211,995 (144,667) in the near (far) detector. Based on an observed energy-dependent reactor neutrino disappearance, neutrino oscillation parameters of $\theta_{13}$ and $\lvert\Delta m_{ee}^2\rvert$ are precisely determined as $\sin^{2}2\theta_{13}=0.0920_{-0.0042}^{+0.0044}(\text{stat.})_{-0.0041}^{+0.0041}(\text{syst.})$ and $\lvert\Delta m_{ee}^2\rvert=\left[2.57_{-0.11}^{+0.10}(\text{stat.})_{-0.05}^{+0.05}(\text{syst.})\right]\times10^{-3}~\text{eV}^{2}$. Compared to the previous RENO results published in Ref.~\cite{PhysRevLett.121.201801}, the precision is improved from 7.5\% to 6.4\% for $\sin^{2}2\theta_{13}$ and from 5.2\% to 4.5\% for $\lvert\Delta m_{ee}^2\rvert$. The statistical error of the measurement has reached our goal and is hardly improved with additional data-taking., Comment: 13 pages, 11 figures

Recent advancements in graph neural networks (GNNs) have highlighted the critical need of calibrating model predictions, with neighborhood prediction similarity recognized as a pivotal component. Existing studies suggest that nodes with analogous neighborhood prediction similarity often exhibit similar calibration characteristics. Building on this insight, recent approaches incorporate neighborhood similarity into node-wise temperature scaling techniques. However, our analysis reveals that this assumption does not hold universally. Calibration errors can differ significantly even among nodes with comparable neighborhood similarity, depending on their confidence levels. This necessitates a re-evaluation of existing GNN calibration methods, as a single, unified approach may lead to sub-optimal calibration. In response, we introduce **Simi-Mailbox**, a novel approach that categorizes nodes by both neighborhood similarity and their own confidence, irrespective of proximity or connectivity. Our method allows fine-grained calibration by employing *group-specific* temperature scaling, with each temperature tailored to address the specific miscalibration level of affiliated nodes, rather than adhering to a uniform trend based on neighborhood similarity. Extensive experiments demonstrate the effectiveness of our **Simi-Mailbox** across diverse datasets on different GNN architectures, achieving up to 13.79\% error reduction compared to uncalibrated GNN predictions., Comment: Accepted at AAAI 2025

One major challenge that institutions face when launching transformation efforts is that they operate in "silos," meaning that they have separate departments--for example, student services, financial aid, and advising--that operate independently with little communication or coordination. Many successful transformation efforts include a core group of people who are working together to influence outcomes on a specific issue. The decentralized nature of most colleges can create challenges to implementing a shared vision and making progress that students can see. Siloed teams may not have access to all the information needed to address a problem adequately, and multiple teams may implement contradictory solutions. Cross-functional teams (CFTs) are one potential response to the problem of silos. CFTs are defined as staff members from different functional areas working together to achieve common institutional goals. Bringing together members with different specialties can lead to faster transfers of knowledge, fewer redundancies, and higher-quality decisions. But CFTs do not always work effectively: they may have unclear governance, a lack of accountability, or unclear goals. However there has been little research on CFTs in the context of higher education. Launched in 2021, this project explores how institutions are assembling and using CFTs to advance their transformation efforts. MDRC partnered with three community colleges and one state agency overseeing higher education to explore their efforts to create these cross-functional teams. The researchers set out to document the interpersonal dynamics of these teams and to pilot test a tool at one institution to address barriers to CFTs' smooth operation. This brief shares descriptive findings and recommendations, with the goal of providing funders, researchers, and practitioners suggestions for future research.

Hallucination in large language models (LLMs) remains a significant challenge for their safe deployment, particularly due to its potential to spread misinformation. Most existing solutions address this challenge by focusing on aligning the models with credible sources or by improving how models communicate their confidence (or lack thereof) in their outputs. While these measures may be effective in most contexts, they may fall short in scenarios requiring more nuanced approaches, especially in situations where access to accurate data is limited or determining credible sources is challenging. In this study, we take North Korea - a country characterised by an extreme lack of reliable sources and the prevalence of sensationalist falsehoods - as a case study. We explore and evaluate how some of the best-performing multilingual LLMs and specific language-based models generate information about North Korea in three languages spoken in countries with significant geo-political interests: English (United States, United Kingdom), Korean (South Korea), and Mandarin Chinese (China). Our findings reveal significant differences, suggesting that the choice of model and language can lead to vastly different understandings of North Korea, which has important implications given the global security challenges the country poses., Comment: Accepted at COLING 2025

The growing demand for halal cosmetic products has exposed significant challenges, especially in Muslim-majority countries. Recently, various machine learning-based strategies, e.g., image-based methods, have shown remarkable success in predicting the halal status of cosmetics. However, these methods mainly focus on analyzing the discrete and specific ingredients within separate cosmetics, which ignore the high-order and complex relations between cosmetics and ingredients. To address this problem, we propose a halal cosmetic recommendation framework, namely HaCKG, that leverages a knowledge graph of cosmetics and their ingredients to explicitly model and capture the relationships between cosmetics and their components. By representing cosmetics and ingredients as entities within the knowledge graph, HaCKG effectively learns the high-order and complex relations between entities, offering a robust method for predicting halal status. Specifically, we first construct a cosmetic knowledge graph representing the relations between various cosmetics, ingredients, and their properties. We then propose a pre-trained relational graph attention network model with residual connections to learn the structural relation between entities in the knowledge graph. The pre-trained model is then fine-tuned on downstream cosmetic data to predict halal status. Extensive experiments on the cosmetic dataset over halal prediction tasks demonstrate the superiority of our model over state-of-the-art baselines., Comment: 10 pages

Aerodynamic loads play a central role in many fluid dynamics applications, and we present a method for identifying the structures (or modes) in a flow that make dominant contributions to the time-varying aerodynamic loads in a flow. The method results from the combination of the force partitioning method (Menon and Mittal, J. Fluid Mech., 907:A37, 2021) and modal decomposition techniques such as Reynolds decomposition, triple decomposition, and proper orthogonal decomposition, and is applied here to three distinct flows - two-dimensional flows past a circular cylinder and an airfoil, and the three-dimensional flow over a revolving rectangular wing. We show that force partitioning applied to modal decomposition of velocity fields results in complex, and difficult to interpret inter-modal interactions. We therefore propose and apply modal decomposition directly to the $Q$-field associated with these flows. The variable $Q$ is a non-linear observable that is typically used to identify vortices in a flow, and we find that the direct decomposition of $Q$ leads to results that are more amenable to interpretation. We also demonstrate that this modal force partitioning can be extended to provide insights into the far-field aeroacoustic loading noise of these flows., Comment: Submitted for peer review to Journal of Fluid Mechanics

Knowledge graphs have proven successful in integrating heterogeneous data across various domains. However, there remains a noticeable dearth of research on their seamless integration among heterogeneous recommender systems, despite knowledge graph-based recommender systems garnering extensive research attention. This study aims to fill this gap by proposing RecKG, a standardized knowledge graph for recommender systems. RecKG ensures the consistent representation of entities across different datasets, accommodating diverse attribute types for effective data integration. Through a meticulous examination of various recommender system datasets, we select attributes for RecKG, ensuring standardized formatting through consistent naming conventions. By these characteristics, RecKG can seamlessly integrate heterogeneous data sources, enabling the discovery of additional semantic information within the integrated knowledge graph. We apply RecKG to standardize real-world datasets, subsequently developing an application for RecKG using a graph database. Finally, we validate RecKG's achievement in interoperability through a qualitative evaluation between RecKG and other studies., Comment: Accepted by The 39th ACM/SIGAPP Symposium On Applied Computing(SAC) 2024

When states in a tower like the Kaluza-Klein or the string tower couple to another state through the irrelevant operators of the same type, their contributions to the loop corrections of the relevant or the marginal operators are not negligible, threatening the perturbativity. This can be avoided provided the cutoff scale is lower than the species scale associated with the irrelevant operator. We apply this to towers of states associated with the neutrino which couple to the Higgs through the Weinberg operator, the dimension-5 irrelevant operator generating the Majorana neutrino mass. Requiring the `Majorana species scale', the species scale associated with the Weinberg operator, to be below the gravitational species scale, one finds the lower bound on the Majorana neutrino mass determined by the species number. The Festina-Lente bound also gives the lower bound on the Majorana neutrino mass, but it is not so stringent. Meanwhile, even if the neutrino mass is of the Dirac type at the renormalizable level, the Majorana mass term still can be written in the effective field theory action so far as the Weinberg operator is not forbidden. Even if the Majorana neutrino mass is larger than the Dirac one, so far as there are sufficient degrees of freedom with mass smaller than the scale of the cosmological constant, the observation of the Majorana nature of the neutrino may not contradict to quantum gravity constraints which rules out the neutrino mass purely given by the Majorana type., Comment: 15 pages, 3 figures

We employ a novel computational modeling framework to perform high-fidelity direct numerical simulations of aero-structural interactions in bat-inspired membrane wings. The wing of a bat consists of an elastic membrane supported by a highly articulated skeleton, enabling localized control over wing movement and deformation during flight. By modeling these complex deformations, along with realistic wing movements and interactions with the surrounding airflow, we expect to gain new insights into the performance of these unique wings. Our model achieves a high degree of realism by incorporating experimental measurements of the skeleton's joint movements to guide the fluid-structure interaction simulations. The simulations reveal that different segments of the wing undergo distinct aeroelastic deformations, impacting flow dynamics and aerodynamic loads. Specifically, the simulations show significant variations in the effectiveness of the wing in generating lift, drag, and thrust forces across different segments and regions of the wing. We employ a force partitioning method to analyze the causality of pressure loads over the wing, demonstrating that vortex-induced pressure forces are dominant while added mass contributions to aerodynamic loads are minimal. This approach also elucidates the role of various flow structures in shaping pressure distributions. Finally, we compare the fully articulated, flexible bat wing to equivalent stiff wings derived from the same kinematics, demonstrating the critical impact of wing articulation and deformation on aerodynamic efficiency., Comment: Preprint

This work studies an information-theoretic performance limit of an integrated sensing and communication (ISAC) system where the goal of sensing is to estimate a random continuous state. Considering the mean-squared error (MSE) for estimation performance metric, the Bayesian Cram\'{e}r-Rao lower bound (BCRB) is widely used in literature as a proxy of the MSE; however, the BCRB is not generally tight even asymptotically except for restrictive distributions. Instead, we characterize the full tradeoff between information rate and the exact MSE using the asymptotically tight BCRB (ATBCRB) analysis, a recent variant of the BCRB. Our characterization is applicable for general channels as long as the regularity conditions are met, and the proof relies on constant composition codes and ATBCRB analysis with the codes. We also perform a numerical evaluation of the tradeoff in a variance estimation example, which commonly arises in spectrum sensing scenarios.

The application of unmanned aerial vehicles (UAVs) is surging across several industries, paralleled by growing demand for these UAVs. However, the noise emitted by UAVs remains a significant impediment to their widespread use even though in areas such as product delivery, they can be more environmentally friendly than traditional delivery methods. Nature has often been a source of inspiration for devices that are efficient and eco-friendly. In the current study, we leverage the previous work by Seo et al. (Bioinsp. Biomimetics, 16 (4):046019, 2021) on the aeroacoustics of flapping wing flight in mosquitoes and fruit flies to propose and examine a simple strategy for reducing the aeroacoustic noise from drone rotors. In particular, inspired by these insects, we explore how an increase in the planform area of the rotor could be used to reduce the rotation rate and the associated aeroacoustic noise from small-scale rotors. The study employs a sharp-interface immersed boundary solver for the flow simulations and the aeroacoustic sound is predicted by the Ffowcs Williams-Hawkings equation. Simulations indicate that the simple strategy of employing rotors with larger planform areas could lead not just to reduced aeroacoustic noise but improved power economy as well.

The dimensionality of vortical structures has recently been extended beyond two dimensions, providing higher-order topological characteristics and robustness for high-capacity information processing and turbulence control. The generation of high-dimensional vortical structures has mostly been demonstrated in classical systems through the complex interference of fluidic, acoustic, or electromagnetic waves. However, natural materials rarely support three- or higher-dimensional vortical structures and their physical interactions. Here, we present a high-dimensional gradient thickness optical cavity (GTOC) in which the optical coupling of planar metal-dielectric multilayers implements topological interactions across multiple dimensions. Topological interactions in high-dimensional GTOC construct non-trivial topological phases, which induce high-dimensional vortical structures in generalized parameter space in three, four dimensions, and beyond. These emergent high-dimensional vortical structures are observed under electro-optic tomography as optical vortex dynamics in two-dimensional real-space, employing the optical thicknesses of the dielectric layers as synthetic dimensions. We experimentally demonstrate emergent vortical structures, optical vortex lines and vortex rings, in a three-dimensional generalized parameter space and their topological transitions. Furthermore, we explore four-dimensional vortical structures, termed optical vortex sheets, which provide the programmability of real-space optical vortex dynamics. Our findings hold significant promise for emulating high-dimensional physics and developing active topological photonic devices., Comment: 21 pages,5 figures

For blind and low-vision (BLV) individuals, digital math communication is uniquely difficult due to the lack of accessible tools. Currently, the state of the art is either code-based, like LaTeX, or WYSIWYG, like visual editors. However, both paradigms view math communication as primarily a visual typesetting problem, and may be accessible but difficult to use. In this paper, we present an equation editor that is built from the ground up with BLV accessibility in mind. Specifically, we notice that two of the biggest barriers with current technology are the high cognitive load and the lack of spatial relationships. Thus, we build an editor that uses spatial audio cues, muscle memory, tones, and more intuitive navigation to properly contextualize math equations. We discuss how this new paradigm can enable new levels of math communication, engagement, and literacy. Finally, we discuss natural next steps., Comment: 5 pages, 2 figures, accepted as demo paper at ASSETS '24

Cultural values alignment in Large Language Models (LLMs) is a critical challenge due to their tendency to embed Western-centric biases from training data, leading to misrepresentations and fairness issues in cross-cultural contexts. Recent approaches, such as role-assignment and few-shot learning, often struggle with reliable cultural alignment as they heavily rely on pre-trained knowledge, lack scalability, and fail to capture nuanced cultural values effectively. To address these issues, we propose ValuesRAG, a novel and effective framework that applies Retrieval-Augmented Generation (RAG) with in-context learning to integrate cultural and demographic knowledge dynamically during text generation. Leveraging the World Values Survey (WVS) dataset, ValuesRAG first generates summaries of values for each individual. Subsequently, we curated several representative regional datasets to serve as test datasets and retrieve relevant summaries of values based on demographic features, followed by a reranking step to select the top-k relevant summaries. ValuesRAG consistently outperforms baseline methods, both in the main experiment and in the ablation study where only the values summary was provided, highlighting ValuesRAG's potential to foster culturally aligned AI systems and enhance the inclusivity of AI-driven applications., Comment: preprint

Sr-doped nickelate, Nd1-xSrxNiO3 (NSNO), perovskite thin films and Ruddlesden-Popper (RP) phases are actively investigated because of their physical properties, such as the metal-insulator transition and superconductivity. However, achieving epitaxial growth of NSNO perovskite and RP phase films in a sputtering system is challenging compared to pulsed laser deposition and molecular beam epitaxy, due to the difficulty in stabilizing nickel oxidation states and minimizing structural defects. Here, we used an off-axis radio frequency (RF) magnetron sputtering to fabricate epitaxial NSNO perovskite and RP phase thin films on SrTiO3 (001) substrates, systematically controlling the growth temperatures. We investigated the thermal stability of the perovskite phase and the structural and electronic characteristics of the RP phase films. These findings provide valuable insights into the synthesis of nickelate RP phase films using RF magnetron sputtering, paving the way for scalable thin films fabrication technologies., Comment: 13 pages, 4 figures

Precise measurements from sensors are crucial, but data is usually collected from low-cost, low-tech systems, which are often inaccurate. Thus, they require further calibrations. To that end, we first identify three requirements for effective calibration under practical low-tech sensor conditions. Based on the requirements, we develop a model called TESLA, Transformer for effective sensor calibration utilizing logarithmic-binned attention. TESLA uses a high-performance deep learning model, Transformers, to calibrate and capture non-linear components. At its core, it employs logarithmic binning to minimize attention complexity. TESLA achieves consistent real-time calibration, even with longer sequences and finer-grained time series in hardware-constrained systems. Experiments show that TESLA outperforms existing novel deep learning and newly crafted linear models in accuracy, calibration speed, and energy efficiency., Comment: Accepted by AAAI 2025

The use of knowledge graphs in recommender systems has become one of the common approaches to addressing data sparsity and cold start problems. Recent advances in large language models (LLMs) offer new possibilities for processing side and context information within knowledge graphs. However, consistent integration across various systems remains challenging due to the need for domain expert intervention and differences in system characteristics. To address these issues, we propose a consistent approach that extracts both general and specific topics from both side and context information using LLMs. First, general topics are iteratively extracted and updated from side information. Then, specific topics are extracted using context information. Finally, to address synonymous topics generated during the specific topic extraction process, a refining algorithm processes and resolves these issues effectively. This approach allows general topics to capture broad knowledge across diverse item characteristics, while specific topics emphasize detailed attributes, providing a more comprehensive understanding of the semantic features of items and the preferences of users. Experimental results demonstrate significant improvements in recommendation performance across diverse knowledge graphs., Comment: Accepted by The 40th ACM/SIGAPP Symposium On Applied Computing(SAC) 2025

One of the key strengths of Large Language Models (LLMs) is their ability to interact with humans by generating appropriate responses to given instructions. This ability, known as instruction-following capability, has established a foundation for the use of LLMs across various fields and serves as a crucial metric for evaluating their performance. While numerous evaluation benchmarks have been developed, most focus solely on clear and coherent instructions. However, we have noted that LLMs can become easily distracted by instruction-formatted statements, which may lead to an oversight of their instruction comprehension skills. To address this issue, we introduce the Intention of Instruction (IoInst) benchmark. This benchmark evaluates LLMs' capacity to remain focused and understand instructions without being misled by extraneous instructions. The primary objective of this benchmark is to identify the appropriate instruction that accurately guides the generation of a given context. Our findings suggest that even recently introduced state-of-the-art models still lack instruction understanding capability. Along with the proposition of IoInst in this study, we also present broad analyses of the several strategies potentially applicable to IoInst., Comment: 21 pages

In current mobile networks, optimizing which base station a mobile phone in a particular area connects to is crucial for ensuring good communication quality for each mobile phone but presents a challenging combinatorial optimization problem. In this study, we optimize the connection patterns to base stations using quantum annealing which is a generic solver using quantum fluctuation. However, since the number of qubits on a quantum annealer is limited, it is necessary to consider a formulation that efficiently utilizes qubits. By adopting a variable reduction formulation, we significantly reduce the qubit requirements compared to the naive formulation that is typically used when considering pattern-matching problems. Furthermore, experiments using quantum annealing revealed that the accuracy of the approximate solution obtained by the new formulation is superior to that of the conventional formulation. Additionally, we demonstrate that the new formulation provides better solutions than the conventional formulation as the problem size increases, even when using simulated annealing, the classical counterpart of quantum annealing., Comment: 10 pages, 7 figures, 1 table

Long Context Language Models (LCLMs) have emerged as a new paradigm to perform Information Retrieval (IR), which enables the direct ingestion and retrieval of information by processing an entire corpus in their single context, showcasing the potential to surpass traditional sparse and dense retrieval methods. However, processing a large number of passages within in-context for retrieval is computationally expensive, and handling their representations during inference further exacerbates the processing time; thus, we aim to make LCLM retrieval more efficient and potentially more effective with passage compression. Specifically, we propose a new compression approach tailored for LCLM retrieval, which is trained to maximize the retrieval performance while minimizing the length of the compressed passages. To accomplish this, we generate the synthetic data, where compressed passages are automatically created and labeled as chosen or rejected according to their retrieval success for a given query, and we train the proposed Compression model for Long context Retrieval (CoLoR) with this data via preference optimization while adding the length regularization loss on top of it to enforce brevity. Through extensive experiments on 9 datasets, we show that CoLoR improves the retrieval performance by 6% while compressing the in-context size by a factor of 1.91.

Group distributionally robust optimization, which aims to improve robust accuracies -- worst-group and unbiased accuracies -- is a prominent algorithm used to mitigate spurious correlations and address dataset bias. Although existing approaches have reported improvements in robust accuracies, these gains often come at the cost of average accuracy due to inherent trade-offs. To control this trade-off flexibly and efficiently, we propose a simple class-specific scaling strategy, directly applicable to existing debiasing algorithms with no additional training. We further develop an instance-wise adaptive scaling technique to alleviate this trade-off, even leading to improvements in both robust and average accuracies. Our approach reveals that a na\"ive ERM baseline matches or even outperforms the recent debiasing methods by simply adopting the class-specific scaling technique. Additionally, we introduce a novel unified metric that quantifies the trade-off between the two accuracies as a scalar value, allowing for a comprehensive evaluation of existing algorithms. By tackling the inherent trade-off and offering a performance landscape, our approach provides valuable insights into robust techniques beyond just robust accuracy. We validate the effectiveness of our framework through experiments across datasets in computer vision and natural language processing domains.

Video Action Detection (VAD) involves localizing and categorizing action instances in videos. Videos inherently contain various information sources, including audio, visual cues, and surrounding scene contexts. Effectively leveraging this multi-modal information for VAD is challenging, as the model must accurately focus on action-relevant cues. In this study, we introduce a novel multi-modal VAD architecture called the Joint Actor-centric Visual, Audio, Language Encoder (JoVALE). JoVALE is the first VAD method to integrate audio and visual features with scene descriptive context derived from large image captioning models. The core principle of JoVALE is the actor-centric aggregation of audio, visual, and scene descriptive contexts, where action-related cues from each modality are identified and adaptively combined. We propose a specialized module called the Actor-centric Multi-modal Fusion Network, designed to capture the joint interactions among actors and multi-modal contexts through Transformer architecture. Our evaluation conducted on three popular VAD benchmarks, AVA, UCF101-24, and JHMDB51-21, demonstrates that incorporating multi-modal information leads to significant performance gains. JoVALE achieves state-of-the-art performances. The code will be available at \texttt{https://github.com/taeiin/AAAI2025-JoVALE}., Comment: Accepted to AAAI Conference on Artificial Intelligence 2025, 9 pages, 5 figures

Large language models (LLMs) have exhibited outstanding performance in natural language processing tasks. However, these models remain susceptible to adversarial attacks in which slight input perturbations can lead to harmful or misleading outputs. A gradient-based defensive suffix generation algorithm is designed to bolster the robustness of LLMs. By appending carefully optimized defensive suffixes to input prompts, the algorithm mitigates adversarial influences while preserving the models' utility. To enhance adversarial understanding, a novel total loss function ($L_{\text{total}}$) combining defensive loss ($L_{\text{def}}$) and adversarial loss ($L_{\text{adv}}$) generates defensive suffixes more effectively. Experimental evaluations conducted on open-source LLMs such as Gemma-7B, mistral-7B, Llama2-7B, and Llama2-13B show that the proposed method reduces attack success rates (ASR) by an average of 11\% compared to models without defensive suffixes. Additionally, the perplexity score of Gemma-7B decreased from 6.57 to 3.93 when applying the defensive suffix generated by openELM-270M. Furthermore, TruthfulQA evaluations demonstrate consistent improvements with Truthfulness scores increasing by up to 10\% across tested configurations. This approach significantly enhances the security of LLMs in critical applications without requiring extensive retraining., Comment: 9 pages, 2 figures

Colossal optical anisotropy in the entire visible spectrum is crucial for advanced photonic applications, enabling precise light manipulation without optical loss across a broad spectral range. Here, we demonstrate that CuAlO2 exhibits colossal optical anisotropy and transparency across the visible spectrum, enabled by its unique three-dimensional O-Cu-O dumbbell structure and two-dimensionally confined excitons. Using mm-sized single crystals, we independently measured ab-plane and c-axis optical properties, revealing maximum birefringence (= 3.67) and linear dichroism (= 5.21), the highest reported to date. CuAlO2 retains birefringence over 0.5 throughout the entire visible range and possesses a wide direct bandgap of 3.71 eV, surpassing the birefringence of commercial anisotropic crystals transparent in the visible spectrum. From the two-dimensional screened hydrogen model and first-principles calculations, we demonstrate that the colossal anisotropy arises from a unique excitonic Cu d-p transition confined to the atomic-thick layer. This colossal optical anisotropy and transparency across the entire visible spectrum makes CuAlO2 a promising candidate for future photonic technologies.

Recent advancements in 3D editing have highlighted the potential of text-driven methods in real-time, user-friendly AR/VR applications. However, current methods rely on 2D diffusion models without adequately considering multi-view information, resulting in multi-view inconsistency. While 3D Gaussian Splatting (3DGS) significantly improves rendering quality and speed, its 3D editing process encounters difficulties with inefficient optimization, as pre-trained Gaussians retain excessive source information, hindering optimization. To address these limitations, we propose \textbf{EditSplat}, a novel 3D editing framework that integrates Multi-view Fusion Guidance (MFG) and Attention-Guided Trimming (AGT). Our MFG ensures multi-view consistency by incorporating essential multi-view information into the diffusion process, leveraging classifier-free guidance from the text-to-image diffusion model and the geometric properties of 3DGS. Additionally, our AGT leverages the explicit representation of 3DGS to selectively prune and optimize 3D Gaussians, enhancing optimization efficiency and enabling precise, semantically rich local edits. Through extensive qualitative and quantitative evaluations, EditSplat achieves superior multi-view consistency and editing quality over existing methods, significantly enhancing overall efficiency.

Accurate precipitation forecasting is crucial for early warnings of disasters, such as floods and landslides. Traditional forecasts rely on ground-based radar systems, which are space-constrained and have high maintenance costs. Consequently, most developing countries depend on a global numerical model with low resolution, instead of operating their own radar systems. To mitigate this gap, we propose the Neural Precipitation Model (NPM), which uses global-scale geostationary satellite imagery. NPM predicts precipitation for up to six hours, with an update every hour. We take three key channels to discriminate rain clouds as input: infrared radiation (at a wavelength of 10.5 $\mu m$), upper- (6.3 $\mu m$), and lower- (7.3 $\mu m$) level water vapor channels. Additionally, NPM introduces positional encoders to capture seasonal and temporal patterns, accounting for variations in precipitation. Our experimental results demonstrate that NPM can predict rainfall in real-time with a resolution of 2 km. The code and dataset are available at https://github.com/seominseok0429/Data-driven-Precipitation-Nowcasting-Using-Satellite-Imagery., Comment: Accepted by AAAI2025

This study examines the effectiveness of traditional machine learning classifiers versus deep learning models for detecting the imagined speech using electroencephalogram data. Specifically, we evaluated conventional machine learning techniques such as CSP-SVM and LDA-SVM classifiers alongside deep learning architectures such as EEGNet, ShallowConvNet, and DeepConvNet. Machine learning classifiers exhibited significantly lower precision and recall, indicating limited feature extraction capabilities and poor generalization between imagined speech and idle states. In contrast, deep learning models, particularly EEGNet, achieved the highest accuracy of 0.7080 and an F1 score of 0.6718, demonstrating their enhanced ability in automatic feature extraction and representation learning, essential for capturing complex neurophysiological patterns. These findings highlight the limitations of conventional machine learning approaches in brain-computer interface (BCI) applications and advocate for adopting deep learning methodologies to achieve more precise and reliable classification of detecting imagined speech. This foundational research contributes to the development of imagined speech-based BCI systems.

Existing Video Frame interpolation (VFI) models tend to suffer from time-to-location ambiguity when trained with video of non-uniform motions, such as accelerating, decelerating, and changing directions, which often yield blurred interpolated frames. In this paper, we propose (i) a novel motion description map, Bidirectional Motion field (BiM), to effectively describe non-uniform motions; (ii) a BiM-guided Flow Net (BiMFN) with Content-Aware Upsampling Network (CAUN) for precise optical flow estimation; and (iii) Knowledge Distillation for VFI-centric Flow supervision (KDVCF) to supervise the motion estimation of VFI model with VFI-centric teacher flows. The proposed VFI is called a Bidirectional Motion field-guided VFI (BiM-VFI) model. Extensive experiments show that our BiM-VFI model significantly surpasses the recent state-of-the-art VFI methods by 26% and 45% improvements in LPIPS and STLPIPS respectively, yielding interpolated frames with much fewer blurs at arbitrary time instances., Comment: The last two authors are co-corresponding authors

We highlight two significant issues leading to the passivity of current merchant non-player characters (NPCs): pricing and communication. While immersive interactions have been a focus, negotiations between merchant NPCs and players on item prices have not received sufficient attention. First, we define passive pricing as the limited ability of merchants to modify predefined item prices. Second, passive communication means that merchants can only interact with players in a scripted manner. To tackle these issues and create an active merchant NPC, we propose a merchant framework based on large language models (LLMs), called MART, which consists of an appraiser module and a negotiator module. We conducted two experiments to guide game developers in selecting appropriate implementations by comparing different training methods and LLM sizes. Our findings indicate that finetuning methods, such as supervised finetuning (SFT) and knowledge distillation (KD), are effective in using smaller LLMs to implement active merchant NPCs. Additionally, we found three irregular cases arising from the responses of LLMs. We expect our findings to guide developers in using LLMs for developing active merchant NPCs., Comment: Under review / Modified the links to code and dataset

Chatbot-based customer support services have significantly advanced with the introduction of large language models (LLMs), enabling enhanced response quality and broader application across industries. However, while these advancements focus on reducing business costs and improving customer satisfaction, limited attention has been given to the experiences of customer service agents, who are critical to the service ecosystem. A major challenge faced by agents is the stress caused by unnecessary emotional exhaustion from harmful texts, which not only impairs their efficiency but also negatively affects customer satisfaction and business outcomes. In this work, we propose an LLM-powered system designed to enhance the working conditions of customer service agents by addressing emotionally intensive communications. Our proposed system leverages LLMs to transform the tone of customer messages, preserving actionable content while mitigating the emotional impact on human agents. Furthermore, the application is implemented as a Chrome extension, making it highly adaptable and easy to integrate into existing systems. Our method aims to enhance the overall service experience for businesses, customers, and agents.

Multi-scale image resolution is a de facto standard approach in modern object detectors, such as DETR. This technique allows for the acquisition of various scale information from multiple image resolutions. However, manual hyperparameter selection of the resolution can restrict its flexibility, which is informed by prior knowledge, necessitating human intervention. This work introduces a novel strategy for learnable resolution, called Elastic-DETR, enabling elastic utilization of multiple image resolutions. Our network provides an adaptive scale factor based on the content of the image with a compact scale prediction module (< 2 GFLOPs). The key aspect of our method lies in how to determine the resolution without prior knowledge. We present two loss functions derived from identified key components for resolution optimization: scale loss, which increases adaptiveness according to the image, and distribution loss, which determines the overall degree of scaling based on network performance. By leveraging the resolution's flexibility, we can demonstrate various models that exhibit varying trade-offs between accuracy and computational complexity. We empirically show that our scheme can unleash the potential of a wide spectrum of image resolutions without constraining flexibility. Our models on MS COCO establish a maximum accuracy gain of 3.5%p or 26% decrease in computation than MS-trained DN-DETR.

Large language models (LLMs) often struggle to objectively identify latent characteristics in large datasets due to their reliance on pre-trained knowledge rather than actual data patterns. To address this data grounding issue, we propose Data Scientist AI (DSAI), a framework that enables unbiased and interpretable feature extraction through a multi-stage pipeline with quantifiable prominence metrics for evaluating extracted features. On synthetic datasets with known ground-truth features, DSAI demonstrates high recall in identifying expert-defined features while faithfully reflecting the underlying data. Applications on real-world datasets illustrate the framework's practical utility in uncovering meaningful patterns with minimal expert oversight, supporting use cases such as interpretable classification. The title of our paper is chosen from multiple candidates based on DSAI-generated criteria.

Beam Drift Chamber (BDC) is designed to reconstruct the trajectories of incident rare isotope beams provided by RAON (Rare isotope Accelerator complex for ON-line experiments) into the experimental target of LAMPS (Large Acceptance Multi-Purpose Spectrometer). To conduct the performance test of the BDC, the prototype BDC (pBDC) is manufactured and evaluated with the high energy ion beams from HIMAC (Heavy Ion Medical Accelerator in Chiba) facility in Japan. Two kinds of ion beams, 100 MeV proton, and 200 MeV/u $^{12}$C, have been utilized for this evaluation, and the track reconstruction efficiency and position resolution have been measured as the function of applied high voltage. This paper introduces the construction details and presents the track reconstruction efficiency and position resolution of pBDC., Comment: 13 pages, 15 figures

We analyzed the 7.92$\times 10^{11}$ cosmic-ray-induced muon events collected by the IceCube Neutrino Observatory from May 13, 2011, when the fully constructed experiment started to take data, to May 12, 2023. This dataset provides an up-to-date cosmic-ray arrival direction distribution in the Southern Hemisphere with unprecedented statistical accuracy covering more than a full period length of a solar cycle. Improvements in Monte Carlo event simulation and better handling of year-to-year differences in data processing significantly reduce systematic uncertainties below the level of statistical fluctuations compared to the previously published results. We confirm the observation of a change in the angular structure of the cosmic-ray anisotropy between 10 TeV and 1 PeV, more specifically in the 100-300 TeV energy range.

Deep learning-based image denoising techniques often struggle with poor generalization performance to out-of-distribution real-world noise. To tackle this challenge, we propose a novel noise translation framework that performs denoising on an image with translated noise rather than directly denoising an original noisy image. Specifically, our approach translates complex, unknown real-world noise into Gaussian noise, which is spatially uncorrelated and independent of image content, through a noise translation network. The translated noisy images are then processed by an image denoising network pretrained to effectively remove Gaussian noise, enabling robust and consistent denoising performance. We also design well-motivated loss functions and architectures for the noise translation network by leveraging the mathematical properties of Gaussian noise. Experimental results demonstrate that the proposed method substantially improves robustness and generalizability, outperforming state-of-the-art methods across diverse benchmarks. Visualized denoising results and the source code are available on our project page., Comment: The project page is available at https://hij1112.github.io/learning-to-translate-noise/

The stability of the double-layered antiferromagnets and their magnonic properties are investigated by considering two model systems, the linear chain (LC) and more complex chain of railroad trestle (RT) geometry, and a real example of chromium nitride CrN. The spin-paired order ($\cdots{+}{+}{-}{-}\cdots$) in LC requires an alternation of the ferromagnetic and antiferromagnetic (AFM) interactions, while analogous spin-paired order in RT can be stable even for all magnetic exchange interactions being AFM. The rock-salt structure of CrN evokes clear magnetic frustration since Cr atoms in face-centered cubic lattice form links to twelve nearest neighbors all equivalent and AFM. Nonetheless, the magnetostructural transition to an orthorhombically distorted phase below $T_\text{N} = 287~\text{K}$ leads to a diversification of Cr-Cr links, which suppresses the frustration and allows for stable double-layered AFM order of CrN. Based on $\textit{ab initio}$ calculated exchange parameters, the magnon spectra and temperature evolution of ordered magnetic moments are derived., Comment: 7 pages, 8 figures

We present the first circularly polarized Floquet engineering time-resolved Resonant Inelastic X-ray Scattering (tr-RIXS) experiment in H$_3$LiIr$_2$O$_6$, an iridium-based Kitaev system. Our calculations and experimental results are consistent with the modification of the low energy magnetic excitations in H$_3$LiIr$_2$O$_6$ only during illumination by the laser pulse, consistent with the Floquet engineering of the exchange interactions. However, the penetration length mismatch between the X-ray probe and laser pump and the intrinsic complexity of Kitaev magnets prevented us from unequivocally extracting towards which ground H$_3$LiIr$_2$O$_6$ was driven. We outline possible solutions to these challenges for Floquet stabilization and observation of the Kitaev Quantum Spin Liquid limit by RIXS.

Recently, the spectral geometric mean has been studied by some papers. In this paper, we firstly estimate the H\"{o}lder type inequality of the spectral geometric mean of positive invertible operators on the Hilbert space for all real order in terms of the generalized Kantorovich constant and show the relation between the weighted geometric mean and the spectral geometric one under the usual operator order. Moreover, we show their operator norm version. Next, in the matrix case, we show the log-majorization for the spectral geometric mean and their applications. Among others, we show the order relation among three quantum Tsallis relative entropies. Finally we give a new lower bound of the Tsallis relative entropy., Comment: 20 pages

Current benchmarks for video segmentation are limited to annotating only salient objects (i.e., foreground instances). Despite their impressive architectural designs, previous works trained on these benchmarks have struggled to adapt to real-world scenarios. Thus, developing a new video segmentation dataset aimed at tracking multi-granularity segmentation target in the video scene is necessary. In this work, we aim to generate multi-granularity video segmentation dataset that is annotated for both salient and non-salient masks. To achieve this, we propose a large-scale, densely annotated multi-granularity video object segmentation (MUG-VOS) dataset that includes various types and granularities of mask annotations. We automatically collected a training set that assists in tracking both salient and non-salient objects, and we also curated a human-annotated test set for reliable evaluation. In addition, we present memory-based mask propagation model (MMPM), trained and evaluated on MUG-VOS dataset, which leads to the best performance among the existing video object segmentation methods and Segment SAM-based video segmentation methods. Project page is available at https://cvlab-kaist.github.io/MUG-VOS., Comment: Project Page: https://cvlab-kaist.github.io/MUG-VOS

Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ~2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the ``standard'' reconstruction framework, in which the displacement field is estimated from the observed density field by solving the linearized continuity equation in redshift space, and galaxy and random positions are shifted in order to partially remove nonlinearities. There are several approaches to solving for the displacement field in real survey data, including the multigrid (MG), iterative Fast Fourier Transform (iFFT), and iterative Fast Fourier Transform particle (iFFTP) algorithms. In this work, we analyze these algorithms and compare them with various metrics including two-point statistics and the displacement itself using realistic DESI mocks. We focus on three representative DESI samples, the emission line galaxies (ELG), quasars (QSO), and the bright galaxy sample (BGS), which cover the extreme redshifts and number densities, and potential wide-angle effects. We conclude that the MG and iFFT algorithms agree within 0.4% in post-reconstruction power spectrum on BAO scales with the RecSym convention, which does not remove large-scale redshift space distortions (RSDs), in all three tracers. The RecSym convention appears to be less sensitive to displacement errors than the RecIso convention, which attempts to remove large-scale RSDs. However, iFFTP deviates from the first two; thus, we recommend against using iFFTP without further development. In addition, we provide the optimal settings for reconstruction for five years of DESI observation. The analyses presented in this work pave the way for DESI DR1 analysis as well as future BAO analyses., Comment: 51 pages, 28 figures. Supporting publication of DESI 2024 III: Baryon Acoustic Oscillations from Galaxies and Quasars

We show that the use of 1,3-diaminopropane (DAP) as a chemical modifier at the perovskite/electron-transport layer (ETL) interface enhances the power conversion efficiency (PCE) of 1.7 eV bandgap FACs mixed-halide perovskite single-junction cells, primarily by boosting the open-circuit voltage (VOC) from 1.06 V to 1.15 V. Adding a post-processing annealing step after C60 evaporation, further improves the fill factor (FF) by 20% from the control to the DAP + post-annealing devices. Using hyperspectral photoluminescence microscopy, we demonstrate that annealing helps improve compositional homogeneity at the top and bottom interfaces of the solar cell, which prevents detrimental bandgap pinning in the devices and improves C60 adhesion. Using time-of-flight secondary ion mass spectrometry, we show that DAP reacts with formamidinium present near the surface of the perovskite lattice to form a larger molecular cation, 1,4,5,6-tetrahydropyrimidinium (THP) that remains at the interface. Combining the use of DAP and the annealing of C60 interface, we fabricate Si-perovskite tandems with PCE of 25.29%, compared to 23.26% for control devices. Our study underscores the critical role of chemical reactivity and thermal post-processing of the C60/Lewis-base passivator interface in minimizing device losses and advancing solar-cell performance of wide-bandgap mixed-cation mixed-halide perovskite for tandem application.

The geometry and motility of the stomach play a critical role in the digestion of ingested liquid meals. Sleeve gastrectomy, a common type of bariatric surgery used to reduce the size of the stomach, significantly alters the stomach's anatomy and motility, which impacts gastric emptying and digestion. In this study, we use an imaging data-based computational model, StomachSim, to investigate the consequences of sleeve gastrectomy. The pre-operative stomach anatomy was derived from imaging data and the post-sleeve gastrectomy shapes were generated for different resection volumes. We investigate the effect of sleeve sizes and motility patterns on gastric mixing and emptying. Simulations were conducted using an immersed-boundary flow solver, modeling a liquid meal to analyze changes in gastric content mixing and emptying rates. The results reveal that different degrees of volume reduction and impaired gastric motility have complex effects on stomach's mixing and emptying functions, which are important factors in gastric health of the patient. These findings provide insights into the biomechanical effects of sleeve gastrectomy on gastric digestion and emptying functions, highlighting the potential of computational models to inform surgical planning and post-operative management.

The majority of satellite galaxies around the Milky Way (MW) show disk-like distributions (the disk of satellites; DoS), which is a small-scale problem of the $\Lambda$CDM cosmology. The conventional definition of the MW-like DoS is a satellite system with a minor-to-major axis ratio ($c$/$a$) lower than the MW's $c$/$a$ value of 0.181. Here we question the validity of the $c$/$a$-based DoS rarity assessment and propose an alternative approach. How satellites are placed around a galaxy is dictated mainly by two factors: the distributions of satellites' orbital poles and distances from the host. Based on this premise, we construct the `satellite distribution generator' code and generate 10$^5$ `spatially and kinematically analogous systems (SKASs)' sharing these two factors. The SKAS can disclose the intrinsic, underlying $c$/$a$ probability distribution function (PDF), from which a present-day $c$/$a$ value is fortuitously determined. We find that the $c$/$a$ PDF of the MW DoS defined by 11 classical satellites is quite broad ($\sigma_{c/a}$$\sim$0.105), implying that a simple present-day $c$/$a$ value, combined with its highly time-variable nature, cannot fully represent the degree of flatness. Moreover, based on the intrinsic $c$/$a$ PDF, we re-evaluate the rarity of the MW DoS by comparing it with IllustrisTNG50-1 host-satellite systems and find that even with the new measure, the MW DoS remains rare (0.00$\sim$3.40%). We show that the reason behind the rareness is that both orbital poles and distances of the 11 MW satellites are far more plane-friendly than those of simulated host-satellite systems, challenging the current structure and galaxy formation model., Comment: 23 pages, 15 figures

We analyse the large-scale clustering of the Luminous Red Galaxy (LRG) and Quasar (QSO) sample from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). In particular, we constrain the primordial non-Gaussianity (PNG) parameter $f_{\rm NL}^{\rm loc}$ via the large-scale scale-dependent bias in the power spectrum using $1,631,716$ LRGs ($0.6 < z < 1.1$) and $1,189,129$ QSOs ($0.8 < z < 3.1$). This new measurement takes advantage of the enormous statistical power at large scales of DESI DR1 data, surpassing the latest data release (DR16) of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). For the first time in this kind of analysis, we use a blinding procedure to mitigate the risk of confirmation bias in our results. We improve the model of the radial integral constraint proposing an innovative correction of the window function. We also carefully test the mitigation of the dependence of the target selection on the photometry qualities by incorporating an angular integral constraint contribution to the window function, and validate our methodology with the blinded data. Finally, combining the two samples, we measure $f_{\rm NL}^{\rm loc} = {-3.6}_{-9.1}^{+9.0}$ at $68\%$ confidence, where we assume the universality relation for the LRG sample and a recent merger model for the QSO sample about the response of bias to primordial non-Gaussianity. Adopting the universality relation for the PNG bias in the QSO analysis leads to $f_{\rm NL}^{\rm loc} = 3.5_{-7.4}^{+10.7}$ at $68\%$ confidence. This measurement is the most precise determination of primordial non-Gaussianity using large-scale structure to date, surpassing the latest result from eBOSS by a factor of $2.3$.

Large language models (LLMs) often reflect real-world biases, leading to efforts to mitigate these effects and make the models unbiased. Achieving this goal requires defining clear criteria for an unbiased state, with any deviation from these criteria considered biased. Some studies define an unbiased state as equal treatment across diverse demographic groups, aiming for balanced outputs from LLMs. However, differing perspectives on equality and the importance of pluralism make it challenging to establish a universal standard. Alternatively, other approaches propose using fact-based criteria for more consistent and objective evaluations, though these methods have not yet been fully applied to LLM bias assessments. Thus, there is a need for a metric with objective criteria that offers a distinct perspective from equality-based approaches. Motivated by this need, we introduce a novel metric to assess bias using fact-based criteria and real-world statistics. In this paper, we conducted a human survey demonstrating that humans tend to perceive LLM outputs more positively when they align closely with real-world demographic distributions. Evaluating various LLMs with our proposed metric reveals that model bias varies depending on the criteria used, highlighting the need for multi-perspective assessment., Comment: Accepted in NeurIPS 2024 Workshop on Socially Responsible Language Modelling Research (SoLaR)

Recent approaches in Conversational Recommender Systems (CRSs) have tried to simulate real-world users engaging in conversations with CRSs to create more realistic testing environments that reflect the complexity of human-agent dialogue. Despite the significant advancements, reliably evaluating the capability of CRSs to elicit user preferences still faces a significant challenge. Existing evaluation metrics often rely on target-biased user simulators that assume users have predefined preferences, leading to interactions that devolve into simplistic guessing game. These simulators typically guide the CRS toward specific target items based on fixed attributes, limiting the dynamic exploration of user preferences and struggling to capture the evolving nature of real-user interactions. Additionally, current evaluation metrics are predominantly focused on single-turn recall of target items, neglecting the intermediate processes of preference elicitation. To address this, we introduce PEPPER, a novel CRS evaluation protocol with target-free user simulators constructed from real-user interaction histories and reviews. PEPPER enables realistic user-CRS dialogues without falling into simplistic guessing games, allowing users to gradually discover their preferences through enriched interactions, thereby providing a more accurate and reliable assessment of the CRS's ability to elicit personal preferences. Furthermore, PEPPER presents detailed measures for comprehensively evaluating the preference elicitation capabilities of CRSs, encompassing both quantitative and qualitative measures that capture four distinct aspects of the preference elicitation process. Through extensive experiments, we demonstrate the validity of PEPPER as a simulation environment and conduct a thorough analysis of how effectively existing CRSs perform in preference elicitation and recommendation., Comment: Work in progress

The physical size of the sound horizon at recombination is a powerful source of information for early-time measurements of the Hubble constant $H_0$, and many proposed solutions to the Hubble tension therefore involve modifications to this scale. In light of this, there has been growing interest in measuring $H_0$ independently of the sound horizon. We present the first such measurement to use data from the Dark Energy Spectroscopic Instrument (DESI), jointly analyzing the full-shape galaxy power spectra of DESI luminous red galaxies, emission line galaxies, quasars, and the bright galaxy sample, in a total of six redshift bins. Information from the sound horizon scale is removed from our constraints via a rescaling procedure at the power spectrum level, with our sound horizon-marginalized measurement being driven instead primarily by the matter-radiation equality scale. This measurement is then combined with additional sound horizon-free information from Planck+ACT CMB lensing, uncalibrated type Ia supernovae, and the DESI Lyman-$\alpha$ forest. We agnostically combine with the DESY5, Pantheon+, and Union3 supernova datasets, with our tightest respective constraints being $H_0=66.7^{+1.7}_{-1.9},~67.9^{+1.9}_{-2.1},$ and $67.8^{+2.0}_{-2.2}$ km s-1 Mpc-1. This corresponds to a sub-3% sound horizon-free constraint of the Hubble constant, and is the most precise measurement of its kind to date. Even without including information from the sound horizon, our measurement is still in 2.2-3.0$\sigma$ tension with SH0ES. Additionally, the consistency between our result and other measurements that do rely on the sound horizon scale provides no evidence for new early-Universe physics (e.g. early dark energy). Future DESI data releases will allow unprecedented measurements of $H_0$ and place strong constraints on models that use beyond-$\Lambda$CDM physics to ameliorate the Hubble tension., Comment: 17+7 pages; 5 figures. Submitted to JCAP

We demonstrate efficient edge couplers by fabricating a 3D mode size converter on a lithium niobate-on-insulator photonic platform. The 3D mode size converter is fabricated using an etching process that employs a Si external mask to provide height variation and adjust the width variation through tapering patterns via lithography. The measured edge coupling efficiency with a 3D mode size converter was approximately 1.16 dB/facet for the TE mode and approximately 0.71 dB/facet for the TM mode at a wavelength of 1550 nm., Comment: 8 pages, 4 figures