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This paper presents evS2CP, an optimization-based framework for simultaneous speed and charging planning designed for connected electric vehicles (EVs). With EVs emerging as competitive alternatives to internal combustion engine vehicles, overcoming challenges such as limited charging infrastructure is crucial. evS2CP addresses these issues by minimizing the travel time, charging time, and energy consumption, providing practical solutions for both human-operated and autonomous vehicles. This framework leverages V2X communication to integrate essential EV planning data, including route geometry, real-time traffic conditions, and charging station availability, while simulating dynamic driving environments using open-web API services. The speed and charging planning problem was initially formulated as a nonlinear programming model, which was then convexified into a quadratic programming model without charging-stop constraints. Additionally, a mixed-integer programming approach was employed to optimize charging station selection and minimize the frequency of charging events. A mixed-integer quadratic programming implementation exhibited exceptional computational efficiency and scalability, effectively solving trip plans over distances exceeding 700 km in a few seconds. Simulations conducted using open-source and commercial solvers validated the framework's near-global optimality, demonstrating its robustness and feasibility for real-world applications in connected EV ecosystems., Comment: 15 pages, 9 figures, 2 tables

Maritime environments often present hazardous situations due to factors such as moving ships or buoys, which become obstacles under the influence of waves. In such challenging conditions, the ability to detect and track potentially hazardous objects is critical for the safe navigation of marine robots. To address the scarcity of comprehensive datasets capturing these dynamic scenarios, we introduce a new multi-modal dataset that includes image and point-wise annotations of maritime hazards. Our dataset provides detailed ground truth for obstacle detection and tracking, including objects as small as 10$\times$10 pixels, which are crucial for maritime safety. To validate the dataset's effectiveness as a reliable benchmark, we conducted evaluations using various methodologies, including \ac{SOTA} techniques for object detection and tracking. These evaluations are expected to contribute to performance improvements, particularly in the complex maritime environment. To the best of our knowledge, this is the first dataset offering multi-modal annotations specifically tailored to maritime environments. Our dataset is available at https://sites.google.com/view/polaris-dataset.

Diffusion models excel in generating high-quality images. However, current diffusion models struggle to produce reliable images without guidance methods, such as classifier-free guidance (CFG). Are guidance methods truly necessary? Observing that noise obtained via diffusion inversion can reconstruct high-quality images without guidance, we focus on the initial noise of the denoising pipeline. By mapping Gaussian noise to `guidance-free noise', we uncover that small low-magnitude low-frequency components significantly enhance the denoising process, removing the need for guidance and thus improving both inference throughput and memory. Expanding on this, we propose \ours, a novel method that replaces guidance methods with a single refinement of the initial noise. This refined noise enables high-quality image generation without guidance, within the same diffusion pipeline. Our noise-refining model leverages efficient noise-space learning, achieving rapid convergence and strong performance with just 50K text-image pairs. We validate its effectiveness across diverse metrics and analyze how refined noise can eliminate the need for guidance. See our project page: https://cvlab-kaist.github.io/NoiseRefine/., Comment: Project page: https://cvlab-kaist.github.io/NoiseRefine/

Recent advances in 3D object detection leveraging multi-view cameras have demonstrated their practical and economical value in various challenging vision tasks. However, typical supervised learning approaches face challenges in achieving satisfactory adaptation toward unseen and unlabeled target datasets (\ie, direct transfer) due to the inevitable geometric misalignment between the source and target domains. In practice, we also encounter constraints on resources for training models and collecting annotations for the successful deployment of 3D object detectors. In this paper, we propose Unified Domain Generalization and Adaptation (UDGA), a practical solution to mitigate those drawbacks. We first propose Multi-view Overlap Depth Constraint that leverages the strong association between multi-view, significantly alleviating geometric gaps due to perspective view changes. Then, we present a Label-Efficient Domain Adaptation approach to handle unfamiliar targets with significantly fewer amounts of labels (\ie, 1$\%$ and 5$\%)$, while preserving well-defined source knowledge for training efficiency. Overall, UDGA framework enables stable detection performance in both source and target domains, effectively bridging inevitable domain gaps, while demanding fewer annotations. We demonstrate the robustness of UDGA with large-scale benchmarks: nuScenes, Lyft, and Waymo, where our framework outperforms the current state-of-the-art methods., Comment: Accepted to NeurIPS 2024

Translating conversational text, particularly in customer support contexts, presents unique challenges due to its informal and unstructured nature. We propose a context-aware LLM translation system that leverages conversation summarization and dialogue history to enhance translation quality for the English-Korean language pair. Our approach incorporates the two most recent dialogues as raw data and a summary of earlier conversations to manage context length effectively. We demonstrate that this method significantly improves translation accuracy, maintaining coherence and consistency across conversations. This system offers a practical solution for customer support translation tasks, addressing the complexities of conversational text., Comment: Accepted to WMT 2024

We calculate the scalar-induced one-loop correction to the power spectrum of tensor perturbations produced during single-field slow-roll inflation. We find that the correction is given by the square of the product of the slow-roll parameter and the tree-level scalar power spectrum. We also discuss the implications of the logarithmic contribution., Comment: (v1) 10 pages, 2 figures; (v2) typos corrected, references added

Clustered stellar feedback creates expanding voids in the magnetized interstellar medium known as superbubbles. Although theory suggests that superbubble expansion is influenced by interstellar magnetic fields, direct observational data on 3D superbubble magnetic field geometry is limited. The Sun's location inside the Local Bubble provides a unique opportunity to infer a superbubble's 3D magnetic field orientation, under the assumptions that: $\mathrm{I}$) the Local Bubble's surface is the primary contributor to plane-of-the-sky polarization observations across much of the sky, and $\mathrm{II}$) the Local Bubble's magnetic field is tangent to its dust-traced shell. In this work, we validate these assumptions and construct a model of the Local Bubble's 3D B-field orientation from $\textit{Planck}$ 353 GHz polarization observations and a 3D-dust-derived model of the Local Bubble's shell. We test Assumption $\mathrm{I}$ by examining correlations between the Local Bubble's 3D geometry, dust polarization, and starlight polarization. We find that the Local Bubble likely dominates the polarized signal in the majority of lines of sight. We jointly test Assumptions $\mathrm{I}$ and $\mathrm{II}$ by applying our reconstruction method to a simulated superbubble, where we successfully reconstruct the 3D magnetic field orientation over the bulk of its surface. Finally, we use our 3D B-field model to infer the initial magnetic field orientation in the solar neighborhood prior to the Local Bubble's formation, and derive an orientation parallel to the present-day Local Arm of the galaxy. These findings provide new insights into the co-evolution of superbubbles and the magnetized interstellar medium., Comment: 29 pages, 16 figures. Submitted to ApJ

The Large Magellanic Cloud (LMC) is a Milky Way (MW) satellite that is massive enough to gravitationally attract the MW disc and inner halo, causing significant motion of the inner MW with respect to the outer halo. In this work, we probe this interaction by constructing a sample of 9,866 blue horizontal branch (BHB) stars with radial velocities from the DESI spectroscopic survey out to 120 kpc from the Galactic centre. This is the largest spectroscopic set of BHB stars in the literature to date, and it contains four times more stars with Galactocentric distances beyond 50 kpc than previous BHB catalogues. Using the DESI BHB sample combined with SDSS BHBs, we measure the bulk radial velocity of stars in the outer halo and observe that the velocity in the Southern Galactic hemisphere is different by 3.7$\sigma$ from the North. Modelling the projected velocity field shows that its dipole component is directed at a point 22 degrees away from the LMC along its orbit, which we interpret as the travel direction of the inner MW. The velocity field includes a monopole term that is -24 km/s, which we refer to as compression velocity. This velocity is significantly larger than predicted by the current models of the MW and LMC interaction. This work uses DESI data from its first two years of observations, but we expect that with upcoming DESI data releases, the sample of BHB stars will increase and our ability to measure the MW-LMC interaction will improve significantly., Comment: 22 pages, 19 figures. Submitted to MNRAS

Deep Generative Models (DGMs) have rapidly advanced in recent years, becoming essential tools in various fields due to their ability to learn complex data distributions and generate synthetic data. Their importance in transportation research is increasingly recognized, particularly for applications like traffic data generation, prediction, and feature extraction. This paper offers a comprehensive introduction and tutorial on DGMs, with a focus on their applications in transportation. It begins with an overview of generative models, followed by detailed explanations of fundamental models, a systematic review of the literature, and practical tutorial code to aid implementation. The paper also discusses current challenges and opportunities, highlighting how these models can be effectively utilized and further developed in transportation research. This paper serves as a valuable reference, guiding researchers and practitioners from foundational knowledge to advanced applications of DGMs in transportation research., Comment: 64 pages, 21 figures, 4 tables

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory and cognitive decline, affecting millions worldwide. Diagnosing AD is challenging due to its heterogeneous nature and variable progression. This study introduces a novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs), designed to improve interpretability and predictive accuracy in neuroimaging for early AD diagnosis. By clustering brain regions based on functional connectivity and node embedding, this layer improves the GNN's capability to capture complex brain network characteristics. We analyzed neuroimaging data from 383 participants, including both cognitively normal and preclinical AD individuals, using T1-weighted MRI, resting-state fMRI, and FBB-PET to construct brain graphs. Our results show that GNNs with the BA readout layer significantly outperform traditional models in predicting the Preclinical Alzheimer's Cognitive Composite (PACC) score, demonstrating higher robustness and stability. The adaptive BA readout layer also offers enhanced interpretability by highlighting task-specific brain regions critical to cognitive functions impacted by AD. These findings suggest that our approach provides a valuable tool for the early diagnosis and analysis of Alzheimer's disease.

3D object detection with omnidirectional views enables safety-critical applications such as mobile robot navigation. Such applications increasingly operate on resource-constrained edge devices, facilitating reliable processing without privacy concerns or network delays. To enable cost-effective deployment, cameras have been widely adopted as a low-cost alternative to LiDAR sensors. However, the compute-intensive workload to achieve high performance of camera-based solutions remains challenging due to the computational limitations of edge devices. In this paper, we present Panopticus, a carefully designed system for omnidirectional and camera-based 3D detection on edge devices. Panopticus employs an adaptive multi-branch detection scheme that accounts for spatial complexities. To optimize the accuracy within latency limits, Panopticus dynamically adjusts the model's architecture and operations based on available edge resources and spatial characteristics. We implemented Panopticus on three edge devices and conducted experiments across real-world environments based on the public self-driving dataset and our mobile 360{\deg} camera dataset. Experiment results showed that Panopticus improves accuracy by 62% on average given the strict latency objective of 33ms. Also, Panopticus achieves a 2.1{\times} latency reduction on average compared to baselines., Comment: Published at MobiCom 2024

Persuasive scholarship presents how individual daily travel habits implicate congestion, environmental pollution, and the travel experience. However, the empirical characteristics and dynamics of travel habits remain poorly understood. Quantifying both our individual travel habits and how these habits aggregate to form system-wide dynamics is of critical importance to enable the smart design of public transit systems that are better tailored to our daily mobility needs. We contribute to this need through the development and implementation of a new measurement framework capturing the 'peakedness' of users' departure time distributions. Departure time 'peakedness' reflects a user's tendency to repeatedly choose the same departure time for a given origin-destination trip, offering a clearer and more intuitive representation of regularity and habitual patterns compared to traditional metrics like standard deviation or entropy. Our framework demonstrates that system-wide departure time peakedness can be decomposed into individual users' departure time peakedness and the alignment of their peak times. This allows for a systematic analysis of both individual and collective behaviours. We apply our framework to departure time data from a 12-month period, encompassing 5,947,907 bus journeys made by 29,640 individuals across three urban networks within a large regional metropolis. Our findings reveal that departure time peakedness is more deeply tied to inherent, passenger-specific characteristics, such as passenger type, rather than external factors like weather or holidays. Additionally, individual-level departure time peakedness shows notable dynamics over time, indicating that habitual routines can evolve in the long term, while system-level peakedness exhibits remarkable long-term stability., Comment: 21 pages, 14 figures, 2 tables

Our understanding of the assembly timeline of the Milky Way has been transforming along with the dramatic increase in astrometric and spectroscopic data available over the past several years. Many substructures in chemo-dynamical space have been discovered and identified as the remnants of various galactic mergers. To investigate the timeline of these mergers we select main sequence turn off & subgiant stars (MSTOs) from the H3 survey, finding members in seven metal poor components of the halo: GSE, the Helmi Streams, Thamnos, Sequoia, Wukong/LMS-1, Arjuna, and I'itoi. We also select out the metal poor in situ disk to facilitate comparison to the evolution of the Milky Way itself at these early epochs. We fit individual isochrone ages to the MSTOs in each of these substructures and use the resulting age distributions to infer simple star formation histories. For GSE we resolve an extended star formation history that truncates $\approx10$ Gyr ago, as well as a clear age -- metallicity relation. From this age distribution and measured star formation history we infer that GSE merged with the Milky Way at a time $9.5-10.2$ Gyr ago, in agreement with previous estimates. We infer that the other mergers occurred at various times ranging from $9-13$ Gyr ago, and that the metal poor component of the disk built up within only a few billion years. These results reinforce the emerging picture that both the disk and halo of the Milky Way experienced a rapid assembly., Comment: 27 pages, 17 figures

We propose a new T, Y dwarf search model using machine learning (ML), called the "BROWn Dwarf Image Explorer (BROWDIE). Brown dwarfs (BD) are estimated to make up 25 percent of all celestial objects in the Galaxy, yet only a small number have been thoroughly studied. Homogeneous and complete samples of BDs are essential to advance the studies. However, due to their faintness, conducting spectral studies of BDs can be challenging. T\&Y brown dwarfs, a redder and fainter subclass of BDs, are even harder to detect. As a result, only a few T\&Y dwarfs have been extensively studied. Numerous attempts, including ML using various color band observations, have been made to identify BDs based on their colors. However, those models often require a large number of color observations, which can be a limitation. This study implemented an ML model by utilizing data from the J, H, and K photometry of the UKIRT Infrared Deep Sky Surveys (UKIDSS) simultaneously, effectively distinguishing celestial objects similar to BDs. The BROWDIE model was trained using UKIDSS data, and based on the model, 118 T dwarfs and 14 Y dwarfs were found in the UKIDSS DR11PLUS LAS L4 zone.

Color programmers manipulate lights, materials, and the resulting colors from light-material interactions. Existing libraries for color programming provide only a thin layer of abstraction around matrix operations. Color programs are, thus, vulnerable to bugs arising from mathematically permissible but physically meaningless matrix computations. Correct implementations are difficult to write and optimize. We introduce CoolerSpace to facilitate physically correct and computationally efficient color programming. CoolerSpace raises the level of abstraction of color programming by allowing programmers to focus on describing the logic of color physics. Correctness and efficiency are handled by CoolerSpace. The type system in CoolerSpace assigns physical meaning and dimensions to user-defined objects. The typing rules permit only legal computations informed by color physics and perception. Along with type checking, CoolerSpace also generates performance-optimized programs using equality saturation. CoolerSpace is implemented as a Python library and compiles to ONNX, a common intermediate representation for tensor computations. CoolerSpace not only prevents common errors in color programming, but also does so without run-time overhead: even unoptimized CoolerSpace programs out-perform existing Python-based color programming systems by up to 5.7 times; our optimizations provide up to an additional 1.4 times speed-up.

Managing high-frequency data in a limit order book (LOB) is a complex task that often exceeds the capabilities of conventional time-series forecasting models. Accurately predicting the entire multi-level LOB, beyond just the mid-price, is essential for understanding high-frequency market dynamics. However, this task is challenging due to the complex interdependencies among compound attributes within each dimension, such as order types, features, and levels. In this study, we explore advanced multidimensional sequence-to-sequence models to forecast the entire multi-level LOB, including order prices and volumes. Our main contribution is the development of a compound multivariate embedding method designed to capture the complex relationships between spatiotemporal features. Empirical results show that our method outperforms other multivariate forecasting methods, achieving the lowest forecasting error while preserving the ordinal structure of the LOB.

In this work, we introduce the demonstration of symbolic music generation, focusing on providing short musical motifs that serve as the central theme of the narrative. For the generation, we adopt an autoregressive model which takes musical metadata as inputs and generates 4 bars of multitrack MIDI sequences. During training, we randomly drop tokens from the musical metadata to guarantee flexible control. It provides users with the freedom to select input types while maintaining generative performance, enabling greater flexibility in music composition. We validate the effectiveness of the strategy through experiments in terms of model capacity, musical fidelity, diversity, and controllability. Additionally, we scale up the model and compare it with other music generation model through a subjective test. Our results indicate its superiority in both control and music quality. We provide a URL link https://www.youtube.com/watch?v=-0drPrFJdMQ to our demonstration video.

We often encounter limited FOV situations due to various factors such as sensor fusion or sensor mount in real-world robot navigation. However, the limited FOV interrupts the generation of descriptions and impacts place recognition adversely. Therefore, we suffer from correcting accumulated drift errors in a consistent map using LiDAR-based place recognition with limited FOV. Thus, in this paper, we propose a robust LiDAR-based place recognition method for handling narrow FOV scenarios. The proposed method establishes spatial organization based on the range-elevation bin and azimuth-elevation bin to represent places. In addition, we achieve a robust place description through reweighting based on vertical direction information. Based on these representations, our method enables addressing rotational changes and determining the initial heading. Additionally, we designed a lightweight and fast approach for the robot's onboard autonomy. For rigorous validation, the proposed method was tested across various LiDAR place recognition scenarios (i.e., single-session, multi-session, and multi-robot scenarios). To the best of our knowledge, we report the first method to cope with the restricted FOV. Our place description and SLAM codes will be released. Also, the supplementary materials of our descriptor are available at \texttt{\url{https://sites.google.com/view/lidar-solid}}., Comment: Accepted in IEEE Robotics and Automation Letters (2024)

Within the domain of person re-identification (ReID), partial ReID methods are considered mainstream, aiming to measure feature distances through comparisons of body parts between samples. However, in practice, previous methods often lack sufficient awareness of anatomical aspect of body parts, resulting in the failure to capture features of the same body parts across different samples. To address this issue, we introduce \textbf{Part Aware Transformer (PAFormer)}, a pose estimation based ReID model which can perform precise part-to-part comparison. In order to inject part awareness to pose tokens, we introduce learnable parameters called `pose token' which estimate the correlation between each body part and partial regions of the image. Notably, at inference phase, PAFormer operates without additional modules related to body part localization, which is commonly used in previous ReID methodologies leveraging pose estimation models. Additionally, leveraging the enhanced awareness of body parts, PAFormer suggests the use of a learning-based visibility predictor to estimate the degree of occlusion for each body part. Also, we introduce a teacher forcing technique using ground truth visibility scores which enables PAFormer to be trained only with visible parts. A set of extensive experiments show that our method outperforms existing approaches on well-known ReID benchmark datasets., Comment: 34 pages, 8 figures

We present Blind-Match, a novel biometric identification system that leverages homomorphic encryption (HE) for efficient and privacy-preserving 1:N matching. Blind-Match introduces a HE-optimized cosine similarity computation method, where the key idea is to divide the feature vector into smaller parts for processing rather than computing the entire vector at once. By optimizing the number of these parts, Blind-Match minimizes execution time while ensuring data privacy through HE. Blind-Match achieves superior performance compared to state-of-the-art methods across various biometric datasets. On the LFW face dataset, Blind-Match attains a 99.63% Rank-1 accuracy with a 128-dimensional feature vector, demonstrating its robustness in face recognition tasks. For fingerprint identification, Blind-Match achieves a remarkable 99.55% Rank-1 accuracy on the PolyU dataset, even with a compact 16-dimensional feature vector, significantly outperforming the state-of-the-art method, Blind-Touch, which achieves only 59.17%. Furthermore, Blind-Match showcases practical efficiency in large-scale biometric identification scenarios, such as Naver Cloud's FaceSign, by processing 6,144 biometric samples in 0.74 seconds using a 128-dimensional feature vector., Comment: Accepted to CIKM 2024 (Applied Research Track)

Global Positioning System (GPS) plays a critical role in navigation by utilizing satellite signals, but its accuracy in urban environments is often compromised by signal obstructions. Previous research has categorized GPS reception conditions into line-of-sight (LOS), non-line-of-sight (NLOS), and LOS+NLOS scenarios to enhance accuracy. This paper introduces a novel approach using quantum support vector machines (QSVM) with a ZZ feature map and fidelity quantum kernel to classify urban GPS signal reception conditions, comparing its performance against classical SVM methods. While classical SVM has been previously explored for this purpose, our study is the first to apply QSVM to this classification task. We conducted experiments using datasets from two distinct urban locations to train and evaluate SVM and QSVM models. Our results demonstrate that QSVM achieves superior classification accuracy compared to classical SVM for urban GPS signal datasets. Additionally, we emphasize the importance of appropriately scaling raw data when utilizing QSVM., Comment: Submitted to IEEE QCE 2024

In this study, we propose a novel positioning and searching system for emergency location services, namely the hyper-enhanced local positioning system (HELPS), which is applicable to all mobile phone users, including legacy feature phone users. In the case of an emergency, rescuers are dispatched with portable signal measurement equipment around the estimated location of the emergency caller. Each signal measurement device measures the uplink signal from the mobile phone of the caller. After calculating the rough location of the caller's mobile phone based on these measurements, rescuers can efficiently search for the caller using the received uplink signal strength. Thus, the positioning accuracy in a conventional sense is not a limitation for rescuers in finding the caller. HELPS is not a traditional positioning system but rather a system with humans in the loop designed to reduce search time in emergencies. HELPS can provide emergency location information even in environments where the GPS or Wi-Fi is not functional. Furthermore, for HELPS operation, no hardware changes or software installations are required on the caller's mobile phone., Comment: Submitted to IEEE Wireless Communications

End-to-end automatic speech recognition (E2E ASR) systems have significantly improved speech recognition through training on extensive datasets. Despite these advancements, they still struggle to accurately recognize domain specific words, such as proper nouns and technical terminologies. To address this problem, we propose a method to utilize the state-of-the-art Whisper without modifying its architecture, preserving its generalization performance while enabling it to leverage descriptions effectively. Moreover, we propose two additional training techniques to improve the domain specific ASR: decoder fine-tuning, and context perturbation. We also propose a method to use a Large Language Model (LLM) to generate descriptions with simple metadata, when descriptions are unavailable. Our experiments demonstrate that proposed methods notably enhance domain-specific ASR accuracy on real-life datasets, with LLM-generated descriptions outperforming human-crafted ones in effectiveness., Comment: Accepted to INTERSPEECH 2024

Understanding students' preferences and learning satisfaction during COVID-19 has focused on learning attributes such as self-efficacy, performance, and engagement. Although existing efforts have constructed statistical models capable of accurately identifying significant factors impacting learning satisfaction, they do not necessarily explain the complex relationships among these factors in depth. This study aimed to understand several facets related to student learning preferences and satisfaction during the pandemic such as individual learner characteristics, instructional design elements and social and environmental factors. Responses from 302 students from Sungkyunkwan University, South Korea were collected between 2021 and 2022. Information gathered included their gender, study major, satisfaction and motivation levels when learning, perceived performance, emotional state and learning environment. Wilcoxon Rank sum test and Explainable Boosting Machine (EBM) were performed to determine significant differences in specific cohorts. The two core findings of the study are as follows:1) Using Wilcoxon Rank Sum test, we can attest with 95% confidence that students who took offline classes had significantly higher learning satisfaction, among other attributes, than those who took online classes, as with STEM versus HASS students; 2) An explainable boosting machine (EBM) model fitted to 95.08% accuracy determined the top five factors affecting students' learning satisfaction as their perceived performance, their perception on participating in class activities, their study majors, their ability to conduct discussions in class and the study space availability at home. Positive perceived performance and ability to discuss with classmates had a positive impact on learning satisfaction, while negative perception on class activities participation had a negative impact on learning satisfaction.

In recent years, advancements in representation learning and language models have propelled Automated Captioning (AC) to new heights, enabling the generation of human-level descriptions. Leveraging these advancements, we propose AVCap, an Audio-Visual Captioning framework, a simple yet powerful baseline approach applicable to audio-visual captioning. AVCap utilizes audio-visual features as text tokens, which has many advantages not only in performance but also in the extensibility and scalability of the model. AVCap is designed around three pivotal dimensions: the exploration of optimal audio-visual encoder architectures, the adaptation of pre-trained models according to the characteristics of generated text, and the investigation into the efficacy of modality fusion in captioning. Our method outperforms existing audio-visual captioning methods across all metrics and the code is available on https://github.com/JongSuk1/AVCap, Comment: Interspeech 2024

Full spectrum fitting is a powerful tool for estimating the stellar populations of galaxies, but the fitting results are often significantly influenced by internal dust attenuation. For understanding how the choice of the internal dust correction method affects the detailed stellar populations estimated from the full spectrum fitting, we analyze the Sydney-Australian Astronomical Observatory Multi-object Integral field spectrograph (SAMI) galaxy survey data using the Penalized PiXel-Fitting (PPXF) package. Three choices are compared: (Choice-1) using the PPXF reddening option, (Choice-2) using the multiplicative Legendre polynomial, and (Choice-3) using none of them (no dust correction). In any case, the total mean stellar populations show reasonable mass-age and mass-metallicity relations (MTR and MZR), although the correlations appear to be strongest for Choice-1 (MTR) and Choice-2 (MZR). When we compare the age-divided mean stellar populations, the MZR of young (< 10^9.5 yr ~ 3.2 Gyr) stellar components in Choice-2 is consistent with the gas-phase MZR, whereas those in the other two choices hardly are. On the other hand, the MTR of old (>= 10^9.5 yr) stellar components in Choice-1 seems to be more reasonable than that in Choice-2, because the old stellar components in low-mass galaxies tend to be relatively younger than those in massive galaxies. Based on the results, we provide empirical guidelines for choosing the optimal options for dust correction., Comment: 10 pages, 8 figures, accepted for publication in Journal of the Korean Astronomical Society

The kinematics of the stellar halo hold important clues to the assembly history and mass distribution of the Galaxy. In this study, we map the kinematics of stars across the Galactic halo with the H3 Survey. We find a complex distribution that breaks both azimuthal symmetry about the $Z$-axis and mirror symmetry about the Galactic plane. This asymmetry manifests as large variations in the radial velocity dispersion $\sigma_r$ from as ``cold'' as 70 $\text{km}\text{ s}^{-1}$ to as ``hot'' as 160 $\text{km}\text{ s}^{-1}$. We use stellar chemistry to distinguish accreted stars from in-situ stars in the halo, and find that the accreted population has higher $\sigma_r$ and radially biased orbits, while the in-situ population has lower $\sigma_r$ and isotropic orbits. As a result, the Galactic halo kinematics are highly heterogeneous and poorly approximated as being spherical or axisymmetric. We measure radial profiles of $\sigma_r$ and the anisotropy parameter $\beta$ over Galactocentric radii $10-80\text{ kpc}$, and find that discrepancies in the literature are due to the nonspherical geometry and heterogeneous nature of the halo. Investigating the effect of strongly asymmetric $\sigma_r$ and $\beta$ on equilibrium models is a path forward to accurately constraining the Galactic gravitational field, including its total mass., Comment: submitted to ApJ; comments welcome

We present novel insights into the interplay between tidal forces and star formation in interacting galaxies before their first pericentre passage. We investigate seven close pair galaxies devoid of visible tidal disturbances, such as tails, bridges, and shells. Using integral field spectroscopy (IFS) data of extended Calar Alto Legacy Integral Field Area (eCALIFA), we unveil a previously unreported phenomenon: H alhpa emission, a proxy for recent star formation, exhibits a significant enhancement in regions facing the companion galaxy, reaching up to 1.9 times higher flux compared to opposite directions. Notably, fainter companions within pairs display a more pronounced one-sided H alpha excess, exceeding the typical range observed in isolated galaxies with 2 sigma confidence level. Furthermore, the observed H alpha excess in fainter companion galaxies exhibits a heightened prominence at the outer galactic regions. These findings suggest that tidal forces generated before the first pericentre passage exert a stronger influence on fainter galaxies due to their shallower potential wells by their brighter companions. This unveils a more intricate interplay between gravitational interactions and star formation history within interacting galaxies than previously understood, highlighting the need further to explore the early stages of interaction in galaxy evolution., Comment: 7 pages, 4 figgures, Accepted for publication in MNRAS Letters

The origin of our Galaxy's high-velocity clouds (HVCs) remains a mystery after many decades of effort. In this paper, we use the TNG50 simulation of the IllustrisTNG project to identify cool, dense clouds that match observations of Galactic HI HVCs. We track these clouds back in time to determine their origin. For a TNG50 Milky Way-like galaxy, we find that only 17% of HVCs can be tracked directly to the disk, and 21% to material stripped out of satellites. The majority of HVCs (62%) arise from warm and hot circumgalactic gas that cools through thermal instability. They then obtain their anomalous velocities through interactions with the turbulent circumgalactic medium. At TNG50 resolution, we do not see evidence for HVCs forming out of very low metallicity intergalactic material. Instead, low metallicity HVCs are most likely associated with satellites. These results suggest that Galactic HVCs are highly heterogeneous in their origin, and can provide insight into the physical processes that shape the circumgalactic medium such as disk outflows, satellite accretion, and thermal instabilities., Comment: 17 pages, 12 figures, accepted to ApJ

The infall of the Large Magellanic Cloud (LMC) is predicted to displace the inner Milky Way (MW), imprinting an apparent 'reflex motion' on the observed velocities of distant halo stars. We construct the largest all-sky spectroscopic dataset of luminous red giant stars from $50-160$ kpc, including a new survey of the southern celestial hemisphere. We fit the full 6D kinematics of our data to measure the amplitude and direction of the inner MW's motion towards the outer halo. The observed velocity grows with distance such that, relative to halo stars at $100$ kpc, the inner MW is lurching at $\approx 40$ km s$^{-1}$ towards a recent location along the LMC's past orbit. Our measurements align with N-body simulations of the halo's response to a $1.8 \times 10^{11} M_\odot$ LMC on first infall, suggesting that the LMC is at least 15% as massive as the MW. Our findings highlight the dramatic disequilibrium of the MW outskirts, and will enable more accurate measurements of the total mass of our Galaxy., Comment: 25 pages, 15 figures. Submitted to ApJ

Creating multilingual task-oriented dialogue (TOD) agents is challenging due to the high cost of training data acquisition. Following the research trend of improving training data efficiency, we show for the first time, that in-context learning is sufficient to tackle multilingual TOD. To handle the challenging dialogue state tracking (DST) subtask, we break it down to simpler steps that are more compatible with in-context learning where only a handful of few-shot examples are used. We test our approach on the multilingual TOD dataset X-RiSAWOZ, which has 12 domains in Chinese, English, French, Korean, Hindi, and code-mixed Hindi-English. Our turn-by-turn DST accuracy on the 6 languages range from 55.6% to 80.3%, seemingly worse than the SOTA results from fine-tuned models that achieve from 60.7% to 82.8%; our BLEU scores in the response generation (RG) subtask are also significantly lower than SOTA. However, after manual evaluation of the validation set, we find that by correcting gold label errors and improving dataset annotation schema, GPT-4 with our prompts can achieve (1) 89.6%-96.8% accuracy in DST, and (2) more than 99% correct response generation across different languages. This leads us to conclude that current automatic metrics heavily underestimate the effectiveness of in-context learning.

Quantum computing holds the potential to solve problems that are practically unsolvable by classical computers due to its ability to significantly reduce time complexity. We aim to harness this potential to enhance ray casting, a pivotal technique in computer graphics for simplifying the rendering of 3D objects. To perform ray casting in a quantum computer, we need to encode the defining parameters of primitives into qubits. However, during the current noisy intermediate-scale quantum (NISQ) era, challenges arise from the limited number of qubits and the impact of noise when executing multiple gates. Through logic optimization, we reduced the depth of quantum circuits as well as the number of gates and qubits. As a result, the event count of correct measurements from an IBM quantum computer significantly exceeded that of incorrect measurements., Comment: Submitted to ITC-CSCC 2024

Here, we present an EfficientNet-B0-based model to directly predict multiple properties of lithium manganese nickel oxides (LMNO) using their crystal structure images. The model is supposed to predict the energy above the convex hull, bandgap energy, crystal systems, and crystal space groups of LMNOs. In the last layer of the model, a linear function is used to predict the bandgap energy and energy above the convex hull, while a SoftMax function is used to classify the crystal systems and crystal space groups. In the test set, the percentages of coefficient of determination (R2) scores are 97.73% and 96.50% for the bandgap energy and energy above the convex hull predictions, respectively, while the percentages of accuracy are 99.45% and 99.27% for the crystal system and crystal space group classifications, respectively. The class saliency maps explain that the model pays more attention to the shape of the crystal lattices and gradients around the lattice region occupied by the larger ions. This work provides new insight into using an intelligent model to directly relate the crystal structures of LMNO materials with their properties.

Prompt tuning methods have shown comparable performance to general training methods as parameter-efficient fine-tuning (PEFT) methods in various natural language understanding tasks. However, existing prompt tuning methods still utilize the entire model architecture even when solving a specific task, which prevents them from accelerating inference speed during the application procedure. In this paper, we propose a novel prompt tuning framework called Skeleton to efficiently utilize a language model in terms of memory and time complexity for solving various tasks, retaining only task-relevant neurons by using an explainability method. From our framework, we can efficiently solve various tasks by using only task-relevant neurons and prepending adequate task-specific prompt tokens with only a single language model. Experiments reveal that our method significantly enhances inference efficiency (at most x 1.73 speed up) for various widely used benchmarks, showing comparable performance to the prompt tuning method. Moreover, our method is applicable across various transformer-based architectures, confirming its practicality and scalability., Comment: 11 pages

This paper presents design techniques for an energy-efficient multi-lane receiver (RX) with baud-rate clock and data recovery (CDR), which is essential for high-throughput low-latency communication in high-performance computing systems. The proposed low-power global clock distribution not only significantly reduces power consumption across multi-lane RXs but is capable of compensating for the frequency offset without any phase interpolators. To this end, a fractional divider controlled by CDR is placed close to the global phase locked loop. Moreover, in order to address the sub-optimal lock point of conventional baud-rate phase detectors, the proposed CDR employs a background eye-climbing algorithm, which optimizes the sampling phase and maximizes the vertical eye margin (VEM). Fabricated in a 28nm CMOS process, the proposed 4x32Gb/s RX shows a low integrated fractional spur of -40.4dBc at a 2500ppm frequency offset. Furthermore, it improves bit-error-rate performance by increasing the VEM by 17%. The entire RX achieves the energy efficiency of 1.8pJ/bit with the aggregate data rate of 128Gb/s.

This paper presents an I/O interface with Xtalk Minimizing Affine Signaling (XMAS), which is designed to support high-speed data transmission in die-to-die communication over silicon interposers or similar high-density interconnects susceptible to crosstalk. The operating principles of XMAS are elucidated through rigorous analyses, and its advantages over existing signaling are validated through numerical experiments. XMAS not only demonstrates exceptional crosstalk removing capabilities but also exhibits robustness against noise, especially simultaneous switching noise. Fabricated in a 28-nm CMOS process, the prototype XMAS transceiver achieves an edge density of 3.6TB/s/mm and an energy efficiency of 0.65pJ/b. Compared to the single-ended signaling, the crosstalk-induced peak-to-peak jitter of the received eye with XMAS is reduced by 75% at 10GS/s/pin data rate, and the horizontal eye opening extends to 0.2UI at a bit error rate < 10$^{-12}$.

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