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End-to-end style autonomous driving models have been developed recently. These models lack interpretability of decision-making process from perception to control of the ego vehicle, resulting in anxiety for passengers. To alleviate it, it is effective to build a model which outputs captions describing future behaviors of the ego vehicle and their reason. However, the existing approaches generate reasoning text that inadequately reflects the future plans of the ego vehicle, because they train models to output captions using momentary control signals as inputs. In this study, we propose a reasoning model that takes future planning trajectories of the ego vehicle as inputs to solve this limitation with the dataset newly collected., Comment: Accepted and presented at ECCV 2024 2nd Workshop on Vision-Centric Autonomous Driving (VCAD) on September 30, 2024. 13 pages, 5 figures

In this study, we propose an ensemble learning framework for electroencephalogram-based overt speech classification, leveraging denoising diffusion probabilistic models with varying convolutional kernel sizes. The ensemble comprises three models with kernel sizes of 51, 101, and 201, effectively capturing multi-scale temporal features inherent in signals. This approach improves the robustness and accuracy of speech decoding by accommodating the rich temporal complexity of neural signals. The ensemble models work in conjunction with conditional autoencoders that refine the reconstructed signals and maximize the useful information for downstream classification tasks. The results indicate that the proposed ensemble-based approach significantly outperforms individual models and existing state-of-the-art techniques. These findings demonstrate the potential of ensemble methods in advancing brain signal decoding, offering new possibilities for non-verbal communication applications, particularly in brain-computer interface systems aimed at aiding individuals with speech impairments.

This study investigates the sleep characteristics and brain activity of individuals in the gray zone of insomnia, a population that experiences sleep disturbances yet does not fully meet the clinical criteria for chronic insomnia. Thirteen healthy participants and thirteen individuals from the gray zone were assessed using polysomnography and electroencephalogram to analyze both sleep architecture and neural activity. Although no significant differences in objective sleep quality or structure were found between the groups, gray zone individuals reported higher insomnia severity index scores, indicating subjective sleep difficulties. Electroencephalogram analysis revealed increased delta and alpha activity during the wake stage, suggesting lingering sleep inertia, while non-rapid eye movement stages 1 and 2 exhibited elevated beta and gamma activity, often associated with chronic insomnia. However, these high-frequency patterns were not observed in non-rapid eye movement stage 3 or rapid eye movement sleep, suggesting less severe disruptions compared to chronic insomnia. This study emphasizes that despite normal polysomnography findings, EEG patterns in gray zone individuals suggest a potential risk for chronic insomnia, highlighting the need for early identification and tailored intervention strategies to prevent progression., Comment: 4 pages, 2 figures

Brain signals accompany various information relevant to human actions and mental imagery, making them crucial to interpreting and understanding human intentions. Brain-computer interface technology leverages this brain activity to generate external commands for controlling the environment, offering critical advantages to individuals with paralysis or locked-in syndrome. Within the brain-computer interface domain, brain-to-speech research has gained attention, focusing on the direct synthesis of audible speech from brain signals. Most current studies decode speech from brain activity using invasive techniques and emphasize spoken speech data. However, humans express various speech states, and distinguishing these states through non-invasive approaches remains a significant yet challenging task. This research investigated the effectiveness of deep learning models for non-invasive-based neural signal decoding, with an emphasis on distinguishing between different speech paradigms, including perceived, overt, whispered, and imagined speech, across multiple frequency bands. The model utilizing the spatial conventional neural network module demonstrated superior performance compared to other models, especially in the gamma band. Additionally, imagined speech in the theta frequency band, where deep learning also showed strong effects, exhibited statistically significant differences compared to the other speech paradigms.

Numerical simulation is a predominant tool for studying the dynamics in complex systems, but large-scale simulations are often intractable due to computational limitations. Here, we introduce the Neural Graph Simulator (NGS) for simulating time-invariant autonomous systems on graphs. Utilizing a graph neural network, the NGS provides a unified framework to simulate diverse dynamical systems with varying topologies and sizes without constraints on evaluation times through its non-uniform time step and autoregressive approach. The NGS offers significant advantages over numerical solvers by not requiring prior knowledge of governing equations and effectively handling noisy or missing data with a robust training scheme. It demonstrates superior computational efficiency over conventional methods, improving performance by over $10^5$ times in stiff problems. Furthermore, it is applied to real traffic data, forecasting traffic flow with state-of-the-art accuracy. The versatility of the NGS extends beyond the presented cases, offering numerous potential avenues for enhancement.

Observations of the Milky Way and external galaxies support the idea that large-scale magnetic fields are concentrated in galactic disks, with halo magnetic fields at least an order of magnitude weaker. However, very little is known about the transition between the two. We present the discovery of linearly polarized radio emission at the interface between interacting shells of gas within a well-known grouping of high-velocity clouds (HVCs), the Anti-Center Shell. Faraday rotation of diffuse emission and of background extragalactic compact sources demonstrates an enhancement of the field at the interface. This is the clearest observed example of an HVC altering the large-scale magnetic field at the disk-halo interface and is the first image of magnetic field effects in an HVC. These results demonstrate the possibility of future three-dimensional reconstruction of the Galactic magnetic field and showcase the versatility of the Synthesis Telescope at the Dominion Radio Astrophysical Observatory as one of the few existing telescopes which can exploit this new method of probing Galactic magnetism., Comment: Accepted for publication in the Astrophysical Journal Nov. 13, 2024

Eye gaze is considered a promising interaction modality in extende reality (XR) environments. However, determining selection intention from gaze data often requires additional manual selection techniques. We present a Bayesian-based machine learning (ML) model to predict user selection intention in real-time using only gaze data. Our model uses a Bayesian approach to transform gaze data into selection probabilities, which are then fed into an ML model to discriminate selection intentions. In Study 1, our model achieved real-time inference with an accuracy of 0.97 and an F1 score of 0.96. In Study 2, we found that the selection intention inferred by our model enables more comfortable and accurate interactions compared to traditional techniques., Comment: 18 pages, 6 figures, 2 Tables

Machine learning models in astrophysics are often limited in scope and cannot adapt to data from new instruments or tasks. We introduce SpectraFM, a Transformer-based foundation model architecture that can be pre-trained on stellar spectra from any wavelength range and instrument. SpectraFM excels in generalization by combining flexibility with knowledge transfer from pre-training, allowing it to outperform traditional machine learning methods, especially in scenarios with limited training data. Our model is pre-trained on approximately 90k examples of synthetic spectra to predict the chemical abundances (Fe, Mg, O), temperature, and specific gravity of stars. We then fine-tune the model on real spectra to adapt it to observational data before fine-tuning it further on a restricted 100-star training set in a different wavelength range to predict iron abundance. Despite a small iron-rich training set of real spectra, transfer learning from the synthetic spectra pre-training enables the model to perform well on iron-poor stars. In contrast, a neural network trained from scratch fails at this task. We investigate the Transformer attention mechanism and find that the wavelengths receiving attention carry physical information about chemical composition. By leveraging the knowledge from pre-training and its ability to handle non-spectra inputs, SpectraFM reduces the need for large training datasets and enables cross-instrument and cross-domain research. Its adaptability makes it well-suited for tackling emerging challenges in astrophysics, like extracting insights from multi-modal datasets., Comment: Accepted at the NeurIPS 2024 Workshop on Foundation Models for Science

Short video platforms, such as YouTube, Instagram, or TikTok, are used by billions of users globally. These platforms expose users to harmful content, ranging from clickbait or physical harms to misinformation or online hate. Yet, detecting harmful videos remains challenging due to an inconsistent understanding of what constitutes harm and limited resources and mental tolls involved in human annotation. As such, this study advances measures and methods to detect harm in video content. First, we develop a comprehensive taxonomy for online harm on video platforms, categorizing it into six categories: Information, Hate and harassment, Addictive, Clickbait, Sexual, and Physical harms. Next, we establish multimodal large language models as reliable annotators of harmful videos. We analyze 19,422 YouTube videos using 14 image frames, 1 thumbnail, and text metadata, comparing the accuracy of crowdworkers (Mturk) and GPT-4-Turbo with domain expert annotations serving as the gold standard. Our results demonstrate that GPT-4-Turbo outperforms crowdworkers in both binary classification (harmful vs. harmless) and multi-label harm categorization tasks. Methodologically, this study extends the application of LLMs to multi-label and multi-modal contexts beyond text annotation and binary classification. Practically, our study contributes to online harm mitigation by guiding the definitions and identification of harmful content on video platforms.

Deep learning models generally display catastrophic forgetting when learning new data continuously. Many incremental learning approaches address this problem by reusing data from previous tasks while learning new tasks. However, the direct access to past data generates privacy and security concerns. To address these issues, we present a novel method that preserves previous knowledge without storing previous data. This method is inspired by the architecture of a vision transformer and employs a unique token capable of encapsulating the compressed knowledge of each task. This approach generates task-specific embeddings by directing attention differently based on the task associated with the data, thereby effectively mimicking the impact of having multiple models through tokens. Our method incorporates a distillation process that ensures efficient interactions even after multiple additional learning steps, thereby optimizing the model against forgetting. We measured the performance of our model in terms of accuracy and backward transfer using a benchmark dataset for different task-incremental learning scenarios. Our results demonstrate the superiority of our approach, which achieved the highest accuracy and lowest backward transfer among the compared methods. In addition to presenting a new model, our approach lays the foundation for various extensions within the spectrum of vision-transformer architectures.

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mis-modeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.

Improved constraints on current model parameters in a heavy-ion collision model are established using the latest measurements from three distinct collision systems. Various observables are utilized from Au--Au collisions at $\sqrt{s_\mathrm{NN}}=200$~GeV and Pb--Pb collisions at $\sqrt{s_\mathrm{NN}}=5.02$~TeV and $\sqrt{s_\mathrm{NN}}=2.76$~TeV. Additionally, the calibration of centrality is now carried out separately for all parametrizations. The inclusion of an Au--Au collision system with an order of magnitude lower beam energy, along with separate centrality calibration, suggests a preference for smaller values of nucleon width, minimum volume per nucleon, and free-streaming time. The results with the acquired \textit{maximum a posteriori} parameters show improved agreement with the data for the second-order flow coefficient, identified particle yields, and mean transverse momenta. This work contributes to a more comprehensive understanding of heavy-ion collision dynamics and sets the stage for future improvements in theoretical modeling and experimental measurements., Comment: 17 pages, 19 figures

Radio astronomy observatories, such as the Dominion Radio Astrophysical Observatory in Penticton, British Columbia, try to limit radio frequency interference to observe incredibly faint astronomical signals. These protective measures include placing observatories in geographically remote locations, the implementation of radio-frequency-interference-free quiet zones, or removal of interference in data processing. In 2018, we set out to explore how necessary radio-quiet zones are, by studying the radio frequency emission around the Observatory and around our local environment in Calgary, Alberta. We studied five well-used radio frequency bands and found the emission to be variable and environment dependent. While the radio frequency environment has changed since then, as a consequence of increased satellite activity and other forms of emission, we present these results as documentation of the past environment with the aim to redo the measurements. Overall, as there is use from both public and private services across the radio spectrum, protective measures at astronomical observatories are essential to reduce radio frequency interference., Comment: This article was submitted to Physics in Canada in June 2019. Due to a series of unfortunate events including the pandemic and changes within the journal, referee comments and subsequent responses have been quite delayed. This version is currently in revision with most of the referee comments addressed, and we hope it serves as a record of past ambient radio frequency emissions

Pt-based electrocatalysts are the primary choice for fuel cells due to their superior oxygen reduction reaction (ORR) activity. To enhance ORR performance and durability, extensive studies have investigated transition metal alloying, doping, and shape control to optimize the three key governing factors for ORR: geometry, local chemistry, and strain of their surface and subsurface. However, systematic optimization remains incomplete, as it requires an atomic-scale understanding of these factors and their dynamics over potential cycling, as well as their relationship to ORR activity. Here, we implement neural network-assisted atomic electron tomography to measure the 3D atomic structural dynamics and their effects on the functional degradation of PtNi alloy catalysts. Our results reveal that PtNi catalysts undergo shape changes, surface alloying, and strain relaxation during cycling, which can be effectively mitigated by Ga doping. By combining geometry, local chemistry, and strain analysis, we calculated the changes in ORR activity over thousands of cycles and observed that Ga doping leads to higher initial activity and greater stability. These findings offer a pathway to understanding 3D atomic structural dynamics and their relation to ORR activity during cycling, paving the way for the systematic design of durable, high-efficiency nanocatalysts., Comment: 45 pages, 4 main figures, 15 supplementary figures

Social media platforms have led to online consumption communities, or fandoms, that involve complex networks of ancillary creators and consumers focused on some core product or intellectual property. For example, video game communities include networks of players and content creators centered around a specific video game. These networks are complex in that video game publishers often sponsor creators, but creators and publishers may have divergent incentives. Specifically, creators can potentially benefit from content that builds their own following at the expense of the core game. Our research investigates the relationship between consuming live-streamed content and engagement with a specific video game. We examine the causal effect of viewing live-streamed content on subsequent gameplay for a specific game, using an unexpected service interruption of the livestreaming platform and time zone differences among users. We find live-streamed content significantly increases gameplay as a 10% increase in live-streamed viewing minutes results in a 3.08% increase in gameplay minutes. We also explore how this effect varies by user loyalty to different types of streamer channels (firm-owned, mega, and micro). The positive effects of live-streamed content are greatest for micro-streamers and smallest for mega-streamers. These findings are salient for firms allocating sponsorship resources., Comment: 51 pages, 9 Tables, 6 Figures

Diffusion models have demonstrated remarkable success in various domains, including molecular generation. However, conditional molecular generation remains a fundamental challenge due to an intrinsic trade-off between targeting specific chemical properties and generating meaningful samples from the data distribution. In this work, we present Time-Aware Conditional Synthesis (TACS), a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties while maintaining validity and stability. A key component of our algorithm is our new type of diffusion sampler, Time Correction Sampler (TCS), which is used to control guidance and ensure that the generated molecules remain on the correct manifold at each reverse step of the diffusion process at the same time. Our proposed method demonstrates significant performance in conditional 3D molecular generation and offers a promising approach towards inverse molecular design, potentially facilitating advancements in drug discovery, materials science, and other related fields., Comment: NeurIPS 2024

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science.

We study which lens spaces can bound smooth 4-manifolds with second Betti number one under various topological conditions. Specifically, we show that there are infinite families of lens spaces that bound compact, simply-connected, smooth 4-manifolds with second Betti number one, yet cannot bound a 4-manifold consisting of a single 0-handle and 2-handle. Additionally, we establish the existence of infinite families of lens spaces that bound compact, smooth 4-manifolds with first Betti number zero and second Betti number one, but cannot bound simply-connected 4-manifolds with second Betti number one. The construction of such 4-manifolds with lens space boundaries is motivated by the study of rational homology projective planes with cyclic quotient singularities., Comment: 21 pages, 15 figures; Added comments and a discussion on Ballinger's related results; comments are welcome

In this article, we study the effects of topological and smooth obstructions on the existence of rational homology complex projective planes that admit quotient singularities of small indices. In particular, we provide a classification of the types of quotient singularities that can be realized on rational homology complex projective planes with indices up to three, whose smooth loci have trivial first integral homology group., Comment: 22 pages, 16 figures, 10 tables; comments are welcome

In this study, we investigate the impact of covariance within uncertainties on the inference of cosmological and astrophysical parameters, specifically focusing on galaxy stellar mass functions derived from the CAMELS simulation suite. Utilizing both Fisher analysis and Implicit Likelihood Inference (ILI), we explore how different covariance structures, including simple toy models and physics-motivated uncertainties, affect posterior distributions and parameter variances. Our methodology utilizes forward modeling via emulators that are trained on CAMELS simulations to produce stellar mass functions based on input parameters, subsequently incorporating Gaussian noise as defined by covariance matrices. We examine both toy model covariance matrices and physically motivated covariance matrices derived from observational factors like the stellar Initial Mass Function (IMF) and photometric aperture size. Our results demonstrate that covariance terms significantly influence parameter inference, often leading to tighter constraints or revealing complex, multimodal posterior distributions. These findings underscore the necessity of accounting for covariance when interpreting astrophysical observations, especially in fields where accurate parameter estimation is critical for model validation and hypothesis testing., Comment: 19 pages, 6 figures, submitted to ApJ

This paper introduces NeuGPT, a groundbreaking multi-modal language generation model designed to harmonize the fragmented landscape of neural recording research. Traditionally, studies in the field have been compartmentalized by signal type, with EEG, MEG, ECoG, SEEG, fMRI, and fNIRS data being analyzed in isolation. Recognizing the untapped potential for cross-pollination and the adaptability of neural signals across varying experimental conditions, we set out to develop a unified model capable of interfacing with multiple modalities. Drawing inspiration from the success of pre-trained large models in NLP, computer vision, and speech processing, NeuGPT is architected to process a diverse array of neural recordings and interact with speech and text data. Our model mainly focus on brain-to-text decoding, improving SOTA from 6.94 to 12.92 on BLEU-1 and 6.93 to 13.06 on ROUGE-1F. It can also simulate brain signals, thereby serving as a novel neural interface. Code is available at \href{https://github.com/NeuSpeech/NeuGPT}{NeuSpeech/NeuGPT (https://github.com/NeuSpeech/NeuGPT) .}

Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips, present in their data. We demonstrate new calorimetric and particle discrimination capabilities at the MeV energy scale using reconstructed blips in data from the MicroBooNE LArTPC at Fermilab. We observe a concentration of low energy ($<$3 MeV) blips around fiberglass mechanical support struts along the TPC edges with energy spectrum features consistent with the Compton edge of 2.614 MeV $^{208}$Tl decay $\gamma$ rays. These features are used to verify proper calibration of electron energy scales in MicroBooNE's data to few percent precision and to measure the specific activity of $^{208}$Tl in the fiberglass composing these struts, $(11.7 \pm 0.2 ~\text{(stat)} \pm 2.8~\text{(syst)})~\text{Bq/kg}$. Cosmogenically-produced blips above 3 MeV in reconstructed energy are used to showcase the ability of large LArTPCs to distinguish between low-energy proton and electron energy depositions. An enriched sample of low-energy protons selected using this new particle discrimination technique is found to be smaller in data than in dedicated CORSIKA cosmic ray simulations, suggesting either incorrect CORSIKA modeling of incident cosmic fluxes or particle transport modeling issues in Geant4., Comment: 19 pages, 15 figures total including the supplementary material section, 1 table. CC BY license

The high electron temperature in galaxy clusters ($>1\,$keV or $>10^7\,$K) leads to corrections at the level of a few percent in their thermal Sunyaev-Zeldovich effect signatures. Both the size and frequency dependence of these corrections, which are known as relativistic temperature corrections, depend upon the temperature of the objects. In this work we exploit this effect to measure the average temperature of a stack of Compton-$y$ selected clusters. Specifically, we apply the "spectroscopic method" and search for the temperature that best fits the clusters' signal measured at frequencies from 30 to 545 GHz by the Atacama Cosmology Telescope and Planck satellite. We measure the average temperature of clusters detected in the ACT maps to be $8.5\pm 2.4\,$keV, with an additional systematic error of comparable amplitude dominated by passband uncertainty. Upcoming surveys, such as the Simons Observatory and CMB-S4, have the potential to dramatically improve upon these measurements and thereby enable precision studies of cluster temperatures with millimeter observations. The key challenge for future observations will be mitigating instrumental systematic effects, which already limit this analysis., Comment: 21 pages with 17 figures

Altermagnets are magnetic materials with antiferromagnetic spin ordering but exhibit ferromagnetic properties. Understanding the microscopic origin of the latter is a central problem. Ferromagnet-like properties such as the anomalous Hall effect are linked with weak ferromagnetism, whose microscopic origin in altermagnets remains unclear however. We show theoretically that the alternating $g$-tensor anisotropy in altermagnets can induce weak ferromagnetism even when the Dzyaloshinskii-Moriya interaction is forbidden. We demonstrate this mechanism for both collinear and noncollinear spin altermagnets and explain various properties of the weak ferromagnetism in altermagnets. Our findings provide insights for characterizing and manipulating magnetic configurations in altermagnets.

This paper explores the nuanced landscape of generative AI (genAI), particularly focusing on neural network-based models like Large Language Models (LLMs). While genAI garners both optimistic enthusiasm and sceptical criticism, this work seeks to provide a balanced examination of its capabilities, limitations, and the profound impact it may have on societal functions and personal interactions. The first section demystifies language-based genAI through detailed discussions on how LLMs learn, their computational needs, distinguishing features from supporting technologies, and the inherent limitations in their accuracy and reliability. Real-world examples illustrate the practical applications and implications of these technologies. The latter part of the paper adopts a systems perspective, evaluating how the integration of LLMs with existing technologies can enhance productivity and address emerging concerns. It highlights the need for significant investment to understand the implications of recent advancements, advocating for a well-informed dialogue to ethically and responsibly integrate genAI into diverse sectors. The paper concludes with prospective developments and recommendations, emphasizing a forward-looking approach to harnessing genAI`s potential while mitigating its risks.

At frequencies below 1\,Hz, fluctuations in atmospheric emission in the Chajnantor region in northern Chile are the primary source of interference for bolometric millimeter-wave observations. This paper focuses on the statistics of these fluctuations using measurements from the Atacama Cosmology Telescope (ACT) and the Atacama Pathfinder Experiment (APEX) water vapor radiometer. After introducing a method for separating atmospheric effects from other systematic effects, we present a direct measurement of the temporal outer scale of turbulence of $\tau_0\approx50$s corresponding to a spatial scale of $L_0\approx500$m. At smaller scales, the fluctuations are well described by the Kolmogorov 2/3 power law until, at yet smaller scales, the effects of beam smearing become important. As a part of this study, we present measurements of the atmosphere by the APEX radiometer over 20 years, focused on fluctuations in precipitable water vapor (PWV). We find that the 30-minute mean of the total PWV is not in general a robust estimator of the level of fluctuations. We show that the microwave frequency spectrum of these fluctuations is in good agreement with predictions by the \texttt{am} code for bands above 90~GHz. We then show that the variance of fluctuations in ACT's mm-wave bands correlates with the variance of fluctuations in PWV measured by APEX, even though the observatories are 6\,km apart and observe different lines of sight. We find that ACT's atmosphere-determined optical efficiencies are consistent with previous planet-based results., Comment: 13 pages plus appendix, 17 figures. Submitted to PRD

Visual localization determines an agent's precise position and orientation within an environment using visual data. It has become a critical task in the field of robotics, particularly in applications such as autonomous navigation. This is due to the ability to determine an agent's pose using cost-effective sensors such as RGB cameras. Recent methods in visual localization employ scene coordinate regression to determine the agent's pose. However, these methods face challenges as they attempt to regress 2D-3D correspondences across the entire image region, despite not all regions providing useful information. To address this issue, we introduce an attention network that selectively targets informative regions of the image. Using this network, we identify the highest-scoring features to improve the feature selection process and combine the result with edge detection. This integration ensures that the features chosen for the training buffer are located within robust regions, thereby improving 2D-3D correspondence and overall localization performance. Our approach was tested on the outdoor benchmark dataset, demonstrating superior results compared to previous methods.

Radiative transfer interactions with material ablation are critical contributors to vehicle heating during high-altitude, high-velocity atmospheric entry. However, the inherent complexity of fully coupled multi-physics models often necessitates simplifying assumptions, which may overlook key phenomena that significantly affect heat loads, particularly radiative heating. Common approximations include neglecting the contribution of ablation products, applying simplified frozen wall boundary conditions, or treating radiative transfer in a loosely coupled manner. This study introduces a high-fidelity, tightly coupled multi-solver framework designed to accurately capture the multi-physics challenges of hypersonic flow around an ablative body. The proposed approach consistently accounts for the interactions between shock-heated gases, surface material response, and radiative transfer. Our results demonstrate that including radiative heating in the surface energy balance substantially influences the ablation rate. Ablation products are shown to absorb radiative heat flux in the vacuum-ultraviolet spectrum along the stagnation line, while strongly emitting in off-stagnation regions. These findings emphasize the necessity of a tightly coupled multiphysics framework to faithfully capture the complex, multidimensional interactions in hypersonic flow environments, which conventional, loosely coupled models fail to represent accurately.

Recent advances in computing hardware and modeling software have given rise to new applications for numerical optimization. These new applications occasionally uncover bottlenecks in existing optimization algorithms and necessitate further specialization of the algorithms. However, such specialization requires expert knowledge of the underlying mathematical theory and the software implementation of existing algorithms. To address this challenge, we present modOpt, an open-source software framework that facilitates the construction of optimization algorithms from modules. The modular environment provided by modOpt enables developers to tailor an existing algorithm for a new application by only altering the relevant modules. modOpt is designed as a platform to support students and beginner developers in quickly learning and developing their own algorithms. With that aim, the entirety of the framework is written in Python, and it is well-documented, well-tested, and hosted open-source on GitHub. Several additional features are embedded into the framework to assist both beginner and advanced developers. In addition to providing stock modules, the framework also includes fully transparent implementations of pedagogical optimization algorithms in Python. To facilitate testing and benchmarking of new algorithms, the framework features built-in visualization and recording capabilities, interfaces to modeling frameworks such as OpenMDAO and CSDL, interfaces to general-purpose optimization algorithms such as SNOPT and SLSQP, an interface to the CUTEst test problem set, etc. In this paper, we present the underlying software architecture of modOpt, review its various features, discuss several educational and performance-oriented algorithms within modOpt, and present numerical studies illustrating its unique benefits., Comment: 37 pages with 13 figures. For associated code, see https://github.com/LSDOlab/modopt

Understanding protein motion within the cell is crucial for predicting reaction rates and macromolecular transport in the cytoplasm. A key question is how crowded environments affect protein dynamics through hydrodynamic and direct interactions at molecular length scales. Using megahertz X-ray Photon Correlation Spectroscopy (MHz-XPCS) at the European X-ray Free Electron Laser (EuXFEL), we investigate ferritin diffusion at microsecond time scales. Our results reveal anomalous diffusion, indicated by the non-exponential decay of the intensity autocorrelation function $g_2(q,t)$ at high concentrations. This behavior is consistent with the presence of cage-trapping in between the short- and long-time protein diffusion regimes. Modeling with the $\delta\gamma$-theory of hydrodynamically interacting colloidal spheres successfully reproduces the experimental data by including a scaling factor linked to the protein direct interactions. These findings offer new insights into the complex molecular motion in crowded protein solutions, with potential applications for optimizing ferritin-based drug delivery, where protein diffusion is the rate-limiting step.

A major challenge in autonomous flights is unknown disturbances, which can jeopardize safety and lead to collisions, especially in obstacle-rich environments. This paper presents a disturbance-aware motion planning and control framework designed for autonomous aerial flights. The framework is composed of two key components: a disturbance-aware motion planner and a tracking controller. The disturbance-aware motion planner consists of a predictive control scheme and a learned model of disturbances that is adapted online. The tracking controller is designed using contraction control methods to provide safety bounds on the quadrotor behaviour in the vicinity of the obstacles with respect to the disturbance-aware motion plan. Finally, the algorithm is tested in simulation scenarios with a quadrotor facing strong crosswind and ground-induced disturbances.

The kinematic Sunyaev--Zel'dovich (kSZ) effect induces a non-zero density-density-temperature bispectrum, which we can use to reconstruct the large-scale velocity field from a combination of cosmic microwave background (CMB) and galaxy density measurements, in a procedure known as ``kSZ velocity reconstruction''. This method has been forecast to constrain large-scale modes with future galaxy and CMB surveys, improving their measurement beyond what is possible with the galaxy surveys alone. Such measurements will enable tighter constraints on large-scale signals such as primordial non-Gaussianity, deviations from homogeneity, and modified gravity. In this work, we demonstrate a statistically significant measurement of kSZ velocity reconstruction for the first time, by applying quadratic estimators to the combination of the ACT DR6 CMB+kSZ map and the DESI LRG galaxies (with photometric redshifts) in order to reconstruct the velocity field. We do so using a formalism appropriate for the 2-dimensional projected galaxy fields that we use, which naturally incorporates the curved-sky effects important on the largest scales. We find evidence for the signal by cross-correlating with an external estimate of the velocity field from the spectroscopic BOSS survey and rejecting the null (no-kSZ) hypothesis at $3.8\sigma$. Our work presents a first step towards the use of this observable for cosmological analyses., Comment: 16 pages (main)+5 pages (Appendix); 13 figures (main) + 8 figures (appendix)

Liquid Argon Time Projection Chamber (LArTPC) is an exceptional dual calorimeter capable of estimating the energy of incident particles through both the ionization charge and the scintillation light. Our studies show that due to the mechanisms of charge recombination and light generation involved in the energy dissipation in liquid argon, light calorimetry in LArTPCs is inherently self-compensating: the missing energy in the hadronic component is compensated for by the extra recombination luminescence compared to the electromagnetic component. Good compensation of the electron-to-hadron response ratio (e/h) around unity can be achieved across a broad range of drift electric fields from 0.2 to 1.8 kV/cm.This inherent self-compensation enhances the appeal of light calorimetry in LArTPCs, complementing the well-established charge calorimetry. Using GeV neutrinos as a case study, we show that light calorimetry can achieve an energy resolution comparable to the more sophisticated charge imaging calorimetry. The synergy between light and charge calorimetry offers a novel approach to evaluating and mitigating systematic uncertainties in energy measurements with LArTPCs., Comment: 15 pages, 11 figures

Distributed acoustic sensing (DAS) technology leverages fiber optic cables to detect vibrations and acoustic events, which is a promising solution for real-time traffic monitoring. In this paper, we introduce a novel methodology for detecting and tracking vehicles using DAS data, focusing on real-time processing through edge computing. Our approach applies the Hough transform to detect straight-line segments in the spatiotemporal DAS data, corresponding to vehicles crossing the Astfjord bridge in Norway. These segments are further clustered using the Density-based spatial clustering of applications with noise (DBSCAN) algorithm to consolidate multiple detections of the same vehicle, reducing noise and improving accuracy. The proposed workflow effectively counts vehicles and estimates their speed with only tens of seconds latency, enabling real-time traffic monitoring on the edge. To validate the system, we compare DAS data with simultaneous video footage, achieving high accuracy in vehicle detection, including the distinction between cars and trucks based on signal strength and frequency content. Results show that the system is capable of processing large volumes of data efficiently. We also analyze vehicle speeds and traffic patterns, identifying temporal trends and variations in traffic flow. Real-time deployment on edge devices allows immediate analysis and visualization via cloud-based platforms. In addition to traffic monitoring, the method successfully detected structural responses in the bridge, highlighting its potential use in structural health monitoring., Comment: 11 pages, 17 figures

We introduce a black-box auxiliary field quantum Monte Carlo (AFQMC) approach to perform highly accurate electronic structure calculations using configuration interaction singles and doubles (CISD) trial states. This method consistently provides more accurate energy estimates than coupled cluster singles and doubles with perturbative triples (CCSD(T)), often regarded as the gold standard in quantum chemistry. This level of precision is achieved at a lower asymptotic computational cost, scaling as $O(N^6)$ compared to the $O(N^7)$ scaling of CCSD(T). We provide numerical evidence supporting these findings through results for challenging main group and transition metal-containing molecules., Comment: 17 pages, 11 figures

The Sejong dictionary dataset offers a valuable resource, providing extensive coverage of morphology, syntax, and semantic representation. This dataset can be utilized to explore linguistic information in greater depth. The labeled linguistic structures within this dataset form the basis for uncovering relationships between words and phrases and their associations with target verbs. This paper introduces a user-friendly web interface designed for the collection and consolidation of verb-related information, with a particular focus on subcategorization frames. Additionally, it outlines our efforts in mapping this information by aligning subcategorization frames with corresponding illustrative sentence examples. Furthermore, we provide a Python library that would simplify syntactic parsing and semantic role labeling. These tools are intended to assist individuals interested in harnessing the Sejong dictionary dataset to develop applications for Korean language processing., Comment: COLING2025 System Demonstrations (Submitted)

In abstractive summarization, the challenge of producing concise and accurate summaries arises from the vast amount of information contained in the source document. Consequently, although Large Language Models (LLMs) can generate fluent text, they often introduce inaccuracies by hallucinating content not found in the original source. While supervised fine-tuning methods that maximize likelihood contribute to this issue, they do not consistently enhance the faithfulness of the summaries. Preference-based optimization methods, such as Direct Preference Optimization (DPO), can further refine the model to align with human preferences. However, these methods still heavily depend on costly human feedback. In this work, we introduce a novel and straightforward approach called Model-based Preference Optimization (MPO) to fine-tune LLMs for improved summarization abilities without any human feedback. By leveraging the model's inherent summarization capabilities, we create a preference dataset that is fully generated by the model using different decoding strategies. Our experiments on standard summarization datasets and various metrics demonstrate that our proposed MPO significantly enhances the quality of generated summaries without relying on human feedback., Comment: Accepted by EMNLP 2024

This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

A permutation $\pi: [n] \rightarrow [n]$ is a Baxter permutation if and only if it does not contain either of the patterns $2-41-3$ and $3-14-2$. Baxter permutations are one of the most widely studied subclasses of general permutation due to their connections with various combinatorial objects such as plane bipolar orientations and mosaic floorplans, etc. In this paper, we introduce a novel succinct representation (i.e., using $o(n)$ additional bits from their information-theoretical lower bounds) for Baxter permutations of size $n$ that supports $\pi(i)$ and $\pi^{-1}(j)$ queries for any $i \in [n]$ in $O(f_1(n))$ and $O(f_2(n))$ time, respectively. Here, $f_1(n)$ and $f_2(n)$ are arbitrary increasing functions that satisfy the conditions $\omega(\log n)$ and $\omega(\log^2 n)$, respectively. This stands out as the first succinct representation with sub-linear worst-case query times for Baxter permutations. Additionally, we consider a subclass of Baxter permutations called \textit{separable permutations}, which do not contain either of the patterns $2-4-1-3$ and $3-1-4-2$. In this paper, we provide the first succinct representation of the separable permutation $\rho: [n] \rightarrow [n]$ of size $n$ that supports both $\rho(i)$ and $\rho^{-1}(j)$ queries in $O(1)$ time. In particular, this result circumvents Golynski's [SODA 2009] lower bound result for trade-offs between redundancy and $\rho(i)$ and $\rho^{-1}(j)$ queries. Moreover, as applications of these permutations with the queries, we also introduce the first succinct representations for mosaic/slicing floorplans, and plane bipolar orientations, which can further support specific navigational queries on them efficiently.

EuAl$_4$ is proposed to host a topological Hall state. This material also undergoes four consecutive antiferromagnetic (AFM) transitions upon cooling below TN1 = 15.4 K in the presence of charge density wave (CDW) order that sets in below TCDW = 140 K. We use angle-resolved photoemission spectroscopy and density-functional-theory calculations to study how magnetic ordering affects the electronic properties in EuAl$_4$. We found changes in the band structure upon each of the four consecutive AFM transitions including band splitting, renormalizations, and appearance of new bands forming additional Fermi sheets. In addition we also found significant enhancement of the quasiparticles' lifetime due to suppression of spin flip scattering, similar to what was previously reported for ferromagnetic EuCd$_2$As$_2$. Surprisingly, we observe that most significant changes in electronic properties occur not at TN1, but instead at the AFM3 to AFM4 transition, which coincides with the largest drop in resistivity.

Video anomaly detection (VAD) is a crucial task in video analysis and surveillance within computer vision. Currently, VAD is gaining attention with memory techniques that store the features of normal frames. The stored features are utilized for frame reconstruction, identifying an abnormality when a significant difference exists between the reconstructed and input frames. However, this approach faces several challenges due to the simultaneous optimization required for both the memory and encoder-decoder model. These challenges include increased optimization difficulty, complexity of implementation, and performance variability depending on the memory size. To address these challenges,we propose an effective memory method for VAD, called VideoPatchCore. Inspired by PatchCore, our approach introduces a structure that prioritizes memory optimization and configures three types of memory tailored to the characteristics of video data. This method effectively addresses the limitations of existing memory-based methods, achieving good performance comparable to state-of-the-art methods. Furthermore, our method requires no training and is straightforward to implement, making VAD tasks more accessible. Our code is available online at github.com/SkiddieAhn/Paper-VideoPatchCore., Comment: Accepted to ACCV 2024

Trust is essential in human-robot collaboration. Even more so in multi-human multi-robot teams where trust is vital to ensure teaming cohesion in complex operational environments. Yet, at the moment, trust is rarely considered a factor during task allocation and reallocation in algorithms used in multi-human, multi-robot collaboration contexts. Prior work on trust in single-human-robot interaction has identified that including trust as a parameter in human-robot interaction significantly improves both performance outcomes and human experience with robotic systems. However, very little research has explored the impact of trust in multi-human multi-robot collaboration, specifically in the context of task allocation. In this paper, we introduce a new trust model, the Expectation Comparison Trust (ECT) model, and employ it with three trust models from prior work and a baseline no-trust model to investigate the impact of trust on task allocation outcomes in multi-human multi-robot collaboration. Our experiment involved different team configurations, including 2 humans, 2 robots, 5 humans, 5 robots, and 10 humans, 10 robots. Results showed that using trust-based models generally led to better task allocation outcomes in larger teams (10 humans and 10 robots) than in smaller teams. We discuss the implications of our findings and provide recommendations for future work on integrating trust as a variable for task allocation in multi-human, multi-robot collaboration.

d-wave altermagnets have magnetic octupoles as their order parameters [Phys. Rev. X 14, 011019 (2024)]. We theoretically show that magnetic octupoles injected from outside generate torque on the d-wave altermagnets. The injection can be achieved by the magnetic octupole Hall effect in an adjacent layer. We calculate the magnetic octupole Hall conductivity of the heavy metal Pt and find a sizable value comparable to its spin Hall conductivity. Our work generalizes the spin Hall phenomenology (generation by heavy metals and detection by torque in ferromagnets) to the magnetic octupole Hall phenomenology (generation by heavy metals and detection by torque in altermagnets), which can be utilized to electrically control magnetic configurations of altermagnets.

We conduct a systematic search for astrophysical transients using data from the Atacama Cosmology Telescope (ACT). The data were taken from 2017 to 2022 in three frequency bands spanning 77 GHz to 277 GHz. In this paper we present a pipeline for transient detection using single observation maps where each pixel of a map contains one observation with an integration time of approximately four minutes. We find 34 transient events at 27 unique locations. All but two of the transients are associated with Galactic stars and exhibit a wide range of properties. We also detect an event coincident with the classical nova, YZ Ret and one event consistent with a flaring active galactic nucleus. We notably do not detect any reverse shock emission from gamma ray bursts, a non-detection which is in tension with current models., Comment: 23 pages, 10 figures, 10 tables. First and second author share equal contributions. Article and accompanying data submitted to ApJ. Data tables will be made available upon publication

The spin structure functions of the proton and the deuteron were measured during the EG4 experiment at Jefferson Lab in 2006. Data were collected for longitudinally polarized electron scattering off longitudinally polarized NH$_3$ and ND$_3$ targets, for $Q^2$ values as small as 0.012 and 0.02 GeV$^2$, respectively, using the CEBAF Large Acceptance Spectrometer (CLAS). This is the archival paper of the EG4 experiment that summaries the previously reported results of the polarized structure functions $g_1$, $A_1F_1$, and their moments $\overline \Gamma_1$, $\overline \gamma_0$, and $\overline I_{TT}$, for both the proton and the deuteron. In addition, we report on new results on the neutron $g_1$ extracted by combining proton and deuteron data and correcting for Fermi smearing, and on the neutron moments $\overline \Gamma_1$, $\overline \gamma_0$, and $\overline I_{TT}$ formed directly from those of the proton and the deuteron. Our data are in good agreement with the Gerasimov-Drell-Hearn sum rule for the proton, deuteron, and neutron. Furthermore, the isovector combination was formed for $g_1$ and the Bjorken integral $\overline \Gamma_1^{p-n}$, and compared to available theoretical predictions. All of our results provide for the first time extensive tests of spin observable predictions from chiral effective field theory ($\chi$EFT) in a $Q^2$ range commensurate with the pion mass. They motivate further improvement in $\chi$EFT calculations from other approaches such as the lattice gauge method., Comment: 33 pages. 26 figures. Data table provided in supplementary material (30 pages)

In-situ diffuse neutron scattering experiments revealed that when electric current is passed through single crystals of rutile TiO2 under conditions conducive to flash sintering, it induces the formation of parallel planes of oxygen vacancies. Specifically, a current perpendicular to the c-axis generates planes normal to the (132) reciprocal lattice vector, whereas currents aligned with the c-axis form planes normal to the (132) and to the (225) vector. The concentration of defects increases with incresing current. The structural modifications are linked to the appearance of signatures of interacting Ti3+ moments in magnetic susceptibility, signifying a structural collapse around the vacancy planes. Electrical conductivity measurements of the modified material reveal several electronic transitions between semiconducting states (via a metal-like intermediate state) with the smallest gap being 27 meV. Pristine TiO2 can be restored by heating followed by slow cooling in air. Our work suggests a novel paradigm for achieving switching of electrical conductivity related to the flash phenomenon

Entangled photon sources are essential for quantum information applications, including quantum computation, quantum communication, and quantum metrology. Periodically poled (PP) crystals are commonly used to generate bright photon sources through quasi-phase matching. However, fabricating uniform micron-scale periodic structures poses significant technical difficulties, typically limiting the crystal thickness to less than a millimeter. Here, we adopt non-critical phase matching to produce a robust and bright polarization-entangled photon source based on a Sagnac interferometer. This method is tolerant of variations in pump incidence angles and temperature, and theoretically offers about a 2.5-fold brightness enhancement compared to quasi-phase matching. Additionally, the absence of periodic poling allows for a larger crystal cross-section. Using a bulk KTP crystal without a PP structure, we experimentally produce the four Bell states with a brightness of 25.1 kHz/mW, achieving purity, concurrence, and fidelity values close to 0.99. We believe our scheme will serve as a key building block for scalable and practical photonic quantum information applications., Comment: 7 pages, 5 figures, 2 tables, and supplementary note

Non-magnetic FeNb$_3$Se$_{10}$ has been demonstrated to be an insulator at ambient pressure through both theoretical calculations and experimental measurements and it does not host topological surface states. Here we show that on the application of pressure, FeNb$_3$Se$_{10}$ transitions to a metallic state at around 3.0 GPa. With a further increase in pressure, its resistivity becomes independent of both temperature and pressure. Its crystal structure is maintained to at least 4.4 GPa., Comment: 20 pages, 5 figures

Context. (4015) Wilson-Harrington (hereafter, WH) was discovered as a comet in 1949 but has a dynamical property consistent with that of a near-Earth asteroid. Although there is a report that the 1949 activity is associated with an ion tail, the cause of the activity has not yet been identified. Aims. This work aims to reveal the mysterious comet-like activity of the near-Earth asteroid. Methods. We conducted new polarimetric observations of WH from May 2022 to January 2023, reanalyses of the photographic plate images taken at the time of its discovery in 1949, and dust tail simulation modelings, where the dust terminal velocity and ejection epoch are taken into account. Results. We found that this object shows polarization characteristics similar to those of low-albedo asteroids. We derived the geometric albedo ranging from pV = 0.076 +- 0.010 to pV = 0.094 +- 0.018 from our polarimetry (the values vary depending on the data used for fitting and the slope-albedo relationship coefficients). In addition, the 1949 image showed an increase in brightness around the nucleus. Furthermore, we found that the color of the tail is consistent with sunlight, suggesting that the 1949 activity is associated with dust ejection. From the dust tail analysis, ~9 x 10^5 kg of material was ejected episodically at a low velocity equivalent to or even slower than the escape velocity. Conclusions. We conclude that WH is most likely an active asteroid of main belt origin and that the activity in 1949 was likely triggered by mass shedding due to fast rotation., Comment: 12 pages, 7 figures (Accepted for the publication of Astronomy and Astrophysics)