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We study the phenomenology of the charged Higgs boson, $H^\pm$,appearing in the fermionic dark matter model mediated by the dark $Z$ boson. This model is in favor of the light dark $Z$ boson, $Z'$, and the light additional neutral Higgs boson, $h$. We find that $H^\pm \to W^\pm h$ and the $H^\pm \to W^\pm Z'$ are dominant decay channels. Thus the promising final states are trilepton signals, $e \mu \mu$ or $\mu \mu \mu$ following $Z' \to \mu^+ \mu^-$ decays and leptonic decays of the $W^\pm$ boson. The charged Higgs boson will be produced from the top quark decays $t \to b H^\pm$ following $t \bar{t}$ production, if $H^\pm$ is light. Whereas $H^\pm$ is heavier than the top quark, the dominant production processes are associated productions with either $Z'$ or $h$, $pp \to W^\star \to H^\pm h$ and $pp \to W^\star \to H^\pm Z'$. We explore the discovery potential of the charged Higgs boson at the LHC. We also discuss the implications of dark matter in relation with the charged Higgs phenomenology., Comment: 12 pages, 4 figures

Heterogeneous graph neural networks (HGNNs) have significantly propelled the information retrieval (IR) field. Still, the effectiveness of HGNNs heavily relies on high-quality labels, which are often expensive to acquire. This challenge has shifted attention towards Heterogeneous Graph Contrastive Learning (HGCL), which usually requires pre-defined meta-paths. However, our findings reveal that meta-path combinations significantly affect performance in unsupervised settings, an aspect often overlooked in current literature. Existing HGCL methods have considerable variability in outcomes across different meta-path combinations, thereby challenging the optimization process to achieve consistent and high performance. In response, we introduce \textsf{LAMP} (\underline{\textbf{L}}earn\underline{\textbf{A}}ble \underline{\textbf{M}}eta-\underline{\textbf{P}}ath), a novel adversarial contrastive learning approach that integrates various meta-path sub-graphs into a unified and stable structure, leveraging the overlap among these sub-graphs. To address the denseness of this integrated sub-graph, we propose an adversarial training strategy for edge pruning, maintaining sparsity to enhance model performance and robustness. \textsf{LAMP} aims to maximize the difference between meta-path and network schema views for guiding contrastive learning to capture the most meaningful information. Our extensive experimental study conducted on four diverse datasets from the Heterogeneous Graph Benchmark (HGB) demonstrates that \textsf{LAMP} significantly outperforms existing state-of-the-art unsupervised models in terms of accuracy and robustness., Comment: 19 pages, 7 figures

Mixture-of-experts (MoEs) have been adopted for reducing inference costs by sparsely activating experts in Large language models (LLMs). Despite this reduction, the massive number of experts in MoEs still makes them expensive to serve. In this paper, we study how to address this, by pruning MoEs. Among pruning methodologies, unstructured pruning has been known to achieve the highest performance for a given pruning ratio, compared to structured pruning, since the latter imposes constraints on the sparsification structure. This is intuitive, as the solution space of unstructured pruning subsumes that of structured pruning. However, our counterintuitive finding reveals that expert pruning, a form of structured pruning, can actually precede unstructured pruning to outperform unstructured-only pruning. As existing expert pruning, requiring $O(\frac{k^n}{\sqrt{n}})$ forward passes for $n$ experts, cannot scale for recent MoEs, we propose a scalable alternative with $O(1)$ complexity, yet outperforming the more expensive methods. The key idea is leveraging a latent structure between experts, based on behavior similarity, such that the greedy decision of whether to prune closely captures the joint pruning effect. Ours is highly effective -- for Snowflake Arctic, a 480B-sized MoE with 128 experts, our method needs only one H100 and two hours to achieve nearly no loss in performance with 40% sparsity, even in generative tasks such as GSM8K, where state-of-the-art unstructured pruning fails to. The code will be made publicly available.

Filaments are believed to play a key role in high-mass star formation. We present a systematic study of the filaments and their hosting clumps in the G35 molecular complex using JCMT SCUBA-2 850 $\micron$ continuum data. We identified five clouds in the complex and 91 filaments within them, some of which form 10 hub-filament systems (HFSs), each with at least 3 hub-composing filaments. We also compiled a catalogue of 350 dense clumps, 183 of which are associated with the filaments. We investigated the physical properties of the filaments and clumps, such as mass, density, and size, and their relation to star formation. We find that the global mass-length trend of the filaments is consistent with a turbulent origin, while the hub-composing filaments of high line masses ($m_{\rm l}\,>$\,230\,$\mathrm{M_{\odot}~pc^{-1}}$) in HFSs deviate from this relation, possibly due to feedback from massive star formation. We also find that the most massive and densest clumps (R\,$>$\,0.2\,pc, M\,$>35\,\mathrm{M_{\odot}}$, $\mathrm{\Sigma}>\,0.05\,\mathrm{g~cm^{-2}}$) are located in the filaments and in the hubs of HFS with the latter bearing a higher probability of occurrence of high-mass star-forming signatures, highlighting the preferential sites of HFSs for high-mass star formation. We do not find significant variation in the clump mass surface density across different evolutionary environments of the clouds, which may reflect the balance between mass accretion and stellar feedback., Comment: 34 pages, 17 figures. Accepted for publication in ApJ

In this paper, we solve the joint probability density for the passive and active particles with harmonic, viscous, and perturbative forces. After deriving the Fokker-Planck equation for a passive and a run-and-tumble particles, we approximately get and analyze the solution for the joint distribution density subject to an exponential correlated Gaussian force in three kinds of time limit domains. Mean squared displacement (velocity) for a particle with harmonic and viscous forces behaviors in the form of super-diffusion, consistent with a particle having viscous and perturbative forces. A passive particle with both harmonic, viscous forces and viscous, perturbative forces has the Gaussian form with mean squared velocity ~t. Particularly, In our case of a run-and-tumble particle, the mean squared displacement scales as super-diffusion, while the mean squared velocity has a normal diffusive form.In addition, the kurtosis, the correlation coefficient, and the moment from moment equation are numerically calculated., Comment: 20 pages, 3 Tables. arXiv admin note: text overlap with arXiv:2409.02401

Accurate pain assessment is crucial in healthcare for effective diagnosis and treatment; however, traditional methods relying on self-reporting are inadequate for populations unable to communicate their pain. Cutting-edge AI is promising for supporting clinicians in pain recognition using facial video data. In this paper, we enhance pain recognition by employing facial video analysis within a Transformer-based deep learning model. By combining a powerful Masked Autoencoder with a Transformers-based classifier, our model effectively captures pain level indicators through both expressions and micro-expressions. We conducted our experiment on the AI4Pain dataset, which produced promising results that pave the way for innovative healthcare solutions that are both comprehensive and objective.

The creation of listener facial responses aims to simulate interactive communication feedback from a listener during a face-to-face conversation. Our goal is to generate believable videos of listeners' heads that respond authentically to a single speaker by a sequence-to-sequence model with an combination of WaveNet and Long short-term memory network. Our approach focuses on capturing the subtle nuances of listener feedback, ensuring the preservation of individual listener identity while expressing appropriate attitudes and viewpoints. Experiment results show that our method surpasses the baseline models on ViCo benchmark Dataset.

Sea ice is a key element of the global Earth system, with a major impact on global climate and regional weather. Unfortunately, accurate sea ice modeling is challenging due to the diversity and complexity of underlying physics happening there, and a relative lack of ground truth observations. This is especially true for the Marginal Ice Zone (MIZ), which is the area where sea ice is affected by incoming ocean waves. Waves contribute to making the area dynamic, and due to the low survival time of the buoys deployed there, the MIZ is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs are affordable enough to be deployed in large number, and gather information about drift (GPS position) and waves (1-dimensional elevation spectrum). This provides data focusing on the area around Svalbard with unprecedented spatial and temporal resolution. We expect that this will allow to perform validation and calibration of ice models and remote sensing algorithms.

Drawing tests like the Rey Complex Figure Test (RCFT) are widely used to assess cognitive functions such as visuospatial skills and memory, making them valuable tools for detecting mild cognitive impairment (MCI). Despite their utility, existing predictive models based on these tests often suffer from limitations like small sample sizes and lack of external validation, which undermine their reliability. We developed a multi-stream deep learning framework that integrates two distinct processing streams: a multi-head self-attention based spatial stream using raw RCFT images and a scoring stream employing a previously developed automated scoring system. Our model was trained on data from 1,740 subjects in the Korean cohort and validated on an external hospital dataset of 222 subjects from Korea. The proposed multi-stream model demonstrated superior performance over baseline models (AUC = 0.872, Accuracy = 0.781) in external validation. The integration of both spatial and scoring streams enables the model to capture intricate visual details from the raw images while also incorporating structured scoring data, which together enhance its ability to detect subtle cognitive impairments. This dual approach not only improves predictive accuracy but also increases the robustness of the model, making it more reliable in diverse clinical settings. Our model has practical implications for clinical settings, where it could serve as a cost-effective tool for early MCI screening., Comment: 20 pages, 3 figures, 2 tables

We study the Fokker-Planck equation for an active particle with both the radial and tangential forces and the perturbative force. We find the solution of the joint probability density. In the limit of the long-time domain and for the characteristic time=0 domain, the mean squared radial velocity for an active particle leads to a super-diffusive distribution, while the mean squared tangential velocity with both the radial and tangential forces and the perturbative force behaviors as the Gaussian diffusion. Compared with the self-propelled particle, the mean squared tangential velocity is matched with the same value to the time ~t^2, while the mean squared radial velocity is the same as the time ~t., Comment: 10 pages, 2 Tables

We derive a Fokker-Planck equation for joint probability density for an active particle coupled two heat reservoirs with harmonic, viscous, random forces. The approximate solution for the joint distribution density of all-to-all and three others topologies is solved, which apply an exponential correlated Gaussian force in three-time regions of correlation time. Mean squared displacement, velocity behaviors in the form of super-diffusion, while the mean squared displacement, velocity has the Gaussian form, normal diffusion. Concomitantly, the Kurtosis, correlation coefficient, and moment from moment equation are approximately and numerically calculated. In this paper, we derive an altered Fokker-Planck equation for an active particle with the harmonic, viscous, and random forces, coupled to two heat reservoirs. We attain the solution for the joint distribution density of our topology, including the center topology, the ring topology, and the chain topology, subject to an exponential correlated Gaussian force. The mean squared displacement and the mean squared velocity behavior as the super-diffusions in the short-time domain and for the characteristic time=0, while those have the Gaussian forms in the long-time domain and for the characteristic time=0. We concomitantly calculate and analyze the non-equilibrium characteristics of the kurtosis, the correlation coefficient, and the moment from the derived moment equation., Comment: 10 pages, 1 Figure, 3 Tables

We firstly study the Navier-Stokes equation for the motion of a passive particle with harmonic, viscous, perturbative forces, subject to an exponentially correlated Gaussian force. Secondly, from the Fokker-Planck equation in an incompressible conducting fluid of magnetic field, we approximately obtain the solution of the joint probability density by using double Fourier transforms in three-time domains. In addition, the kurtosis, the correlation coefficient, and the moment from moment equation are numerically calculated., Comment: 19 pages, 5 Tables

Lip reading aims to predict spoken language by analyzing lip movements. Despite advancements in lip reading technologies, performance degrades when models are applied to unseen speakers due to their sensitivity to variations in visual information such as lip appearances. To address this challenge, speaker adaptive lip reading technologies have advanced by focusing on effectively adapting a lip reading model to target speakers in the visual modality. The effectiveness of adapting language information, such as vocabulary choice, of the target speaker has not been explored in the previous works. Moreover, existing datasets for speaker adaptation have limited vocabulary size and pose variations, limiting the validation of previous speaker-adaptive methods in real-world scenarios. To address these issues, we propose a novel speaker-adaptive lip reading method that adapts a pre-trained model to target speakers at both vision and language levels. Specifically, we integrate prompt tuning and the LoRA approach, applying them to a pre-trained lip reading model to effectively adapt the model to target speakers. In addition, to validate its effectiveness in real-world scenarios, we introduce a new dataset, VoxLRS-SA, derived from VoxCeleb2 and LRS3. It contains a vocabulary of approximately 100K words, offers diverse pose variations, and enables the validation of adaptation methods in wild, sentence-level lip reading for the first time. Through various experiments, we demonstrate that the existing speaker-adaptive method also improves performance in the wild at the sentence level. Moreover, with the proposed adaptation method, we show that the proposed method achieves larger improvements when applied to the target speaker, compared to the previous works., Comment: Code available: https://github.com/JeongHun0716/Personalized-Lip-Reading

Video action detection (VAD) is a formidable vision task that involves the localization and classification of actions within the spatial and temporal dimensions of a video clip. Among the myriad VAD architectures, two-stage VAD methods utilize a pre-trained person detector to extract the region of interest features, subsequently employing these features for action detection. However, the performance of two-stage VAD methods has been limited as they depend solely on localized actor features to infer action semantics. In this study, we propose a new two-stage VAD framework called Joint Actor-scene context Relation modeling based on Visual Semantics (JARViS), which effectively consolidates cross-modal action semantics distributed globally across spatial and temporal dimensions using Transformer attention. JARViS employs a person detector to produce densely sampled actor features from a keyframe. Concurrently, it uses a video backbone to create spatio-temporal scene features from a video clip. Finally, the fine-grained interactions between actors and scenes are modeled through a Unified Action-Scene Context Transformer to directly output the final set of actions in parallel. Our experimental results demonstrate that JARViS outperforms existing methods by significant margins and achieves state-of-the-art performance on three popular VAD datasets, including AVA, UCF101-24, and JHMDB51-21., Comment: 31 pages, 10 figures, update references

Location-based services offer immense utility, but also pose significant privacy risks. In response, we propose LocPIR, a novel framework using homomorphic encryption (HE), specifically the TFHE scheme, to preserve user location privacy when retrieving data from public clouds. Our system employs TFHE's expertise in non-polynomial evaluations, crucial for comparison operations. LocPIR showcases minimal client-server interaction, reduced memory overhead, and efficient throughput. Performance tests confirm its computational speed, making it a viable solution for practical scenarios, demonstrated via application to a COVID-19 alert model. Thus, LocPIR effectively addresses privacy concerns in location-based services, enabling secure data sharing from the public cloud., Comment: Accepted at the IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS) 2024

3D object detection is crucial for applications like autonomous driving and robotics. However, in real-world environments, variations in sensor data distribution due to sensor upgrades, weather changes, and geographic differences can adversely affect detection performance. Semi-Supervised Domain Adaptation (SSDA) aims to mitigate these challenges by transferring knowledge from a source domain, abundant in labeled data, to a target domain where labels are scarce. This paper presents a new SSDA method referred to as Target-Oriented Domain Augmentation (TODA) specifically tailored for LiDAR-based 3D object detection. TODA efficiently utilizes all available data, including labeled data in the source domain, and both labeled data and unlabeled data in the target domain to enhance domain adaptation performance. TODA consists of two stages: TargetMix and AdvMix. TargetMix employs mixing augmentation accounting for LiDAR sensor characteristics to facilitate feature alignment between the source-domain and target-domain. AdvMix applies point-wise adversarial augmentation with mixing augmentation, which perturbs the unlabeled data to align the features within both labeled and unlabeled data in the target domain. Our experiments conducted on the challenging domain adaptation tasks demonstrate that TODA outperforms existing domain adaptation techniques designed for 3D object detection by significant margins. The code is available at: https://github.com/rasd3/TODA., Comment: Accepted to IEEE Transactions on Intelligent Vehicles (T-IV). The code is available at: https://github.com/rasd3/TODA

Homomorphic encryption (HE) is pivotal for secure computation on encrypted data, crucial in privacy-preserving data analysis. However, efficiently processing high-dimensional data in HE, especially for machine learning and statistical (ML/STAT) algorithms, poses a challenge. In this paper, we present an effective acceleration method using the kernel method for HE schemes, enhancing time performance in ML/STAT algorithms within encrypted domains. This technique, independent of underlying HE mechanisms and complementing existing optimizations, notably reduces costly HE multiplications, offering near constant time complexity relative to data dimension. Aimed at accessibility, this method is tailored for data scientists and developers with limited cryptography background, facilitating advanced data analysis in secure environments., Comment: Submitted as a preprint

Engagement between instructors and students plays a crucial role in enhancing students'academic performance. However, instructors often struggle to provide timely and personalized support in large classes. To address this challenge, we propose a novel Virtual Teaching Assistant (VTA) named YA-TA, designed to offer responses to students that are grounded in lectures and are easy to understand. To facilitate YA-TA, we introduce the Dual Retrieval-augmented Knowledge Fusion (DRAKE) framework, which incorporates dual retrieval of instructor and student knowledge and knowledge fusion for tailored response generation. Experiments conducted in real-world classroom settings demonstrate that the DRAKE framework excels in aligning responses with knowledge retrieved from both instructor and student sides. Furthermore, we offer additional extensions of YA-TA, such as a Q&A board and self-practice tools to enhance the overall learning experience. Our video is publicly available., Comment: 9 pages, 5 figures

In magnetically ordered insulators, elementary quasiparticles manifest as spin waves - collective motions of localized magnetic moments propagating through the lattice - observed via inelastic neutron scattering. In effective spin-1/2 systems where geometric frustrations suppress static magnetic order, spin excitation continua can emerge, either from degenerate classical spin ground states or from entangled quantum spins characterized by emergent gauge fields and deconfined fractionalized excitations. Comparing the spin Hamiltonian with theoretical models can unveil the microscopic origins of these zero-field spin excitation continua. Here, we use neutron scattering to study spin excitations of the two-dimensional (2D) triangular-lattice effective spin-1/2 antiferromagnet CeMgAl11O19. Analyzing the spin waves in the field-polarized ferromagnetic state, we find that the spin Hamiltonian is close to an exactly solvable 2D triangular-lattice XXZ model, where degenerate 120$^\circ$ ordered ground states - umbrella states - develop in the zero temperature limit. We then find that the observed zero-field spin excitation continuum matches the calculated ensemble of spin waves from the umbrella state manifold, and thus conclude that CeMgAl11O19 is the first example of an exactly solvable spin liquid on a triangular lattice where the spin excitation continuum arises from the ground state degeneracy., Comment: 11 pages, 5 figures

Collaborative filtering (CF) remains essential in recommender systems, leveraging user--item interactions to provide personalized recommendations. Meanwhile, a number of CF techniques have evolved into sophisticated model architectures based on multi-layer perceptrons (MLPs). However, MLPs often suffer from catastrophic forgetting, and thus lose previously acquired knowledge when new information is learned, particularly in dynamic environments requiring continual learning. To tackle this problem, we propose CF-KAN, a new CF method utilizing Kolmogorov-Arnold networks (KANs). By learning nonlinear functions on the edge level, KANs are more robust to the catastrophic forgetting problem than MLPs. Built upon a KAN-based autoencoder, CF-KAN is designed in the sense of effectively capturing the intricacies of sparse user--item interactions and retaining information from previous data instances. Despite its simplicity, our extensive experiments demonstrate 1) CF-KAN's superiority over state-of-the-art methods in recommendation accuracy, 2) CF-KAN's resilience to catastrophic forgetting, underscoring its effectiveness in both static and dynamic recommendation scenarios, and 3) CF-KAN's edge-level interpretation facilitating the explainability of recommendations., Comment: 9 pages, 7 figures, 4 tables

The explosion of machine learning model size has led to its execution on distributed clusters at a very large scale. Many works have tried to optimize the process of producing collective algorithms and running collective communications, which act as a bottleneck to distributed machine learning. However, different works use their own collective algorithm representation, pushing away from co-optimizing collective communication and the rest of the workload. The lack of a standardized collective algorithm representation has also hindered interoperability between collective algorithm producers and consumers. Additionally, tool-specific conversions and modifications have to be made for each pair of tools producing and consuming collective algorithms which adds to engineering efforts. In this position paper, we propose a standardized workflow leveraging a common collective algorithm representation. Upstream producers and downstream consumers converge to a common representation format based on Chakra Execution Trace, a commonly used graph based representation of distributed machine learning workloads. Such a common representation enables us to view collective communications at the same level as workload operations and decouple producer and consumer tools, enhance interoperability, and relieve the user from the burden of having to focus on downstream implementations. We provide a proof-of-concept of this standardized workflow by simulating collective algorithms generated by the MSCCLang domain-specific language through the ASTRA-sim distributed machine learning simulator using various network configurations.

Detecting out-of-distribution (OOD) samples is a critical task for reliable machine learning. However, it becomes particularly challenging when the models are trained on long-tailed datasets, as the models often struggle to distinguish tail-class in-distribution samples from OOD samples. We examine the main challenges in this problem by identifying the trade-offs between OOD detection and in-distribution (ID) classification, faced by existing methods. We then introduce our method, called \textit{Representation Norm Amplification} (RNA), which solves this challenge by decoupling the two problems. The main idea is to use the norm of the representation as a new dimension for OOD detection, and to develop a training method that generates a noticeable discrepancy in the representation norm between ID and OOD data, while not perturbing the feature learning for ID classification. Our experiments show that RNA achieves superior performance in both OOD detection and classification compared to the state-of-the-art methods, by 1.70\% and 9.46\% in FPR95 and 2.43\% and 6.87\% in classification accuracy on CIFAR10-LT and ImageNet-LT, respectively. The code for this work is available at https://github.com/dgshin21/RNA., Comment: 30 pages, 8 figures, 17 tables

Measuring the strength of three dimensional (3D) magnetic field vector is challenging as it is not easy to recognize whether its line-of-sight (LOS) and plane-of-sky (POS) components are obtained from the same region. CN ($N = 1 - 0$) emission has been used to get the LOS component of a magnetic field (B$_\mathrm{LOS}$) from its Zeeman splitting lines, while dust continuum emission has been used to get the POS component of a magnetic field (B$_\mathrm{POS}$). We use the CN ($N = 1 - 0$) data observed with the Taeduk Radio Astronomy Observatory (TRAO) 14-m telescope and the dust continuum data from $Herschel$ archive toward six high-mass star-forming regions in order to test whether CN line and dust continuum emission can trace a similar region and thus can be used for inferring 3D magnetic field strength. Our comparison between CN and H$_2$ column densities for all targets indicates that CN line emission tends to be strong toward bright continuum regions. The positions of peak CN column densities are particularly well correlated with those of peak H$_2$ column densities at least over the H$_2$ column density of 8.0 $\times$ 10$^{22}$ cm$^{-2}$ within one or two telescope beam size in all targets, implying that CN line and dust continuum emitting regions are likely spatially coincident. This enabled us to make the reliable measurement of 3D magnetic field strengths of five targets by taking a vector sum of their B$_\mathrm{LOS}$ and B$_\mathrm{POS}$, helping to decide the magnetical criticality of the targets as supercritical or transcritical., Comment: Accepted in ApJ, 14th Aug 2024

Analysis and visualization of 3D microscopy images pose challenges due to anisotropic axial resolution, demanding volumetric super-resolution along the axial direction. While training a learning-based 3D super-resolution model seems to be a straightforward solution, it requires ground truth isotropic volumes and suffers from the curse of dimensionality. Therefore, existing methods utilize 2D neural networks to reconstruct each axial slice, eventually piecing together the entire volume. However, reconstructing each slice in the pixel domain fails to give consistent reconstruction in all directions leading to misalignment artifacts. In this work, we present a reconstruction framework based on implicit neural representation (INR), which allows 3D coherency even when optimized by independent axial slices in a batch-wise manner. Our method optimizes a continuous volumetric representation from low-resolution axial slices, using a 2D diffusion prior trained on high-resolution lateral slices without requiring isotropic volumes. Through experiments on real and synthetic anisotropic microscopy images, we demonstrate that our method surpasses other state-of-the-art reconstruction methods. The source code is available on GitHub: https://github.com/hvcl/INR-diffusion., Comment: MICCAI2024 accepted

Synthetic Aperture Radar (SAR) imaging technology provides the unique advantage of being able to collect data regardless of weather conditions and time. However, SAR images exhibit complex backscatter patterns and speckle noise, which necessitate expertise for interpretation. Research on translating SAR images into optical-like representations has been conducted to aid the interpretation of SAR data. Nevertheless, existing studies have predominantly utilized low-resolution satellite imagery datasets and have largely been based on Generative Adversarial Network (GAN) which are known for their training instability and low fidelity. To overcome these limitations of low-resolution data usage and GAN-based approaches, this paper introduces a conditional image-to-image translation approach based on Brownian Bridge Diffusion Model (BBDM). We conducted comprehensive experiments on the MSAW dataset, a paired SAR and optical images collection of 0.5m Very-High-Resolution (VHR). The experimental results indicate that our method surpasses both the Conditional Diffusion Models (CDMs) and the GAN-based models in diverse perceptual quality metrics., Comment: 5 pages, 2 figures, 1 table

With the increasing adoption of social virtual reality (VR), it is critical to design inclusive avatars. While researchers have investigated how and why blind and d/Deaf people wish to disclose their disabilities in VR, little is known about the preferences of many others with invisible disabilities (e.g., ADHD, dyslexia, chronic conditions). We filled this gap by interviewing 15 participants, each with one to three invisible disabilities, who represented 22 different invisible disabilities in total. We found that invisibly disabled people approached avatar-based disclosure through contextualized considerations informed by their prior experiences. For example, some wished to use VR's embodied affordances, such as facial expressions and body language, to dynamically represent their energy level or willingness to engage with others, while others preferred not to disclose their disability identity in any context. We define a binary framework for embodied invisible disability expression (public and private) and discuss three disclosure patterns (Activists, Non-Disclosers, and Situational Disclosers) to inform the design of future inclusive VR experiences., Comment: To appear at ASSETS 2024

Complex urban road networks with high vehicle occupancy frequently face severe traffic congestion. Designing an effective strategy for managing multiple traffic lights plays a crucial role in managing congestion. However, most current traffic light management systems rely on human-crafted decisions, which may not adapt well to diverse traffic patterns. In this paper, we delve into two pivotal design components of the traffic light management system that can be dynamically adjusted to various traffic conditions: phase combination and phase time allocation. While numerous studies have sought an efficient strategy for managing traffic lights, most of these approaches consider a fixed traffic pattern and are limited to relatively small road networks. To overcome these limitations, we introduce a novel and practical framework to formulate the optimization of such design components using an offline meta black-box optimization. We then present a simple yet effective method to efficiently find a solution for the aforementioned problem. In our framework, we first collect an offline meta dataset consisting of pairs of design choices and corresponding congestion measures from various traffic patterns. After collecting the dataset, we employ the Attentive Neural Process (ANP) to predict the impact of the proposed design on congestion across various traffic patterns with well-calibrated uncertainty. Finally, Bayesian optimization, with ANP as a surrogate model, is utilized to find an optimal design for unseen traffic patterns through limited online simulations. Our experiment results show that our method outperforms state-of-the-art baselines on complex road networks in terms of the number of waiting vehicles. Surprisingly, the deployment of our method into a real-world traffic system was able to improve traffic throughput by 4.80\% compared to the original strategy., Comment: 12 pages, 7 figures, 10 tables

In this paper, we propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, SVBRDF, and 3D spatially-varying lighting. While multi-view images have been widely used for object-level inverse rendering, scene-level inverse rendering has primarily been studied using single-view images due to the lack of a dataset containing high dynamic range multi-view images with ground-truth geometry, material, and spatially-varying lighting. To improve the quality of scene-level inverse rendering, a novel framework called Multi-view Attention Inverse Rendering (MAIR) was recently introduced. MAIR performs scene-level multi-view inverse rendering by expanding the OpenRooms dataset, designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Although MAIR showed impressive results, its lighting representation is fixed to spherical Gaussians, which limits its ability to render images realistically. Consequently, MAIR cannot be directly used in applications such as material editing. Moreover, its multi-view aggregation networks have difficulties extracting rich features because they only focus on the mean and variance between multi-view features. In this paper, we propose its extended version, called MAIR++. MAIR++ addresses the aforementioned limitations by introducing an implicit lighting representation that accurately captures the lighting conditions of an image while facilitating realistic rendering. Furthermore, we design a directional attention-based multi-view aggregation network to infer more intricate relationships between views. Experimental results show that MAIR++ not only achieves better performance than MAIR and single-view-based methods, but also displays robust performance on unseen real-world scenes.

Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design., Comment: page 23

We report on the discovery of a dual supermassive black hole system in the radio galaxy J1543-0757, with a projected separation between the two black holes of 46 mas. The result is based on recent multifrequency observations using the Very Long Baseline Array and European VLBI Network, which reveal two compact, variable, flat-spectrum, active nuclei within the elliptical host galaxy of J1543-0757. Multiepoch observations from the VLBA also provide constraints on the spectral index and proper motions of all components. The flat spectra of both N and S at both frequencies strongly support that these radio components are associated with two separate, accreting supermassive black holes (SMBHs). The two nuclei appear stationary, while the jets emanating from the weaker of the two nuclei appear to move out and terminate in bright hot spots. The discovery of this system has implications for the number of close dual black holes that might be sources of gravitational radiation., Comment: 18 pages, 5 figures, 4 tables, accepted for publication in ApJ

The geometric characteristics of Bloch wave functions play a crucial role in electronic transport properties. We show that the thermoelectric performance of materials is governed by the geometric structure of Bloch wave functions within the framework of the Boltzmann equation. The essential geometric notion is the Hilbert-Schmidt quantum distance, measuring the resemblance between two quantum states. We establish a geometric characterization of the scattering rate by extending the concept of quantum distance between two states in momentum space at a distance.Employing isotropic quadratic band touching semimetals, where one can concentrate on the role of quantum geometric effects other than the Berry curvature, we find that the response functions for electrical quantum transport and, therefore, the thermoelectric power factor can be succinctly expressed in terms of the maximum quantum distance, $d_\mathrm{max}$. Specifically, when $d_\mathrm{max}$ reaches one, the power factor doubles compared to the case with trivial geometry ($d_\mathrm{max}=0$). Our finding highlights the significance of quantum geometry in improving the performance of thermoelectric devices.

Trained neural networks (NN) have attractive features for closing governing equations, but in the absence of additional constraints, they can stray from physical reality. A NN formulation is introduced to preclude spurious oscillations that violate solution boundedness or positivity. It is embedded in the discretized equations as a machine learning closure and strictly constrained, inspired by total variation diminishing (TVD) methods for hyperbolic conservation laws. The constraint is exactly enforced during gradient-descent training by rescaling the NN parameters, which maps them onto an explicit feasible set. Demonstrations show that the constrained NN closure model usefully recovers linear and nonlinear hyperbolic phenomena and anti-diffusion while enforcing the non-oscillatory property. Finally, the model is applied to subgrid-scale (SGS) modeling of a turbulent reacting flow, for which it suppresses spurious oscillations in scalar fields that otherwise violate the solution boundedness. It outperforms a simple penalization of oscillations in the loss function.

In the present paper, firstly we obtain the canal hypersurfaces that are formed as the envelope of a family of pseudo hyperspheres or pseudo hyperbolic hyperspheres whose centers lie on a spacelike curve with parallel timelike normal vector field $B_{2}$ in Minkowski space-time and we give some geometric characterizations for them by obtaining the Gaussian curvature, mean curvature and principal curvatures of these canal hypersurfaces. Also, we give the general expression of parametrizations of the canal hypersurfaces generated by non-null curves with parallel frame in $E_{1}^{4}$ and we obtain some important geometric invariants and characterizations for them.

MoodPupilar introduces a novel method for mood evaluation using pupillary response captured by a smartphone's front-facing camera during daily use. Over a four-week period, data was gathered from 25 participants to develop models capable of predicting daily mood averages. Utilizing the GLOBEM behavior modeling platform, we benchmarked the utility of pupillary response as a predictor for mood. Our proposed model demonstrated a Matthew's Correlation Coefficient (MCC) score of 0.15 for Valence and 0.12 for Arousal, which is on par with or exceeds those achieved by existing behavioral modeling algorithms supported by GLOBEM. This capability to accurately predict mood trends underscores the effectiveness of pupillary response data in providing crucial insights for timely mental health interventions and resource allocation. The outcomes are encouraging, demonstrating the potential of real-time and predictive mood analysis to support mental health interventions., Comment: Accepted to IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN 2024)

This paper aims to extend the code generation capability of large language models (LLMs) to automatically manage comprehensive software requirements from given textual descriptions. Such requirements include both functional (i.e. achieving expected behavior for inputs) and non-functional (e.g., time/space performance, robustness, maintainability) requirements. However, textual descriptions can either express requirements verbosely or may even omit some of them. We introduce ARCHCODE, a novel framework that leverages in-context learning to organize requirements observed in descriptions and to extrapolate unexpressed requirements from them. ARCHCODE generates requirements from given descriptions, conditioning them to produce code snippets and test cases. Each test case is tailored to one of the requirements, allowing for the ranking of code snippets based on the compliance of their execution results with the requirements. Public benchmarks show that ARCHCODE enhances to satisfy functional requirements, significantly improving Pass@k scores. Furthermore, we introduce HumanEval-NFR, the first evaluation of LLMs' non-functional requirements in code generation, demonstrating ARCHCODE's superiority over baseline methods. The implementation of ARCHCODE and the HumanEval-NFR benchmark are both publicly accessible., Comment: Accepted by ACL 2024 main conference

Person search is the task to localize a query person in gallery datasets of scene images. Existing methods have been mainly developed to handle a single target dataset only, however diverse datasets are continuously given in practical applications of person search. In such cases, they suffer from the catastrophic knowledge forgetting in the old datasets when trained on new datasets. In this paper, we first introduce a novel problem of lifelong person search (LPS) where the model is incrementally trained on the new datasets while preserving the knowledge learned in the old datasets. We propose an end-to-end LPS framework that facilitates the knowledge distillation to enforce the consistency learning between the old and new models by utilizing the prototype features of the foreground persons as well as the hard background proposals in the old domains. Moreover, we also devise the rehearsal-based instance matching to further improve the discrimination ability in the old domains by using the unlabeled person instances additionally. Experimental results demonstrate that the proposed method achieves significantly superior performance of both the detection and re-identification to preserve the knowledge learned in the old domains compared with the existing methods., Comment: 10 pages, 6 figure

With the proliferation of Graph Neural Network (GNN) methods stemming from contrastive learning, unsupervised node representation learning for graph data is rapidly gaining traction across various fields, from biology to molecular dynamics, where it is often used as a dimensionality reduction tool. However, there remains a significant gap in understanding the quality of the low-dimensional node representations these methods produce, particularly beyond well-curated academic datasets. To address this gap, we propose here the first comprehensive benchmarking of various unsupervised node embedding techniques tailored for dimensionality reduction, encompassing a range of manifold learning tasks, along with various performance metrics. We emphasize the sensitivity of current methods to hyperparameter choices -- highlighting a fundamental issue as to their applicability in real-world settings where there is no established methodology for rigorous hyperparameter selection. Addressing this issue, we introduce GNUMAP, a robust and parameter-free method for unsupervised node representation learning that merges the traditional UMAP approach with the expressivity of the GNN framework. We show that GNUMAP consistently outperforms existing state-of-the-art GNN embedding methods in a variety of contexts, including synthetic geometric datasets, citation networks, and real-world biomedical data -- making it a simple but reliable dimensionality reduction tool.

We present the results of the ALMA Large Program Early Planet Formation in Embedded disks observations of the Class 0 protostar GSS30 IRS3. Our observations included 1.3 mm continuum with a resolution of 0.''05 (7.8 au) and several molecular species including $^{12}$CO, $^{13}$CO, C$^{18}$O, H$_{2}$CO and c-C$_{3}$H$_{2}$. The dust continuum analysis unveiled a disk-shaped structure with a major axis size of $\sim$200 au. We observed an asymmetry in the minor axis of the continuum emission suggesting that the emission is optically thick and the disk is flared. On the other hand, we identified two prominent bumps along the major axis located at distances of 26 and 50 au from the central protostar. The origin of the bumps remains uncertain and might be due to an embedded substructure within the disk or the result of the temperature distribution instead of surface density due to optically thick continuum emission. The $^{12}$CO emission reveals a molecular outflow consisting of three distinct components: a collimated one, an intermediate velocity component exhibiting an hourglass shape, and a wider angle low-velocity component. We associate these components with the coexistence of a jet and a disk-wind. The C$^{18}$O emission traces both a Keplerian rotating circumstellar disk and the infall of the rotating envelope. We measured a stellar dynamical mass of 0.35$\pm$0.09 M$_{\odot}$., Comment: 25 pages, 19 figures. Accepted for publication in A&A

We investigate the properties of AGB and post-AGB (PAGB) stars, planetary nebulae, and young stellar objects (YSOs) in our Galaxy through an analysis of observational data covering visual and infrared (IR) wavelengths. Utilizing datasets from IRAS, 2MASS, AllWISE, Gaia DR3, and the SIMBAD object database, we perform an in-depth comparison between observational data and theoretical models. For this comparison, we present various color-magnitude diagrams (CMDs) in visual and IR bands, as well as IR two-color diagrams (2CDs). Our results demonstrate that the CMDs, incorporating the latest distance and extinction data from Gaia DR3 for a majority of sample stars, are effective in distinguishing different classes of stars. To improve the precision of our analysis, we apply theoretical radiative transfer models for dust shells around AGB and PAGB stars. A thorough comparison of the theoretical models with observations across various IR 2CDs and CMDs shows a significant agreement. We find that AGB and PAGB stars are among the brightest classes in visual and IR bands. Furthermore, most YSOs are clearly distinguishable from AGB stars on various IR CMDs, exhibiting fainter absolute magnitudes in IR bands., Comment: 12 pages, 10 Figures; Accepted for publication in JKAS

We present a measurement of $|V_{ub}|$ from a simultaneous study of the charmless semileptonic decays $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$, where $\ell = e, \mu$. This measurement uses a data sample of 387 million $B\overline{B}$ meson pairs recorded by the Belle~II detector at the SuperKEKB electron-positron collider between 2019 and 2022. The two decays are reconstructed without identifying the partner $B$ mesons. We simultaneously measure the differential branching fractions of $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$ decays as functions of $q^2$ (momentum transfer squared). From these, we obtain total branching fractions $B(B^0\to\pi^- \ell^+ \nu_{\ell}) = (1.516 \pm 0.042 (\mathrm{stat}) \pm 0.059 (\mathrm{syst})) \times 10^{-4}$ and $B(B^+\to\rho^0 \ell^+\nu_{\ell}) = (1.625 \pm 0.079 (\mathrm{stat}) \pm 0.180 (\mathrm{syst})) \times 10^{-4}$. By fitting the measured $B^0\to\pi^- \ell^+ \nu_{\ell}$ partial branching fractions as functions of $q^2$, together with constraints on the non-perturbative hadronic contribution from lattice QCD calculations, we obtain $|V_{ub}|$ = $(3.93 \pm 0.09 \pm 0.13 \pm 0.19) \times 10^{-3}$. Here, the first uncertainty is statistical, the second is systematic, and the third is theoretical.

We present Atacama Large Millimeter/submillimeter Array (ALMA) observations of the binary Class 0 protostellar system BHR 71 IRS1 and IRS2 as part of the Early Planet Formation in Embedded Disks (eDisk) ALMA Large Program. We describe the $^{12}$CO ($J$=2--1), $^{13}$CO ($J$=2--1), C$^{18}$O ($J$=2--1), H$_2$CO ($J=3_{2,1}$--$2_{2,0}$), and SiO ($J$=5--4) molecular lines along with the 1.3 mm continuum at high spatial resolution ($\sim$0.08" or $\sim$5 au). Dust continuum emission is detected toward BHR 71 IRS1 and IRS2, with a central compact component and extended continuum emission. The compact components are smooth and show no sign of substructures such as spirals, rings or gaps. However, there is a brightness asymmetry along the minor axis of the presumed disk in IRS1, possibly indicative of an inclined geometrically and optically thick disk-like component. Using a position-velocity diagram analysis of the C$^{18}$O line, clear Keplerian motions were not detected toward either source. If Keplerian rotationally-supported disks are present, they are likely deeply embedded in their envelope. However, we can set upper limits of the central protostellar mass of 0.46 M$_\odot$ and 0.26 M$_\odot$ for BHR 71 IRS1 and BHR 71 IRS2, respectively. Outflows traced by $^{12}$CO and SiO are detected in both sources. The outflows can be divided into two components, a wide-angle outflow and a jet. In IRS1, the jet exhibits a double helical structure, reflecting the removal of angular momentum from the system. In IRS2, the jet is very collimated and shows a chain of knots, suggesting episodic accretion events., Comment: 38 pages, 29 figures, accepted in ApJ

GroundBIRD is a ground-based cosmic microwave background (CMB) experiment for observing the polarization pattern imprinted on large angular scales ($\ell > 6$ ) from the Teide Observatory in Tenerife, Spain. Our primary scientific objective is a precise measurement of the optical depth $\tau$ ($\sigma(\tau) \sim 0.01$) to the reionization epoch of the Universe to cross-check systematic effects in the measurements made by previous experiments. GroundBIRD observes a wide sky area in the Northern Hemisphere ($\sim 40\%$ of the full sky) while continuously rotating the telescope at a high speed of up to 20 rotations per minute (rpm) to overcome the fluctuations of atmospheric radiation. We have adopted the NbTiN/Al hybrid microwave kinetic inductance detectors (MKIDs) as focal plane detectors. We observe two frequency bands centered at 145 GHz and 220 GHz. The 145 GHz band picks up the peak frequency of the CMB spectrum. The 220 GHz band helps accurate removal of the contamination of thermal emission from the Galactic interstellar dust. The MKID arrays (138 MKIDs for 145GHz and 23 MKIDs for 220GHz) were designed and optimized so as to minimize the contamination of the two-level-system noise and maximize the sensitivity. The MKID arrays were successfully installed in May 2023 after the performance verification tests were performed at a laboratory. GroundBIRD has been upgraded to use the full MKID arrays, and scientific observations are now underway. The telescope is automated, so that all observations are performed remotely. Initial validations, including polarization response tests and observations of Jupiter and the moon, have been completed successfully. We are now running scientific observations., Comment: Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan; paper number 13102-7, Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy XII

Modern graphics processing units (GPUs) provide an unprecedented level of computing power. In this study, we present a high-performance, multi-GPU implementation of the analytical nuclear gradient for Kohn-Sham time-dependent density functional theory (TDDFT), employing the Tamm-Dancoff approximation (TDA) and Gaussian-type atomic orbitals as basis functions. We discuss GPU-efficient algorithms for the derivatives of electron repulsion integrals and exchange-correlation functionals within the range-separated scheme. As an illustrative example, we calculated the TDA-TDDFT gradient of the S1 state of a full-scale green fluorescent protein with explicit water solvent molecules, totaling 4353 atoms, at the wB97X/def2-SVP level of theory. Our algorithm demonstrates favorable parallel efficiencies on a high-speed distributed system equipped with 256 Nvidia A100 GPUs, achieving >70% with up to 64 GPUs and 31% with 256 GPUs, effectively leveraging the capabilities of modern high-performance computing systems., Comment: 13 pages, 9 figures

Recent advancements in physics-based character animation leverage deep learning to generate agile and natural motion, enabling characters to execute movements such as backflips, boxing, and tennis. However, reproducing the selection and use of diverse motor skills in dynamic environments to solve complex tasks, as humans do, still remains a challenge. We present a strategy and skill learning approach for physics-based table tennis animation. Our method addresses the issue of mode collapse, where the characters do not fully utilize the motor skills they need to perform to execute complex tasks. More specifically, we demonstrate a hierarchical control system for diversified skill learning and a strategy learning framework for effective decision-making. We showcase the efficacy of our method through comparative analysis with state-of-the-art methods, demonstrating its capabilities in executing various skills for table tennis. Our strategy learning framework is validated through both agent-agent interaction and human-agent interaction in Virtual Reality, handling both competitive and cooperative tasks., Comment: SIGGRAPH 2024

The physicochemical characterization of trivalent ions is limited due to a lack of accurate force fields. By leveraging the latest machine learning force field to model aqueous $\text{AlCl}_{3}$, we discover that upon dissolution of $\text{Al}^{3+}$, water molecules beyond the second hydration shell involve in the hydration process. A combination of scissoring of coordinating water is followed by synchronized secondary motion of water in the second solvation shell due to hydrogen bonding. Consequently, the water beyond the second solvation penetrates through the second solvation shell and coordinates to the $\text{Al}^{3+}$. Our study reveals a novel microscopic understanding of solvation dynamics for trivalent ion., Comment: 15 pages, 4 figures

Deep neural networks have demonstrated remarkable advancements in various fields using large, well-annotated datasets. However, real-world data often exhibit long-tailed distributions and label noise, significantly degrading generalization performance. Recent studies addressing these issues have focused on noisy sample selection methods that estimate the centroid of each class based on high-confidence samples within each target class. The performance of these methods is limited because they use only the training samples within each class for class centroid estimation, making the quality of centroids susceptible to long-tailed distributions and noisy labels. In this study, we present a robust training framework called Distribution-aware Sample Selection and Contrastive Learning (DaSC). Specifically, DaSC introduces a Distribution-aware Class Centroid Estimation (DaCC) to generate enhanced class centroids. DaCC performs weighted averaging of the features from all samples, with weights determined based on model predictions. Additionally, we propose a confidence-aware contrastive learning strategy to obtain balanced and robust representations. The training samples are categorized into high-confidence and low-confidence samples. Our method then applies Semi-supervised Balanced Contrastive Loss (SBCL) using high-confidence samples, leveraging reliable label information to mitigate class bias. For the low-confidence samples, our method computes Mixup-enhanced Instance Discrimination Loss (MIDL) to improve their representations in a self-supervised manner. Our experimental results on CIFAR and real-world noisy-label datasets demonstrate the superior performance of the proposed DaSC compared to previous approaches.

In multi-class histopathology nuclei analysis tasks, the lack of training data becomes a main bottleneck for the performance of learning-based methods. To tackle this challenge, previous methods have utilized generative models to increase data by generating synthetic samples. However, existing methods often overlook the importance of considering the context of biological tissues (e.g., shape, spatial layout, and tissue type) in the synthetic data. Moreover, while generative models have shown superior performance in synthesizing realistic histopathology images, none of the existing methods are capable of producing image-label pairs at the same time. In this paper, we introduce a novel framework for co-synthesizing histopathology nuclei images and paired semantic labels using a context-conditioned joint diffusion model. We propose conditioning of a diffusion model using nucleus centroid layouts with structure-related text prompts to incorporate spatial and structural context information into the generation targets. Moreover, we enhance the granularity of our synthesized semantic labels by generating instance-wise nuclei labels using distance maps synthesized concurrently in conjunction with the images and semantic labels. We demonstrate the effectiveness of our framework in generating high-quality samples on multi-institutional, multi-organ, and multi-modality datasets. Our synthetic data consistently outperforms existing augmentation methods in the downstream tasks of nuclei segmentation and classification., Comment: ECCV 2024 accepted