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We proposed a haptic dial based on magnetorheological fluid (MRF) which enhances performance by increasing the MRF-exposed area through concave shaft and housing structure. We developed a breakout-style game to show that the proposed haptic dial allows users to efficiently interact with virtual objects., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

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

Stability is a well investigated concept in extremal combinatorics. The main idea is that if some object is close in size to an extremal object, then it retains the structure of the extremal construction. In the present paper we study stability in the context of forbidden configurations. $(0,1)$-matrix $F$ is a configuration in a $(0,1)$-matrix $A$ if $F$ is a row and columns permutation of a submatrix of $A$. $\mathrm{Avoid}(m,F)$ denotes the set of $m$-rowed $(0,1)$-matrices with pairwise distinct columns without configuration $F$, $\mathrm{forb}(m,F)$ is the largest number of columns of a matrix in $\mathrm{Avoid}(m,F)$, while $\mathrm{ext}(m,F)$ is the set of matrices in $\mathrm{Avoid}(m,F)$ of size $\mathrm{forb}(m,F)$. We show cases (i) when each element of $\mathrm{Avoid}(m,F)$ have the structure of element(s) in $\mathrm{ext}(m,F)$, (ii) $\mathrm{forb}(m,F)=\Theta(m^2)$ and the size of $A\in \mathrm{Avoid}(m,F)$ deviates from $\mathrm{forb}(m,F)$ by a linear amount, or (iii) $\mathrm{forb}(m,F)=\Theta(m)$ and the size of $A$ is smaller by a constant, then the structure of $A$ is same as the structure of a matrix in $\mathrm{ext}(m,F)$., Comment: 25 pages, 2 figures

A pre-trained visual-language model, contrastive language-image pre-training (CLIP), successfully accomplishes various downstream tasks with text prompts, such as finding images or localizing regions within the image. Despite CLIP's strong multi-modal data capabilities, it remains limited in specialized environments, such as medical applications. For this purpose, many CLIP variants-i.e., BioMedCLIP, and MedCLIP-SAMv2-have emerged, but false positives related to normal regions persist. Thus, we aim to present a simple yet important goal of reducing false positives in medical anomaly detection. We introduce a Contrastive LAnguage Prompting (CLAP) method that leverages both positive and negative text prompts. This straightforward approach identifies potential lesion regions by visual attention to the positive prompts in the given image. To reduce false positives, we attenuate attention on normal regions using negative prompts. Extensive experiments with the BMAD dataset, including six biomedical benchmarks, demonstrate that CLAP method enhances anomaly detection performance. Our future plans include developing an automated fine prompting method for more practical usage., Comment: 4 pages, 3 figures, 2 tables

Emotional text-to-speech (TTS) technology has achieved significant progress in recent years; however, challenges remain owing to the inherent complexity of emotions and limitations of the available emotional speech datasets and models. Previous studies typically relied on limited emotional speech datasets or required extensive manual annotations, restricting their ability to generalize across different speakers and emotional styles. In this paper, we present EmoSphere++, an emotion-controllable zero-shot TTS model that can control emotional style and intensity to resemble natural human speech. We introduce a novel emotion-adaptive spherical vector that models emotional style and intensity without human annotation. Moreover, we propose a multi-level style encoder that can ensure effective generalization for both seen and unseen speakers. We also introduce additional loss functions to enhance the emotion transfer performance for zero-shot scenarios. We employ a conditional flow matching-based decoder to achieve high-quality and expressive emotional TTS in a few sampling steps. Experimental results demonstrate the effectiveness of the proposed framework.

This work investigates a collaborative sensing and data collection system in which multiple unmanned aerial vehicles (UAVs) sense an area of interest and transmit images to a cloud server (CS) for processing. To accelerate the completion of sensing missions, including data transmission, the sensing task is divided into individual private sensing tasks for each UAV and a common sensing task that is executed by all UAVs to enable cooperative transmission. Unlike existing studies, we explore the use of an advanced cell-free multiple-input multiple-output (MIMO) network, which effectively manages inter-UAV interference. To further optimize wireless channel utilization, we propose a hybrid transmission strategy that combines time-division multiple access (TDMA), non-orthogonal multiple access (NOMA), and cooperative transmission. The problem of jointly optimizing task splitting ratios and the hybrid TDMA-NOMA-cooperative transmission strategy is formulated with the objective of minimizing mission completion time. Extensive numerical results demonstrate the effectiveness of the proposed task allocation and hybrid transmission scheme in accelerating the completion of sensing missions., Comment: This work has been accepted for publication in the IEEE Internet of Things Journal

To automatically detect Anterior Mediastinum Lesions (AMLs) in the Anterior Mediastinum (AM), the primary requirement will be an automatic segmentation model specifically designed for the AM. The prevalence of AML is extremely low, making it challenging to conduct screening research similar to lung cancer screening. Retrospectively reviewing chest CT scans over a specific period to investigate the prevalence of AML requires substantial time. Therefore, developing an Artificial Intelligence (AI) model to find location of AM helps radiologist to enhance their ability to manage workloads and improve diagnostic accuracy for AMLs. In this paper, we introduce a U-shaped structure network to segment AM. Two attention mechanisms were used for maintaining long-range dependencies and localization. In order to have the potential of Multi-Head Self-Attention (MHSA) and a lightweight network, we designed a parallel MHSA named Wide-MHSA (W-MHSA). Maintaining long-range dependencies is crucial for segmentation when we upsample feature maps. Therefore, we designed a Dilated Depth-Wise Parallel Path connection (DDWPP) for this purpose. In order to design a lightweight architecture, we introduced an expanding convolution block and combine it with the proposed W-MHSA for feature extraction in the encoder part of the proposed U-shaped network. The proposed network was trained on 2775 AM cases, which obtained an average Dice Similarity Coefficient (DSC) of 87.83%, mean Intersection over Union (IoU) of 79.16%, and Sensitivity of 89.60%. Our proposed architecture exhibited superior segmentation performance compared to the most advanced segmentation networks, such as Trans Unet, Attention Unet, Res Unet, and Res Unet++.

The quantum state over time formalism provides an extension of the density operator formalism into the time domain, so that quantum correlations across both space and time may be treated with a common mathematical formalism. While bipartite quantum states over time have been uniquely characterized from various perspectives, it is not immediately clear how to extend the uniqueness result to multipartite temporal scenarios, such as those considered in the context of Legget-Garg inequalities. In this Letter, we show that two simple assumptions uniquely single out a multipartite extension of bipartite quantum states over time, namely, linearity in the initial state and a quantum analog of conditionability for multipartite probability distributions. As a direct consequence of our uniqueness result we arrive at a canonical multipartite extension of Kirkwood-Dirac type quasi-probability distributions, and we conclude by showing how our result yields a new characterization of quantum Markovianity., Comment: 5+7 pages, comments welcome

Large-scale foundation models have demonstrated exceptional performance in language and vision tasks. However, the numerous dense matrix-vector operations involved in these large networks pose significant computational challenges during inference. To address these challenges, we introduce the Block-Level Adaptive STructured (BLAST) matrix, designed to learn and leverage efficient structures prevalent in the weight matrices of linear layers within deep learning models. Compared to existing structured matrices, the BLAST matrix offers substantial flexibility, as it can represent various types of structures that are either learned from data or computed from pre-existing weight matrices. We demonstrate the efficiency of using the BLAST matrix for compressing both language and vision tasks, showing that (i) for medium-sized models such as ViT and GPT-2, training with BLAST weights boosts performance while reducing complexity by 70% and 40%, respectively; and (ii) for large foundation models such as Llama-7B and DiT-XL, the BLAST matrix achieves a 2x compression while exhibiting the lowest performance degradation among all tested structured matrices. Our code is available at https://github.com/changwoolee/BLAST.

The Federal Funds rate in the United States plays a significant role in both domestic and international financial markets. However, research has predominantly focused on the effects of adjustments to the Federal Funds rate rather than on the decision-making process itself. Recent advancements in large language models(LLMs) offer a potential method for reconstructing the original FOMC meetings, which are responsible for setting the Federal Funds rate. In this paper, we propose a five-stage FOMC meeting simulation framework, MiniFed, which employs LLM agents to simulate real-world FOMC meeting members and optimize the FOMC structure. This framework effectively revitalizes the FOMC meeting process and facilitates projections of the Federal Funds rate. Experimental results demonstrate that our proposed MiniFed framework achieves both high accuracy in Federal Funds rate projections and behavioral alignment with the agents' real-world counterparts. Given that few studies have focused on employing LLM agents to simulate large-scale real-world conferences, our work can serve as a benchmark for future developments.

Numerous researchers have utilized GPS-enabled vehicle data and SafeGraph mobility data to analyze human movements. However, the comparison of their ability to capture human mobility remains unexplored. This study investigates differences in human mobility using taxi trip records and the SafeGraph dataset in New York City neighborhoods. The analysis includes neighborhood clustering to identify population characteristics and a comparative analysis of mobility patterns. Our findings show that taxi data tends to capture human mobility to and from locations such as Lower Manhattan, where taxi demand is consistently high, while often underestimating the volume of trips originating from areas with lower taxi demand, particularly in the suburbs of NYC. In contrast, SafeGraph data excels in capturing trips to and from areas where commuting by driving one's own car is common, but underestimates trips in pedestrian-heavy areas. The comparative analysis also sheds new light on transportation mode choices for trips across various neighborhoods. The results of this study underscore the importance of understanding the representativeness of human mobility big data and highlight the necessity for careful consideration when selecting the most suitable dataset for human mobility research.

Accelerating end-to-end inference of transformer-based large language models (LLMs) is a critical component of AI services in datacenters. However, diverse compute characteristics of end-to-end LLM inference present challenges as previously proposed accelerators only address certain operations or stages (e.g., self-attention, generation stage, etc.). To address the unique challenges of accelerating end-to-end inference, we propose IANUS -- Integrated Accelerator based on NPU-PIM Unified Memory System. IANUS is a domain-specific system architecture that combines a Neural Processing Unit (NPU) with a Processing-in-Memory (PIM) to leverage both the NPU's high computation throughput and the PIM's high effective memory bandwidth. In particular, IANUS employs a unified main memory system where the PIM memory is used both for PIM operations and for NPU's main memory. The unified main memory system ensures that memory capacity is efficiently utilized and the movement of shared data between NPU and PIM is minimized. However, it introduces new challenges since normal memory accesses and PIM computations cannot be performed simultaneously. Thus, we propose novel PIM Access Scheduling that manages normal memory accesses and PIM computations through workload mapping and scheduling across the PIM and the NPU. Our detailed simulation evaluations show that IANUS improves the performance of GPT-2 by 6.2$\times$ and 3.2$\times$, on average, compared to the NVIDIA A100 GPU and the state-of-the-art accelerator. As a proof-of-concept, we develop a prototype of IANUS with a commercial PIM, NPU, and an FPGA-based PIM controller to demonstrate the feasibility of IANUS., Comment: Updated version of the paper accepted to ASPLOS 2024

Automatic question generation (QG) is essential for AI and NLP, particularly in intelligent tutoring, dialogue systems, and fact verification. Generating multiple-choice questions (MCQG) for professional exams, like the United States Medical Licensing Examination (USMLE), is particularly challenging, requiring domain expertise and complex multi-hop reasoning for high-quality questions. However, current large language models (LLMs) like GPT-4 struggle with professional MCQG due to outdated knowledge, hallucination issues, and prompt sensitivity, resulting in unsatisfactory quality and difficulty. To address these challenges, we propose MCQG-SRefine, an LLM self-refine-based (Critique and Correction) framework for converting medical cases into high-quality USMLE-style questions. By integrating expert-driven prompt engineering with iterative self-critique and self-correction feedback, MCQG-SRefine significantly enhances human expert satisfaction regarding both the quality and difficulty of the questions. Furthermore, we introduce an LLM-as-Judge-based automatic metric to replace the complex and costly expert evaluation process, ensuring reliable and expert-aligned assessments., Comment: Equal contribution for the first two authors

We propose a novel principal component analysis in the graph frequency domain for dimension reduction of multivariate data residing on graphs. The proposed method not only effectively reduces the dimensionality of multivariate graph signals, but also provides a closed-form reconstruction of the original data. In addition, we investigate several propositions related to principal components and the reconstruction errors, and introduce a graph spectral envelope that aids in identifying common graph frequencies in multivariate graph signals. We demonstrate the validity of the proposed method through a simulation study and further analyze the boarding and alighting patterns of Seoul Metropolitan Subway passengers using the proposed method.

Deep neural networks (DNNs) exhibit a remarkable ability to automatically learn data representations, finding appropriate features without human input. Here we present a method for analysing feature learning by decomposing DNNs into 1) a forward feature-map $\Phi$ that maps the input dataspace to the post-activations of the penultimate layer, and 2) a final linear layer that classifies the data. We diagonalize $\Phi$ with respect to the gradient descent operator and track feature learning by measuring how the eigenfunctions and eigenvalues of $\Phi$ change during training. Across many popular architectures and classification datasets, we find that DNNs converge, after just a few epochs, to a minimal feature (MF) regime dominated by a number of eigenfunctions equal to the number of classes. This behaviour resembles the neural collapse phenomenon studied at longer training times. For other DNN-data combinations, such as a fully connected network on CIFAR10, we find an extended feature (EF) regime where significantly more features are used. Optimal generalisation performance upon hyperparameter tuning typically coincides with the MF regime, but we also find examples of poor performance within the MF regime. Finally, we recast the phenomenon of neural collapse into a kernel picture which can be extended to broader tasks such as regression.

Hydrogen, while a promising sustainable energy carrier, presents challenges such as the embrittlement of materials due to its ability to penetrate and weaken their crystal structures. Here we investigate Fe4N nitride layers, formed on iron through a cost-effective gas nitriding process, as an effective hydrogen permeation barrier. A combination of screening using advanced characterization, density functional theory calculations, and hydrogen permeation analysis reveals that a nitride layer reduces hydrogen diffusion by a factor of 20 at room temperature. This reduction is achieved by creating energetically unfavorable states due to stronger H-binding at the surface and high energy barriers for diffusion. The findings demonstrate the potential of Fe4N as a cost-efficient and easy-to-process solution to protecting metallic materials exposed to hydrogen, with great advantages for large-scale applications.

Recent studies have shown that regularization techniques using soft labels, e.g., label smoothing, Mixup, and CutMix, not only enhance image classification accuracy but also improve model calibration and robustness against adversarial attacks. However, the underlying mechanisms of such improvements remain underexplored. In this paper, we offer a novel explanation from the perspective of the representation space (i.e., the space of the features obtained at the penultimate layer). Our investigation first reveals that the decision regions in the representation space form cone-like shapes around the origin after training regardless of the presence of regularization. However, applying regularization causes changes in the distribution of features (or representation vectors). The magnitudes of the representation vectors are reduced and subsequently the cosine similarities between the representation vectors and the class centers (minimal loss points for each class) become higher, which acts as a central mechanism inducing improved calibration and robustness. Our findings provide new insights into the characteristics of the high-dimensional representation space in relation to training and regularization using soft labels.

Josephson junction spectroscopy is a powerful local microwave spectroscopy technique that has promising potential as a diagnostic tool to probe the microscopic origins of noise in superconducting qubits. We present advancements toward realizing Josephson junction spectroscopy in a scanning geometry, where the Josephson junction is formed between a superconducting sample and a high capacitance superconducting STM tip. Data from planar Nb-based Josephson junction devices first demonstrate the benefits of including a high capacitance shunt across the junction, which decreases linewidth and improves performance at elevated temperatures. We show how an equivalent circuit can be implemented by utilizing a planarized STM tip with local prominences, which are fabricated via electron beam lithography and reactive ion etching, followed by coating with a superconducting layer. Differential conductance measurements on a superconducting NbN surface demonstrate the ability of these high capacitance tips to decrease both thermal noise and P(E)-broadening in comparison to typical wire tips., Comment: 8 pages, 7 figures

Artificial intelligence (AI) and large language models (LLMs) in healthcare require advanced clinical skills (CS), yet current benchmarks fail to evaluate these comprehensively. We introduce MedQA-CS, an AI-SCE framework inspired by medical education's Objective Structured Clinical Examinations (OSCEs), to address this gap. MedQA-CS evaluates LLMs through two instruction-following tasks, LLM-as-medical-student and LLM-as-CS-examiner, designed to reflect real clinical scenarios. Our contributions include developing MedQA-CS, a comprehensive evaluation framework with publicly available data and expert annotations, and providing the quantitative and qualitative assessment of LLMs as reliable judges in CS evaluation. Our experiments show that MedQA-CS is a more challenging benchmark for evaluating clinical skills than traditional multiple-choice QA benchmarks (e.g., MedQA). Combined with existing benchmarks, MedQA-CS enables a more comprehensive evaluation of LLMs' clinical capabilities for both open- and closed-source LLMs.

In this work we propose and demonstrate a method to estimate the flowing gas-oil ratio and composition of a hydrocarbon well stream using measurements of pressure and temperature across a production choke. The method consists of using a numerical solver on a thermodynamic simulator to recombine a seed oil and gas until the simulated temperature drop across the choke is equal to the measured value. This method is meant for cases where it is not possible to measure periodically individual well composition. A study case and reference solution were generated using the reservoir model presented in the SPE (Society of Petroleum Engineers) comparative case Nr. 5 linked with a process simulator. Time profiles of well producing gas-oil ratio, wellstream compositions, compositions of surface conditions oil and gas, and temperature drop across the choke were generated with the models. The method proposed was then employed to estimate the flowing gas-oil ratio of the reference solution. Results show that the proposed method predicts with reasonable accuracy (maximum 12% percent error) the well gas-oil ratio and compositions during the life of the field when using compositions of surface oil and gas from initial time. When using compositions of surface oil and gas from later times, the prediction accuracy of the gas-oil ratio improves at those times but worsens for times before and after. A measurement error for the temperature drop across the choke of at least 0.01 {\deg}C is required to achieve convergence of the method. The mean percent error between the predicted and real mole fractions has an upper bound in time of 21% when using initial surface oil and gas as seed compositions., Comment: 21 pages, 11 figures

The growing demand for ultra-fast telecommunications, autonomous driving, and futuristic technologies highlights the crucial role of active beam steering at the nanoscale. This is essential for applications like LiDAR, beam-forming, and holographic displays, especially as devices reduce in form-factor. Although device with active beam switching capability is a potential candidate for realizing those applications, there have been only a few works to realize beam switching in reconfigurable metasurfaces with active tuning materials. In this paper, we theoretically present a multi-level beam-switching dielectric metasurface with a graphene layer for active tuning, addressing challenges associated with achieving high directivity and diffraction efficiency, and doing so while using a single-gate setup. For two-level switching, the directivities reached above 95%, and the diffraction efficiencies were near 50% at the operation wavelength ${\lambda}_0$ = 8 ${\mu}$m. Through quasi-normal mode expansion, we illustrate the physics of the beam switching metasurface inverse-designed by the adjoint method, highlighting the role of resonant modes and their response to charge carrier tuning. Under the same design scheme, we design and report characteristics of a three-level and four-level beam switching device, suggesting a possibility of generalizing to multi-level beam switching.

Electro-optic active metasurfaces have attracted attention due to their ability to electronically control optical wavefront with unprecedented spatiotemporal resolutions. In most studies, such devices require gate arrays composed of a large number of independently-controllable local gate electrodes that address local scattering response of individual metaatoms. Although this approach in principle enables arbitrary wavefront control, the complicated driving mechanism and low optical efficiency have been hindering its practical applications. In this work, we demonstrate an active beam switching device that provides high directivity, uniform efficiency across diffraction orders, and a wide field of view while operating with only a single-gate bias. Experimentally, the metasurface achieves 57{\deg} of active beam switching from the 0th to the -1st order diffraction, with efficiencies of 0.084 and 0.078 and directivities of 0.765 and 0.836, respectively. Furthermore, an analytical framework using nonlocal quasinormal mode expansion provides deeper insight into the operating mechanism of active beam switching. Finally, we discuss the performance limitations of this design platform and provide insights into potential improvements.

We demonstrate the Patterns of Life Simulation to create realistic simulations of human mobility in a city. This simulation has recently been used to generate massive amounts of trajectory and check-in data. Our demonstration focuses on using the simulation twofold: (1) using the graphical user interface (GUI), and (2) running the simulation headless by disabling the GUI for faster data generation. We further demonstrate how the Patterns of Life simulation can be used to simulate any region on Earth by using publicly available data from OpenStreetMap. Finally, we also demonstrate recent improvements to the scalability of the simulation allows simulating up to 100,000 individual agents for years of simulation time. During our demonstration, as well as offline using our guides on GitHub, participants will learn: (1) The theories of human behavior driving the Patters of Life simulation, (2) how to simulate to generate massive amounts of synthetic yet realistic trajectory data, (3) running the simulation for a region of interest chosen by participants using OSM data, (4) learn the scalability of the simulation and understand the properties of generated data, and (5) manage thousands of parallel simulation instances running concurrently., Comment: Accepted paper to SIGSPATIAL 2024 main conference

Charged Black holes in $AdS_5 \times S^5$ suffer from superradiant instabilities over a range of energies. Hairy black hole solutions (constructed within gauged supergravity) have previously been proposed as endpoints to this instability. We demonstrate that these hairy black holes are themselves unstable to the emission of large dual giant gravitons. We propose that the endpoint to this instability is given by Dual Dressed Black Holes (DDBH)s; configurations consisting of one, two, or three very large dual giant gravitons surrounding a core $AdS$ black hole with one, two, or three $SO(6)$ chemical potentials equal to unity. The dual giants each live at $AdS$ radial coordinates of order $\sqrt{N}$ and each carry charge of order $N^2$. The large separation makes DDBHs a very weakly interacting mix of their components and allows for a simple computation of their thermodynamics. We conjecture that DDBHs dominate the phase diagram of ${\cal N}=4$ Yang-Mills over a range of energies around the BPS plane, and provide an explicit construction of this phase diagram, briefly discussing the interplay with supersymmetry. We develop the quantum description of dual giants around black hole backgrounds and explicitly verify that DDBHs are stable to potential tunneling instabilities, precisely when the chemical potentials of the core black holes equal unity. We also construct the 10-dimensional DDBH supergravity solutions., Comment: 114 pages, 12 figures

This paper begins by deriving the uniform bounds for the regular Coulomb wave function $F_{\ell,\eta}$ and its derivative $F_{\ell,\eta}'$. We then examine detailed zero configurations of $F_{\ell,\eta}$ and $F_{\ell+1,\eta}$, extending insights into the earlier work that was restricted to $\ell>-3/2$. Our investigation also includes an analysis of the monotonicity of the zeros of $F_{\ell,\eta}$ with respect to parameters $\ell$ and $\eta$, respectively. Furthermore, we expand our exploration to associated orthogonal polynomials, as well as the functions involving both $F_{\ell,\eta}$ and $F_{\ell,\eta}'$. Finally, we explore the breakdown of the Sturm separation theorem by means of the zeros of associated orthogonal polynomials., Comment: 29 pages, 6 figures

In this paper, we present an effective data augmentation framework leveraging the Large Language Model (LLM) and Diffusion Model (DM) to tackle the challenges inherent in data-scarce scenarios. Recently, DMs have opened up the possibility of generating synthetic images to complement a few training images. However, increasing the diversity of synthetic images also raises the risk of generating samples outside the target distribution. Our approach addresses this issue by embedding novel semantic information into text prompts via LLM and utilizing real images as visual prompts, thus generating semantically rich images. To ensure that the generated images remain within the target distribution, we dynamically adjust the guidance weight based on each image's CLIPScore to control the diversity. Experimental results show that our method produces synthetic images with enhanced diversity while maintaining adherence to the target distribution. Consequently, our approach proves to be more efficient in the few-shot setting on several benchmarks. Our code is available at https://github.com/kkyuhun94/dalda ., Comment: Accepted to ECCV Synthetic Data for Computer Vision Workshop (Oral)

Photoinduced ultrafast phenomena in materials exhibiting nonequilibrium behavior can lead to the emergence of exotic phases beyond the limits of thermodynamics, presenting opportunities for femtosecond photoexcitation. Despite extensive research, the ability to actively control quantum materials remains elusive owing to the lack of clear evidence demonstrating the explicit control of phase-changing kinetics through light-matter interactions. To address this drawback, we leveraged single-pulse time-resolved X-ray imaging of Au nanorods undergoing photoinduced melting to showcase control over the solid-to-liquid transition process through the use of localized surface plasmons. Our study uncovers transverse or longitudinal melting processes accompanied by characteristic oscillatory distortions at different laser intensities. Numerical simulations confirm that the localized surface plasmons, excited by polarized laser fields, dictate the melting modes through anharmonic lattice deformations. These results provide direct evidence of photoinduced surface plasmon-mediated ultrafast control of matter, establishing a foundation for the customization of material kinetics using femtosecond laser fields., Comment: 17 pages, 3 figures

We derive an exact formula $F(e) = 2 \hbar \frac{dN}{dJ}$ which provides a concrete estimate for the total number and angular momentum of gravitons emitted during the nonrelativistic inspiral of two black holes. We show that the function $F(e)$ is a slowly growing monotonic function of the eccentricity $0 \le e \le 1$ and $F(1) = 1.0128 \cdots $. We confirm and extend the results of Page for the function $F(e)$. We also get an exact result for the ratio $\nu (e_i) = \frac{2\hbar N(L_i, e_i)}{L_i}$ of the sum of the spin angular momentum magnitudes of gravitons emitted to the magnitude $L_i$ of the total angular momentum emitted in the graviational waves by inspiraling binary black holes. If the orbit starts off with unit eccentricity $e_i=1$, we get the exact value $\nu(1) = 1.002\, 268\, 666\, 2 \pm 10^{-10}$ which confirms the Page conjecture. We also show that the formula $F(e)$ for gravitons emitted can be determined by a single function., Comment: 14 pages, 3 figures, v3: In section 5, Note Added

Data-driven visual-inertial odometry (VIO) has received highlights for its performance since VIOs are a crucial compartment in autonomous robots. However, their deployment on resource-constrained devices is non-trivial since large network parameters should be accommodated in the device memory. Furthermore, these networks may risk failure post-deployment due to environmental distribution shifts at test time. In light of this, we propose UL-VIO -- an ultra-lightweight (<1M) VIO network capable of test-time adaptation (TTA) based on visual-inertial consistency. Specifically, we perform model compression to the network while preserving the low-level encoder part, including all BatchNorm parameters for resource-efficient test-time adaptation. It achieves 36X smaller network size than state-of-the-art with a minute increase in error -- 1% on the KITTI dataset. For test-time adaptation, we propose to use the inertia-referred network outputs as pseudo labels and update the BatchNorm parameter for lightweight yet effective adaptation. To the best of our knowledge, this is the first work to perform noise-robust TTA on VIO. Experimental results on the KITTI, EuRoC, and Marulan datasets demonstrate the effectiveness of our resource-efficient adaptation method under diverse TTA scenarios with dynamic domain shifts.

The C IV doublet in the UV has long been associated with accretion in T Tauri stars. However, it is still unclear where and how the lines are formed. Here, we present a new C IV line model based on the currently available accretion shock and accretion flow models. We assume axisymmetric, dipolar accretion flows with different energy fluxes and calculate the properties of the accretion shock. We use Cloudy to obtain the carbon level populations and calculate the emerging line profiles assuming a plane-parallel geometry near the shock. Our model generally reproduces the intensities and shapes of the C IV emission lines observed from T Tauri stars. We find that the narrow component is optically thin and originates in the postshock, while the broad component is optically thick and emerges from the preshock. We apply our model to seven T Tauri stars from the Hubble Ultraviolet Legacy Library of Young Stars as Essential Standards Director's Discretionary program (ULLYSES), for which consistently determined accretion shock properties are available. We can reproduce the observations of four stars, finding that the accretion flows are carbon-depleted. We also find that the chromospheric emission accounts for less than 10 percent of the observed C IV line flux in accreting T Tauri stars. This work paves the way toward a better understanding of hot line formation and provides a potential probe of abundances in the inner disk., Comment: Accepted for publication in the Astrophysical Journal, 26 pages, 14 figures

Fault-tolerant implementation of non-Clifford gates is a major challenge for achieving universal fault-tolerant quantum computing with quantum error-correcting codes. Magic state distillation is the most well-studied method for this but requires significant resources. Hence, it is crucial to tailor and optimize magic state distillation for specific codes from both logical- and physical-level perspectives. In this work, we perform such optimization for two-dimensional color codes, which are promising due to their higher encoding rates compared to surface codes, transversal implementation of Clifford gates, and efficient lattice surgery. We propose two distillation schemes based on the 15-to-1 distillation circuit and lattice surgery, which differ in their methods for handling faulty rotations. Our first scheme uses faulty T-measurement, offering resource efficiency when the target infidelity is above a certain threshold ($\sim 35p^3$ for physical error rate $p$). To achieve lower infidelities while maintaining resource efficiency, our second scheme exploits a distillation-free fault-tolerant magic state preparation protocol, achieving significantly lower infidelities (e.g., $\sim 10^{-19}$ for $p = 10^{-4}$) than the first scheme. Notably, our schemes outperform the best existing magic state distillation methods for color codes by up to about two orders of magnitude in resource costs for a given achievable target infidelity., Comment: 42 pages (22 pages for main text), 21 figures, 3 tables; v2 - updated combined MSD scheme (without autocorrection qubits) thanks to Sam Roberts's suggestion & additional comparison with a previous color code MSD scheme in Fig. 14

In this paper, we introduce and study the Dirichlet series enumerating (proper) equivalence classes of full rank subforms/sublattices of a given quadratic form/lattice, focusing on the positive definite binary case. We obtain formulas linking this Dirichlet series with Dirichlet series counting ideal classes of the imaginary quadratic field associated with the quadratic form. Utilizing the result, we provide explicit formulas of the Dirichlet series for several lattices, including square lattice and hexagonal lattice. Moreover, we investigate some analytic properties of this Dirichlet series., Comment: 37 pages

Autism Spectrum Disorder (ASD) can affect individuals at varying degrees of intensity, from challenges in overall health, communication, and sensory processing, and this often begins at a young age. Thus, it is critical for medical professionals to be able to accurately diagnose ASD in young children, but doing so is difficult. Deep learning can be responsibly leveraged to improve productivity in addressing this task. The availability of data, however, remains a considerable obstacle. Hence, in this work, we introduce the Video ASD dataset--a dataset that contains video frame convolutional and attention map feature data--to foster further progress in the task of ASD classification. The original videos showcase children reacting to chemo-sensory stimuli, among auditory, touch, and vision This dataset contains the features of the frames spanning 2,467 videos, for a total of approximately 1.4 million frames. Additionally, head pose angles are included to account for head movement noise, as well as full-sentence text labels for the taste and smell videos that describe how the facial expression changes before, immediately after, and long after interaction with the stimuli. In addition to providing features, we also test foundation models on this data to showcase how movement noise affects performance and the need for more data and more complex labels.

Graph and network visualization supports exploration, analysis and communication of relational data arising in many domains: from biological and social networks, to transportation and powergrid systems. With the arrival of AI-based question-answering tools, issues of trustworthiness and explainability of generated answers motivate a greater role for visualization. In the context of graphs, we see the need for visualizations that can convince a critical audience that an assertion about the graph under analysis is valid. The requirements for such representations that convey precisely one specific graph property are quite different from standard network visualization criteria which optimize general aesthetics and readability. In this paper, we aim to provide a comprehensive introduction to visual proofs of graph properties and a foundation for further research in the area. We present a framework that defines what it means to visually prove a graph property. In the process, we introduce the notion of a visual certificate, that is, a specialized faithful graph visualization that leverages the viewer's perception, in particular, pre-attentive processing (e.g. via pop-out effects), to verify a given assertion about the represented graph. We also discuss the relationships between visual complexity, cognitive load and complexity theory, and propose a classification based on visual proof complexity. Finally, we provide examples of visual certificates for problems in different visual proof complexity classes., Comment: Appears in the Proceedings of the 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024)

Logit-based knowledge distillation (KD) for classification is cost-efficient compared to feature-based KD but often subject to inferior performance. Recently, it was shown that the performance of logit-based KD can be improved by effectively delivering the probability distribution for the non-target classes from the teacher model, which is known as `implicit (dark) knowledge', to the student model. Through gradient analysis, we first show that this actually has an effect of adaptively controlling the learning of implicit knowledge. Then, we propose a new loss that enables the student to learn explicit knowledge (i.e., the teacher's confidence about the target class) along with implicit knowledge in an adaptive manner. Furthermore, we propose to separate the classification and distillation tasks for effective distillation and inter-class relationship modeling. Experimental results demonstrate that the proposed method, called adaptive explicit knowledge transfer (AEKT) method, achieves improved performance compared to the state-of-the-art KD methods on the CIFAR-100 and ImageNet datasets., Comment: 19 pages, 5 figures

Not every directed acyclic graph (DAG) whose underlying undirected graph is planar admits an upward planar drawing. We are interested in pushing the notion of upward drawings beyond planarity by considering upward $k$-planar drawings of DAGs in which the edges are monotonically increasing in a common direction and every edge is crossed at most $k$ times for some integer $k \ge 1$. We show that the number of crossings per edge in a monotone drawing is in general unbounded for the class of bipartite outerplanar, cubic, or bounded pathwidth DAGs. However, it is at most two for outerpaths and it is at most quadratic in the bandwidth in general. From the computational point of view, we prove that upward-$k$-planarity testing is NP-complete already for $k =1$ and even for restricted instances for which upward planarity testing is polynomial. On the positive side, we can decide in linear time whether a single-source DAG admits an upward $1$-planar drawing in which all vertices are incident to the outer face., Comment: This is the extended version, with full appendix, of a paper to appear in the Proc. 32nd Int. Symp. Graph Drawing & Network Visualization (GD 2024)

Magnetic anisotropy in atomically thin correlated heterostructures is essential for exploring quantum magnetic phases for next-generation spintronics. Whereas previous studies have mostly focused on van der Waals systems, here, we investigate the impact of dimensionality of epitaxially-grown correlated oxides down to the monolayer limit on structural, magnetic, and orbital anisotropies. By designing oxide superlattices with a correlated ferromagnetic SrRuO3 and nonmagnetic SrTiO3 layers, we observed modulated ferromagnetic behavior with the change of the SrRuO3 thickness. Especially, for three-unit-cell-thick layers, we observe a significant 1,500% improvement of coercive field in the anomalous Hall effect, which cannot be solely attributed to the dimensional crossover in ferromagnetism. The atomic-scale heterostructures further reveal the systematic modulation of anisotropy for the lattice structure and orbital hybridization, explaining the enhanced magnetic anisotropy. Our findings provide valuable insights into engineering the anisotropic hybridization of synthetic magnetic crystals, offering a tunable spin order for various applications., Comment: 23 pages

Let $G$ be a finite group. By a sequence over $G$, we mean a finite unordered string of terms from $G$ with repetition allowed, and we say that it is a product-one sequence if its terms can be ordered so that their product is the identity element of $G$. Then, the Davenport constant $\mathsf D (G)$ is the maximal length of a minimal product-one sequence, that is a product-one sequence which cannot be partitioned into two non-trivial product-one subsequences. The Davenport constant is a combinatorial group invariant that has been studied fruitfully over several decades in additive combinatorics, invariant theory, and factorization theory, etc. Apart from a few cases of finite groups, the precise value of the Davenport constant is unknown. Even in the abelian case, little is known beyond groups of rank at most two. On the other hand, for a fixed positive integer $r$, structural results characterizing which groups $G$ satisfy $\mathsf D (G) = r$ are rare. We only know that there are finitely many such groups. In this paper, we study the classification of finite groups based on the Davenport constant.

PyFR is an open-source cross-platform computational fluid dynamics framework based on the high-order Flux Reconstruction approach, specifically designed for undertaking high-accuracy scale-resolving simulations in the vicinity of complex engineering geometries. Since the initial release of PyFR v0.1.0 in 2013, a range of new capabilities have been added to the framework, with a view to enabling industrial adoption of the capability. This paper provides details of those enhancements as released in PyFR v2.0.3, explains efforts to grow an engaged developer and user community, and provides latest performance and scaling results on up to 1024 AMD Instinct MI250X accelerators of Frontier at ORNL (each with two GCDs), and up to 2048 NVIDIA GH200 GPUs on Alps at CSCS.

Generally, the components constituting a metaverse are classified into hardware, software, and content categories. As a content component, text design is known to positively affect user immersion and usability. Unlike English, where designing texts involves only 26 letters, designing texts in Korean and Chinese requires creating 11,172 and over 60,000 individual glyphs, respectively, owing to the nature of the languages. Consequently, applying new text designs to enhance user immersion within the metaverse can be tedious and expensive, particularly for certain languages. Recently, efforts have been devoted toward addressing this issue using generative artificial intelligence (AI). However, challenges remain in creating new text designs for the metaverse owing to inaccurate character structures. This study proposes a new AI learning method known as Legacy Learning, which enables high-quality text design at a lower cost. Legacy Learning involves recombining existing text designs and intentionally introducing variations to produce fonts that are distinct from the originals while maintaining high quality. To demonstrate the effectiveness of the proposed method in generating text designs for the metaverse, we performed evaluations from the following three aspects: 1) Quantitative performance evaluation 2) Qualitative evaluationand 3) User usability evaluation. The quantitative and qualitative performance results indicated that the generated text designs differed from the existing ones by an average of over 30% while still maintaining high visual quality. Additionally, the SUS test performed with metaverse content designers achieved a score of 95.8, indicating high usability.

Text-to-Speech (TTS) models have advanced significantly, aiming to accurately replicate human speech's diversity, including unique speaker identities and linguistic nuances. Despite these advancements, achieving an optimal balance between speaker-fidelity and text-intelligibility remains a challenge, particularly when diverse control demands are considered. Addressing this, we introduce DualSpeech, a TTS model that integrates phoneme-level latent diffusion with dual classifier-free guidance. This approach enables exceptional control over speaker-fidelity and text-intelligibility. Experimental results demonstrate that by utilizing the sophisticated control, DualSpeech surpasses existing state-of-the-art TTS models in performance. Demos are available at https://bit.ly/48Ewoib., Comment: Accepted to INTERSPEECH 2024

We propose a quantum-assisted solution for the maximum likelihood detection (MLD) of generalized spatial modulation (GSM) signals. Specifically, the MLD of GSM is first formulated as a novel polynomial optimization problem, followed by the application of a quantum algorithm, namely, the Grover adaptive search. The performance in terms of query complexity of the proposed method is evaluated and compared to the classical alternative via a numerical analysis, which reveals that under fault-tolerant quantum computation, the proposed method outperforms the classical solution if the number of data symbols and the constellation size are relatively large., Comment: 5 pages, 4 figures, accepted for presentation at the IEEE 100th Vehicular Technology Conference (VTC2024-Fall)

The prevention of falls is paramount in modern healthcare, particularly for the elderly, as falls can lead to severe injuries or even fatalities. Additionally, the growing incidence of falls among the elderly, coupled with the urgent need to prevent suicide attempts resulting from medication overdose, underscores the critical importance of accurate and efficient fall detection methods. In this scenario, a computer-aided fall detection system is inevitable to save elderly people's lives worldwide. Many researchers have been working to develop fall detection systems. However, the existing fall detection systems often struggle with issues such as unsatisfactory performance accuracy, limited robustness, high computational complexity, and sensitivity to environmental factors due to a lack of effective features. In response to these challenges, this paper proposes a novel three-stream spatial-temporal feature-based fall detection system. Our system incorporates joint skeleton-based spatial and temporal Graph Convolutional Network (GCN) features, joint motion-based spatial and temporal GCN features, and residual connections-based features. Each stream employs adaptive graph-based feature aggregation and consecutive separable convolutional neural networks (Sep-TCN), significantly reducing computational complexity and model parameters compared to prior systems. Experimental results across multiple datasets demonstrate the superior effectiveness and efficiency of our proposed system, with accuracies of 99.51\%, 99.15\%, 99.79\% and 99.85 \% achieved on the ImViA, UR-Fall, Fall-UP and FU-Kinect datasets, respectively. The remarkable performance of our system highlights its superiority, efficiency, and generalizability in real-world fall detection scenarios, offering significant advancements in healthcare and societal well-being.

Ambiguity in language presents challenges in developing more enhanced language models, particularly in preference learning, where variability among annotators results in inconsistently annotated datasets used for model alignment. To address this issue, we introduce a self-curation method that preprocesses annotated datasets by leveraging proxy models trained directly on these datasets. Our method enhances preference learning by automatically detecting and removing ambiguous annotations within the dataset. The proposed approach is validated through extensive experiments, demonstrating a marked improvement in performance across various instruction-following tasks. Our work provides a straightforward and reliable method to overcome annotation inconsistencies, serving as an initial step towards the development of more advanced preference learning techniques.

We introduce novel measures, L1 prestige and L1 centrality, for quantifying the prominence of each vertex in a strongly connected and directed graph by utilizing the concept of L1 data depth (Vardi and Zhang, Proc. Natl. Acad. Sci. U.S.A.\ 97(4):1423--1426, 2000). The former measure quantifies the degree of prominence of each vertex in receiving choices, whereas the latter measure evaluates the degree of importance in giving choices. The proposed measures can handle graphs with both edge and vertex weights, as well as undirected graphs. However, examining a graph using a measure defined over a single `scale' inevitably leads to a loss of information, as each vertex may exhibit distinct structural characteristics at different levels of locality. To this end, we further develop local versions of the proposed measures with a tunable locality parameter. Using these tools, we present a multiscale network analysis framework that provides much richer structural information about each vertex than a single-scale inspection. By applying the proposed measures to the networks constructed from the Seoul Mobility Flow Data, it is demonstrated that these measures accurately depict and uncover the inherent characteristics of individual city regions.

In four dimensions, 't Hooft symbols offer a compact and powerful framework for describing the self-dual structures fundamental to instanton physics. Extending this to six dimensions, the six-dimensional 't Hooft symbols can be constructed using the isomorphism between the Lorentz group $Spin(6)$ and the unitary group $SU(4)$. We demonstrate that the six-dimensional self-dual structures governed by the Hermitian Yang-Mills equations can be elegantly organized using these generalized 't Hooft symbols. We also present a systematic method for constructing Hermitian Yang-Mills instantons from spin connections on six-dimensional manifolds using the generalized 't Hooft symbols. We provide a thorough analysis of the topological invariants such as instanton and Euler numbers., Comment: 35 pages, no figure; to be published in JHEP

BGK (Bhatnagar-Gross-Krook) model is a relaxation-type model of the Boltzmann equation, which is popularly used in place of the Boltzmann equation in physics and engineering. In this paper, we address the ill-posedness problem for the BGK model, in which the solution instantly escapes the initial solution space. For this, we propose two ill-posedness scenarios, namely, the homogeneous and the inhomogeneous ill-posedness mechanisms. In the former case, we find a class of spatially homogeneous solutions to the BGK model, where removing the small velocity part of the initial data triggers ill-posedness by increasing temperature. For the latter, we construct a spatially inhomogeneous solution to the BGK model such that the local temperature constructed from the solution has a polynomial growth in spatial variable. These ill-posedness properties for the BGK model pose a stark contrast with the Boltzmann equation for which the solution map is, at least for a finite time, stable in the corresponding solution spaces., Comment: 79 pages, 1 figures; remark 1.5 and acknowledgement modified, references added for section 1