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High-achieving minority students have fewer friends than their majority counterparts. Exploring patterns of friendship formation in the Add Health data, we find strong racial homophily in friendship formations as well as strong achievement homophily within race. However, we find that achievement matters less in cross-racial friendships. As a result, high-achieving Black students lose Black friends as they move away from the mean achievement of their group, but do not gain high-achieving White friends in offsetting fashion. We find that high-achieving Black students have 0.9 fewer friends, mainly attributable to the fact that they are exposed to fewer high-achieving peers within their own race. We find that this could account for as much as 5 to 9 percent of the racial wage gap observed among high achievers.

The rapid advancement of generative models has introduced serious risks, including deepfake techniques for facial synthesis and editing. Traditional approaches rely on training classifiers and enhancing generalizability through various feature extraction techniques. Meanwhile, training-free detection methods address issues like limited data and overfitting by directly leveraging statistical properties from vision foundation models to distinguish between real and fake images. The current leading training-free approach, RIGID, utilizes DINOv2 sensitivity to perturbations in image space for detecting fake images, with fake image embeddings exhibiting greater sensitivity than those of real images. This observation prompts us to investigate how detection performance varies across model backbones, perturbation types, and datasets. Our experiments reveal that detection performance is closely linked to model robustness, with self-supervised (SSL) models providing more reliable representations. While Gaussian noise effectively detects general objects, it performs worse on facial images, whereas Gaussian blur is more effective due to potential frequency artifacts. To further improve detection, we introduce Contrastive Blur, which enhances performance on facial images, and MINDER (MINimum distance DetEctoR), which addresses noise type bias, balancing performance across domains. Beyond performance gains, our work offers valuable insights for both the generative and detection communities, contributing to a deeper understanding of model robustness property utilized for deepfake detection.

We carried out a project involving the systematic analysis of microlensing data from the Korea Microlensing Telescope Network survey. The aim of this project is to identify lensing events with complex anomaly features that are difficult to explain using standard binary-lens or binary-source models. Our investigation reveals that the light curves of microlensing events KMT-2021-BLG-0284, KMT-2022-BLG-2480, and KMT-2024-BLG-0412 display highly complex patterns with three or more anomaly features. These features cannot be adequately explained by a binary-lens (2L1S) model alone. However, the 2L1S model can effectively describe certain segments of the light curve. By incorporating an additional source into the modeling, we identified a comprehensive model that accounts for all the observed anomaly features. Bayesian analysis, based on constraints provided by lensing observables, indicates that the lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are binary systems composed of M dwarfs. For KMT-2022-BLG-2480, the primary lens is an early K-type main-sequence star with an M dwarf companion. The lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are likely located in the bulge, whereas the lens of KMT-2022-BLG-2480 is more likely situated in the disk. In all events, the binary stars of the sources have similar magnitudes due to a detection bias favoring binary source events with a relatively bright secondary source star, which increases detection efficiency., Comment: 9 pages, 9 figures

We analysed microlensing data to uncover the nature of the anomaly that appeared near the peak of the short-timescale microlensing event KMT-2024-BLG-1044. Despite the anomaly's brief duration of less than a day, it was densely observed through high-cadence monitoring conducted by the KMTNet survey. Detailed modelling of the light curve confirmed the planetary origin of the anomaly and revealed two possible solutions, due to an inner--outer degeneracy. The two solutions provide different measured planet parameters: $(s, q)_{\rm inner} = [1.0883 \pm 0.0027, (3.125 \pm 0.248)\times 10^{-4}]$ for the inner solutions and $(s, q)_{\rm outer} = [1.0327 \pm 0.0054, (3.350 \pm 0.316)\times 10^{-4}]$ for the outer solutions. Using Bayesian analysis with constraints provided by the short event timescale ($t_{\rm E} \sim 9.1$~day) and the small angular Einstein radius ($\theta_{\rm E}\sim 0.16$~mas for the inner solution and $\sim 0.10$~mas for the outer solutio), we determined that the lens is a planetary system consisting of a host near the boundary between a star and a brown dwarf and a planet with a mass lower than that of Uranus. The discovery of the planetary system highlights the crucial role of the microlensing technique in detecting planets that orbit substellar brown dwarfs or very low-mass stars., Comment: 8 pages, 10 figures

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS., Comment: Accepted for publication in ApJ. 21 pages, 12 figures

We use the determinant method of Bombieri-Pila and Heath-Brown and its Arakelov reformulation by Chen utilizing Bost's slope method to estimate the number of hypersurfaces required to cover the regular rational points with bounded Arakelov height on a projective variety. Using complex cellular structures introduced by Binyamini-Novikov, we replace the usual subpolynomial factor by a polylogarithmic factor in the estimation., Comment: 13 pages, comments welcome!

The Hubble constant $H_0$, the current expansion rate of the universe, is one of the most important parameters in cosmology. The cosmic expansion regulates the mutually approaching motion of a pair of celestial objects due to their gravity. Therefore, the mean pairwise peculiar velocity of celestial objects, which quantifies their relative motion, is sensitive to both $H_0$ and the dimensionless total matter density $\Omega_m$. Based on this, using the Cosmicflows-4 data, we measured $H_0$ for the first time via the galaxy pairwise velocity in the nonlinear and quasi-linear range. Our results yield $H_0=75.5\pm1.4$ km s$^{-1}$ Mpc$^{-1}$ and $\Omega_m=0.311^{+0.029}_{-0.028}$ . The uncertainties of $H_0$ and $\Omega_m$ can be improved to around 0.6% and 2%, respectively, if the statistical errors become negligible in the future., Comment: 10 pages, 3 main + 2 appendix figures, accepted for publication in ApJL

The exponential growth of data-intensive machine learning workloads has exposed significant limitations in conventional GPU-accelerated systems, especially when processing datasets exceeding GPU DRAM capacity. We propose MQMS, an augmented in-storage GPU architecture and simulator that is aware of internal SSD states and operations, enabling intelligent scheduling and address allocation to overcome performance bottlenecks caused by CPU-mediated data access patterns. MQMS introduces dynamic address allocation to maximize internal parallelism and fine-grained address mapping to efficiently handle small I/O requests without incurring read-modify-write overheads. Through extensive evaluations on workloads ranging from large language model inference to classical machine learning algorithms, MQMS demonstrates orders-of-magnitude improvements in I/O request throughput, device response time, and simulation end time compared to existing simulators.

We introduce the Aya Expanse model family, a new generation of 8B and 32B parameter multilingual language models, aiming to address the critical challenge of developing highly performant multilingual models that match or surpass the capabilities of monolingual models. By leveraging several years of research at Cohere For AI and Cohere, including advancements in data arbitrage, multilingual preference training, and model merging, Aya Expanse sets a new state-of-the-art in multilingual performance. Our evaluations on the Arena-Hard-Auto dataset, translated into 23 languages, demonstrate that Aya Expanse 8B and 32B outperform leading open-weight models in their respective parameter classes, including Gemma 2, Qwen 2.5, and Llama 3.1, achieving up to a 76.6% win-rate. Notably, Aya Expanse 32B outperforms Llama 3.1 70B, a model with twice as many parameters, achieving a 54.0% win-rate. In this short technical report, we present extended evaluation results for the Aya Expanse model family and release their open-weights, together with a new multilingual evaluation dataset m-ArenaHard.

With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis.

We present the development of a decay energy spectroscopy (DES) method for the analysis of radioactive impurities using magnetic microcalorimeters (MMCs). The DES system was designed to analyze radionuclides, such as Ra-226, Th-228, and their daughter nuclides, in materials like copper, commonly used in rare-event search experiments. We tested the DES system with a gold foil absorber measuring 20x20x0.05 mm^3, large enough to accommodate a significant drop of source solution. Using this large absorber and an MMC sensor, we conducted a long-term measurement over ten days of live time, requiring 11 ADR cooling cycles. The combined spectrum achieved an energy resolution of 45 keV FWHM, sufficient to identify most alpha and DES peaks of interest. Specific decay events from radionuclide contaminants in the absorber were identified. This experiment confirms the capability of the DES system to measure alpha decay chains of Ra-226 and Th-228, offering a promising method for radio-impurity evaluation in ultra-low background experiments., Comment: 5 pages, 5 figures

Despite significant advancements in vision-language models (VLMs), there lacks effective approaches to enhance response quality by scaling inference-time computation. This capability is known to be a core step towards the self-improving models in recent large language model studies. In this paper, we present Vision Value Model (VisVM) that can guide VLM inference-time search to generate responses with better visual comprehension. Specifically, VisVM not only evaluates the generated sentence quality in the current search step, but also anticipates the quality of subsequent sentences that may result from the current step, thus providing a long-term value. In this way, VisVM steers VLMs away from generating sentences prone to hallucinations or insufficient detail, thereby producing higher quality responses. Experimental results demonstrate that VisVM-guided search significantly enhances VLMs' ability to generate descriptive captions with richer visual details and fewer hallucinations, compared with greedy decoding and search methods with other visual reward signals. Furthermore, we find that self-training the model with the VisVM-guided captions improve VLM's performance across a wide range of multimodal benchmarks, indicating the potential for developing self-improving VLMs. Our value model and code are available at https://github.com/si0wang/VisVM.

Exemplar-based semantic image synthesis aims to generate images aligned with given semantic content while preserving the appearance of an exemplar image. Conventional structure-guidance models, such as ControlNet, are limited in that they cannot directly utilize exemplar images as input, relying instead solely on text prompts to control appearance. Recent tuning-free approaches address this limitation by transferring local appearance from the exemplar image to the synthesized image through implicit cross-image matching in the augmented self-attention mechanism of pre-trained diffusion models. However, these methods face challenges when applied to content-rich scenes with significant geometric deformations, such as driving scenes. In this paper, we propose the Appearance Matching Adapter (AM-Adapter), a learnable framework that enhances cross-image matching within augmented self-attention by incorporating semantic information from segmentation maps. To effectively disentangle generation and matching processes, we adopt a stage-wise training approach. Initially, we train the structure-guidance and generation networks, followed by training the AM-Adapter while keeping the other networks frozen. During inference, we introduce an automated exemplar retrieval method to efficiently select exemplar image-segmentation pairs. Despite utilizing a limited number of learnable parameters, our method achieves state-of-the-art performance, excelling in both semantic alignment preservation and local appearance fidelity. Extensive ablation studies further validate our design choices. Code and pre-trained weights will be publicly available.: https://cvlab-kaist.github.io/AM-Adapter/

In Bayesian inverse problems, it is common to consider several hyperparameters that define the prior and the noise model that must be estimated from the data. In particular, we are interested in linear inverse problems with additive Gaussian noise and Gaussian priors defined using Mat\'{e}rn covariance models. In this case, we estimate the hyperparameters using the maximum a posteriori (MAP) estimate of the marginalized posterior distribution. However, this is a computationally intensive task since it involves computing log determinants. To address this challenge, we consider a stochastic average approximation (SAA) of the objective function and use the preconditioned Lanczos method to compute efficient approximations of the function and gradient evaluations. We propose a new preconditioner that can be updated cheaply for new values of the hyperparameters and an approach to compute approximations of the gradient evaluations, by reutilizing information from the function evaluations. We demonstrate the performance of our approach on static and dynamic seismic tomography problems.

The first spectroscopy of $^{52}$K was investigated via in-beam $\gamma$-ray spectroscopy at the RIKEN Radioactive Isotope Beam Factory after one-proton and one-neutron knockout from $^{53}$Ca and $^{53}$K beams impinging on a 15-cm liquid hydrogen target at $\approx$ 230~MeV/nucleon. The energy level scheme of $^{52}$K was built using single $\gamma$ and $\gamma$-$\gamma$ coincidence spectra. The spins and parities of the excited states were established based on momentum distributions of the fragment after the knockout reaction and based on exclusive cross sections. The results were compared to state-of-the-art shell model calculations with the SDPF-Umod interaction and ab initio IMSRG calculations with chiral effective field theory nucleon-nucleon and three-nucleon forces.

We prove that, for any two polyhedral manifolds P, Q, there is a polyhedral manifold I such that P, I share a common unfolding and I, Q share a common unfolding. In other words, we can unfold P, refold (glue) that unfolding into I, unfold I, and then refold into Q. Furthermore, if P, Q are embedded in 3D, then I can be embedded in 3D (without self-intersection). These results generalize to n given manifolds P_1, P_2, ..., P_n; they all have a common unfolding with an intermediate manifold I. Allowing more than two unfold/refold steps, we obtain stronger results for two special cases: for doubly covered convex planar polygons, we achieve that all intermediate polyhedra are planar; and for tree-shaped polycubes, we achieve that all intermediate polyhedra are tree-shaped polycubes., Comment: 18 pages, 15 figures. Presented at JCDCGGG 2024

We introduce a novel framework for representation learning in head pose estimation (HPE). Previously such a scheme was difficult due to head pose data sparsity, making triplet sampling infeasible. Recent progress in 3D generative adversarial networks (3D-aware GAN) has opened the door for easily sampling triplets (anchor, positive, negative). We perform contrastive learning on extensively augmented data including geometric transformations and demonstrate that contrastive learning allows networks to learn genuine features that contribute to accurate HPE. On the other hand, we observe that existing HPE works struggle to predict head poses as accurately when test image rotation matrices are slightly out of the training dataset distribution. Experiments show that our methodology performs on par with state-of-the-art models on standard test datasets and outperforms them when images are slightly rotated/ flipped or full range head pose. To the best of our knowledge, we are the first to deliver a true full range HPE model capable of accurately predicting any head pose including upside-down pose. Furthermore, we compared with other existing full-yaw range models and demonstrated superior results.

The domain decomposition (DD) nonlinear-manifold reduced-order model (NM-ROM) represents a computationally efficient method for integrating underlying physics principles into a neural network-based, data-driven approach. Compared to linear subspace methods, NM-ROMs offer superior expressivity and enhanced reconstruction capabilities, while DD enables cost-effective, parallel training of autoencoders by partitioning the domain into algebraic subdomains. In this work, we investigate the scalability of this approach by implementing a "bottom-up" strategy: training NM-ROMs on smaller domains and subsequently deploying them on larger, composable ones. The application of this method to the two-dimensional time-dependent Burgers' equation shows that extrapolating from smaller to larger domains is both stable and effective. This approach achieves an accuracy of 1% in relative error and provides a remarkable speedup of nearly 700 times., Comment: To be included in the proceedings of the Machine Learning and the Physical Sciences Workshop at NeurIPS 2024

Planning safe and efficient trajectories through signal-free intersections presents significant challenges for autonomous vehicles (AVs), particularly in dynamic, multi-task environments with unpredictable interactions and an increased possibility of conflicts. This study aims to address these challenges by developing a robust, adaptive framework to ensure safety in such complex scenarios. Existing approaches often struggle to provide reliable safety mechanisms in dynamic and learn multi-task behaviors from demonstrations in signal-free intersections. This study proposes a safety-critical planning method that integrates Dynamic High-Order Control Barrier Functions (DHOCBF) with a diffusion-based model, called Dynamic Safety-Critical Diffuser (DSC-Diffuser), offering a robust solution for adaptive, safe, and multi-task driving in signal-free intersections. Our approach incorporates a goal-oriented, task-guided diffusion model, enabling the model to learn multiple driving tasks simultaneously from real-world data. To further ensure driving safety in dynamic environments, the proposed DHOCBF framework dynamically adjusts to account for the movements of surrounding vehicles, offering enhanced adaptability compared to traditional control barrier functions. Validity evaluations of DHOCBF, conducted through numerical simulations, demonstrate its robustness in adapting to variations in obstacle velocities, sizes, uncertainties, and locations, effectively maintaining driving safety across a wide range of complex and uncertain scenarios. Performance evaluations across various scenes confirm that DSC-Diffuser provides realistic, stable, and generalizable policies, equipping it with the flexibility to adapt to diverse driving tasks., Comment: 7 figures, 3 tables, 12 pages

In this paper, we introduce V2SFlow, a novel Video-to-Speech (V2S) framework designed to generate natural and intelligible speech directly from silent talking face videos. While recent V2S systems have shown promising results on constrained datasets with limited speakers and vocabularies, their performance often degrades on real-world, unconstrained datasets due to the inherent variability and complexity of speech signals. To address these challenges, we decompose the speech signal into manageable subspaces (content, pitch, and speaker information), each representing distinct speech attributes, and predict them directly from the visual input. To generate coherent and realistic speech from these predicted attributes, we employ a rectified flow matching decoder built on a Transformer architecture, which models efficient probabilistic pathways from random noise to the target speech distribution. Extensive experiments demonstrate that V2SFlow significantly outperforms state-of-the-art methods, even surpassing the naturalness of ground truth utterances.

Recent years have seen soft robotic grippers gain increasing attention due to their ability to robustly grasp soft and fragile objects. However, a commonly available standardised evaluation protocol has not yet been developed to assess the performance of varying soft robotic gripper designs. This work introduces a novel protocol, the Soft Grasping Benchmarking and Evaluation (SoGraB) method, to evaluate grasping quality, which quantifies object deformation by using the Density-Aware Chamfer Distance (DCD) between point clouds of soft objects before and after grasping. We validated our protocol in extensive experiments, which involved ranking three Fin-Ray gripper designs with a subset of the EGAD object dataset. The protocol appropriately ranked grippers based on object deformation information, validating the method's ability to select soft grippers for complex grasping tasks and benchmark them for comparison against future designs., Comment: 6 pages, 7 figures

The goal of music style transfer is to convert a music performance by one instrument into another while keeping the musical contents unchanged. In this paper, we investigate another style transfer scenario called ``failed-music style transfer''. Unlike the usual music style transfer where the content remains the same and only the instrumental characteristics are changed, this scenario seeks to transfer the music from the source instrument to the target instrument which is deliberately performed off-pitch. Our work attempts to transfer normally played music into off-pitch recorder music, which we call ``failed-style recorder'', and study the results of the conversion. To carry out this work, we have also proposed a dataset of failed-style recorders for this task, called ``FR109 Dataset''. Such an experiment explores the music style transfer task in a more expressive setting, as the generated audio should sound like an ``off-pitch recorder'' while maintaining a certain degree of naturalness., Comment: Accepted by APSIPA 2024

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has been fully operational since 11 January 2020. We present a comprehensive analysis of the beam structure for each of the 19 feed horns on FAST's L-band receiver across the Stokes I, Q, U, and V parameters. Using an on-the-fly mapping pattern, we conducted simultaneous sky mapping using all 19 beams directed towards polarization calibrators J1407+2827 and J0854+2006 from 2020 to 2022. Electromagnetic simulations were also performed to model the telescope's beam patterns in all Stokes parameters. Our findings reveal a symmetrical Gaussian pattern in the Stokes I parameter of the central beam without strong sidelobes, while the off-center beams exhibit significant asymmetrical shapes that can be fitted using a combination of log-normal and Gaussian distributions. The inner beams have higher relative beam efficiencies and smaller beam sizes compared to those of the outer beams. The sidelobes of the inner beams contribute approximately 2% of the total flux in the main lobe, increasing to 5% for outer beams, with a peak at 6.8%. In Stokes U, a distinct four-lobed cloverleaf beam squash structure is observed, with similar intensity levels in both inner and outer beams. In Stokes V, a two-lobed beam squint structure is observed in the central beam, along with a secondary eight-lobed structure. The highest squint peak in Stokes V is about 0.3% of the Stokes I in the outer beams. These results align closely with the simulations, providing valuable insights for the design of radio multi-beam observations., Comment: 26 pages, 20 figures. Submitted to AJ

We present the polarization calibration of the 19-beam receiver at 1420 MHz within the full illumination of the Five-hundred-meter Aperture Spherical Telescope from October 2018 to March 2023. We perform spider observations to characterize the on-axis Mueller matrix of the central beam. The calibrated polarization percentage and polarization angle of a source with strong linear polarization emission are about 0.2\% and 0.5$^{\circ}$. Several parameters of the central-beam Mueller matrix show time variability from months to years, suggesting relatively frequent polarization calibrations are needed. We obtain the Mueller matrix parameters of the 18 off-center beams with the combination of on-the-fly observations and spider observations. The polarization calibration provides consistent fractional Stokes parameters of the 19 beams, although the Mueller matrix parameters of the off-center beams are not as accurate as those of the central beam. The Mueller matrix parameters of the central beam do not show a strong dependence on the reflector surface. However, we notice different off-center Mueller matrix parameters between the eastern and western sides of the reflector surface. We provide average parameters of the 19-beam Mueller matrices which should be applicable to observations from 2020 to 2022 with several caveats. After applying the average parameters, on-axis fractional linear polarization measurements $\gtrsim$ 10\% and on-axis fractional circular polarization measurements $\gtrsim$ 1.5\% can be considered high-confidence detections. For sources with weak polarization, timely polarization calibrations using spider observations are required., Comment: 16 pages, 13 figures, , Submitted to AJ

The comparable abundances of dark matter and baryons imply a deep connection between the dark sector and the QCD sector. In models of asymmetric dark matter, the number densities of both sectors are ensured to be similar. However, a complete solution should also include a mechanism for comparable masses. In this letter, we present a solution in which the dark matter mass is generated through the QCD vacuum, ensuring the mass is at the GeV scale. The model features $\mathcal{O}(1)$ GeV dark baryons as dark matter, together with dark pions (axion-like particles) and dark photons., Comment: 10 pages, 1 figures, 4 tables

Text-to-image diffusion models have emerged as powerful priors for real-world image super-resolution (Real-ISR). However, existing methods may produce unintended results due to noisy text prompts and their lack of spatial information. In this paper, we present HoliSDiP, a framework that leverages semantic segmentation to provide both precise textual and spatial guidance for diffusion-based Real-ISR. Our method employs semantic labels as concise text prompts while introducing dense semantic guidance through segmentation masks and our proposed Segmentation-CLIP Map. Extensive experiments demonstrate that HoliSDiP achieves significant improvement in image quality across various Real-ISR scenarios through reduced prompt noise and enhanced spatial control., Comment: Project page: https://liyuantsao.github.io/HoliSDiP/

The ability of robotic grippers to not only grasp but also re-position and re-orient objects in-hand is crucial for achieving versatile, general-purpose manipulation. While recent advances in soft robotic grasping has greatly improved grasp quality and stability, their manipulation capabilities remain under-explored. This paper presents the DexGrip, a multi-modal soft robotic gripper for in-hand grasping, re-orientation and manipulation. DexGrip features a 3 Degrees of Freedom (DoFs) active suction palm and 3 active (rotating) grasping surfaces, enabling soft, stable, and dexterous grasping and manipulation without ever needing to re-grasp an object. Uniquely, these features enable complete 360 degree rotation in all three principal axes. We experimentally demonstrate these capabilities across a diverse set of objects and tasks. DexGrip successfully grasped, re-positioned, and re-oriented objects with widely varying stiffnesses, sizes, weights, and surface textures; and effectively manipulated objects that presented significant challenges for existing robotic grippers., Comment: 6 pages, 5 figures

Multispectral pedestrian detection is a crucial component in various critical applications. However, a significant challenge arises due to the misalignment between these modalities, particularly under real-world conditions where data often appear heavily misaligned. Conventional methods developed on well-aligned or minimally misaligned datasets fail to address these discrepancies adequately. This paper introduces a new framework for multispectral pedestrian detection designed specifically to handle heavily misaligned datasets without the need for costly and complex traditional pre-processing calibration. By leveraging Large-scale Vision-Language Models (LVLM) for cross-modal semantic alignment, our approach seeks to enhance detection accuracy by aligning semantic information across the RGB and thermal domains. This method not only simplifies the operational requirements but also extends the practical usability of multispectral detection technologies in practical applications.

Inverse rendering seeks to reconstruct both geometry and spatially varying BRDFs (SVBRDFs) from captured images. To address the inherent ill-posedness of inverse rendering, basis BRDF representations are commonly used, modeling SVBRDFs as spatially varying blends of a set of basis BRDFs. However, existing methods often yield basis BRDFs that lack intuitive separation and have limited scalability to scenes of varying complexity. In this paper, we introduce a differentiable inverse rendering method that produces interpretable basis BRDFs. Our approach models a scene using 2D Gaussians, where the reflectance of each Gaussian is defined by a weighted blend of basis BRDFs. We efficiently render an image from the 2D Gaussians and basis BRDFs using differentiable rasterization and impose a rendering loss with the input images. During this analysis-by-synthesis optimization process of differentiable inverse rendering, we dynamically adjust the number of basis BRDFs to fit the target scene while encouraging sparsity in the basis weights. This ensures that the reflectance of each Gaussian is represented by only a few basis BRDFs. This approach enables the reconstruction of accurate geometry and interpretable basis BRDFs that are spatially separated. Consequently, the resulting scene representation, comprising basis BRDFs and 2D Gaussians, supports physically-based novel-view relighting and intuitive scene editing., Comment: This is a different paper from my previous paper "Differentiable Point-based Inverse Rendering". It must not be removed automatically

We formulate a stable reduction conjecture that extends Deligne-Mumford's stable reduction to higher dimensions and provide a simple proof that it holds in large characteristic, assuming two standard conjectures of the Minimal Model Program. As a result, we recover the Hacon-Kov\'acs theorem on the properness of the moduli stack $\overline{\mathscr{M}}_{2,v,k}$ of stable surfaces of volume $v$ defined over $k=\overline{k}$, provided that $\operatorname{char}k>C(v)$, a constant depending only on $v$.

The relationship between B-field orientation and density structure in molecular clouds is often assessed using the Histogram of Relative Orientations (HRO). We perform a plane-of-the-sky geometrical analysis of projected B-fields, by interpreting HROs in dense, spheroidal, prestellar and protostellar cores. We use James Clerk Maxwell Telescope (JCMT) POL-2 850 $\mu$m polarisation maps and Herschel column density maps to study dense cores in the Ophiuchus molecular cloud complex. We construct two-dimensional core models, assuming Plummer column density profiles and modelling both linear and hourglass B-fields. We find high-aspect-ratio ellipsoidal cores produce strong HRO signals, as measured using the shape parameter $\xi$. Cores with linear fields oriented $< 45^{\circ}$ from their minor axis produce constant HROs with $-1 < \xi < 0$, indicating fields are preferentially parallel to column density gradients. Fields parallel to the core minor axis produce the most negative value of $\xi$. For low-aspect-ratio cores, $\xi \approx 0$ for linear fields. Hourglass fields produce a minimum in $\xi$ at intermediate densities in all cases, converging to the minor-axis-parallel linear field value at high and low column densities. We create HROs for six dense cores in Ophiuchus. $\rho$ Oph A and IRAS 16293 have high aspect ratios and preferentially negative HROs, consistent with moderately strong-field behaviour. $\rho$ Oph C, L1689A and L1689B have low aspect ratios, and $\xi \approx 0$. $\rho$ Oph B is too complex to be modelled using a simple spheroidal field geometry. We see no signature of hourglass fields, agreeing with previous findings that dense cores generally exhibit linear fields on these size scales., Comment: 16 pages, 19 figures, 2 tables. Accepted for publication in MNRAS

As Classifier-Free Guidance (CFG) has proven effective in conditional diffusion model sampling for improved condition alignment, many applications use a negated CFG term to filter out unwanted features from samples. However, simply negating CFG guidance creates an inverted probability distribution, often distorting samples away from the marginal distribution. Inspired by recent advances in conditional diffusion models for inverse problems, here we present a novel method to enhance negative CFG guidance using contrastive loss. Specifically, our guidance term aligns or repels the denoising direction based on the given condition through contrastive loss, achieving a nearly identical guiding direction to traditional CFG for positive guidance while overcoming the limitations of existing negative guidance methods. Experimental results demonstrate that our approach effectively removes undesirable concepts while maintaining sample quality across diverse scenarios, from simple class conditions to complex and overlapping text prompts., Comment: 14 pages, 8 figures

We investigate a theory of $SU(9)$ dark grand unification, where dark matter consists of asymmetric dark baryons from the $Sp(4)_D$ dark QCD sector. By unifying the dark color gauge group with the Standard Model gauge group, the asymmetry generation in both sectors originates from a common process that preserves a $U(1)_{D-(B-L)}$ symmetry, resulting in comparable number densities. Furthermore, thanks to dark grand unification, the $Sp(4)_D$ dark QCD sector shares a similar matter content with the QCD sector, leading to comparable running of the gauge couplings from the scale as high as $10^{15}$ GeV. This predicts a dark color confinement scale and thus dark baryon masses around the GeV scale, comparable to visible baryon masses. Together with the similar number densities, the model provides a natural explanation for the observed ratio between the energy densities of dark matter and baryon, $\rho_D/\rho_B\approx 5$. The model also features some novel phenomenology, including a flavored dark sector with chimera dark baryons and GeV-scale dark $\rho$ mesons., Comment: 30 pages, 6 figures, 4 tables

Van Hove singularities (vHs) located close to the Fermi level in Kagome superconductors AV3Sb5 (A = K, Rb, Cs) have profound influence on their electronic and transport characteristics. Specifically, magneto-transport and susceptibility measurements on CsV3Sb5 reveal an anomalous temperature dependence of the upper critical field H_c2 (T), characterized by a pronounced upward curvature for both in-plane and c-axis magnetic fields, with zero-temperature H_c2 values of ~6.0 T and ~1.2 T, respectively. Our theoretical analysis, using a newly developed single-band model incorporating vHs and gap anisotropy, suggests that the observed upper critical field behavior is predominantly driven by the anisotropy of the Fermi velocity originating from vHs, instead of multi-band effects or gap anisotropy. Increased electron scattering introduced by proton irradiation defects smears out the vHs, reduces anisotropy, and recovers the conventional H_c2 (T) behavior, corroborating our proposed model., Comment: 31 pages, 11 figures

The quality and size of a pretraining dataset significantly influence the performance of large language models (LLMs). While there have been numerous efforts in the curation of such a dataset for English users, there is a relative lack of similar initiatives for Traditional Chinese. Building upon this foundation of FineWeb, we introduce FineWeb-zhtw, a dataset tailored specifically for Traditional Chinese users. We came up with multiple stages of meticulously designed filters to cater to the linguistic difference between English and Traditional Chinese, to ensure comprehensiveness and quality. We determined effectiveness from querying dataset samples with three main objectives. Our code and datasets are publicly available.

Recent developments in All-in-One (AiO) RGB image restoration and prompt learning have enabled the representation of distinct degradations through prompts, allowing degraded images to be effectively addressed by a single restoration model. However, this paradigm faces significant challenges when transferring to hyperspectral image (HSI) restoration tasks due to: 1) the domain gap between RGB and HSI features and difference on their structures, 2) information loss in visual prompts under severe composite degradations, and 3) difficulties in capturing HSI-specific degradation representations through text prompts. To address these challenges, we propose PromptHSI, the first universal AiO HSI restoration framework. By leveraging the frequency-aware feature modulation based on characteristics of HSI degradations, we decompose text prompts into intensity and bias controllers to effectively guide the restoration process while avoiding domain gaps. Our unified architecture excels at both fine-grained recovery and global information restoration tasks. Experimental results demonstrate superior performance under various degradation combinations, indicating great potential for practical remote sensing applications. The source code and dataset will be publicly released., Comment: 11 pages, 8 figures

This paper explores the potential of leveraging language priors learned by text-to-image diffusion models to address ambiguity and visual nuisance in monocular depth estimation. Particularly, traditional monocular depth estimation suffers from inherent ambiguity due to the absence of stereo or multi-view depth cues, and nuisance due to lack of robustness of vision. We argue that language prior in diffusion models can enhance monocular depth estimation by leveraging the geometric prior aligned with the language description, which is learned during text-to-image pre-training. To generate images that reflect the text properly, the model must comprehend the size and shape of specified objects, their spatial relationship, and the scale of the scene. Thus, we propose PriorDiffusion, using a pre-trained text-to-image diffusion model that takes both image and text description that aligned with the scene to infer affine-invariant depth through a denoising process. We also show that language priors can guide the model's attention to specific regions and help it perceive the 3D scene in alignment with user intent. Simultaneously, it acts as a constraint to accelerate the convergence of the diffusion trajectory, since learning 3D properties from a condensed, low-dimensional language feature is more efficient compared with learning from a redundant, high-dimensional image feature. By training on HyperSim and Virtual KITTI, we achieve state-of-the-art zero-shot performance and a faster convergence speed, compared with other diffusion-based depth estimators, across NYUv2, KITTI, ETH3D, and ScanNet.

We present Model Predictive Trees (MPT), a receding horizon tree search algorithm that improves its performance by reusing information efficiently. Whereas existing solvers reuse only the highest-quality trajectory from the previous iteration as a "hotstart", our method reuses the entire optimal subtree, enabling the search to be simultaneously guided away from the low-quality areas and towards the high-quality areas. We characterize the restrictions on tree reuse by analyzing the induced tracking error under time-varying dynamics, revealing a tradeoff between the search depth and the timescale of the changing dynamics. In numerical studies, our algorithm outperforms state-of-the-art sampling-based cross-entropy methods with hotstarting. We demonstrate our planner on an autonomous vehicle testbed performing a nonprehensile manipulation task: pushing a target object through an obstacle field. Code associated with this work will be made available at https://github.com/jplathrop/mpt., Comment: Presented at the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems

We report the detection of H$_2$O maser emission in 4 out of 77 (5.2%) mid-IR red galaxies that meet the color criteria of $W1-W2 > 0.5$ and $W1-W4 > 7$ and are classified as Type-2 AGNs based on optical, near-IR, and mid-IR spectral energy distribution (SED) fitting. Here, $W1$, $W2$, and $W4$ represent the IR magnitudes at 3.4, 4.6, and 22 micron, respectively, as measured by the Wide-field Infrared Survey Explorer. Three of the four newly identified maser galaxies are classified as either Seyfert 2 or LINER systems, but none are disk maser systems. Our analysis indicates that AGN identifications based solely on SED fitting are unreliable, resulting in an unexpectedly low detection rate. By restricting our sample to optically classified Type 2 AGNs that satisfy the mid-IR color criteria, we achieve a maser detection rate of ~13-18%, aligning with previous predictions for mid-IR red sources. These selection criteria are the most effective to date for facilitating new maser detections, particularly in light of the recent identification of additional Type 2 AGNs identified from ongoing galaxy and AGN surveys., Comment: 11 pages, 3 tables, 3 figures

Gradient-based methods are a prototypical family of explainability techniques, especially for image-based models. Nonetheless, they have several shortcomings in that they (1) require white-box access to models, (2) are vulnerable to adversarial attacks, and (3) produce attributions that lie off the image manifold, leading to explanations that are not actually faithful to the model and do not align well with human perception. To overcome these challenges, we introduce Derivative-Free Diffusion Manifold-Constrainted Gradients (FreeMCG), a novel method that serves as an improved basis for explainability of a given neural network than the traditional gradient. Specifically, by leveraging ensemble Kalman filters and diffusion models, we derive a derivative-free approximation of the model's gradient projected onto the data manifold, requiring access only to the model's outputs. We demonstrate the effectiveness of FreeMCG by applying it to both counterfactual generation and feature attribution, which have traditionally been treated as distinct tasks. Through comprehensive evaluation on both tasks, counterfactual explanation and feature attribution, we show that our method yields state-of-the-art results while preserving the essential properties expected of XAI tools., Comment: 19 pages, 5 figures

Two enigmatic gamma-ray features in the Galactic central region, known as Fermi Bubbles (FBs), were found from Fermi-LAT data. An energy release (e.g., by tidal disruption events in the Galactic center, GC), generates a cavity with a shock that expands into the local ambient medium of the Galactic halo. A decade or so ago, a phenomenological model of the FBs was suggested as a result of routine star disruptions by the supermassive black hole in the GC which might provide enough energy for large-scale structures, like the FBs. In 2020, analytical and numerical models of the FBs as a process of routine tidal disruption of stars near the GC were developed, which can provide enough cumulative energy to form and maintain large scale structures like the FBs. The disruption events are expected to be ten to hundred events per million years, providing the average power of energy release from the GC into the halo of 3E41 erg/s, which is needed to support the FBs. Analysis of the evolution of superbubbles in exponentially stratified disks concluded that the FB envelope would be destroyed by the Rayleigh-Taylor (RT) instabilities at late stages. The shell is composed of a swept-up gas of the bubble, whose thickness is much thinner in comparison to the size of the envelope. We assume that hydrodynamic turbulence is excited in the FB envelope by the RT instability. In this case, the universal energy spectrum of turbulence may be developed in the inertial range of wavenumbers of fluctuations (the Kolmogorov-Obukhov spectrum). From our model we suppose the power of the FBs is transformed partly into the energy of hydrodynamic turbulence in the envelope. If so, hydrodynamic turbulence may generate MHD-fluctuations, which accelerate cosmic rays there and generate gamma-ray and radio emission from the FBs. We hope that this model may interpret the observed nonthermal emission from the bubbles., Comment: 21 pages, 13 figures

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data., Comment: 21 pages, 11 figures, 2 tables

Silicon-based ion trap chips can benefit from existing advanced fabrication technologies, such as multi-metal layer techniques for two-dimensional architectures and silicon photonics for the integration of on-chip optical components. However, the scalability of these technologies may be compromised by semiconductor charging, where photogenerated charge carriers produce electric potentials that disrupt ion motion. Inspired by recent studies on charge distribution mechanisms in semiconductors, we developed a silicon-based chip with gold coated on all exposed silicon surfaces. This modification significantly stabilized ion motion compared to a chip without such metallic shielding, a result that underscores the detrimental effects of exposed silicon. With the mitigation of background silicon-induced fields to negligible levels, quantum operations such as sideband cooling and two-ion entangling gates, which were previously infeasible with the unshielded chip, can now be implemented.