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Reducing the radiation dose in computed tomography (CT) is crucial, but it often results in sparse-view CT, where the number of available projections is significantly reduced. This reduction in projection data makes it challenging to accurately reconstruct high-quality CT images. In this condition, a sinogram, which is a collection of these projections, becomes incomplete. Sinogram inpainting then becomes essential because it enables accurate image reconstruction with limited projections. Existing models performing well on conventional RGB images for inpainting mostly fail in the case of sinograms. Further, these models usually do not make full use of unique properties, e.g., frequency features and absorption characteristics in the sinogram, and cannot handle large-area masks and complex real-world projections well. To address these limitations, we propose a novel model called the Frequency Convolution Diffusion Model (FCDM). It employs frequency domain convolutions to extract frequency information from various angles and capture the intricate relationships between these angles, which is essential for high-quality CT reconstruction. We also design a specific loss function based on the unique properties of a sinogram to maintain the consistency in physical properties, which allows the model to learn more effectively even in larger mask areas. We compare FCDM using both simulations and real data with nine inpainting models examples, among which two are designed for sinogram and seven for RGB. The results indicate that our model significantly improves the quality of the inpainted sinograms in terms of both visually and quantitatively, with an SSIM of more than 0.95 and PSNR of more than 30, achieving up to a 33% improvement in SSIM and a 29% improvement in PSNR compared to the baseline.

The integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. However, this combination often struggles with capturing semantic information effectively. Moreover, relying solely on point features within regions of interest can lead to information loss and limitations in local feature representation. To tackle these challenges, we propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN). PVAFN leverages an attention mechanism to improve multi-modal feature fusion during the feature extraction phase. In the refinement stage, it utilizes a multi-pooling strategy to integrate both multi-scale and region-specific information effectively. The point-voxel attention mechanism adaptively combines point cloud and voxel-based Bird's-Eye-View (BEV) features, resulting in richer object representations that help to reduce false detections. Additionally, a multi-pooling enhancement module is introduced to boost the model's perception capabilities. This module employs cluster pooling and pyramid pooling techniques to efficiently capture key geometric details and fine-grained shape structures, thereby enhancing the integration of local and global features. Extensive experiments on the KITTI and Waymo datasets demonstrate that the proposed PVAFN achieves competitive performance. The code and models will be available., Comment: 3D Object Detection

In speaker tracking research, integrating and complementing multi-modal data is a crucial strategy for improving the accuracy and robustness of tracking systems. However, tracking with incomplete modalities remains a challenging issue due to noisy observations caused by occlusion, acoustic noise, and sensor failures. Especially when there is missing data in multiple modalities, the performance of existing multi-modal fusion methods tends to decrease. To this end, we propose a Global-Local Distillation-based Tracker (GLDTracker) for robust audio-visual speaker tracking. GLDTracker is driven by a teacher-student distillation model, enabling the flexible fusion of incomplete information from each modality. The teacher network processes global signals captured by camera and microphone arrays, and the student network handles local information subject to visual occlusion and missing audio channels. By transferring knowledge from teacher to student, the student network can better adapt to complex dynamic scenes with incomplete observations. In the student network, a global feature reconstruction module based on the generative adversarial network is constructed to reconstruct global features from feature embedding with missing local information. Furthermore, a multi-modal multi-level fusion attention is introduced to integrate the incomplete feature and the reconstructed feature, leveraging the complementarity and consistency of audio-visual and global-local features. Experimental results on the AV16.3 dataset demonstrate that the proposed GLDTracker outperforms existing state-of-the-art audio-visual trackers and achieves leading performance on both standard and incomplete modalities datasets, highlighting its superiority and robustness in complex conditions. The code and models will be available., Comment: Audio-Visual Speaker Tracking with Incomplete Modalities

3D Gaussian Splatting (3DGS) has become the de facto method of 3D representation in many vision tasks. This calls for the 3D understanding directly in this representation space. To facilitate the research in this direction, we first build a large-scale dataset of 3DGS using the commonly used ShapeNet and ModelNet datasets. Our dataset ShapeSplat consists of 65K objects from 87 unique categories, whose labels are in accordance with the respective datasets. The creation of this dataset utilized the compute equivalent of 2 GPU years on a TITAN XP GPU. We utilize our dataset for unsupervised pretraining and supervised finetuning for classification and segmentation tasks. To this end, we introduce \textbf{\textit{Gaussian-MAE}}, which highlights the unique benefits of representation learning from Gaussian parameters. Through exhaustive experiments, we provide several valuable insights. In particular, we show that (1) the distribution of the optimized GS centroids significantly differs from the uniformly sampled point cloud (used for initialization) counterpart; (2) this change in distribution results in degradation in classification but improvement in segmentation tasks when using only the centroids; (3) to leverage additional Gaussian parameters, we propose Gaussian feature grouping in a normalized feature space, along with splats pooling layer, offering a tailored solution to effectively group and embed similar Gaussians, which leads to notable improvement in finetuning tasks.

Epsilon Eridani ($\epsilon$ Eri) is one of the first debris disk systems detected by the Infrared Astronomical Satellite (IRAS). However, the system has thus far eluded detection in scattered light with no components having been directly imaged. Its similarity to a relatively young Solar System combined with its proximity makes it an excellent candidate to further our understanding of planetary system evolution. We present a set of coronagraphic images taken using the Space Telescope Imaging Spectrograph (STIS) coronagraph on the Hubble space telescope at a small inner working angle to detect a predicted warm inner debris disk inside 1". We used three different post-processing approaches; Non-negative Matrix Factorization (NMF), Karhunen-Lo`eve Image Processing (KLIP), and Classical reference differential imaging (RDI), to best optimize reference star subtraction, and find that NMF performed the best overall while KLIP produced the absolute best contrast inside 1". We present limits on scattered light from warm dust, with constraints on surface brightness at 6 mJy/as$^2$ at our inner working angle of 0.6". We also place a constraint of 0.5 mJy/as$^2$ outside 1", which gives us an upper limit on the brightness for outer disks and substellar companions. Finally, we calculated an upper limit on the dust albedo at $\omega <$ 0.487., Comment: 13+2 pages, 7+2 figures; Accepted for publication in the Astronomical Journal

Recent Atacama Large Millimeter/submillimeter Array (ALMA) observations of protoplanetary disks in the millimeter continuum have shown a variety of radial gaps, cavities, and spiral features. These substructures may be signposts for ongoing planet formation, and therefore these systems are promising targets for direct imaging planet searches in the near-infrared. To this end, we present results from a deep imaging survey in the $L'$-band (3.8 $\mu$m) with the Keck/NIRC2 vortex coronagraph to search for young planets in 43 disks with resolved features in the millimeter continuum or evidence for gaps/central cavities from their spectral energy distributions. Although we do not detect any new point sources, using the vortex coronagraph allows for high sensitivity to faint sources at small angular separations (down to ${\sim}$0$^{\prime\prime}$.1), allowing us to place strong upper limits on the masses of potential gas giant planets. We compare our mass sensitivities to the masses of planets derived using ALMA observations, and while we are sensitive to $\sim$1 M$_{Jup}$ planets in the gaps in some of our systems, we are generally not sensitive to planets of the masses expected from the ALMA observations. In addition to placing upper limits on the masses of gas giant planets that could be interacting with the dust in the disks to form the observed millimeter substructures, we are also able to map the micron-sized dust as seen in scattered light for 8 of these systems. Our large sample of systems also allows us to investigate limits on planetary accretion rates and disk viscosities., Comment: 23 pages, 14 figures, 3 tables, accepted for publication in AJ

With the emergence of mobile devices, there is a growing demand for an efficient model to restore any degraded image for better perceptual quality. However, existing models often require specific learning modules tailored for each degradation, resulting in complex architectures and high computation costs. Different from previous work, in this paper, we propose a unified manner to achieve joint embedding by leveraging the inherent similarities across various degradations for efficient and comprehensive restoration. Specifically, we first dig into the sub-latent space of each input to analyze the key components and reweight their contributions in a gated manner. The intrinsic awareness is further integrated with contextualized attention in an X-shaped scheme, maximizing local-global intertwining. Extensive comparison on benchmarking all-in-one restoration setting validates our efficiency and effectiveness, i.e., our network sets new SOTA records while reducing model complexity by approximately -82% in trainable parameters and -85\% in FLOPs. Our code will be made publicly available at:https://github.com/Amazingren/AnyIR., Comment: Efficient Any Image Restoration

Contrastive learning (CL) for Vision Transformers (ViTs) in image domains has achieved performance comparable to CL for traditional convolutional backbones. However, in 3D point cloud pretraining with ViTs, masked autoencoder (MAE) modeling remains dominant. This raises the question: Can we take the best of both worlds? To answer this question, we first empirically validate that integrating MAE-based point cloud pre-training with the standard contrastive learning paradigm, even with meticulous design, can lead to a decrease in performance. To address this limitation, we reintroduce CL into the MAE-based point cloud pre-training paradigm by leveraging the inherent contrastive properties of MAE. Specifically, rather than relying on extensive data augmentation as commonly used in the image domain, we randomly mask the input tokens twice to generate contrastive input pairs. Subsequently, a weight-sharing encoder and two identically structured decoders are utilized to perform masked token reconstruction. Additionally, we propose that for an input token masked by both masks simultaneously, the reconstructed features should be as similar as possible. This naturally establishes an explicit contrastive constraint within the generative MAE-based pre-training paradigm, resulting in our proposed method, Point-CMAE. Consequently, Point-CMAE effectively enhances the representation quality and transfer performance compared to its MAE counterpart. Experimental evaluations across various downstream applications, including classification, part segmentation, and few-shot learning, demonstrate the efficacy of our framework in surpassing state-of-the-art techniques under standard ViTs and single-modal settings. The source code and trained models are available at: https://github.com/Amazingren/Point-CMAE., Comment: Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning

Image Restoration (IR), a classic low-level vision task, has witnessed significant advancements through deep models that effectively model global information. Notably, the Vision Transformers (ViTs) emergence has further propelled these advancements. When computing, the self-attention mechanism, a cornerstone of ViTs, tends to encompass all global cues, even those from semantically unrelated objects or regions. This inclusivity introduces computational inefficiencies, particularly noticeable with high input resolution, as it requires processing irrelevant information, thereby impeding efficiency. Additionally, for IR, it is commonly noted that small segments of a degraded image, particularly those closely aligned semantically, provide particularly relevant information to aid in the restoration process, as they contribute essential contextual cues crucial for accurate reconstruction. To address these challenges, we propose boosting IR's performance by sharing the key semantics via Transformer for IR (i.e., SemanIR) in this paper. Specifically, SemanIR initially constructs a sparse yet comprehensive key-semantic dictionary within each transformer stage by establishing essential semantic connections for every degraded patch. Subsequently, this dictionary is shared across all subsequent transformer blocks within the same stage. This strategy optimizes attention calculation within each block by focusing exclusively on semantically related components stored in the key-semantic dictionary. As a result, attention calculation achieves linear computational complexity within each window. Extensive experiments across 6 IR tasks confirm the proposed SemanIR's state-of-the-art performance, quantitatively and qualitatively showcasing advancements., Comment: 9 pages

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $ n \rightarrow { inv} $ and $ nn \rightarrow { inv} $. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$., Comment: 28 pages, 7 figures, 4 tables

This work is motivated by recent developments in Deep Neural Networks, particularly the Transformer architectures underlying applications such as ChatGPT, and the need for performing inference on mobile devices. Focusing on emerging transformers (specifically the ones with computationally efficient Swin-like architectures) and large models (e.g., Stable Diffusion and LLMs) based on transformers, we observe that layout transformations between the computational operators cause a significant slowdown in these applications. This paper presents SmartMem, a comprehensive framework for eliminating most layout transformations, with the idea that multiple operators can use the same tensor layout through careful choice of layout and implementation of operations. Our approach is based on classifying the operators into four groups, and considering combinations of producer-consumer edges between the operators. We develop a set of methods for searching such layouts. Another component of our work is developing efficient memory layouts for 2.5 dimensional memory commonly seen in mobile devices. Our experimental results show that SmartMem outperforms 5 state-of-the-art DNN execution frameworks on mobile devices across 18 varied neural networks, including CNNs, Transformers with both local and global attention, as well as LLMs. In particular, compared to DNNFusion, SmartMem achieves an average speedup of 2.8$\times$, and outperforms TVM and MNN with speedups of 6.9$\times$ and 7.9$\times$, respectively, on average.

This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor of x4 based on pairs of low and corresponding high-resolution images. The primary objective is to develop networks that optimize various aspects such as runtime, parameters, and FLOPs, while still maintaining a peak signal-to-noise ratio (PSNR) of approximately 26.90 dB on the DIV2K_LSDIR_valid dataset and 26.99 dB on the DIV2K_LSDIR_test dataset. In addition, this challenge has 4 tracks including the main track (overall performance), sub-track 1 (runtime), sub-track 2 (FLOPs), and sub-track 3 (parameters). In the main track, all three metrics (ie runtime, FLOPs, and parameter count) were considered. The ranking of the main track is calculated based on a weighted sum-up of the scores of all other sub-tracks. In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking. In sub-track 2, the number of FLOPs was considered. The score calculated based on the corresponding FLOPs was used to determine the ranking. In sub-track 3, the number of parameters was considered. The score calculated based on the corresponding parameters was used to determine the ranking. RLFN is set as the baseline for efficiency measurement. The challenge had 262 registered participants, and 34 teams made valid submissions. They gauge the state-of-the-art in efficient single-image super-resolution. To facilitate the reproducibility of the challenge and enable other researchers to build upon these findings, the code and the pre-trained model of validated solutions are made publicly available at https://github.com/Amazingren/NTIRE2024_ESR/., Comment: The report paper of NTIRE2024 Efficient Super-resolution, accepted by CVPRW2024

Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary. While several robotic platforms have been used in gerontological healthcare, the question of whether or not a social interactive robot with multi-modal conversational capabilities will be useful and accepted in real-life facilities is yet to be answered. This paper is an attempt to partially answer this question, via two waves of experiments with patients and companions in a day-care gerontological facility in Paris with a full-sized humanoid robot endowed with social and conversational interaction capabilities. The software architecture, developed during the H2020 SPRING project, together with the experimental protocol, allowed us to evaluate the acceptability (AES) and usability (SUS) with more than 60 end-users. Overall, the users are receptive to this technology, especially when the robot perception and action skills are robust to environmental clutter and flexible to handle a plethora of different interactions.

Though many compilation and runtime systems have been developed for DNNs in recent years, the focus has largely been on static DNNs. Dynamic DNNs, where tensor shapes and sizes and even the set of operators used are dependent upon the input and/or execution, are becoming common. This paper presents SoD$^2$, a comprehensive framework for optimizing Dynamic DNNs. The basis of our approach is a classification of common operators that form DNNs, and the use of this classification towards a Rank and Dimension Propagation (RDP) method. This framework statically determines the shapes of operators as known constants, symbolic constants, or operations on these. Next, using RDP we enable a series of optimizations, like fused code generation, execution (order) planning, and even runtime memory allocation plan generation. By evaluating the framework on 10 emerging Dynamic DNNs and comparing it against several existing systems, we demonstrate both reductions in execution latency and memory requirements, with RDP-enabled key optimizations responsible for much of the gains. Our evaluation results show that SoD$^2$ runs up to $3.9\times$ faster than these systems while saving up to $88\%$ peak memory consumption.

Context.Spatially-resoved cicrumstellar disk spectrum and composition can provide valuable insights into the bulk composition of forming planets, as well as the mineralogical signatures that emerge during and after planet formation. Aims. We aim to systemically extract the RX~J1604.3-213010 (J1604 hereafter) protoplanetary disk in high-contrast imaging observations, and obtain its multi-band reflectance in visible to near-infrared wavelengths. Methods. We obtained coronagraphic observations of J1604 from the Keck Observatory's NIRC2 instrument, and archival data from the Very Large Telescope's SPHERE instrument. Using archival images to remove star light and speckles, we recovered the J1604 disk and obtained its surface brightness using forward modeling. Together with polarization data, we obtained the relative reflectance of the disk in $R$, $J$, $H$ ($H2$ and $H3$), $K$ ($K1$ and $K2$), and $L'$ bands spanning two years. Results. Relative to the J1604 star, the resolved disk has a reflectance of ${\sim}10^{-1}$~arcsec$^{-2}$ in $R$ through $H$ bands and ${\sim}10^{-2}$~arcsec$^{-2}$ in $K$ and $L'$ bands, showing a blue color. Together with other systems, we summarized the multi-band reflectance for 9 systems. We also identified varying disk geometry structure, and a shadow that vanished between June and August in 2015. Conclusions. Motivated by broad-band observations, the deployment of cutting-edge technologies could yield higher-resolution reflection spectra, thereby informing the dust composition of disks in scattered light in the future. With multi-epoch observations, variable shadows have the potential to deepen insights into the dynamic characteristics of inner disk regions., Comment: 13 pages, 6 figures

The close-in regions of bright quasars' host galaxies have been difficult to image due to the overwhelming light from the quasars. With coronagraphic observations in visible light using the Space Telescope Imaging Spectrograph (STIS) on the Hubble Space Telescope, we removed 3C 273 quasar light using color-matching reference stars. The observations revealed the host galaxy from 60" to 0.2" with nearly full angular coverage. Isophote modeling revealed a new core jet, a core blob, and multiple smaller-scale blobs within 2.5". The blobs could potentially be satellite galaxies or infalling materials towards the central quasar. Using archival STIS data, we constrained the apparent motion of its large scale jets over a 22 yr timeline. By resolving the 3C 273 host galaxy with STIS, our study validates the coronagraph usage on extragalactic sources in obtaining new insights into the central ~kpc regions of quasar hosts., Comment: 13 pages, 11 figures, 2 tables, A&A Letters accepted

While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, especially with high input resolution. Furthermore, the self-attention mechanism in transformers is prone to considering unnecessary global cues from unrelated objects or regions, introducing computational inefficiencies. In response to these challenges, we introduce the Key-Graph Transformer (KGT) in this paper. Specifically, KGT views patch features as graph nodes. The proposed Key-Graph Constructor efficiently forms a sparse yet representative Key-Graph by selectively connecting essential nodes instead of all the nodes. Then the proposed Key-Graph Attention is conducted under the guidance of the Key-Graph only among selected nodes with linear computational complexity within each window. Extensive experiments across 6 IR tasks confirm the proposed KGT's state-of-the-art performance, showcasing advancements both quantitatively and qualitatively., Comment: 9 pages, 6 figures

Although data-driven methods have achieved success in 3D human pose estimation, they often suffer from domain gaps and exhibit limited generalization. In contrast, optimization-based methods excel in fine-tuning for specific cases but are generally inferior to data-driven methods in overall performance. We observe that previous optimization-based methods commonly rely on projection constraint, which only ensures alignment in 2D space, potentially leading to the overfitting problem. To address this, we propose an Uncertainty-Aware testing-time Optimization (UAO) framework, which keeps the prior information of pre-trained model and alleviates the overfitting problem using the uncertainty of joints. Specifically, during the training phase, we design an effective 2D-to-3D network for estimating the corresponding 3D pose while quantifying the uncertainty of each 3D joint. For optimization during testing, the proposed optimization framework freezes the pre-trained model and optimizes only a latent state. Projection loss is then employed to ensure the generated poses are well aligned in 2D space for high-quality optimization. Furthermore, we utilize the uncertainty of each joint to determine how much each joint is allowed for optimization. The effectiveness and superiority of the proposed framework are validated through extensive experiments on two challenging datasets: Human3.6M and MPI-INF-3DHP. Notably, our approach outperforms the previous best result by a large margin of 4.5% on Human3.6M. Our source code will be open-sourced.

Spatially-resolved images of debris disks are necessary to determine disk morphological properties and the scattering phase function (SPF) which quantifies the brightness of scattered light as a function of phase angle. Current high-contrast imaging instruments have successfully resolved several dozens of debris disks around other stars, but few studies have investigated trends in the scattered-light, resolved population of debris disks in a uniform and consistent manner. We have combined Karhunen-Loeve Image Projection (KLIP) with radiative-transfer disk forward modeling in order to obtain the highest quality image reductions and constrain disk morphological properties of eight debris disks imaged by the Gemini Planet Imager at H-band with a consistent and uniformly-applied approach. In describing the scattering properties of our models, we assume a common SPF informed from solar system dust scattering measurements and apply it to all systems. We identify a diverse range of dust density properties among the sample, including critical radius, radial width, and vertical width. We also identify radially narrow and vertically extended disks that may have resulted from substellar companion perturbations, along with a tentative positive trend in disk eccentricity with relative disk width. We also find that using a common SPF can achieve reasonable model fits for disks that are axisymmetric and asymmetric when fitting models to each side of the disk independently, suggesting that scattering behavior from debris disks may be similar to Solar System dust., Comment: 23+5 pages, 12+6 figures, 15 pages of Online Supplemental Material included; Accepted for publication in MNRAS

Inspired by the success of contrastive learning, we systematically examine recommendation losses, including listwise (softmax), pairwise (BPR), and pointwise (MSE and CCL) losses. In this endeavor, we introduce InfoNCE+, an optimized generalization of InfoNCE with balance coefficients, and highlight its performance advantages, particularly when aligned with our new decoupled contrastive loss, MINE+. We also leverage debiased InfoNCE to debias pointwise recommendation loss (CCL) as Debiased CCL. Interestingly, our analysis reveals that linear models like iALS and EASE are inherently debiased. Empirical results demonstrates the effectiveness of MINE+ and Debiased-CCL., Comment: This manuscript was initially submitted for review in August 2023

The unprecedented medium-resolution (R~1500-3500) near- and mid-infrared (1-18um) spectrum provided by JWST for the young (140+/-20Myr) low-mass (12-20MJup) L-T transition (L7) companion VHS1256b gives access to a catalogue of molecular absorptions. In this study, we present a comprehensive analysis of this dataset utilizing a forward modelling approach, applying our Bayesian framework, ForMoSA. We explore five distinct atmospheric models to assess their performance in estimating key atmospheric parameters: Teff, log(g), [M/H], C/O, gamma, fsed, and R. Our findings reveal that each parameter's estimate is significantly influenced by factors such as the wavelength range considered and the model chosen for the fit. This is attributed to systematic errors in the models and their challenges in accurately replicating the complex atmospheric structure of VHS1256b, notably the complexity of its clouds and dust distribution. To propagate the impact of these systematic uncertainties on our atmospheric property estimates, we introduce innovative fitting methodologies based on independent fits performed on different spectral windows. We finally derived a Teff consistent with the spectral type of the target, considering its young age, which is confirmed by our estimate of log(g). Despite the exceptional data quality, attaining robust estimates for chemical abundances [M/H] and C/O, often employed as indicators of formation history, remains challenging. Nevertheless, the pioneering case of JWST's data for VHS1256b has paved the way for future acquisitions of substellar spectra that will be systematically analyzed to directly compare the properties of these objects and correct the systematics in the models., Comment: 32 pages, 16 figures, 6 tables, 2 appendices

Learning-based point cloud registration approaches have significantly outperformed their traditional counterparts. However, they typically require extensive training on specific datasets. In this paper, we propose , the first zero-shot point cloud registration approach that eliminates the need for training on point cloud datasets. The cornerstone of ZeroReg is the novel transfer of image features from keypoints to the point cloud, enriched by aggregating information from 3D geometric neighborhoods. Specifically, we extract keypoints and features from 2D image pairs using a frozen pretrained 2D backbone. These features are then projected in 3D, and patches are constructed by searching for neighboring points. We integrate the geometric and visual features of each point using our novel parameter-free geometric decoder. Subsequently, the task of determining correspondences between point clouds is formulated as an optimal transport problem. Extensive evaluations of ZeroReg demonstrate its competitive performance against both traditional and learning-based methods. On benchmarks such as 3DMatch, 3DLoMatch, and ScanNet, ZeroReg achieves impressive Recall Ratios (RR) of over 84%, 46%, and 75%, respectively.

The Gemini Planet Imager (GPI) has excelled in imaging debris disks in the near-infrared. The GPI Exoplanet Survey (GPIES) imaged twenty-four debris disks in polarized $H$-band light, while other programs observed half of these disks in polarized $J$- and/or $K1$-bands. Using these data, we present a uniform analysis of the morphology of each disk to find asymmetries suggestive of perturbations, particularly those due to planet-disk interactions. The multi-wavelength surface brightness, the disk color and geometry permit identification of any asymmetries such as warps or disk offsets from the central star. We find that nineteen of the disks in this sample exhibit asymmetries in surface brightness, disk color, disk geometry, or a combination of the three, suggesting that for this sample, perturbations, as seen in scattered light, are common. The relationship between these perturbations and potential planets in the system are discussed. We also explore correlations among stellar temperatures, ages, disk properties, and observed perturbations. We find significant trends between the vertical aspect ratio and the stellar temperature, disk radial extent, and the dust grain size distribution power-law, $q$. We also confirm a trend between the disk color and stellar effective temperature, where the disk becomes increasingly red/neutral with increasing temperature. Such results have important implications on the evolution of debris disk systems around stars of various spectral types., Comment: 46 pages, 20 figures, 6 tables, accepted for publication in ApJ

While there have been nearly two dozen of spiral arms detected from planet-forming disks in near-infrared scattered light, none of their substellar drivers have been confirmed. By observing spiral systems in at least two epochs spanning multiple years, and measuring the motion of the spirals, we can distinguish the cause of the spirals, and locate the orbits of the driving planets if they trigger the spirals. Upon a recent validation of this approach using the co-motion between a stellar companion and a spiral, we obtained a second epoch observation for the spiral system in the disk of V1247 Ori in the $H$-band polarized scattered light using VLT/SPHERE/IRDIS. Combining our observations with archival IRDIS data, we established a $4.8$ yr timeline to constrain the V1247 Ori spiral motion. We obtained a pattern speed of $0.40^{\circ} \pm 0.09^{\circ}$ yr$^{-1}$ for the north-east spiral. This corresponds to an orbital period of $900\pm200$ yr, and thus the semi-major axis of the hidden planetary driver is $118\pm19$ au for a 2.0 $\pm$ 0.1 M$_\odot$ central star. The location agrees with the gap in ALMA dust continuum observations, providing joint support for the existence of a companion driving the scattered-light spirals while carving a millimeter gap. With an angular separation of 0.29" $\pm$ 0.05", this hidden companion is an ideal target for JWST imaging., Comment: Accepted for publication in Astronomy and Astrophysics; 6 pages, 5 figures

We present aperture masking interferometry (AMI) observations of the star HIP 65426 at $3.8\,\rm{\mu m}$ as a part of the JWST Direct Imaging Early Release Science (ERS) program obtained using the Near Infrared Imager and Slitless Spectrograph (NIRISS) instrument. This mode provides access to very small inner working angles (even separations slightly below the Michelson limit of $0.5\lambda/D$ for an interferometer), which are inaccessible with the classical inner working angles of the JWST coronagraphs. When combined with JWST's unprecedented infrared sensitivity, this mode has the potential to probe a new portion of parameter space across a wide array of astronomical observations. Using this mode, we are able to achieve a $5\sigma$ contrast of $\Delta m{\sim}7.62{\pm}0.13$ mag relative to the host star at separations ${\gtrsim}0.07{"}$, and the contrast deteriorates steeply at separations ${\lesssim}0.07{"}$. However, we detect no additional companions interior to the known companion HIP 65426 b (at separation ${\sim}0.82{"}$ or, $87^{+108}_{-31}\,\rm{au}$). Our observations thus rule out companions more massive than $10{-}12\,\rm{M_{Jup}}$ at separations ${\sim}10{-}20\,\rm{au}$ from HIP 65426, a region out of reach of ground or space-based coronagraphic imaging. These observations confirm that the AMI mode on JWST is sensitive to planetary mass companions at close-in separations (${\gtrsim}0.07{"}$), even for thousands of more distant stars at $\sim$100 pc, in addition to the stars in the nearby young moving groups as stated in previous works. This result will allow the planning and successful execution of future observations to probe the inner regions of nearby stellar systems, opening an essentially unexplored parameter space., Comment: 20 pages, 12 figures, submitted to ApJL

We present a performance analysis for the aperture masking interferometry (AMI) mode on board the James Webb Space Telescope Near Infrared Imager and Slitless Spectrograph (JWST/NIRISS). Thanks to self-calibrating observables, AMI accesses inner working angles down to and even within the classical diffraction limit. The scientific potential of this mode has recently been demonstrated by the Early Release Science (ERS) 1386 program with a deep search for close-in companions in the HIP 65426 exoplanetary system. As part of ERS 1386, we use the same data set to explore the random, static, and calibration errors of NIRISS AMI observables. We compare the observed noise properties and achievable contrast to theoretical predictions. We explore possible sources of calibration errors and show that differences in charge migration between the observations of HIP 65426 and point-spread function calibration stars can account for the achieved contrast curves. Lastly, we use self-calibration tests to demonstrate that with adequate calibration NIRISS F380M AMI can reach contrast levels of $\sim9-10$ mag at $\gtrsim \lambda/D$. These tests lead us to observation planning recommendations and strongly motivate future studies aimed at producing sophisticated calibration strategies taking these systematic effects into account. This will unlock the unprecedented capabilities of JWST/NIRISS AMI, with sensitivity to significantly colder, lower-mass exoplanets than lower-contrast ground-based AMI setups, at orbital separations inaccessible to JWST coronagraphy., Comment: 20 pages, 12 figures, accepted to Astrophysical Journal Letters

Diverse protoplanetary disk morphology can result from planet-disk interaction, suggesting planetary presence. To date, most scattered light imaging campaigns have probed polarized light, which is only a fraction of the total light and not very sensitive to planets. To observe and characterize protoplanetary disk systems in the near-infrared in both polarized and total intensity light, we carried out an unprecedented study of scattering properties of disks, as well as of any planetary companions. Using SPHERE with star-hopping at the Very Large Telescope, we observed 29 disk hosts and their reference stars in $K_s$-band polarized light. We extracted disks in total intensity by adopting the data imputation concept with sequential non-negative matrix factorization (DI-sNMF). We obtained high-quality disk images in total intensity for 15 systems and in polarized light for 23. For well-recovered disks in polarized light and total intensity, we parameterized the polarization fraction phase functions using scaled beta distribution: the peak of polarization fraction tentatively correlates with the peak scattering angle, which could be reproduced using certain compact dust, yet more detailed modeling studies are needed. We investigated the empirical DI-sNMF detectability of disks using logistic regression: total intensity detectability of disks primarily depends on host star brightness. For disks with SPHERE data in $Y$-/$J$-/$H$-band, we summarized their polarized color at ~90 deg scattering angle: most of disks are blue in polarized $J-K_s$ color, and they are relatively redder as stellar luminosity increases, indicating larger scatterers. High-quality disk imagery in both total intensity and polarized light thus allows for disk characterization in polarization fraction, and reduces the confusion between disk and planetary signals., Comment: 25 pages, 16 figures, 3 tables, A&A accepted. Data files in FITS format will be publicly available

The core-collapse supernova (CCSN) is considered one of the most energetic astrophysical events in the universe. The early and prompt detection of neutrinos before (pre-SN) and during the supernova (SN) burst presents a unique opportunity for multi-messenger observations of CCSN events. In this study, we describe the monitoring concept and present the sensitivity of the system to pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton liquid scintillator detector currently under construction in South China. The real-time monitoring system is designed to ensure both prompt alert speed and comprehensive coverage of progenitor stars. It incorporates prompt monitors on the electronic board as well as online monitors at the data acquisition stage. Assuming a false alert rate of 1 per year, this monitoring system exhibits sensitivity to pre-SN neutrinos up to a distance of approximately 1.6 (0.9) kiloparsecs and SN neutrinos up to about 370 (360) kiloparsecs for a progenitor mass of 30 solar masses, considering both normal and inverted mass ordering scenarios. The pointing ability of the CCSN is evaluated by analyzing the accumulated event anisotropy of inverse beta decay interactions from pre-SN or SN neutrinos. This, along with the early alert, can play a crucial role in facilitating follow-up multi-messenger observations of the next galactic or nearby extragalactic CCSN., Comment: 24 pages, 9 figures, accepted for the publication at JCAP

Vortex fiber nulling (VFN) is a single-aperture interferometric technique for detecting and characterizing exoplanets separated from their host star by less than a diffracted beam width. VFN uses a vortex mask and single mode fiber to selectively reject starlight while coupling off-axis planet light with a simple optical design that can be readily implemented on existing direct imaging instruments that can feed light to an optical fiber. With its axially symmetric coupling region peaking within the inner working angle of conventional coronagraphs, VFN is more efficient at detecting new companions at small separations than conventional direct imaging, thereby increasing the yield of on-going exoplanet search campaigns. We deployed a VFN mode operating in K band ($2.0{-}2.5~\mu$m) on the Keck Planet Imager and Characterizer (KPIC) instrument at the Keck II Telescope. In this paper we present the instrument design of this first on-sky demonstration of VFN and the results from on-sky commissioning, including planet and star throughput measurements and predicted flux-ratio detection limits for close-in companions. The instrument performance is shown to be sufficient for detecting a companion $10^3$ times fainter than a $5^{\mathrm{th}}$ magnitude host star in 1 hour at a separation of 50 mas (1.1$\lambda/D$). This makes the instrument capable of efficiently detecting substellar companions around young stars. We also discuss several routes for improvement that will reduce the required integration time for a detection by a factor ${>}$3., Comment: 26 pages, 5 figures; Accepted to JATIS

We present a generalized Non-negative factorization (NMF)-based data reduction pipeline for circumstellar disk and exoplanet detection. By using an adaptable pre-processing routine that applies algorithmic masks and corrections to improper data, we are able to easily offload the computationally-intensive NMF algorithm to a graphics processing unit (GPU), significantly increasing computational efficiency. NMF has been shown to better preserve disk structural features compared to other post-processing approaches and has demonstrated improvements in the analysis of archival data. The adaptive pre-processing routine of this pipeline, which automatically aligns and applies image corrections to the raw data, is shown to significantly improve chromatic halo suppression. Utilizing HST-STIS and JWST-MIRI coronagraphic datasets, we demonstrate a factor of five increase in real-time computational efficiency by using GPUs to perform NMF compared to using CPUs. Additionally, we demonstrate the usefulness of higher numbers of NMF components with SNR and contrast improvements, which necessitates the use of a more computationally efficient approach for data reduction.

Detection and characterization of extended structures is a crucial goal in high contrast imaging. However, these structures face challenges in data reduction, leading to over-subtraction from speckles and self-subtraction with most existing methods. Iterative post-processing methods offer promising results, but their integration into existing pipelines is hindered by selective algorithms, high computational cost, and algorithmic regularization. To address this for reference differential imaging (RDI), here we propose the data imputation concept to Karhunen-Lo\`eve transform (DIKL) by modifying two steps in the standard Karhunen-Lo\`eve image projection (KLIP) method. Specifically, we partition an image to two matrices: an anchor matrix which focuses only on the speckles to obtain the DIKL coefficients, and a boat matrix which focuses on the regions of astrophysical interest for speckle removal using DIKL components. As an analytical approach, DIKL achieves high-quality results with significantly reduced computational cost (~3 orders of magnitude less than iterative methods). Being a derivative method of KLIP, DIKL is seamlessly integrable into high contrast imaging pipelines for RDI observations., Comment: 7 pages, 5 figures, A&A accepted

The Keck Planet Imager and Characterizer (KPIC) is an instrument at the Keck II telescope that enables high-resolution spectroscopy of directly imaged exoplanets and substellar companions. KPIC uses single-mode fibers to couple the adaptive optics system to Keck's near-infrared spectrometer (NIRSPEC). However, KPIC's sensitivity at small separations is limited by the leakage of stellar light into the fiber. Speckle nulling uses a deformable mirror to destructively interfere starlight with itself, a technique typically used to reduce stellar signal on a focal-plane imaging detector. We present the first on-sky demonstration of speckle nulling through an optical fiber with KPIC, using NIRSPEC to collect exposures that measure speckle phase for quasi-real-time wavefront control while also serving as science data. We repeat iterations of measurement and correction, each using at least 5 exposures. We show a decrease in the on-sky leaked starlight by a factor of 2.6 to 2.8 in the targeted spectral order, at a spatial separation of 2.0 {\lambda}/D in K-band. This corresponds to an estimated factor of 2.6 to 2.8 decrease in the required exposure time to reach a given SNR, relative to conventional KPIC observations. The performance of speckle nulling is limited by instability in the speckle phase: when the loop is opened, the null-depth degrades by a factor of 2 on the timescale of a single phase measurement, which would limit the suppression that can be achieved. Future work includes exploring gradient-descent methods, which may be faster and thereby able to achieve deeper nulls. In the meantime, the speckle nulling algorithm demonstrated in this work can be used to decrease stellar leakage and improve the signal-to-noise of science observations., Comment: 18 pages, 4 figures

Sparse rewards pose a significant challenge to achieving high sample efficiency in goal-conditioned reinforcement learning (RL). Specifically, in sequential manipulation tasks, the agent receives failure rewards until it successfully completes the entire manipulation task, which leads to low sample efficiency. To tackle this issue and improve sample efficiency, we propose a novel model-based RL framework called Model-based Relay Hindsight Experience Replay (MRHER). MRHER breaks down a continuous task into subtasks with increasing complexity and utilizes the previous subtask to guide the learning of the subsequent one. Instead of using Hindsight Experience Replay (HER) in every subtask, we design a new robust model-based relabeling method called Foresight relabeling (FR). FR predicts the future trajectory of the hindsight state and relabels the expected goal as a goal achieved on the virtual future trajectory. By incorporating FR, MRHER effectively captures more information from historical experiences, leading to improved sample efficiency, particularly in object-manipulation environments. Experimental results demonstrate that MRHER exhibits state-of-the-art sample efficiency in benchmark tasks, outperforming RHER by 13.79% and 14.29% in the FetchPush-v1 environment and FetchPickandPlace-v1 environment, respectively.

Radio and near-infrared observations have observed dozens of protoplanetary disks that host spiral arm features. Numerical simulations have shown that companions may excite spiral density waves in protoplanetary disks via companion-disk interaction. However, the lack of direct observational evidence for spiral-driving companions poses challenges to current theories of companion-disk interaction. Here we report multi-epoch observations of the binary system HD 100453 with the Spectro-Polarimetric High-contrast Exoplanet REsearch (SPHERE) facility at the Very Large Telescope. By recovering the spiral features via robustly removing starlight contamination, we measure spiral motion across 4 yr to perform dynamical motion analyses. The spiral pattern motion is consistent with the orbital motion of the eccentric companion. With this first observational evidence of a companion driving a spiral arm among protoplanetary disks, we directly and dynamically confirm the long-standing theory on the origin of spiral features in protoplanetary disks. With the pattern motion of companion-driven spirals being independent of companion mass, here we establish a feasible way of searching for hidden spiral-arm-driving planets that are beyond the detection of existing ground-based high-contrast imagers., Comment: Accepted for publication in Astronomy and Astrophysics; 12 pages, 9 figures

We discuss JUNO sensitivity to the annihilation of MeV dark matter in the galactic halo via detecting inverse beta decay reactions of electron anti-neutrinos resulting from the annihilation. We study possible backgrounds to the signature, including the reactor neutrinos, diffuse supernova neutrino background, charged- and neutral-current interactions of atmospheric neutrinos, backgrounds from muon-induced fast neutrons and cosmogenic isotopes. A fiducial volume cut, as well as the pulse shape discrimination and the muon veto are applied to suppress the above backgrounds. It is shown that JUNO sensitivity to the thermally averaged dark matter annihilation rate in 10 years of exposure would be significantly better than the present-day best limit set by Super-Kamiokande and would be comparable to that expected by Hyper-Kamiokande., Comment: 25 pages, 9 figures, matches the publised version

HD 53143 is a mature Sun-like star and host to a broad disk of dusty debris, including a cold outer ring of planetesimals near 90 AU. Unlike most other inclined debris disks imaged at visible wavelengths, the cold disk around HD 53143 appears as disconnected "arcs" of material, with no forward scattering side detected to date. We present new, deeper Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) coronagraphic observations of the HD 53143 debris disk and show that the forward scattering side of the disk remains undetected. By fitting our KLIP-reduced observations via forward modeling with an optically thin disk model, we show that fitting the visible wavelength images with an azimuthally symmetric disk with unconstrained orientation results in an unphysical edge-on orientation that is at odds with recent ALMA observations, while constraining the orientation to that observed by ALMA results in nearly isotropically scattering dust. We show that the HD53143 host star exhibits significant stellar variations due to spot rotation and revisit age estimates for this system., Comment: 13 pages, 8 figures, 2 tables Accepted for publication in ApJ

Quantum circuit cutting has been proposed to help execute large quantum circuits using only small and noisy machines. Intuitively, cutting a qubit wire can be thought of as classically passing information of a quantum state along each element in a basis set. As the number of cuts increase, the number of quantum degrees of freedom needed to be passed through scales exponentially. We propose a simple reduction scheme to lower the classical and quantum resources required to perform a cut. Particularly, we recognize that for some cuts, certain basis element might pass "no information" through the qubit wire and can effectively be neglected. We empirically demonstrate our method on circuit simulators as well as IBM quantum hardware, and we observed up to 33 percent reduction in wall time without loss of accuracy., Comment: 7 pages, 5 figures, submitted to 37th IEEE International Parallel & Distributed Processing Symposium

White-light superflares from ultra cool stars are thought to be resulted from magnetic reconnection, but the magnetic dynamics in a fully convective star is not clear yet. In this paper, we report a stellar superflare detected with the Ground Wide Angle Camera (GWAC), along with rapid follow-ups with the F60A, Xinglong 2.16m and LCOGT telescopes. The effective temperature of the counterpart is estimated to be $2200\pm50$K by the BT-Settl model, corresponding to a spectral type of L0. The $R-$band light curve can be modeled as a sum of three exponential decay components, where the impulsive component contributes a fraction of 23\% of the total energy, while the gradual and the shallower decay phases emit 42\% and 35\% of the total energy, respectively. The strong and variable Balmer narrow emission lines indicate the large amplitude flare is resulted from magnetic activity. The bolometric energy released is about $6.4\times10^{33}$ ergs, equivalent to an energy release in a duration of 143.7 hours at its quiescent level. The amplitude of $\Delta R=-8.6 $mag ( or $\Delta V=-11.2$ mag), placing it one of the highest amplitudes of any ultra cool star recorded with excellent temporal resolution. We argue that a stellar flare with such rapidly decaying and huge amplitude at distances greater than 1 kpc may be false positive in searching for counterparts of catastrophic events such as gravitational wave events or gamma-ray bursts, which are valuable in time-domain astronomy and should be given more attention., Comment: 9 pages, 5 figures, 1 table, MNRAS accepted

Planet formation imprints signatures on the physical structures of disks. In this paper, we present high-resolution ($\sim$50 mas, 8 au) Atacama Large Millimeter/submillimeter Array (ALMA) observations of 1.3 mm dust continuum and CO line emission toward the disk around the M3.5 star 2MASS J04124068+2438157. The dust disk consists only of two narrow rings at radial distances of 0.47 and 0.78 arcsec ($\sim$70 and 116 au), with Gaussian $\sigma$ widths of 5.6 and 8.5 au, respectively. The width of the outer ring is smaller than the estimated pressure scale height by $\sim25\%$, suggesting dust trapping in a radial pressure bump. The dust disk size, set by the location of the outermost ring, is significantly larger (by $3\sigma$) than other disks with similar millimeter luminosity, which can be explained by an early formation of local pressure bump to stop radial drift of millimeter dust grains. After considering the disk's physical structure and accretion properties, we prefer planet--disk interaction over dead zone or photoevaporation models to explain the observed dust disk morphology. We carry out high-contrast imaging at $L'$ band using Keck/NIRC2 to search for potential young planets, but do not identify any source above $5\sigma$. Within the dust gap between the two rings, we reach a contrast level of $\sim$7 mag, constraining the possible planet below $\sim$2--4 $M_{\rm Jup}$. Analyses of the gap/ring properties suggest a $\sim$Saturn mass planet at $\sim$90 au is likely responsible for the formation of the outer ring, which can be potentially revealed with JWST., Comment: 15 pages, 5 figures. Accepted for publication in ApJ

As deep convolutional neural networks (DNNs) are widely used in various fields of computer vision, leveraging the overfitting ability of the DNN to achieve video resolution upscaling has become a new trend in the modern video delivery system. By dividing videos into chunks and overfitting each chunk with a super-resolution model, the server encodes videos before transmitting them to the clients, thus achieving better video quality and transmission efficiency. However, a large number of chunks are expected to ensure good overfitting quality, which substantially increases the storage and consumes more bandwidth resources for data transmission. On the other hand, decreasing the number of chunks through training optimization techniques usually requires high model capacity, which significantly slows down execution speed. To reconcile such, we propose a novel method for high-quality and efficient video resolution upscaling tasks, which leverages the spatial-temporal information to accurately divide video into chunks, thus keeping the number of chunks as well as the model size to minimum. Additionally, we advance our method into a single overfitting model by a data-aware joint training technique, which further reduces the storage requirement with negligible quality drop. We deploy our models on an off-the-shelf mobile phone, and experimental results show that our method achieves real-time video super-resolution with high video quality. Compared with the state-of-the-art, our method achieves 28 fps streaming speed with 41.6 PSNR, which is 14$\times$ faster and 2.29 dB better in the live video resolution upscaling tasks. Code available in https://github.com/coulsonlee/STDO-CVPR2023.git, Comment: CVPR 2023 Highlight Paper

The main task of the Top Tracker detector of the neutrino reactor experiment Jiangmen Underground Neutrino Observatory (JUNO) is to reconstruct and extrapolate atmospheric muon tracks down to the central detector. This muon tracker will help to evaluate the contribution of the cosmogenic background to the signal. The Top Tracker is located above JUNO's water Cherenkov Detector and Central Detector, covering about 60% of the surface above them. The JUNO Top Tracker is constituted by the decommissioned OPERA experiment Target Tracker modules. The technology used consists in walls of two planes of plastic scintillator strips, one per transverse direction. Wavelength shifting fibres collect the light signal emitted by the scintillator strips and guide it to both ends where it is read by multianode photomultiplier tubes. Compared to the OPERA Target Tracker, the JUNO Top Tracker uses new electronics able to cope with the high rate produced by the high rock radioactivity compared to the one in Gran Sasso underground laboratory. This paper will present the new electronics and mechanical structure developed for the Top Tracker of JUNO along with its expected performance based on the current detector simulation., Comment: 20 pages

The Jiangmen Underground Neutrino Observatory (JUNO), the first multi-kton liquid scintillator detector, which is under construction in China, will have a unique potential to perform a real-time measurement of solar neutrinos well below the few MeV threshold typical for Water Cherenkov detectors. JUNO's large target mass and excellent energy resolution are prerequisites for reaching unprecedented levels of precision. In this paper, we provide estimation of the JUNO sensitivity to 7Be, pep, and CNO solar neutrinos that can be obtained via a spectral analysis above the 0.45 MeV threshold. This study is performed assuming different scenarios of the liquid scintillator radiopurity, ranging from the most opti mistic one corresponding to the radiopurity levels obtained by the Borexino experiment, up to the minimum requirements needed to perform the neutrino mass ordering determination with reactor antineutrinos - the main goal of JUNO. Our study shows that in most scenarios, JUNO will be able to improve the current best measurements on 7Be, pep, and CNO solar neutrino fluxes. We also perform a study on the JUNO capability to detect periodical time variations in the solar neutrino flux, such as the day-night modulation induced by neutrino flavor regeneration in Earth, and the modulations induced by temperature changes driven by helioseismic waves.

Multi-wavelength scattered light imaging of debris disks may inform dust properties including typical size and mineral composition. Existing studies have investigated a small set of individual systems across a variety of imaging instruments and filters, calling for uniform comparison studies to systematically investigate dust properties. We obtain the surface brightness of dust particles in debris disks by post-processing coronagraphic imaging observations, and compare the multi-wavelength reflectance of dust. For a sample of resolved debris disks, we perform a systematic analysis on the reflectance properties of their birth rings. We reduced the visible and near-infrared images of 23 debris disk systems hosted by A through M stars using two coronagraphs onboard the Hubble Space Telescope: the STIS instrument observations centering at 0.58 $\mu$m, and the NICMOS instrument at 1.12 $\mu$m or 1.60 $\mu$m. For proper recovery of debris disks, we used classical reference differential imaging for STIS, and adopted non-negative matrix factorization with forward modeling for NICMOS. By dividing disk signals by stellar signals to take into account of intrinsic stellar color effects, we systematically obtained and compared the reflectance of debris birth rings at ~90 deg scattering angle. Debris birth rings typically exhibit a blue color at ~90 deg scattering angle. As the stellar luminosity increases, the color tends to be more neutral. A likely L-shaped color-albedo distribution indicates a clustering of scatterer properties. The observed color trend correlates with the expected blow-out size of dust particles. The color-albedo clustering likely suggests different populations of dust in these systems. More detailed radiative transfer models with realistic dust morphology will contribute to explaining the observed color and color-albedo distribution of debris systems., Comment: 17 pages, 8 figures, 3 tables. A&A accepted