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We propose ImGeoNet, a multi-view image-based 3D object detection framework that models a 3D space by an image-induced geometry-aware voxel representation. Unlike previous methods which aggregate 2D features into 3D voxels without considering geometry, ImGeoNet learns to induce geometry from multi-view images to alleviate the confusion arising from voxels of free space, and during the inference phase, only images from multiple views are required. Besides, a powerful pre-trained 2D feature extractor can be leveraged by our representation, leading to a more robust performance. To evaluate the effectiveness of ImGeoNet, we conduct quantitative and qualitative experiments on three indoor datasets, namely ARKitScenes, ScanNetV2, and ScanNet200. The results demonstrate that ImGeoNet outperforms the current state-of-the-art multi-view image-based method, ImVoxelNet, on all three datasets in terms of detection accuracy. In addition, ImGeoNet shows great data efficiency by achieving results comparable to ImVoxelNet with 100 views while utilizing only 40 views. Furthermore, our studies indicate that our proposed image-induced geometry-aware representation can enable image-based methods to attain superior detection accuracy than the seminal point cloud-based method, VoteNet, in two practical scenarios: (1) scenarios where point clouds are sparse and noisy, such as in ARKitScenes, and (2) scenarios involve diverse object classes, particularly classes of small objects, as in the case in ScanNet200., Comment: ICCV'23; project page: https://ttaoretw.github.io/imgeonet/

Large-scale Pre-Training Vision-Language Model such as CLIP has demonstrated outstanding performance in zero-shot classification, e.g. achieving 76.3% top-1 accuracy on ImageNet without seeing any example, which leads to potential benefits to many tasks that have no labeled data. However, while applying CLIP to a downstream target domain, the presence of visual and text domain gaps and cross-modality misalignment can greatly impact the model performance. To address such challenges, we propose ReCLIP, the first source-free domain adaptation method for vision-language models, which does not require any source data or target labeled data. ReCLIP first learns a projection space to mitigate the misaligned visual-text embeddings and learns pseudo labels, and then deploys cross-modality self-training with the pseudo labels, to update visual and text encoders, refine labels and reduce domain gaps and misalignments iteratively. With extensive experiments, we demonstrate ReCLIP reduces the average error rate of CLIP from 30.17% to 25.06% on 22 image classification benchmarks. Code available at https://github.com/michiganleon/ReCLIP_WACV., Comment: Accepted as Oral Paper by 2024 IEEE CVF Winter Conference on Applications of Computer Vision (WACV)

The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy neutrinos, cosmic rays, and gamma rays, with energies above 100 PeV. GRAND targets the radio signals emitted by extensive air showers induced by the interaction of ultra-high-energy particles in the atmosphere, using an array of 200,000 radio antennas split into sub-arrays deployed worldwide. GRANDProto13 (GP13) is a 13-antenna demonstrator array deployed in February 2023 in the Gansu province of China, as a precursor for GRANDProto300, which will validate the detection principle of the GRAND experiment. Its goal is to measure the radio background present at the site, validate the design of the detection units and develop an autonomous radio trigger for air showers. We will describe GP13 and its operation, and show preliminary results on noise monitoring., Comment: Proceedings of the 38th International Cosmic Ray Conference (ICRC2023)

GRANDProto300 is a 300-antenna prototype array of the envisioned GRAND (Giant Radio Array for Neutrino Detection) project. The goal of GRANDProto300 is to detect radio signals emitted by cosmic ray-induced air showers, with energies ranging from $10^{16.5}$~eV to $10^{18.5}$~eV, which covers the transition region between Galactic and extragalactic sources. We use simulations to optimize the layout of GRANDProto300 and develop a shower reconstruction method. Based on them, we present the performance of GRANDProto300 for cosmic-ray detection, by means of its effective area, angular resolution, and energy resolution., Comment: Presented at the 38th International Cosmic Ray Conference (ICRC 2023), 8 pages, 9 figures

It remains unclear what mechanism is driving the evolution of protoplanetary disks. Direct detection of the main candidates, either turbulence driven by magnetorotational instability or magnetohydrodynamical disk winds, has proven difficult, leaving the time evolution of the disk size as one of the most promising observables able to differentiate between these two mechanisms. But to do so successfully, we need to understand what the observed gas disk size actually traces. We studied the relation between $R_{\rm CO,\ 90\%}$, the radius that encloses 90% of the $^{12}$CO flux, and $R_c$, the radius that encodes the physical disk size, in order to provide simple prescriptions for conversions between these two sizes. For an extensive grid of thermochemical models we calculate $R_{\rm CO,\ 90\%}$ from synthetic observations and relate properties measured at this radius, such as the gas column density, to bulk disk properties, such as $R_c$ and the disk mass $M_{\rm disk}$. We found an empirical correlation between the gas column density at $R_{\rm CO,\ 90\%}$ and disk mass: $N_{\rm gas}(R_{\rm CO,\ 90\%}) \approx 3.73\times10^{21}(M_{\rm disk}/\mathrm{M}_{\odot})^{0.34}\ \mathrm{cm}^{-2}$. Using this correlation we derive an analytical prescription of $R_{\rm CO,\ 90\%}$ that only depends on $R_c$ and $M_{\rm disk}$. We derive $R_c$ for disks in Lupus, Upper Sco, Taurus and DSHARP, finding that disks in the older Upper Sco region are significantly smaller ($\langle R_c \rangle$ = 4.8 au) than disks in the younger Lupus and Taurus regions ($\langle R_c \rangle$ = 19.8 and 20.9 au, respectively). This temporal decrease in $R_c$ goes against predictions of both viscous and wind-driven evolution, but could be a sign of significant external photoevaporation having truncated disks in Upper Sco., Comment: 23 pages, 12 figures, accepted in ApJ

Previous analyses of mid-infrared water spectra from young protoplanetary disks observed with the Spitzer-IRS found an anti-correlation between water luminosity and the millimeter dust disk radius observed with ALMA. This trend was suggested to be evidence for a fundamental process of inner disk water enrichment, used to explain properties of the Solar System 40 years ago, in which icy pebbles drift inward from the outer disk and sublimate after crossing the snowline. Previous analyses of IRS water spectra, however, were uncertain due to the low spectral resolution that blended lines together. We present new JWST-MIRI spectra of four disks, two compact and two large with multiple radial gaps, selected to test the scenario that water vapor inside the snowline is regulated by pebble drift. The higher spectral resolving power of MIRI-MRS now yields water spectra that separate individual lines, tracing upper level energies from 900 K to 10,000 K. These spectra clearly reveal excess emission in the low-energy lines in compact disks, compared to the large disks, demonstrating an enhanced cool component with $T \approx$ 170-400 K and equivalent emitting radius $R_{\rm{eq}}\approx$ 1-10 au. We interpret the cool water emission as ice sublimation and vapor diffusion near the snowline, suggesting that there is indeed a higher inwards mass flux of icy pebbles in compact disks. Observation of this process opens up multiple exciting prospects to study planet formation chemistry in inner disks with JWST., Comment: Accepted for publication in ApJ Letters

The Pulmonary Function Test (PFT) is an widely utilized and rigorous classification test for lung function evaluation, serving as a comprehensive tool for lung diagnosis. Meanwhile, Electrical Impedance Tomography (EIT) is a rapidly advancing clinical technique that visualizes conductivity distribution induced by ventilation. EIT provides additional spatial and temporal information on lung ventilation beyond traditional PFT. However, relying solely on conventional isolated interpretations of PFT results and EIT images overlooks the continuous dynamic aspects of lung ventilation. This study aims to classify lung ventilation patterns by extracting spatial and temporal features from the 3D EIT image series. The study uses a Variational Autoencoder network with a MultiRes block to compress the spatial distribution in a 3D image into a one-dimensional vector. These vectors are then concatenated to create a feature map for the exhibition of temporal features. A simple convolutional neural network is used for classification. Data collected from 137 subjects were finally used for training. The model is validated by ten-fold and leave-one-out cross-validation first. The accuracy and sensitivity of normal ventilation mode are 0.95 and 1.00, and the f1-score is 0.94. Furthermore, we check the reliability and feasibility of the proposed pipeline by testing it on newly recruited nine subjects. Our results show that the pipeline correctly predicts the ventilation mode of 8 out of 9 subjects. The study demonstrates the potential of using image series for lung ventilation mode classification, providing a feasible method for patient prescreening and presenting an alternative form of PFT.

Integrated microwave photonics is an intriguing field that leverages integrated photonic technologies for the generation, transmission, and manipulation of microwave signals in chip-scale optical systems. In particular, ultrafast processing and computation of analog electronic signals in the optical domain with high fidelity and low latency could enable a variety of applications such as MWP filters, microwave signal processing, and image recognition. An ideal photonic platform for achieving these integrated MWP processing tasks shall simultaneously offer an efficient, linear and high-speed electro-optic modulation block to faithfully perform microwave-optic conversion at low power, and a low-loss functional photonic network that can be configured for a variety of signal processing tasks, as well as large-scale, low-cost manufacturability to monolithically integrate the two building blocks on the same chip. In this work, we demonstrate such an integrated MWP processing engine based on a thin-film lithium niobate platform capable of performing multi-purpose processing and computation tasks of analog signals up to 92 giga samples per second at CMOS-compatible voltages. We demonstrate high-speed analog computation, i.e., first- and second-order temporal integration and differentiation with computing accuracies up to 98.1 %, and deploy these functions to showcase three proof-of-concept applications, namely, ordinary differential equation solving, ultra-wideband signal generation and high-speed edge detection of images. We further leverage the image edge detector to enable a photonic-assisted image segmentation model that could effectively outline the boundaries of melanoma lesion in medical diagnostic images, achieving orders of magnitude faster processing speed and lower power consumption than conventional electronic processors.

In this paper we prove a perturbative version of a remarkable Bialy-Mironov (Ann.Math:389-413(196), 2022) result. They prove non perturbative Birkhoff conjecture for centrally-symmetric convex domains, namely, a centrally-symmetric convex domain with integrable billiard is ellipse. We combine techniques from Bialy-Mironov (Ann.Math:389-413(196), 2022) with a local result by Kaloshin-Sorrentino (Ann.Math:315-380(188), 2018) and show that a domain close enough to a centrally-symmetric one with integrable billiard is ellipse. To combine these results we derive a slight extension of Bialy-Mironov (Ann.Math:389-413(196), 2022) by proving that a notion of rational integrability is equivalent to the $C^0$-integrability condition used in their paper., Comment: Content and presentation improved. 40 pages

In this paper, we mainly focus on the rigorous convergence analysis for two fully decoupled, unconditional energy stable methods of the two-phase magnetohydrodynamics (MHD) model, which described in our previous work \cite{2022Highly}. The two methods consist of semi-implicit stabilization method/invariant energy quadratization (IEQ) method \cite{2019EfficientCHEN, Yang2016Linear, Yang2017Efficient, 2019EfficientYANG} for the phase field system, the pressure projection correction method for the saddle point MHD system, the exquisite implicit-explicit treatments for nonlinear coupled terms, which leads to only require solving a sequence of small elliptic equations at each time step. As far as we know, it's the first time to establish the optimal convergence analysis of fully decoupled and unconditional energy stable methods for multi-physics nonlinear two-phase MHD model. In addition, several numerical examples are showed to test the accuracy and stability of the presented methods.