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Semi-Supervised Object Detection (SSOD) has achieved resounding success by leveraging unlabeled data to improve detection performance. However, in Open Scene Semi-Supervised Object Detection (O-SSOD), unlabeled data may contains unknown objects not observed in the labeled data, which will increase uncertainty in the model's predictions for known objects. It is detrimental to the current methods that mainly rely on self-training, as more uncertainty leads to the lower localization and classification precision of pseudo labels. To this end, we propose Credible Teacher, an end-to-end framework. Credible Teacher adopts an interactive teaching mechanism using flexible labels to prevent uncertain pseudo labels from misleading the model and gradually reduces its uncertainty through the guidance of other credible pseudo labels. Empirical results have demonstrated our method effectively restrains the adverse effect caused by O-SSOD and significantly outperforms existing counterparts., Comment: Accpet by ICASSP 2024

Planetary Radio Interferometry and Doppler Experiment (PRIDE) is a multi-purpose experimental technique aimed at enhancing the science return of planetary missions. The technique exploits the science payload and spacecraft service systems without requiring a dedicated onboard instrumentation or imposing on the existing instrumentation any special for PRIDE requirements. PRIDE is based on the near-field phase-referencing Very Long Baseline Interferometry (VLBI) and evaluation of the Doppler shift of the radio signal transmitted by spacecraft by observing it with multiple Earth-based radio telescopes. The methodology of PRIDE has been developed initially at the Joint Institute for VLBI ERIC (JIVE) for tracking the ESA's Huygens Probe during its descent in the atmosphere of Titan in 2005. From that point on, the technique has been demonstrated for various planetary and other space science missions. The estimates of lateral position of the target spacecraft are done using the phase-referencing VLBI technique. Together with radial Doppler estimates, these observables can be used for a variety of applications, including improving the knowledge of the spacecraft state vector. The PRIDE measurements can be applied to a broad scope of research fields including studies of atmospheres through the use of radio occultations, the improvement of planetary and satellite ephemerides, as well as gravity field parameters and other geodetic properties of interest, and estimations of interplanetary plasma properties. This paper presents the implementation of PRIDE as a component of the ESA's Jupiter Icy Moons Explorer (JUICE) mission., Comment: Accepted for publication in Space Science Reviews, v. 219 (special issue on the ESA's JUICE mission)

The supernova remnant (SNR) 30 Dor B is associated with the \ion{H}{2} region ionized by the OB association LH99. The complex interstellar environment has made it difficult to study the physical structure of this SNR. We have used Hubble Space Telescope H$\alpha$ images to identify SNR shocks and deep Chandra X-ray observations to detect faint diffuse emission. We find that 30 Dor B hosts three zones with very different X-ray surface brightnesses and nebular kinematics that are characteristic of SNRs in different interstellar environments and/or evolutionary stages. The ASKAP 888 MHz map of 30 Dor B shows counterparts to all X-ray emission features except the faint halo. The ASKAP 888 MHz and 1420 MHz observations are used to produce a spectral index map, but its interpretation is complicated by the background thermal emission and the pulsar PSR J0537$-$6910's flat spectral index. The stellar population in the vicinity of 30 Dor B indicates a continuous star formation in the past 8--10 Myr. The observed very massive stars in LH99 cannot be coeval with the progenitor of 30 Dor B's pulsar. Adopting the pulsar's spin-down timescale, 5000 yr, as the age of the SNR, the X-ray shell would be expanding at $\sim$4000 km\,s$^{-1}$ and the post-shock temperature would be 1--2 orders of magnitude higher than that indicated by the X-ray spectra. Thus, the bright central region of 30 Dor B and the X-ray shell requires two separate SN events, and the faint diffuse X-ray halo perhaps other older SN events.

We analyze the DETR-based framework on semi-supervised object detection (SSOD) and observe that (1) the one-to-one assignment strategy generates incorrect matching when the pseudo ground-truth bounding box is inaccurate, leading to training inefficiency; (2) DETR-based detectors lack deterministic correspondence between the input query and its prediction output, which hinders the applicability of the consistency-based regularization widely used in current SSOD methods. We present Semi-DETR, the first transformer-based end-to-end semi-supervised object detector, to tackle these problems. Specifically, we propose a Stage-wise Hybrid Matching strategy that combines the one-to-many assignment and one-to-one assignment strategies to improve the training efficiency of the first stage and thus provide high-quality pseudo labels for the training of the second stage. Besides, we introduce a Crossview Query Consistency method to learn the semantic feature invariance of object queries from different views while avoiding the need to find deterministic query correspondence. Furthermore, we propose a Cost-based Pseudo Label Mining module to dynamically mine more pseudo boxes based on the matching cost of pseudo ground truth bounding boxes for consistency training. Extensive experiments on all SSOD settings of both COCO and Pascal VOC benchmark datasets show that our Semi-DETR method outperforms all state-of-the-art methods by clear margins. The PaddlePaddle version code1 is at https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/semi_det/semi_detr., Comment: CVPR2023

The EnVision and VERITAS missions to Venus will fly with X and Ka band telecommunications channels which can be used to conduct radio occultation studies of Venus' atmosphere. While link attenuation measurements during prior S and X band occultation experiments have been used to determine vertical profiles of H$_2$SO$_4$ vapor abundance, the addition of the Ka band channel introduces greater sensitivity to the abundances of H$_2$SO$_4$ aerosols and SO$_2$ gas, permitting retrieval of their vertical profiles from dual band measurements. Such measurements would be valuable in the assessment of chemical and dynamical processes governing short and long-term variability in Venus' atmosphere. This paper considers the sensitivity of the X/Ka band radio attenuation measurement to these atmospheric constituents, as well as uncertainties and regularization approaches for conducting retrievals of these atmospheric sulfur species from future occultation experiments. We introduce methods for seeding maximum likelihood estimation retrievals using shape models and simple atmospheric transport constraints. From simulated retrievals, we obtain mean errors of the order of 0.5 ppm, 20 ppm, and 10 mg/m$^3$ for H$_2$SO$_4$ vapor, SO$_2$, and H$_2$SO$_4$ aerosol abundances, respectively, for simultaneous retrieval., Comment: 29 pages, 11 figures, accepted to PSJ

The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest. In addition to spatial-temporal correlations, the functionality of urban areas also plays a crucial role in traffic flow prediction. However, the exploration of regional functional attributes mainly focuses on adding additional topological structures, ignoring the influence of functional attributes on regional traffic patterns. Different from the existing works, we propose a novel module named POI-MetaBlock, which utilizes the functionality of each region (represented by Point of Interest distribution) as metadata to further mine different traffic characteristics in areas with different functions. Specifically, the proposed POI-MetaBlock employs a self-attention architecture and incorporates POI and time information to generate dynamic attention parameters for each region, which enables the model to fit different traffic patterns of various areas at different times. Furthermore, our lightweight POI-MetaBlock can be easily integrated into conventional traffic flow prediction models. Extensive experiments demonstrate that our module significantly improves the performance of traffic flow prediction and outperforms state-of-the-art methods that use metadata., Comment: 15 pages, 8 figures.This work has been accepted by Information Sciences

Science archives are cornerstones of modern astronomical facilities. In this paper we describe the version 1.0 milestone of the Atacama Large Millimeter/submillimeter Array Science Archive. This version features a comprehensive query interface with rich metadata and visualisation of the spatial and spectral locations of the observations, a complete set of virtual observatory services for programmatic access, text-based similarity search, display and query for types of astronomical objects in SIMBAD and NED, browser-based remote visualisation, interactive previews with tentative line identification and extensive documentation including video and Jupyter Notebook tutorials. The development is regularly evaluated by means of user surveys and is entirely focused on providing the best possible user experience with the goal of helping to maximise the scientific productivity of the observatory., Comment: 6 pages, 7 figues

Land remote sensing analysis is a crucial research in earth science. In this work, we focus on a challenging task of land analysis, i.e., automatic extraction of traffic roads from remote sensing data, which has widespread applications in urban development and expansion estimation. Nevertheless, conventional methods either only utilized the limited information of aerial images, or simply fused multimodal information (e.g., vehicle trajectories), thus cannot well recognize unconstrained roads. To facilitate this problem, we introduce a novel neural network framework termed Cross-Modal Message Propagation Network (CMMPNet), which fully benefits the complementary different modal data (i.e., aerial images and crowdsourced trajectories). Specifically, CMMPNet is composed of two deep Auto-Encoders for modality-specific representation learning and a tailor-designed Dual Enhancement Module for cross-modal representation refinement. In particular, the complementary information of each modality is comprehensively extracted and dynamically propagated to enhance the representation of another modality. Extensive experiments on three real-world benchmarks demonstrate the effectiveness of our CMMPNet for robust road extraction benefiting from blending different modal data, either using image and trajectory data or image and Lidar data. From the experimental results, we observe that the proposed approach outperforms current state-of-the-art methods by large margins.Our source code is resealed on the project page http://lingboliu.com/multimodal_road_extraction.html., Comment: This work has been accepted by IEEE Transactions on Neural Networks and Learning Systems

Gaussian processes occupy one of the leading places in modern statistics and probability theory due to their importance and a wealth of strong results. The common use of Gaussian processes is in connection with problems related to estimation, detection, and many statistical or machine learning models. With the fast development of Gaussian process applications, it is necessary to consolidate the fundamentals of vector-valued stochastic processes, in particular multivariate Gaussian processes, which is the essential theory for many applied problems with multiple correlated responses. In this paper, we propose a precise definition of multivariate Gaussian processes based on Gaussian measures on vector-valued function spaces, and provide an existence proof. In addition, several fundamental properties of multivariate Gaussian processes, such as strict stationarity and independence, are introduced. We further derive multivariate Brownian motion including It\^o lemma as a special case of a multivariate Gaussian process, and present a brief introduction to multivariate Gaussian process regression as a useful statistical learning method for multi-output prediction problems., Comment: METRON (2023)

The massive young stellar object S255IR NIRS3 embedded in the star forming core SMA1 has been recently observed with a luminosity burst, which is conjectured as a disc-mediated variable accretion event. In this context, it is imperative to characterize the gas properties around the massive young stellar object. With this in mind, we carried out high angular resolution observations with the Atacama Large Millimeter and submillimeter Array and imaged the 900 $\mu m$ dust continuum and the CH$_3$CN $J$=19$-$18 $K$=0$-$10 transitions of S255IR SMA1. The integrated CH$_3$CN emission exhibits an elongated feature with an extent of 1800 au in the northwest-southeast direction at a position angle of 165 degree, which is nearly perpendicular to the bipolar outflow. We confirm the presence of dense (a few $\times 10^{9}$ cm$^{-3}$) and hot ($\sim$ 400 K) gas immediately surrounding the central protostar. The CH$_3$CN emission features a velocity gradient along the elongated ridge and by modelling the gas kinematics based on features in the position-velocity diagram, we infer that the gas is best described by a flattened rotating infalling envelope (or pseudo-disc). A mass infall rate of a few $\times$ 10$^{-4}$ solar-mass per year is derived. If there exists a putative Keplerian disc directly involved in the mass accretion onto the star and jet/outflow launching, it is likely smaller than 125 au and unresolved by our observations. We show qualitative resemblances between the gas properties (such as density and kinematics) in 255IR SMA1 inferred from our observations and those in a numerical simulation particularly tailored for studying the burst mode of massive star formation., Comment: 23 pages, 11 figures, accepted for publication in the Astrophysical Journal

Iris recognition has emerged as one of the most accurate and convenient biometric for the human identification and has been increasingly employed in a wide range of e-security applications. The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris matching accuracy. The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios. Our analysis of such iris templates also indicates significant degradation and reduction in the region of interest, where the iris recognition can benefit from a similarity distance that can consider importance of different binary bits, instead of the direct use of Hamming distance in the literature. Periocular information can be dynamically reinforced, by incorporating the differences in the effective area of available iris regions, for more accurate iris recognition. This paper presents such a segmentation-assisted adaptive framework for more accurate less-constrained iris recognition. The effectiveness of this framework is evaluated on three publicly available iris databases using within-dataset and cross-dataset performance evaluation and validates the merit of the proposed iris recognition framework.