Form-NLU: Dataset for the Form Language Understanding

Compared to general document analysis tasks, form document structure understanding and retrieval are challenging. Form documents are typically made by two types of authors; A form designer, who develops the form structure and keys, and a form user, who fills out form values based on the provided keys. Hence, the form values may not be aligned with the form designer's intention (structure and keys) if a form user gets confused. In this paper, we introduce Form-NLU, the first novel dataset for form structure understanding and its key and value information extraction, interpreting the form designer's intent and the alignment of user-written value on it. It consists of 857 form images, 6k form keys and values, and 4k table keys and values. Our dataset also includes three form types: digital, printed, and handwritten, which cover diverse form appearances and layouts. We propose a robust positional and logical relation-based form key-value information extraction framework. Using this dataset, Form-NLU, we first examine strong object detection models for the form layout understanding, then evaluate the key information extraction task on the dataset, providing fine-grained results for different types of forms and keys. Furthermore, we examine it with the off-the-shelf pdf layout extraction tool and prove its feasibility in real-world cases.Comment: Accepted by SIGIR 202

Low-mass dark matter search results from full exposure of PandaX-I experiment

We report the results of a weakly-interacting massive particle (WIMP) dark matter search using the full 80.1\;live-day exposure of the first stage of the PandaX experiment (PandaX-I) located in the China Jin-Ping Underground Laboratory. The PandaX-I detector has been optimized for detecting low-mass WIMPs, achieving a photon detection efficiency of 9.6\%. With a fiducial liquid xenon target mass of 54.0\,kg, no significant excess event were found above the expected background. A profile likelihood analysis confirms our earlier finding that the PandaX-I data disfavor all positive low-mass WIMP signals reported in the literature under standard assumptions. A stringent bound on the low mass WIMP is set at WIMP mass below 10\,GeV/c$^2$, demonstrating that liquid xenon detectors can be competitive for low-mass WIMP searches.Comment: v3 as accepted by PRD. Minor update in the text in response to referee comments. Separating Fig. 11(a) and (b) into Fig. 11 and Fig. 12. Legend tweak in Fig. 9(b) and 9(c) as suggested by referee, as well as a missing legend for CRESST-II legend in Fig. 12 (now Fig. 13). Same version as submitted to PR

The Second Monocular Depth Estimation Challenge

This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, including fully-supervised, self-supervised, multi-task or proxy depth. The challenge was based around the SYNS-Patches dataset, which features a wide diversity of environments with high-quality dense ground-truth. This includes complex natural environments, e.g. forests or fields, which are greatly underrepresented in current benchmarks. The challenge received eight unique submissions that outperformed the provided SotA baseline on any of the pointcloud- or image-based metrics. The top supervised submission improved relative F-Score by 27.62%, while the top self-supervised improved it by 16.61%. Supervised submissions generally leveraged large collections of datasets to improve data diversity. Self-supervised submissions instead updated the network architecture and pretrained backbones. These results represent a significant progress in the field, while highlighting avenues for future research, such as reducing interpolation artifacts at depth boundaries, improving self-supervised indoor performance and overall natural image accuracy.Comment: Published at CVPRW202

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

A clinicopathologic multivariate analysis affecting recurrence of borderline ovarian tumors

Abstract Objective. To evaluate the risk factors associated with recurrence of borderline ovarian tumors that may be used as evidence of the efficacy of select preventive procedures. Methods. Various clinicopathologic factors of 234 patients with borderline ovarian tumors admitted to our hospital between January 2001 and June 2007 were reviewed. Univariate and multivariate logistic regression models were constructed to evaluate the risk factors for odds ratio (OR) and statistical significance. The survival was assessed by the Kaplan-Meier method and proportional hazards model. Results. Recurrence of borderline ovarian tumors was observed in 26 cases and the median time to recurrence was 29.4 months. Of these cases, 5 occurred involving the ipsilateral ovary, 9 involved the contralateral ovary, and 12 spread to the pelvic peritoneum, including 3 patients who had progressed to invasive carcinoma. No tumor-related deaths were reported. The results of the multivariate logistic regression analysis showed that conservative surgical procedures (OR = 2.304; p = 0.024), cyst rupture (OR = 2.213; p = 0.038), advanced FIGO stage (OR = 4.114; p = 0.000), microinvasion (OR = 2.291; p = 0.046), and peritoneal implants (OR = 2.101; p = 0.016) may be independent predictive factors of recurrence. The proportional hazards model identified surgical procedure (relative risk, RR = 3.752, p = 0.007), cyst rupture (RR = 1.985, p = 0.006), FIGO stage (RR = 3.746, p = 0.001), microinvasion (RR = 1.153, p = 0.009) and peritoneal implants (RR = 2.742, p = 0.010), as independently related to disease-free survival. Conclusions. Although patients with borderline ovarian tumors have an excellent prognosis, the risk of recurrence remains. Identification of patients with high-risk factors is essential for offering more selective treatments to prevent recurrence

Trajectory Prediction with Attention-Based Spatial–Temporal Graph Convolutional Networks for Autonomous Driving

Accurate and reliable trajectory prediction is crucial for autonomous vehicles to achieve safe and efficient operation. Vehicles perceive the historical trajectories of moving objects and make predictions of behavioral intentions for a future period of time. With the predicted trajectories of moving objects such as obstacle vehicles, pedestrians, and non-motorized vehicles as inputs, self-driving vehicles can make more rational driving decisions and plan more reasonable and safe vehicle motion behaviors. However, due to traffic environments such as intersection scenes with highly interdependent and dynamic attributes, the task of motion anticipation becomes challenging. Existing works focus on the mutual relationships among vehicles while ignoring other potential essential interactions such as vehicle–traffic rules. These studies have not yet deeply explored the intensive learning of interactions between multi-agents, which may result in evaluation deviations. Aiming to meet these issues, we have designed a novel framework, namely trajectory prediction with attention-based spatial–temporal graph convolutional networks (TPASTGCN). In our proposal, the multi-agent interaction mechanisms, including vehicle–vehicle and vehicle–traffic rules, are meticulously highlighted and integrated into one homogeneous graph by transferring the time-series data of traffic lights into the spatial–temporal domains. Through integrating the attention mechanism into the adjacency matrix, we effectively learn the different strengths of interactive association and improve the model’s ability to capture critical features. Simultaneously, we construct a hierarchical structure employing the spatial GCN and temporal GCN to extract the spatial dependencies of traffic networks. Profiting from the gated recurrent unit (GRU), the scene context in temporal dimensions is further attained and enhanced with the encoder. In such a way, the GCN and GRU networks are fused as a features extractor module in the proposed framework. Finally, the future potential trajectories generation tasks are performed by another GRU network. Experiments on real-world datasets demonstrate the superior performance of the scheme compared with several baselines