A large-scale dataset for end-to-end table recognition in the wild

Table recognition (TR) is one of the research hotspots in pattern recognition, which aims to extract information from tables in an image. Common table recognition tasks include table detection (TD), table structure recognition (TSR) and table content recognition (TCR). TD is to locate tables in the image, TCR recognizes text content, and TSR recognizes spatial ogical structure. Currently, the end-to-end TR in real scenarios, accomplishing the three sub-tasks simultaneously, is yet an unexplored research area. One major factor that inhibits researchers is the lack of a benchmark dataset. To this end, we propose a new large-scale dataset named Table Recognition Set (TabRecSet) with diverse table forms sourcing from multiple scenarios in the wild, providing complete annotation dedicated to end-to-end TR research. It is the largest and first bi-lingual dataset for end-to-end TR, with 38.1K tables in which 20.4K are in English\, and 17.7K are in Chinese. The samples have diverse forms, such as the border-complete and -incomplete table, regular and irregular table (rotated, distorted, etc.). The scenarios are multiple in the wild, varying from scanned to camera-taken images, documents to Excel tables, educational test papers to financial invoices. The annotations are complete, consisting of the table body spatial annotation, cell spatial logical annotation and text content for TD, TSR and TCR, respectively. The spatial annotation utilizes the polygon instead of the bounding box or quadrilateral adopted by most datasets. The polygon spatial annotation is more suitable for irregular tables that are common in wild scenarios. Additionally, we propose a visualized and interactive annotation tool named TableMe to improve the efficiency and quality of table annotation

Improving Multi-Person Pose Tracking with A Confidence Network

Human pose estimation and tracking are fundamental tasks for understanding human behaviors in videos. Existing top-down framework-based methods usually perform three-stage tasks: human detection, pose estimation and tracking. Although promising results have been achieved, these methods rely heavily on high-performance detectors and may fail to track persons who are occluded or miss-detected. To overcome these problems, in this paper, we develop a novel keypoint confidence network and a tracking pipeline to improve human detection and pose estimation in top-down approaches. Specifically, the keypoint confidence network is designed to determine whether each keypoint is occluded, and it is incorporated into the pose estimation module. In the tracking pipeline, we propose the Bbox-revision module to reduce missing detection and the ID-retrieve module to correct lost trajectories, improving the performance of the detection stage. Experimental results show that our approach is universal in human detection and pose estimation, achieving state-of-the-art performance on both PoseTrack 2017 and 2018 datasets.Comment: Accepted by IEEE Transactions on Multimedia. 11 pages, 5 figure

The Commutator of the Bergman Projection on Strongly Pseudoconvex Domains with Minimal Smoothness

Consider a bounded, strongly pseudoconvex domain $D\subset \mathbb C^n$ with minimal smoothness (namely, the class $C^2$) and let $b$ be a locally integrable function on $D$. We characterize boundedness (resp., compactness) in $L^p(D), p > 1$, of the commutator $[b, P]$ of the Bergman projection $P$ in terms of an appropriate bounded (resp. vanishing) mean oscillation requirement on $b$. We also establish the equivalence of such notion of BMO (resp., VMO) with other BMO and VMO spaces given in the literature. Our proofs use a dyadic analog of the Berezin transform and holomorphic integral representations going back (for smooth domains) to N. Kerzman & E. M. Stein, and E. Ligocka.Comment: 35 pages with references; published versio

Quantitative assessment of the associations between XRCC1 polymorphisms and bladder cancer risk

BACKGROUND: The XRCC1 polymorphisms have been implicated in bladder cancer risk, but individually published studies show inconsistent results. The aim of our study was to clarify the effects of XRCC1 variants on bladder cancer risk. METHODS: A systematic literature search up to September 13, 2012 was carried out in PubMed, EMBASE and Wanfang databases, and the references of retrieved articles were screened. Crude odds ratios with 95% confidence intervals were used to assess the associations between XRCC1 Arg194Trp and Arg399Gln polymorphisms and bladder cancer risk. Heterogeneity and publication bias were also evaluated. RESULTS: A total of 14 and 18 studies were eligible for meta-analyses of Arg194Trp and Arg399Gln, respectively. Regrouping was adopted in accordance with the most probable appropriate genetic models. No obvious heterogeneity between studies was found. For overall bladder cancer, the pooled odds ratios for Arg194Trp and Arg399Gln were 1.69 (95% confidence interval: 1.25 to 2.28; P = 0.001) and 1.10 (95% confidence interval: 1.03 to 1.19; P = 0.008), respectively. After excluding the studies that were not in Hardy–Weinberg equilibrium, the estimated pooled odds ratio still did not change at all. CONCLUSIONS: The meta-analysis results suggest that XRCC1 Arg194Trp and Arg399Gln polymorphisms may be associated with elevated bladder cancer risk

Relationship between polymorphic interaction of glutamate pathway genes and anhedonia

Objective·To explore the association between gene-gene interaction of glutamate pathway and anhedonia.Methods·A total of 279 patients with schizophrenia (SZ) and 236 patients with major depression disorder (MDD) recruited in the outpatient department and ward of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, and 236 healthy controls (HC) recruited in the community from January 2017 to August 2020 were included in the study. General demographic data and clinical characteristics of the three groups were collected and compared. The Chinese version of Temporal Experience of Pleasure Scale (TEPS) was used to evaluate the pleasure experience ability of the three groups. Generalized multifactor dimensionality reduction (GMDR) method was used to establish the interaction model of the single nucleotide polymorphism (SNP) in glutamate pathway genes (NOS1AP, GSK3β, DAOA, DISC1 and GRIN2A). According to the interaction model, SZ and MDD patients were divided into high-risk group and low-risk group, and the differences in pleasure experience ability were compared between the two groups, so as to analyze the effect of gene-gene interaction on anhedonia.Results·There were significant differences in age and years of education among the three groups, and in age of onset and duration of illness between SZ and MDD groups (all P=0.000). There were significant differences among the three groups of participants in terms of overall pleasure experience, anticipatory pleasure experience and consummatory pleasure experience (all P=0.000); the overall pleasure experience, anticipatory pleasure experience and consummatory pleasure experience in the SZ and MDD group were lower than those in the HC group (all Pcorr=0.000), and there was marginal statistical difference in anticipatory pleasure experience between the SZ and MDD groups (Pcorr=0.051). Through GMDR modeling, it was found that the 2-loci interaction model composed of DAOA-rs3916965 and DISC1-rs821577 had a predictive effect on the overall pleasure experience ability of SZ patients (P=0.003), and the 2-loci interaction model composed of NOS1AP-rs1858232 and GRIN2A-rs1014531 had a predictive effect on the anticipatory pleasure experience ability of MDD patients (P=0.037); moreover, the overall pleasure experience ability of patients in the SZ high-risk group and anticipatory pleasure experience ability of patients in MDD high-risk groups were lower than those in their low-risk groups (t=3.443, P=0.000; t=3.471, P=0.001).Conclusion·The interaction of glutamate pathway gene polymorphisms may be involved in the occurrence of anhedonia

The clinical predictive value of geriatric nutritional risk index in elderly rectal cancer patients received surgical treatment after neoadjuvant therapy

ObjectiveThe assessment of nutritional status has been recognized as crucial in the treatment of geriatric cancer patients. The objective of this study is to determine the clinical predictive value of the geriatric nutritional risk index (GNRI) in predicting the short-term and long-term prognosis of elderly rectal cancer (RC) patients who undergo surgical treatment after neoadjuvant therapy.MethodsBetween January 2014 and December 2020, the clinical materials of 639 RC patients aged ≥70 years who underwent surgical treatment after neoadjuvant therapy were retrospectively analysed. Propensity score matching was performed to adjust for baseline potential confounders. Logistic regression analysis and competing risk analysis were conducted to evaluate the correlation between the GNRI and the risk of postoperative major complications and cumulative incidence of cancer-specific survival (CSS). Nomograms were then constructed for postoperative major complications and CSS. Additionally, 203 elderly RC patients were enrolled between January 2021 and December 2022 as an external validation cohort.ResultsMultivariate logistic regression analysis showed that GNRI [odds ratio = 1.903, 95% confidence intervals (CI): 1.120–3.233, p = 0.017] was an independent risk factor for postoperative major complications. In competing risk analysis, the GNRI was also identified as an independent prognostic factor for CSS (subdistribution hazard ratio = 3.90, 95% CI: 2.46–6.19, p < 0.001). The postoperative major complication nomogram showed excellent performance internally and externally in the area under the receiver operating characteristic curve (AUC), calibration plots and decision curve analysis (DCA). When compared with other models, the competing risk prognosis nomogram incorporating the GNRI achieved the highest outcomes in terms of the C-index, AUC, calibration plots, and DCA.ConclusionThe GNRI is a simple and effective tool for predicting the risk of postoperative major complications and the long-term prognosis of elderly RC patients who undergo surgical treatment after neoadjuvant therapy

Exploiting Magnetic Resonance Angiography Imaging Improves Model Estimation of BOLD Signal

The change of BOLD signal relies heavily upon the resting blood volume fraction () associated with regional vasculature. However, existing hemodynamic data assimilation studies pretermit such concern. They simply assign the value in a physiologically plausible range to get over ill-conditioning of the assimilation problem and fail to explore actual . Such performance might lead to unreliable model estimation. In this work, we present the first exploration of the influence of on fMRI data assimilation, where actual within a given cortical area was calibrated by an MR angiography experiment and then was augmented into the assimilation scheme. We have investigated the impact of on single-region data assimilation and multi-region data assimilation (dynamic cause modeling, DCM) in a classical flashing checkerboard experiment. Results show that the employment of an assumed in fMRI data assimilation is only suitable for fMRI signal reconstruction and activation detection grounded on this signal, and not suitable for estimation of unobserved states and effective connectivity study. We thereby argue that introducing physically realistic in the assimilation process may provide more reliable estimation of physiological information, which contributes to a better understanding of the underlying hemodynamic processes. Such an effort is valuable and should be well appreciated