Building XML data warehouse based on frequent patterns in user queries

[Abstract]: With the proliferation of XML-based data sources available across the Internet, it is increasingly important to provide users with a data warehouse of XML data sources to facilitate decision-making processes. Due to the extremely large amount of XML data available on web, unguided warehousing of XML data turns out to be highly costly and usually cannot well accommodate the users’ needs in XML data acquirement. In this paper, we propose an approach to materialize XML data warehouses based on frequent query patterns discovered from historical queries issued by users. The schemas of integrated XML documents in the warehouse are built using these frequent query patterns represented as Frequent Query Pattern Trees (FreqQPTs). Using hierarchical clustering technique, the integration approach in the data warehouse is flexible with respect to obtaining and maintaining XML documents. Experiments show that the overall processing of the same queries issued against the global schema become much efficient by using the XML data warehouse built than by directly searching the multiple data sources

A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm

Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images. This paper aims to establish a Euler's Elastica based approach that properly deals with random noises to improve the segmentation performance for noisy images. We solve the corresponding optimization problem via using the progressive hedging algorithm (PHA) with a step length suggested by the alternating direction method of multipliers (ADMM). Technically, all the simplified convex versions of the subproblems derived from the major framework of PHA can be obtained by using the curvature weighted approach and the convex relaxation method. Then an alternating optimization strategy is applied with the merits of using some powerful accelerating techniques including the fast Fourier transform (FFT) and generalized soft threshold formulas. Extensive experiments have been conducted on both synthetic and real images, which validated some significant gains of the proposed segmentation models and demonstrated the advantages of the developed algorithm

Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning

In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against the compared MIPL and partial-label learning approaches.Comment: Accepted at NeurIPS 202

Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) enables the training of object detection models using only image-level annotations. State-of-the-art WSOD detectors commonly rely on multi-instance learning (MIL) as the backbone of their detectors and assume that the bounding box proposals of an image are independent of each other. However, since such approaches only utilize the highest score proposal and discard the potentially useful information from other proposals, their independent MIL backbone often limits models to salient parts of an object or causes them to detect only one object per class. To solve the above problems, we propose a novel backbone for WSOD based on our tailored Vision Transformer named Weakly Supervised Transformer Detection Network (WSTDN). Our algorithm is not only the first to demonstrate that self-attention modules that consider inter-instance relationships are effective backbones for WSOD, but also we introduce a novel bounding box mining method (BBM) integrated with a memory transfer refinement (MTR) procedure to utilize the instance dependencies for facilitating instance refinements. Experimental results on PASCAL VOC2007 and VOC2012 benchmarks demonstrate the effectiveness of our proposed WSTDN and modified instance refinement modules

Weakly supervised POS tagging without disambiguation

Weakly supervised part-of-speech (POS) tagging is to learn to predict the POS tag for a given word in context by making use of partial annotated data instead of the fully tagged corpora. Weakly supervised POS tagging would benefit various natural language processing applications in such languages where tagged corpora are mostly unavailable. In this article, we propose a novel framework for weakly supervised POS tagging based on a dictionary of words with their possible POS tags. In the constrained error-correcting output codes (ECOC)-based approach, a unique L-bit vector is assigned to each POS tag. The set of bitvectors is referred to as a coding matrix with value { 1, -1}. Each column of the coding matrix specifies a dichotomy over the tag space to learn a binary classifier. For each binary classifier, its training data is generated in the following way: each pair of words and its possible POS tags are considered as a positive training example only if the whole set of its possible tags falls into the positive dichotomy specified by the column coding and similarly for negative training examples. Given a word in context, its POS tag is predicted by concatenating the predictive outputs of the L binary classifiers and choosing the tag with the closest distance according to some measure. By incorporating the ECOC strategy, the set of all possible tags for each word is treated as an entirety without the need of performing disambiguation. Moreover, instead of manual feature engineering employed in most previous POS tagging approaches, features for training and testing in the proposed framework are automatically generated using neural language modeling. The proposed framework has been evaluated on three corpora for English, Italian, and Malagasy POS tagging, achieving accuracies of 93.21%, 90.9%, and 84.5% individually, which shows a significant improvement compared to the state-of-the-art approaches

Hypoxia-inducible factor 1-mediated regulation of PPP1R3C promotes glycogen accumulation in human MCF-7 cells under hypoxia

AbstractHundreds of genes can be regulated by hypoxia-inducible factor 1 (HIF1) under hypoxia. Here we demonstrated a HIF1-mediated induction of protein phosphatase 1, regulatory subunit 3C gene (PPP1R3C) in human MCF7 cells under hypoxia. By mutation analysis we confirmed the presence of a functional hypoxia response element that is located 229bp upstream from the PPP1R3C gene. PPP1R3C induction correlates with a significant glycogen accumulation in MCF7 cells under hypoxia. Knockdown of either HIF1α or PPP1R3C attenuated hypoxia-induced glycogen accumulation significantly. Knockdown of HIF2α reduced hypoxia-induced glycogen accumulation slightly (but not significantly). Our results demonstrated that HIF1 promotes glycogen accumulation through regulating PPP1R3C expression under hypoxia, which revealed a novel metabolic adaptation of cells to hypoxia