Self-Paced Multi-Task Learning

In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating all tasks and instances equally when training, SPMTL attempts to jointly learn the tasks by taking into consideration the complexities of both tasks and instances. This is inspired by the cognitive process of human brain that often learns from the easy to the hard. We construct a compact SPMTL formulation by proposing a new task-oriented regularizer that can jointly prioritize the tasks and the instances. Thus it can be interpreted as a self-paced learner for MTL. A simple yet effective algorithm is designed for optimizing the proposed objective function. An error bound for a simplified formulation is also analyzed theoretically. Experimental results on toy and real-world datasets demonstrate the effectiveness of the proposed approach, compared to the state-of-the-art methods

Matching-CNN Meets KNN: Quasi-Parametric Human Parsing

Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts, for the human parsing task.Comment: This manuscript is the accepted version for CVPR 201

\u3cem\u3eIn vitro\u3c/em\u3e surface reaction layer formation and dissolution of calcium phosphate cement – bioactive glass composites

Composites of hydrated calcium phosphate cement (CPC) and bioactive glass (BG) containing Si were immersed in vitro to study the effect of chemical composition on surface reaction layer formation and dissolution/precipitation behavior. The solutions used were 0.05M tris hydroxymethyl aminomethane/HCl (tris buffer), tris buffer supplemented with plasma electrolyte (TE) with pH 7.4 at 37°C, and this solution complemented with 10% newborn bovine serum (TES). The post-immersion solutions were analyzed for changes in Ca, PO4 and Si concentrations. The reacted surfaces were analyzed using Fourier transform infrared (FTIR), and scanning electron microscopy (SEM) with energy dispersive X-ray analysis (EDX). The sample weight variations after immersion were also determined. The results showed that the composition of the bioactive composite CPCs greatly affected their behavior in solution and the formation of apatite bioactive surface reaction layers. After immersion in TE solution, Ca ions were taken up by all samples during the entire immersion duration. Initially, the P ion concentration increased sharply, and then decreased. This reaction pattern reveals the formation of an amorphous calcium phosphate layer on the surface of these composite calcium phosphate cements. FTIR revealed that the layer was, in fact, poorly crystallized Ca-deficient carbonate apatite. The thickness of the layer was 12-14 μm and was composed of rod-like apatite with directional arrangement. For immersion in TES solution, the Ca and Si ion concentrations showed a similar behavior as that in TE, but the release rate of Si ion was higher. FTIR revealed that after TES immersion, not only did the typical, poorly crystallized, Ca-deficient carbonated apatite form, as it did in TE, but that the serum proteins co-adsorbed on the surface and thereby affected the surface reaction layer formation. A thinner apatite layer was formed and was composed of a micro-porous layer comprising rounded particles in a glue-like appearing matrix. The addition of BG to the calcium phosphate cements to create composite calcium phosphate cements obviously is at the basis of this altered behavior of the cements. All data combined are useful for the design and optimization of degradable implant materials for use in bone tissue repair and regeneration procedures

Toward Robust and Efficient Interpretations of Idiomatic Expressions in Context

Studies show that a large number of idioms can be interpreted figuratively or literally depending on their contexts. This usage ambiguity has negative impacts on many natural language processing (NLP) applications. In this thesis, we investigate methods of building robust and efficient usage recognizers by modeling interactions between contexts and idioms. We aim to address three problems. First, how do differences in idioms’ linguistic properties affect the performances of automatic usage recognizers? We analyze the interactions between context representations and linguistic properties of idioms and develop ensemble models that predict usages adaptively for different idioms. Second, can an automatic usage recognizer be developed without annotated training examples? We develop a method for estimating the semantic distance between context and components of an idiom and then use that as distant supervision to guide further unsupervised clustering of usages. Third, how can we build one generalized model that reliably predicts the correct usage for a wide range of idioms, despite of variations in their linguistic properties? We recast this as a problem of modeling semantic compatibility between the literal interpretation of an arbitrary idiom and its context. We show that a general model of semantic compatibility can be trained from a large unannotated corpus, and that the resulting model can be applied to an arbitrary idiom without specific parameter tuning. To demonstrate that our work can benefit downstream NLP applications, we perform a case study on machine translation. It shows that our model can help to improve the translation quality of sentences containing idioms

Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China

Large-scale assessments of the potential for food production and its impact on biogeochemical cycling require the best possible information on the distribution of cropland. This information can come from ground-based agricultural census data sets and/or spaceborne remote sensing products, both with strengths and weaknesses. Official cropland statistics for China contain much information on the distribution of crop types, but are known to significantly underestimate total cropland areas and are generally at coarse spatial resolution. Remote sensing products can provide moderate to fine spatial resolution estimates of cropland location and extent, but supply little information on crop type or management. We combined county-scale agricultural census statistics on total cropland area and sown area of 17 major crops in 1990 with a fine-resolution land-cover map derived from 1995–1996 optical remote sensing (Landsat) data to generate 0.5° resolution maps of the distribution of rice agriculture in mainland China. Agricultural census data were used to determine the fraction of crop area in each 0.5° grid cell that was in single rice and each of 10 different multicrop paddy rice rotations (e.g., winter wheat/rice), while the remote sensing land-cover product was used to determine the spatial distribution and extent of total cropland in China. We estimate that there were 0.30 million km2 of paddy rice cropland; 75% of this paddy land was multicropped, and 56% had two rice plantings per year. Total sown area for paddy rice was 0.47 million km2. Paddy rice agriculture occurred on 23% of all cultivated land in China