Active Learning With Complementary Sampling for Instructing Class-Biased Multi-Label Text Emotion Classification

High-quality corpora have been very scarce for the text emotion research. Existing corpora with multi-label emotion annotations have been either too small or too class-biased to properly support a supervised emotion learning. In this paper, we propose a novel active learning method for efficiently instructing the human annotations for a less-biased and high-quality multi-label emotion corpus. Specifically, to compensate annotation for the minority-class examples, we propose a complementary sampling strategy based on unlabeled resources by measuring a probabilistic distance between the expected emotion label distribution in a temporary corpus and an uniform distribution. Qualitative evaluations are also given to the unlabeled examples, in which we evaluate the model uncertainties for multi-label emotion predictions, their syntactic representativeness for the other unlabeled examples, and their diverseness to the labeled examples, for a high-quality sampling. Through active learning, a supervised emotion classifier gets progressively improved by learning from these new examples. Experiment results suggest that by following these sampling strategies we can develop a corpus of high-quality examples with significantly relieved bias for emotion classes. Compared to the learning procedures based on traditional active learning algorithms, our learning procedure indicates the most efficient learning curve and estimates the best multi-label emotion predictions

Involvement of N-6 adenine-specific DNA methyltransferase 1 (N6AMT1) in arsenic biomethylation and its role in arsenic-induced toxicity.

BackgroundIn humans, inorganic arsenic (iAs) is metabolized to methylated arsenical species in a multistep process mainly mediated by arsenic (+3 oxidation state) methyltransferase (AS3MT). Among these metabolites is monomethylarsonous acid (MMAIII), the most toxic arsenic species. A recent study in As3mt-knockout mice suggests that unidentified methyltransferases could be involved in alternative iAs methylation pathways. We found that yeast deletion mutants lacking MTQ2 were highly resistant to iAs exposure. The human ortholog of the yeast MTQ2 is N-6 adenine-specific DNA methyltransferase 1 (N6AMT1), encoding a putative methyltransferase.ObjectiveWe investigated the potential role of N6AMT1 in arsenic-induced toxicity.MethodsWe measured and compared the cytotoxicity induced by arsenicals and their metabolic profiles using inductively coupled plasma-mass spectrometry in UROtsa human urothelial cells with enhanced N6AMT1 expression and UROtsa vector control cells treated with different concentrations of either iAsIII or MMAIII.ResultsN6AMT1 was able to convert MMAIII to the less toxic dimethylarsonic acid (DMA) when overexpressed in UROtsa cells. The enhanced expression of N6AMT1 in UROtsa cells decreased cytotoxicity of both iAsIII and MMAIII. Moreover, N6AMT1 is expressed in many human tissues at variable levels, although at levels lower than those of AS3MT, supporting a potential participation in arsenic metabolism in vivo.ConclusionsConsidering that MMAIII is the most toxic arsenical, our data suggest that N6AMT1 has a significant role in determining susceptibility to arsenic toxicity and carcinogenicity because of its specific activity in methylating MMAIII to DMA and other unknown mechanisms

3D Stretchable Arch Ribbon Array Fabricated via Grayscale Lithography.

Microstructures with flexible and stretchable properties display tremendous potential applications including integrated systems, wearable devices and bio-sensor electronics. Hence, it is essential to develop an effective method for fabricating curvilinear and flexural microstructures. Despite significant advances in 2D stretchable inorganic structures, large scale fabrication of unique 3D microstructures at a low cost remains challenging. Here, we demonstrate that the 3D microstructures can be achieved by grayscale lithography to produce a curved photoresist (PR) template, where the PR acts as sacrificial layer to form wavelike arched structures. Using plasma-enhanced chemical vapor deposition (PECVD) process at low temperature, the curved PR topography can be transferred to the silicon dioxide layer. Subsequently, plasma etching can be used to fabricate the arched stripe arrays. The wavelike silicon dioxide arch microstructure exhibits Young modulus and fracture strength of 52 GPa and 300 MPa, respectively. The model of stress distribution inside the microstructure was also established, which compares well with the experimental results. This approach of fabricating a wavelike arch structure may become a promising route to produce a variety of stretchable sensors, actuators and circuits, thus providing unique opportunities for emerging classes of robust 3D integrated systems

UGC: Unified GAN Compression for Efficient Image-to-Image Translation

Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on ponderous computational costs and labor-expensive training data. Current efficient GAN learning techniques often fall into two orthogonal aspects: i) model slimming via reduced calculation costs; ii)data/label-efficient learning with fewer training data/labels. To combine the best of both worlds, we propose a new learning paradigm, Unified GAN Compression (UGC), with a unified optimization objective to seamlessly prompt the synergy of model-efficient and label-efficient learning. UGC sets up semi-supervised-driven network architecture search and adaptive online semi-supervised distillation stages sequentially, which formulates a heterogeneous mutual learning scheme to obtain an architecture-flexible, label-efficient, and performance-excellent model

GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling

Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding.Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity. In this paper, we present a robust semantic segmentation network by deeply exploring the geometry of point clouds, dubbed GeoSegNet. Our GeoSegNet consists of a multi-geometry based encoder and a boundary-guided decoder. In the encoder, we develop a new residual geometry module from multi-geometry perspectives to extract object-level features. In the decoder, we introduce a contrastive boundary learning module to enhance the geometric representation of boundary points. Benefiting from the geometric encoder-decoder modeling, our GeoSegNet can infer the segmentation of objects effectively while making the intersections (boundaries) of two or more objects clear. Experiments show obvious improvements of our method over its competitors in terms of the overall segmentation accuracy and object boundary clearness. Code is available at https://github.com/Chen-yuiyui/GeoSegNet

Assessing the capacity of biochar to stabilize copper and lead in contaminated sediments using chemical and extraction methods

Because of its high adsorption capacity, biochar has been used to stabilize heavy metals when remediating contaminated soils; to date, however, it has seldom been used to remediate contaminated sediment. In this study, a biochar was used as a stabilization agent to remediate Cu-and Pb-contaminated sediments, collected from three locations in or close to Beijing. The sediments were mixed with a palm sawdust gasified biochar at a range of weight ratios (2.5%, 5%, and 10%) and incubated for 10, 30, or 60 days. The performance of the different treatments and the heavy metal fractions in the sediments were assessed using four extraction methods, including diffusive gradients in thin films, the porewater concentration, a sequential extraction, and the toxicity characteristic leaching procedure. The results showed that biochar could enhance the stability of heavy metals in contaminated sediments. The degree of stability increased as both the dose of biochar and the incubation time increased. The sediment pH and the morphology of the metal crystals adsorbed onto the biochar changed as the contact time increased. Our results showed that adsorption, metal crystallization, and the pH were the main controls on the stabilization of metals in contaminated sediment by biochar