157 research outputs found
On Tree-Based Neural Sentence Modeling
Neural networks with tree-based sentence encoders have shown better results
on many downstream tasks. Most of existing tree-based encoders adopt syntactic
parsing trees as the explicit structure prior. To study the effectiveness of
different tree structures, we replace the parsing trees with trivial trees
(i.e., binary balanced tree, left-branching tree and right-branching tree) in
the encoders. Though trivial trees contain no syntactic information, those
encoders get competitive or even better results on all of the ten downstream
tasks we investigated. This surprising result indicates that explicit syntax
guidance may not be the main contributor to the superior performances of
tree-based neural sentence modeling. Further analysis show that tree modeling
gives better results when crucial words are closer to the final representation.
Additional experiments give more clues on how to design an effective tree-based
encoder. Our code is open-source and available at
https://github.com/ExplorerFreda/TreeEnc.Comment: To Appear at EMNLP 201
The ProfessionAl Go annotation datasEt (PAGE)
The game of Go has been highly under-researched due to the lack of game
records and analysis tools. In recent years, the increasing number of
professional competitions and the advent of AlphaZero-based algorithms provide
an excellent opportunity for analyzing human Go games on a large scale. In this
paper, we present the ProfessionAl Go annotation datasEt (PAGE), containing
98,525 games played by 2,007 professional players and spans over 70 years. The
dataset includes rich AI analysis results for each move. Moreover, PAGE
provides detailed metadata for every player and game after manual cleaning and
labeling. Beyond the preliminary analysis of the dataset, we provide sample
tasks that benefit from our dataset to demonstrate the potential application of
PAGE in multiple research directions. To the best of our knowledge, PAGE is the
first dataset with extensive annotation in the game of Go. This work is an
extended version of [1] where we perform a more detailed description, analysis,
and application.Comment: Journal version of arXiv:2205.00254, under revie
A protein network refinement method based on module discovery and biological information
The identification of essential proteins can help in understanding the
minimum requirements for cell survival and development. Network-based
centrality approaches are commonly used to identify essential proteins from
protein-protein interaction networks (PINs). Unfortunately, these approaches
are limited by the poor quality of the underlying PIN data. To overcome this
problem, researchers have focused on the prediction of essential proteins by
combining PINs with other biological data. In this paper, we proposed a network
refinement method based on module discovery and biological information to
obtain a higher quality PIN. First, to extract the maximal connected subgraph
in the PIN and to divide it into different modules by using Fast-unfolding
algorithm; then, to detect critical modules based on the homology information,
subcellular localization information and topology information within each
module, and to construct a more refined network (CM-PIN). To evaluate the
effectiveness of the proposed method, we used 10 typical network-based
centrality methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR) to compare the
overall performance of the CM-PIN with those the refined dynamic protein
network (RD-PIN). The experimental results showed that the CM-PIN was optimal
in terms of precision-recall curve, jackknife curve and other criteria, and can
help to identify essential proteins more accurately
Convergence analysis of environmental efficiency from the perspective of environmental regulation: evidence from China
The aim of this paper is to analyze the impact of environmental regulation on regional environmental efficiency convergence using the fixed effects model and threshold regression model. The results show that the differences in environmental efficiency have a convergence trend in China, as well as in the eastern, central and western regions. The effect of environmental regulation on regional environmental efficiency is inhibition first and then promotion, research and development investment and outward foreign direct investment have a positive transmission effect; when environmental regulation intensity exceeds a certain threshold, the growth rate of environmental efficiency in the central and western regions will be significantly higher than that in the eastern regions
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
While deep learning demonstrates its strong ability to handle independent and
identically distributed (IID) data, it often suffers from out-of-distribution
(OoD) generalization, where the test data come from another distribution
(w.r.t. the training one). Designing a general OoD generalization framework to
a wide range of applications is challenging, mainly due to possible correlation
shift and diversity shift in the real world. Most of the previous approaches
can only solve one specific distribution shift, such as shift across domains or
the extrapolation of correlation. To address that, we propose DecAug, a novel
decomposed feature representation and semantic augmentation approach for OoD
generalization. DecAug disentangles the category-related and context-related
features. Category-related features contain causal information of the target
object, while context-related features describe the attributes, styles,
backgrounds, or scenes, causing distribution shifts between training and test
data. The decomposition is achieved by orthogonalizing the two gradients
(w.r.t. intermediate features) of losses for predicting category and context
labels. Furthermore, we perform gradient-based augmentation on context-related
features to improve the robustness of the learned representations. Experimental
results show that DecAug outperforms other state-of-the-art methods on various
OoD datasets, which is among the very few methods that can deal with different
types of OoD generalization challenges.Comment: Accepted by AAAI202
NH3 sensor based on 3D hierarchical flower-shaped n-ZnO/p-NiO heterostructures yields outstanding sensing capabilities at ppb level
Hierarchical three-dimensional (3D) flower-like n-ZnO/p-NiO heterostructures with various ZnxNiy molar ratios (Zn5Ni1, Zn2Ni1, Zn1Ni1, Zn1Ni2 and Zn1Ni5) were synthesized by a facile hydrothermal method. Their crystal phase, surface morphology, elemental composition and chemical state were comprehensively investigated by XRD, SEM, EDS, TEM and XPS techniques. Gas sensing measurements were conducted on all the as-developed ZnxNiy-based sensors toward ammonia (NH3) detection under various working temperatures from 160 to 340 °C. In particular, the as-prepared Zn1Ni2 sensor exhibited superior NH3 sensing performance under optimum working temperature (280 °C) including high response (25 toward 100 ppm), fast response/recovery time (16 s/7 s), low detection limit (50 ppb), good selectivity and long-term stability. The enhanced NH3 sensing capabilities of Zn1Ni2 sensor could be attributed to both the specific hierarchical structure which facilitates the adsorption of NH3 molecules and produces much more contact sites, and the improved gas response characteristics of p-n heterojunctions. The obtained results clear demonstrated that the optimum n-ZnO/p-NiO heterostructure is indeed very promising sensing material toward NH3 detection for different applications
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