89 research outputs found
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
The goal of graph representation learning is to embed each vertex in a graph
into a low-dimensional vector space. Existing graph representation learning
methods can be classified into two categories: generative models that learn the
underlying connectivity distribution in the graph, and discriminative models
that predict the probability of edge existence between a pair of vertices. In
this paper, we propose GraphGAN, an innovative graph representation learning
framework unifying above two classes of methods, in which the generative model
and discriminative model play a game-theoretical minimax game. Specifically,
for a given vertex, the generative model tries to fit its underlying true
connectivity distribution over all other vertices and produces "fake" samples
to fool the discriminative model, while the discriminative model tries to
detect whether the sampled vertex is from ground truth or generated by the
generative model. With the competition between these two models, both of them
can alternately and iteratively boost their performance. Moreover, when
considering the implementation of generative model, we propose a novel graph
softmax to overcome the limitations of traditional softmax function, which can
be proven satisfying desirable properties of normalization, graph structure
awareness, and computational efficiency. Through extensive experiments on
real-world datasets, we demonstrate that GraphGAN achieves substantial gains in
a variety of applications, including link prediction, node classification, and
recommendation, over state-of-the-art baselines.Comment: The 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), 8
page
Transcribing Content from Structural Images with Spotlight Mechanism
Transcribing content from structural images, e.g., writing notes from music
scores, is a challenging task as not only the content objects should be
recognized, but the internal structure should also be preserved. Existing image
recognition methods mainly work on images with simple content (e.g., text lines
with characters), but are not capable to identify ones with more complex
content (e.g., structured symbols), which often follow a fine-grained grammar.
To this end, in this paper, we propose a hierarchical Spotlight Transcribing
Network (STN) framework followed by a two-stage "where-to-what" solution.
Specifically, we first decide "where-to-look" through a novel spotlight
mechanism to focus on different areas of the original image following its
structure. Then, we decide "what-to-write" by developing a GRU based network
with the spotlight areas for transcribing the content accordingly. Moreover, we
propose two implementations on the basis of STN, i.e., STNM and STNR, where the
spotlight movement follows the Markov property and Recurrent modeling,
respectively. We also design a reinforcement method to refine the framework by
self-improving the spotlight mechanism. We conduct extensive experiments on
many structural image datasets, where the results clearly demonstrate the
effectiveness of STN framework.Comment: Accepted by KDD2018 Research Track. In proceedings of the 24th ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining
(KDD'18
Tax Arrangement and Regional Industrial Restructuring: Evidence from Panel Data in China
Regional industrial restructuring has been one of the major items in the transformation of economic development mode. An exploration was made into the influence of tax arrangement on the regional industrial structure by setting up a panel data econometric model based on the evaluation and analysis of the regional industrial structure in China. It was shown that tax arrangement influenced the regional industrial restructuring in terms of three aspects. Microlevel: the turnover tax and income tax appeared with a U-path of influence on upgrading of the industrial structure while appearing with an inverted U-path of influence on rationalization of the industrial structure. In addition, the levy of resource tax had a negative impact on both upgrading and rationalization of the industrial structure. Mesolevel: taxation in the secondary and tertiary industries appeared with a U-path of influence on upgrading of the industrial structure. An increase of taxation in the secondary industry had a negative impact on rationalization of the industrial structure. The taxation in the tertiary industry appeared with an inverted U-path of influence on rationalization of the industrial structure. Macrolevel: the macrotax burden had a U-path of influence on both upgrading and rationalization of the industrial structure
Ask One More Time: Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios
Although chain-of-thought (CoT) prompting combined with language models has
achieved encouraging results on complex reasoning tasks, the naive greedy
decoding used in CoT prompting usually causes the repetitiveness and local
optimality. To address this shortcoming, ensemble-optimization tries to obtain
multiple reasoning paths to get the final answer assembly. However, current
ensemble-optimization methods either simply employ rule-based post-processing
such as \textit{self-consistency}, or train an additional model based on
several task-related human annotations to select the best one among multiple
reasoning paths, yet fail to generalize to realistic settings where the type of
input questions is unknown or the answer format of reasoning paths is unknown.
To avoid their limitations, we propose \textbf{self-agreement}, a generalizable
ensemble-optimization method applying in almost all scenarios where the type of
input questions and the answer format of reasoning paths may be known or
unknown. Self-agreement firstly samples from language model's decoder to
generate a \textit{diverse} set of reasoning paths, and subsequently prompts
the language model \textit{one more time} to determine the optimal answer by
selecting the most \textit{agreed} answer among the sampled reasoning paths.
Self-agreement simultaneously achieves remarkable performance on six public
reasoning benchmarks and superior generalization capabilities.Comment: Work in progres
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Placental Mesenchymal Stem Cell-Derived Extracellular Vesicles Promote Myelin Regeneration in an Animal Model of Multiple Sclerosis.
Mesenchymal stem/stromal cells (MSCs) display potent immunomodulatory and regenerative capabilities through the secretion of bioactive factors, such as proteins, cytokines, chemokines as well as the release of extracellular vesicles (EVs). These functional properties of MSCs make them ideal candidates for the treatment of degenerative and inflammatory diseases, including multiple sclerosis (MS). MS is a heterogenous disease that is typically characterized by inflammation, demyelination, gliosis and axonal loss. In the current study, an induced experimental autoimmune encephalomyelitis (EAE) murine model of MS was utilized. At peak disease onset, animals were treated with saline, placenta-derived MSCs (PMSCs), as well as low and high doses of PMSC-EVs. Animals treated with PMSCs and high-dose PMSC-EVs displayed improved motor function outcomes as compared to animals treated with saline. Symptom improvement by PMSCs and PMSC-EVs led to reduced DNA damage in oligodendroglia populations and increased myelination within the spinal cord of treated mice. In vitro data demonstrate that PMSC-EVs promote myelin regeneration by inducing endogenous oligodendrocyte precursor cells to differentiate into mature myelinating oligodendrocytes. These findings support that PMSCs' mechanism of action is mediated by the secretion of EVs. Therefore, PMSC-derived EVs are a feasible alternative to cellular based therapies for MS, as demonstrated in an animal model of the disease
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