Neural Word Segmentation with Rich Pretraining

Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.Comment: Accepted by ACL 201

Neural Reranking for Named Entity Recognition

We propose a neural reranking system for named entity recognition (NER). The basic idea is to leverage recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as \textit{Barack Obama}, into their entity types, such as \textit{PER}. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, "PER was born in LOC" can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.Comment: Accepted as regular paper by RANLP 201

California Rental Price Prediction Using Machine Learning Algorithms

Rental price prediction (price recommendation) is a practical topic in the current online marketplace. In order to support hosts with less experience to set up the competitive rental prices, we utilize the techniques, such as feature engineering and machine learning algorithm, to select useful features and conduct models to predict possible rental prices based on the property information provided by the hosts. In this paper, machine learning algorithms are implemented on the same dataset which contains all the properties in California listed on Airbnb. After feature analysis, we notice the number of bedrooms and property types are the most important features that are highly associated with the rental prices. Among all the methods, XGBoost gives the most satisfying prediction results of rental prices based on RMSE, MAE, and R squared

Impacts of HDAC4-MAP1S Interaction on Autophagy and Diseases

Autophagy is a cellular process to sequester cytoplasmic components for delivery to lysosomes for subsequent degradation, and plays important roles in aging and aging-associated pathogenesis. MAP1S is a ubiquitously distributed autophagy activator, which directly binds to the autophagosome marker LC3, accelerates autophagy initiation and degradation. Elucidating the mechanism of MAP1S-mediated autophagy can generate insights to control aging and aging-associated diseases. We discover that the acetylation of MAP1S maintains the stability of MAP1S protein and promotes autophagy. MAP1S interacts with HDAC4 via an HDAC4-binding domain (HBD). HDAC4 destabilizes MAP1S via deacetylation and suppresses autophagic flux. Huntington’s disease is a fatal progressive neurodegenerative disorder caused by the accumulation of aggregation-prone mutant Huntingtin protein (mHTT). Utilizing cell line stably expressing GFP-mHTT, we demonstrate that an overexpression of HDAC4 causes accumulation of mHTT aggregates by inhibiting MAP1S-mediated autophagy. Either suppression of HDAC4 or overexpression of HBD promotes the stability of MAP1S, and enhances MAP1S-regulated autophagic clearance of mHTT aggregates. This reveals a new potential strategy to treat Huntington’s disease by interrupting HDAC4-MAP1S interaction. Spermidine is a polyamine that activates autophagy and enhances longevity in some model systems. MAP1S-mediated autophagy helps mice maintain their lifespans and suppresses diethylnitrosamine-induced hepatocellular carcinomas. Thus, we hypothesize that spermidine regulates MAP1S-mediated autophagy to prolong lifespans and suppress hepatocarcinogenesis. Our results indicate that spermidine activates autophagy by depleting cytosolic HDAC4 to reduce HDAC4-MAP1S interaction, and enhance the acetylation of MAP1S. Wild-type mice drinking spermidine-containing water have higher levels of MAP1S and autophagy activity, and extended lifespans, compared to wild-type mice drinking water; whereas MAP1S^-/- mice drinking spermidine have no significant increase in autophagy level and medium survival time, compared to MAP1S^-/- mice drinking water. Utilizing diethylnitrosamine-induced hepatocellular carcinoma mouse model, we find that spermidine enhances MAP1S to accelerate autophagic flux and suppress hepatocarcinogenesis in a MAP1S-dependent manner. In sum, we uncover that HDAC4 deacetylates MAP1S, reduces the stability of MAP1S protein and impairs MAP1S-mediated autophagy. Blocking the association of MAP1S with HDAC4 leads to activation of MAP1S-mediated autophagy and suppression of aging and aging-associated diseases

How Belief in a Just World Benefits Mental Health: The Effects of Optimism and Gratitude

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Past research suggests that individuals’ belief in a just world (BJW) is closely connected with their mental health. To clarify the underlying mechanism, the current study proposes that BJW encourages optimism and gratitude which then mediates the relation- ship between BJW and mental health as indicated by subjective well-being (SWB) and depression. A sample of 1,200 undergraduates yields results indicating that (a) BJW influences optimism, gratitude, SWB, and depression after controlling for gender, age, income, and personality; (b) optimism and gratitude mediate BJW effects by increasing SWB and decreasing depression. The issues of BJW’s adaptive functions are discussed