SoK: Play-to-Earn Projects

Play-to-earn is one of the prospective categories of decentralized applications. The play-to-earn projects combine blockchain technology with entertaining games and finance, attracting various participants. While huge amounts of capital have been poured into these projects, the new crypto niche is considered controversial, and the traditional gaming industry is hesitant to embrace blockchain technology. In addition, there is little systematic research on these projects. In this paper, we delineate play-to-earn projects in terms of economic & governance models and implementation and analyze how blockchain technology can benefit these projects by providing system robustness, transparency, composability, and decentralized governance. We begin by identifying the participants and characterizing the tokens, which are products of composability. We then summarize the roadmap and governance model to exposit there is a transition from centralized governance to decentralized governance. We also classify the implementation of the play-to-earn projects with different extents of robustness and transparency. Finally, we discuss the security & societal challenges for future research in terms of possible attacks, the economics of tokens, and governance

A word-building method based on neural network for text classification

Text classification is a foundational task in many natural language processing applications. All traditional text classifiers take words as the basic units and conduct the pre-training process (like word2vec) to directly generate word vectors at the first step. However, none of them have considered the information contained in word structure which is proved to be helpful for text classification. In this paper, we propose a word-building method based on neural network model that can decompose a Chinese word to a sequence of radicals and learn structure information from these radical level features which is a key difference from the existing models. Then, the convolutional neural network is applied to extract structure information of words from radical sequence to generate a word vector, and the long short-term memory is applied to generate the sentence vector for the prediction purpose. The experimental results show that our model outperforms other existing models on Chinese dataset. Our model is also applicable to English as well where an English word can be decomposed down to character level, which demonstrates the excellent generalisation ability of our model. The experimental results have proved that our model also outperforms others on English dataset

Early ice retreat and ocean warming may induce copepod biogeographic boundary shifts in the Arctic Ocean

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 121 (2016): 6137-6158, doi:10.1002/2016JC011784.Early ice retreat and ocean warming are changing various facets of the Arctic marine ecosystem, including the biogeographic distribution of marine organisms. Here an endemic copepod species, Calanus glacialis, was used as a model organism, to understand how and why Arctic marine environmental changes may induce biogeographic boundary shifts. A copepod individual-based model was coupled to an ice-ocean-ecosystem model to simulate temperature- and food-dependent copepod life history development. Numerical experiments were conducted for two contrasting years: a relatively cold and normal sea ice year (2001) and a well-known warm year with early ice retreat (2007). Model results agreed with commonly known biogeographic distributions of C. glacialis, which is a shelf/slope species and cannot colonize the vast majority of the central Arctic basins. Individuals along the northern boundaries of this species' distribution were most susceptible to reproduction timing and early food availability (released sea ice algae). In the Beaufort, Chukchi, East Siberian, and Laptev Seas where severe ocean warming and loss of sea ice occurred in summer 2007, relatively early ice retreat, elevated ocean temperature (about 1–2°C higher than 2001), increased phytoplankton food, and prolonged growth season created favorable conditions for C. glacialis development and caused a remarkable poleward expansion of its distribution. From a pan-Arctic perspective, despite the great heterogeneity in the temperature and food regimes, common biogeographic zones were identified from model simulations, thus allowing a better characterization of habitats and prediction of potential future biogeographic boundary shifts.National Science Foundation Polar Programs Grant Number: (PLR-1417677, PLR-1417339, and PLR-1416920)2017-02-2

Biogeographic responses of the copepod Calanus glacialis to a changing Arctic marine environment

Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Global Change Biology 24 (2018): e159-e170, doi:10.1111/gcb.13890.Dramatic changes have occurred in the Arctic Ocean over the past few decades, especially in terms of sea ice loss and ocean warming. Those environmental changes may modify the planktonic ecosystem with changes from lower to upper trophic levels. This study aimed to understand how the biogeographic distribution of a crucial endemic copepod species, Calanus glacialis, may respond to both abiotic (ocean temperature) and biotic (phytoplankton prey) drivers. A copepod individual-based model coupled to an ice-ocean-biogeochemical model was utilized to simulate temperature- and food-dependent life cycle development of C. glacialis annually from 1980 to 2014. Over the 35-year study period, the northern boundaries of modeled diapausing C. glacialis expanded poleward and the annual success rates of C. glacialis individuals attaining diapause in a circumpolar transition zone increased substantially. Those patterns could be explained by a lengthening growth season (during which time food is ample) and shortening critical development time (the period from the first feeding stage N3 to the diapausing stage C4). The biogeographic changes were further linked to large scale oceanic processes, particularly diminishing sea ice cover, upper ocean warming, and increasing and prolonging food availability, which could have potential consequences to the entire Arctic shelf/slope marine ecosystems.This study was funded by National Science Foundation Arctic System Science (ARCSS) Program (PLR-1417677, PLR-1417339, and PLR-1416920)

Antitumor efficacy of combination of interferon-gamma-inducible protein 10 gene with gemcitabine, a study in murine model

<p>Abstract</p> <p>Background</p> <p>Interferon-γ-inducible protein 10 (IP-10) is a potent inhibitor of tumor angiogenesis. It has been reported that the antiangiogenic therapy combined with chemotherapy has synergistic effects.</p> <p>Methods</p> <p>To elucidate the mechanisms of IP-10 gene combined with a chemotherapy agent, we intramuscularly injected pBLAST-IP-10 expression plasmid combined with gemcitabine into tumor-bearing mice.</p> <p>Results</p> <p>The proliferation of endothelial cells was effectively inhibited by IP-10 combined with gemcitabine <it>in vitro</it>. Treatment with pBLAST-IP-10 twice a week for 4 weeks combined with gemcitabine 10 mg/kg (once a week) resulted in sustained high level of IP-10 protein in serum, inhibition of tumor growth and prolongation of the survival of tumor-bearing mice. Compared with administration of IP-10 plasmid or gemcitabine alone, the angiogenesis in tumors were apparently inhibited, and the numbers of apoptotic cells and lymphocytes in tumor increased in the combination therapy group.</p> <p>Conclusion</p> <p>Our data indicate that the gene therapy of antiangiogenesis by intramuscular delivery of plasmid DNA encoding IP-10 combined with gemcitabine has synergistic effects on tomor by inhibiting the proliferation of endothelail cells, inducing the apoptosis of tumor cells, and recruiting lymphocytes to tumor in murine models. The present findings provided evidence of antitumor effects of genetherapy combined with chemotherapy.</p

Study of (3He, t) charge exchange reactions to isobaric analog states in inverse kinematics

The transition between isobaric analog states (IAS) in the (3He, t) charge exchange reaction presents a unique opportunity to access the isospin structure of the nuclei. In this study not only the Fermi transition but also the Gamow-Teller (G-T) transition of the IAS reaction were investigated for the 13,14C(3He, t) and 17,18,19,20O(3He, t) reactions, in order to explore the neutron number dependence of the IAS reaction for the light neutron-rich nuclei. It was found that the G-T type IAS reaction also exhibited a significant dependence of the transition strength on the neutron number and the angular momentum configuration of the nuclei. Additionally, the inverse kinematics was also discussed for extracting the yields of the interested reaction channels in the proposed experiments on radioactive beams. The calculated triton yields demonstrated the capability of the proposed experiments to obtain meaningful results

Convolution-deconvolution word embedding: an end-to-end multi-prototype fusion embedding method for natural language processing

Existing unsupervised word embedding methods have been proved to be effective to capture latent semantic information on various tasks of Natural Language Processing (NLP). However, existing word representation methods are incapable of tackling both the polysemousunaware and task-unaware problems that are common phenomena in NLP tasks. In this work, we present a novel Convolution-Deconvolution Word Embedding (CDWE), an end-to-end multi-prototype fusion embedding that fuses context-specific information and taskspecific information. To the best of our knowledge, we are the first to extend deconvolution (e.g. convolution transpose), which has been widely used in computer vision, to word embedding generation. We empirically demonstrate the efficiency and generalization ability of CDWE by applying it to two representative tasks in NLP: text classification and machine translation. The models of CDWE significantly outperform the baselines and achieve state-of-the-art results on both tasks. To validate the efficiency of CDWE further, we demonstrate how CDWE solves the polysemous-unaware and task-unaware problems via analyzing the Text Deconvolution Saliency, which is an existing strategy for evaluating the outputs of deconvolution

A sentiment information collector–extractor architecture based neural network for sentiment analysis

Sentiment analysis, also known as opinion mining is a key natural language processing (NLP) task that receives much attention these years, where deep learning based neural network models have achieved great success. However, the existing deep learning models cannot effectively make use of the sentiment information in the sentence for sentiment analysis. In this paper, we propose a Sentiment Information Collector–Extractor architecture based Neural Network (SICENN) for sentiment analysis consisting of a Sentiment Information Collector (SIC) and a Sentiment Information Extractor (SIE). The SIC based on the Bi-directional Long Short Term Memory structure aims at collecting the sentiment information in the sentence and generating the information matrix. The SIE takes the information matrix as input and extracts the sentiment information precisely via three different sub-extractors. A new ensemble strategy is applied to combine the results of different sub-extractors, making the SIE more universal and outperform any single sub-extractor. Experiments results show that the proposed architecture outperforms the state-of-the-art methods on three datasets of different language