Direct reconstruction of dynamical dark energy from observational Hubble parameter data

Reconstructing the evolution history of the dark energy equation of state parameter $w(z)$ directly from observational data is highly valuable in cosmology, since it contains substantial clues in understanding the nature of the accelerated expansion of the Universe. Many works have focused on reconstructing $w(z)$ using Type Ia supernova data, however, only a few studies pay attention to Hubble parameter data. In the present work, we explore the merit of Hubble parameter data and make an attempt to reconstruct $w(z)$ from them through the principle component analysis approach. We find that current Hubble parameter data perform well in reconstructing $w(z)$; though, when compared to supernova data, the data are scant and their quality is worse. Both $\Lambda$CDM and evolving $w(z)$ models can be constrained within $10\%$ at redshifts $z \lesssim 1.5$ and even $5\%$ at redshifts 0.1 $\lesssim$ z $\lesssim$ 1 by using simulated $H(z)$ data of observational quality.Comment: 25 pages, 11 figure

Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an $F_1$-score of 83.7\%, higher than competing methods in the literature.Comment: EMNLP '1

Robust Sound Event Classification using Deep Neural Networks

The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise. This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques

Electrically Tunable Energy Bandgap in Dual-Gated Ultra-Thin Black Phosphorus Field Effect Transistors

The energy bandgap is an intrinsic character of semiconductors, which largely determines their properties. The ability to continuously and reversibly tune the bandgap of a single device during real time operation is of great importance not only to device physics but also to technological applications. Here we demonstrate a widely tunable bandgap of few-layer black phosphorus (BP) by the application of vertical electric field in dual-gated BP field-effect transistors. A total bandgap reduction of 124 meV is observed when the electrical displacement field is increased from 0.10V/nm to 0.83V/nm. Our results suggest appealing potential for few-layer BP as a tunable bandgap material in infrared optoelectronics, thermoelectric power generation and thermal imaging.Comment: 5 pages, 4 figure

Building Program Vector Representations for Deep Learning

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1

Argon laser treatment of central serous chorioretinopathy

AIM: To observe the efficacy of the argon laser photocoagulation treatment of central serous chorioretinopathy(CSC). METHODS: The treatment groups: 18 patients(18 eyes), argon laser photocoagulation and oral jolethin, vitamin B1, inosine and venoruton tablets. Control group: 18 patients(18 eyes), oral lecithin complex iodine, vitamin B1, inosine, venoruton tablets. Foveal thickness and neuroepithelial layer detachment range were measured by optical coherence tomography(OCT)before treatment, after 1 month and 3 months post-operation to compare the decline in value of foveal thickness and neuroepithelial layer detachment range of the two groups. RESULTS: After 1 month of treatment, the decline in value of the center foveal thickness: the value of treatment group was 256±72μm; the value of the control group was 82±57μm, and the difference of the two groups, P <0.05; the decline in value of neuroepithelial layer detachment range: the value of the treatment group was 3 548±168μm, the value of the control group was 1 520±143μm, And the difference of the two groups, P<0.05. After three months of treatment, the decline in value of the center foveal thickness: the value of treatment group was 383±75μm, the value of the control group was 312±67 μm, and the difference of the two groups, P<0.05; decline in value of neuroepithelial layer detachment range: the value of the treatment group was 4 908±172μm, the value of the control group was 4 211±153μm, and the difference of the two groups, P <0.05. The differences were statistically significant between the treatment and the control groups(two independent samples t-test). CONCLUSION:Argon laser photocoagulation treatment of CSC is an effective treatment method and can significantly shorten the course