Harbin Institute of Technology

About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.

Big Data for Modern Industry: Challenges and Trends [Point of View]

Abstract: We are living in an era of data deluge and as a result, the term ‘‘big data’’ is appearing in many contexts, from meteorology, genomics, complex physics simulations, biological and environmental research, finance and business to healthcare. Explores ways in business and industry is working to manage data and reports on applications that support these initiatives.

Synthesis of Electromagnetic Functionalized Nickel/Polypyrrole Core/Shell Composites

Abstract: Microstructured Ni/PPy (PPy: polypyrrole) core/shell composites were prepared from an in situ chemical oxidative polymerization of pyrrole (Py) monomer in the presence of Ni powder, with ammonium persulfate (APS) as oxidant and citric acid (C6H8O7) as dopant. X-ray diffraction and Fourier transform infrared analyses indicate that there is no chemical interaction between Ni powder and protonated PPy. The mass percentages of PPy, calculated from the remanent weight percentages of Ni/PPy composites after thermogravimetric analysis, are in consistent with those as designed. The prepared Ni/PPy composites are soft and ferromagnetic materials, where a linear increase of saturation magnetization (MS) and remanent magnetization (MR) as a function of Ni powder content is proposed. The permeability of Ni/PPy composites presents a natural magnetic resonance at 6.0 GHz, and Cole−Cole semicircle was applied to explain the permittivity. Electromagnetic absorption less than −10 dB is found for Ni/Py = 4:1 (11−15.4 GHz) ...

Improving Lexical Embeddings with Semantic Knowledge

Abstract: Word embeddings learned on unlabeled data are a popular tool in semantics, but may not capture the desired semantics. We propose a new learning objective that incorporates both a neural language model objective (Mikolov et al., 2013) and prior knowledge from semantic resources to learn improved lexical semantic embeddings. We demonstrate that our embeddings improve over those learned solely on raw text in three settings: language modeling, measuring semantic similarity, and predicting human judgements.