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.

Weavable, Conductive Yarn-Based NiCo//Zn Textile Battery with High Energy Density and Rate Capability

Abstract: With intrinsic safety and much higher energy densities than supercapacitors, rechargeable nickel/cobalt–zinc-based textile batteries are promising power sources for next generation personalized wearable electronics. However, high-performance wearable nickel/cobalt–zinc-based batteries are rarely reported because there is a lack of industrially weavable and knittable highly conductive yarns. Here, we use scalably produced highly conductive yarns uniformly covered with zinc (as anode) and nickel cobalt hydroxide nanosheets (as cathode) to fabricate rechargeable yarn batteries. They possess a battery level capacity and energy density, as well as a supercapacitor level power density. They deliver high specific capacity of 5 mAh cm–3 and energy densities of 0.12 mWh cm–2 and 8 mWh cm–3 (based on the whole solid battery). They exhibit ultrahigh rate capabilities of 232 C (liquid electrolyte) and 116 C (solid electrolyte), which endows the batteries excellent power densities of 32.8 mW cm–2 and 2.2 W cm–3 (based...

Distributed Synchronization in Networks of Agent Systems With Nonlinearities and Random Switchings

Abstract: In this paper, the distributed synchronization problem of networks of agent systems with controllers and nonlinearities subject to Bernoulli switchings is investigated. Controllers and adaptive updating laws injected in each vertex of networks depend on the state information of its neighborhood. Three sets of Bernoulli stochastic variables are introduced to describe the occurrence probabilities of distributed adaptive controllers, updating laws and nonlinearities, respectively. By the Lyapunov functions method, we show that the distributed synchronization of networks composed of agent systems with multiple randomly occurring nonlinearities, multiple randomly occurring controllers, and multiple randomly occurring updating laws can be achieved in mean square under certain criteria. The conditions derived in this paper can be solved by semi-definite programming. Moreover, by mathematical analysis, we find that the coupling strength, the probabilities of the Bernoulli stochastic variables, and the form of nonlinearities have great impacts on the convergence speed and the terminal control strength. The synchronization criteria and the observed phenomena are demonstrated by several numerical simulation examples. In addition, the advantage of distributed adaptive controllers over conventional adaptive controllers is illustrated.

The crystal structure of Cpf1 in complex with CRISPR RNA

Abstract: The CRISPR-Cas systems, as exemplified by CRISPR-Cas9, are RNA-guided adaptive immune systems used by bacteria and archaea to defend against viral infection. The CRISPR-Cpf1 system, a new class 2 CRISPR-Cas system, mediates robust DNA interference in human cells. Although functionally conserved, Cpf1 and Cas9 differ in many aspects including their guide RNAs and substrate specificity. Here we report the 2.38 A crystal structure of the CRISPR RNA (crRNA)-bound Lachnospiraceae bacterium ND2006 Cpf1 (LbCpf1). LbCpf1 has a triangle-shaped architecture with a large positively charged channel at the centre. Recognized by the oligonucleotide-binding domain of LbCpf1, the crRNA adopts a highly distorted conformation stabilized by extensive intramolecular interactions and the (Mg(H2O)6)(2+) ion. The oligonucleotide-binding domain also harbours a looped-out helical domain that is important for LbCpf1 substrate binding. Binding of crRNA or crRNA lacking the guide sequence induces marked conformational changes but no oligomerization of LbCpf1. Our study reveals the crRNA recognition mechanism and provides insight into crRNA-guided substrate binding of LbCpf1, establishing a framework for engineering LbCpf1 to improve its efficiency and specificity for genome editing.

Sentiment Embeddings with Applications to Sentiment Analysis

Abstract: We propose learning sentiment-specific word embeddings dubbed sentiment embeddings in this paper. Existing word embedding learning algorithms typically only use the contexts of words but ignore the sentiment of texts. It is problematic for sentiment analysis because the words with similar contexts but opposite sentiment polarity, such as good and bad , are mapped to neighboring word vectors. We address this issue by encoding sentiment information of texts (e.g., sentences and words) together with contexts of words in sentiment embeddings. By combining context and sentiment level evidences, the nearest neighbors in sentiment embedding space are semantically similar and it favors words with the same sentiment polarity. In order to learn sentiment embeddings effectively, we develop a number of neural networks with tailoring loss functions, and collect massive texts automatically with sentiment signals like emoticons as the training data. Sentiment embeddings can be naturally used as word features for a variety of sentiment analysis tasks without feature engineering. We apply sentiment embeddings to word-level sentiment analysis, sentence level sentiment classification, and building sentiment lexicons. Experimental results show that sentiment embeddings consistently outperform context-based embeddings on several benchmark datasets of these tasks. This work provides insights on the design of neural networks for learning task-specific word embeddings in other natural language processing tasks.