Dual Co-Matching Network for Multi-choice Reading Comprehension

Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure. Given passage and question, a correct answer need to be selected from a set of candidate answers. In this paper, we propose \textbf{D}ual \textbf{C}o-\textbf{M}atching \textbf{N}etwork (\textbf{DCMN}) which model the relationship among passage, question and answer bidirectionally. Different from existing approaches which only calculate question-aware or option-aware passage representation, we calculate passage-aware question representation and passage-aware answer representation at the same time. To demonstrate the effectiveness of our model, we evaluate our model on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Experimental result show that our proposed model achieves new state-of-the-art results.Comment: arXiv admin note: text overlap with arXiv:1806.04068 by other author

Semantics-aware BERT for Language Understanding

The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks. However, the existing language representation models including ELMo, GPT and BERT only exploit plain context-sensitive features such as character or word embeddings. They rarely consider incorporating structured semantic information which can provide rich semantics for language representation. To promote natural language understanding, we propose to incorporate explicit contextual semantics from pre-trained semantic role labeling, and introduce an improved language representation model, Semantics-aware BERT (SemBERT), which is capable of explicitly absorbing contextual semantics over a BERT backbone. SemBERT keeps the convenient usability of its BERT precursor in a light fine-tuning way without substantial task-specific modifications. Compared with BERT, semantics-aware BERT is as simple in concept but more powerful. It obtains new state-of-the-art or substantially improves results on ten reading comprehension and language inference tasks.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020

Behavioral/Cognitive Acute and Long-Term Suppression of Feeding Behavior by POMC Neurons in the Brainstem and Hypothalamus, Respectively

POMC-derived melanocortins inhibit food intake. In the adult rodent brain, POMC-expressing neurons are located in the arcuate nucleus (ARC) and the nucleus tractus solitarius (NTS), but it remains unclear how POMC neurons in these two brain nuclei regulate feeding behavior and metabolism differentially. Using pharmacogenetic methods to activate or deplete neuron groups in separate brain areas, in the present study, we show that POMC neurons in the ARC and NTS suppress feeding behavior at different time scales. Neurons were activated using the DREADD (designer receptors exclusively activated by designer drugs) method. The evolved human M3-muscarinic receptor was expressed in a selective population of POMC neurons by stereotaxic infusion of Cre-recombinase–dependent, adenoassociated virus vectors into the ARC or NTS of POMC-Cre mice. After injection of the human M3-muscarinic receptor ligand clozapine-N-oxide (1 mg/kg, i.p.), acute activation of NTS POMC neurons produced an immediate inhibition of feeding behavior. In contrast, chronic stimulation was required for ARC POMC neurons to suppress food intake. Using adeno-associated virus delivery of the diphtheria toxin receptor gene, we found that diphtheria toxin–induced ablation of POMC neurons in the ARC but not the NTS, increased food intake, reduced energy expenditure, and ultimately resulted in obesity and metabolic and endocrine disorders. Our results reveal different behavioral functions of POMC neurons in the ARC and NTS, suggesting that POMC neurons regulate feeding and energy homeostasis by integrating long-term adiposity signals from the hypothalamus and short-term satiety signals from the brainstem

Highly-efficient Cas9-mediated transcriptional programming

The RNA-guided nuclease Cas9 can be reengineered as a programmable transcription factor. However, modest levels of gene activation have limited potential applications. We describe an improved transcriptional regulator obtained through the rational design of a tripartite activator, VP64-p65-Rta (VPR), fused to nuclease-null Cas9. We demonstrate its utility in activating endogenous coding and noncoding genes, targeting several genes simultaneously and stimulating neuronal differentiation of human induced pluripotent stem cells (iPSCs).National Human Genome Research Institute (U.S.) (Grant P50 HG005550)United States. Dept. of Energy (Grant DE-FG02-02ER63445)Wyss Institute for Biologically Inspired EngineeringNational Science Foundation (U.S.). Graduate Research FellowshipMassachusetts Institute of Technology. Department of Biological EngineeringHarvard Medical School. Department of Genetic

Research on Certification and Circulation Mode of Green Electricity Environmental Value Based on Blockchain

In the process of green power trading, green power reflects the value of electricity energy and green environment, and green certificates aim to reduce the pressure of new energy subsidies and guide the concept of green electricity consumption. In order to promote the integration of new energy through market-oriented mechanisms, ensure the basic income of new energy projects, reflect the environmental value of green electricity, promote the sustainable development of the new energy industry, and meet the needs of users for green electricity at the same time, this paper constructs a blockchain-based green electricity environmental value authentication and circulation method by using the characteristics of blockchain centralization, distributed ledger, consensus mechanism, and smart contract

Research on Certification and Circulation Mode of Green Electricity Environmental Value Based on Blockchain

In the process of green power trading, green power reflects the value of electricity energy and green environment, and green certificates aim to reduce the pressure of new energy subsidies and guide the concept of green electricity consumption. In order to promote the integration of new energy through market-oriented mechanisms, ensure the basic income of new energy projects, reflect the environmental value of green electricity, promote the sustainable development of the new energy industry, and meet the needs of users for green electricity at the same time, this paper constructs a blockchain-based green electricity environmental value authentication and circulation method by using the characteristics of blockchain centralization, distributed ledger, consensus mechanism, and smart contract