108 research outputs found
Code Generation as a Dual Task of Code Summarization
Code summarization (CS) and code generation (CG) are two crucial tasks in the
field of automatic software development. Various neural network-based
approaches are proposed to solve these two tasks separately. However, there
exists a specific intuitive correlation between CS and CG, which have not been
exploited in previous work. In this paper, we apply the relations between two
tasks to improve the performance of both tasks. In other words, exploiting the
duality between the two tasks, we propose a dual training framework to train
the two tasks simultaneously. In this framework, we consider the dualities on
probability and attention weights, and design corresponding regularization
terms to constrain the duality. We evaluate our approach on two datasets
collected from GitHub, and experimental results show that our dual framework
can improve the performance of CS and CG tasks over baselines.Comment: To appear at the 33rd Conference on Neural Information Processing
Systems (NeurIPS) 201
Huatuo-26M, a Large-scale Chinese Medical QA Dataset
In this paper, we release a largest ever medical Question Answering (QA)
dataset with 26 million QA pairs. We benchmark many existing approaches in our
dataset in terms of both retrieval and generation. Experimental results show
that the existing models perform far lower than expected and the released
dataset is still challenging in the pre-trained language model era. Moreover,
we also experimentally show the benefit of the proposed dataset in many
aspects: (i) trained models for other QA datasets in a zero-shot fashion; and
(ii) as external knowledge for retrieval-augmented generation (RAG); and (iii)
improving existing pre-trained language models by using the QA pairs as a
pre-training corpus in continued training manner. We believe that this dataset
will not only contribute to medical research but also facilitate both the
patients and clinical doctors. See
\url{https://github.com/FreedomIntelligence/Huatuo-26M}
TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
Life-long user behavior modeling, i.e., extracting a user's hidden interests
from rich historical behaviors in months or even years, plays a central role in
modern CTR prediction systems. Conventional algorithms mostly follow two
cascading stages: a simple General Search Unit (GSU) for fast and coarse search
over tens of thousands of long-term behaviors and an Exact Search Unit (ESU)
for effective Target Attention (TA) over the small number of finalists from
GSU. Although efficient, existing algorithms mostly suffer from a crucial
limitation: the \textit{inconsistent} target-behavior relevance metrics between
GSU and ESU. As a result, their GSU usually misses highly relevant behaviors
but retrieves ones considered irrelevant by ESU. In such case, the TA in ESU,
no matter how attention is allocated, mostly deviates from the real user
interests and thus degrades the overall CTR prediction accuracy. To address
such inconsistency, we propose \textbf{TWo-stage Interest Network (TWIN)},
where our Consistency-Preserved GSU (CP-GSU) adopts the identical
target-behavior relevance metric as the TA in ESU, making the two stages twins.
Specifically, to break TA's computational bottleneck and extend it from ESU to
GSU, or namely from behavior length to length , we build a
novel attention mechanism by behavior feature splitting. For the video inherent
features of a behavior, we calculate their linear projection by efficient
pre-computing \& caching strategies. And for the user-item cross features, we
compress each into a one-dimentional bias term in the attention score
calculation to save the computational cost. The consistency between two stages,
together with the effective TA-based relevance metric in CP-GSU, contributes to
significant performance gain in CTR prediction.Comment: Accepted by KDD 202
Effect of Eucommia ulmoides Leaf Extract on Growth Performance, Carcass Traits, Parameters of Oxidative Stress, and Lipid Metabolism in Broiler Chickens
Eucommia ulmoides bark has been traditionally used as a Chinese medicine to attenuate stress, but the leaf, which is rich in polyphenols and polysaccharides, has been rarely used. This study aimed to investigate the effect of Eucommia ulmoides leaf extracts (EULEs) on oxidative stress and meat quality of broilers. A total of 252 broilers were randomly divided into 3 treatments and fed with a control basal diet (CON), or a diet containing 250 mg/kg or 1,000 mg/kg of EULE for 51 days. Results showed that dietary supplementation of 250 mg/kg EULE increased significantly the average daily gain of broilers in the early stage (1β21 days), while 250 mg/kg or 1,000 mg/kg of EULE decreased the feed conversion ratio in the whole period (P < 0.05). Supplementation of 250 mg/kg EULE reduced the level of MDA in the liver (P < 0.05), while 1,000 mg/kg EULE decreased the serum level of MDA (P < 0.05), and the HDL level in serum was increased by 250 mg/kg or 1,000 mg/kg EULE (P < 0.05). Additionally, 250 mg/kg EULE decreased abdominal fat ratio and serum triglyceride (TC) level in broilers, while 250 or 1,000 mg/kg of EULE reduced drip loss in breast muscle (P < 0.05), and 1,000 mg/kg EULE reduced the cooking loss in thigh muscle (P < 0.05). In conclusion, dietary supplementation of 250 mg/kg of EULE could attenuate oxidative stress and improve the growth performance and meat quality in broilers
Identification and Differential Expression of MicroRNAs during Metamorphosis of the Japanese Flounder (Paralichthys olivaceus)
BACKGROUND: MicroRNAs (miRNAs) are a class of endogenous small non-coding RNAs of 20-25 nucleotides that play a key role in diverse biological processes. Japanese flounder undergo dramatic metamorphosis in their early development. The metamorphosis is characterized by morphological transformation from a bilaterally symmetrical to an asymmetrical body shape concomitant with extensive morphological and physiological remodeling of organs. So far, only a few miRNAs have been identified in fish and there are very few reports about the Japanese flounder miRNA. METHODOLOGY/PRINCIPAL FINDINGS: Solexa sequencing technology was used to perform high throughput sequencing of the small RNA library from the metamorphic period of Japanese flounder. Subsequently, aligning these sequencing data with metazoan known miRNAs, we characterized 140 conserved miRNAs and 57 miRNA: miRNA* pairs from the small RNA library. Among these 57 miRNA: miRNA* pairs, twenty flounder miRNA precursors were amplified from genomic DNA. We also demonstrated evolutionary conservation of Japanese flounder miRNAs and miRNA* in the animal evolution process. Using miRNA microarrays, we identified 66 differentially expressed miRNAs at two metamorphic stages (17 and 29 days post hatching) of Japanese flounder. The results show that miRNAs might play a key role in regulating gene expression during Japanese flounder metamorphosis. CONCLUSIONS/SIGNIFICANCE: We identified a large number of miRNAs during flounder metamorphosis, some of which are differentially expressed at two different metamorphic stages. The study provides an opportunity for further understanding of miRNA function in the regulation of flounder metamorphosis and gives us clues for further studies of the mechanisms of metamorphosis in Japanese flounder
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