109 research outputs found
Effects of different chemical materials and cultural methods on growth and yield of winter wheat
To determine the effects of different chemical and cultural methods on the growth of winter wheat, six treatments were carried out: Conservational irrigation, non-irrigation, water absorbent polymers (WAP), liquid mulching film (LMF), water-saving irrigation (WSI) and subsoiling tillage (SST). The results show that winter wheat could use more water from soil profile though WAP, LMF and SST treatments; only LMF could use extra water for yield while both WAP and SST could not increase yield. SST could not increase yield of winter wheat. Both LMF and WAP treatments could help in maintaining leaf chlorophyll content and leaf water content which may help in maintaining photosynthetic ability in late growing periods. Furthermore, more dry matter partitioning to reproductive organs is observed in LMF and WAP treatments. LMF might be favorable for yield when grown under lower soil moisture conditions, while the application of WAP might not help in yield producing in field both in high or low soil moisture conditions. A reasonable irrigation quantity may be needed when applying WAP, while LMF could be used in any meteorological and/or soil water conditions.Keywords: Winter wheat, water absorbent polymers, liquid mulching film, subsoiling tillageAfrican Journal of Biotechnology Vol. 12(36), pp. 5522-552
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
We present a Few-Shot Relation Classification Dataset (FewRel), consisting of
70, 000 sentences on 100 relations derived from Wikipedia and annotated by
crowdworkers. The relation of each sentence is first recognized by distant
supervision methods, and then filtered by crowdworkers. We adapt the most
recent state-of-the-art few-shot learning methods for relation classification
and conduct a thorough evaluation of these methods. Empirical results show that
even the most competitive few-shot learning models struggle on this task,
especially as compared with humans. We also show that a range of different
reasoning skills are needed to solve our task. These results indicate that
few-shot relation classification remains an open problem and still requires
further research. Our detailed analysis points multiple directions for future
research. All details and resources about the dataset and baselines are
released on http://zhuhao.me/fewrel.Comment: EMNLP 2018. The first four authors contribute equally. The order is
determined by dice rolling. Visit our website http://zhuhao.me/fewre
Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening
Vulvovaginal candidiasis (VVC) is the most prevalent human candidal
infection, estimated to afflict approximately 75% of all women at least once in
their lifetime. It will lead to several symptoms including pruritus, vaginal
soreness, and so on. Automatic whole slide image (WSI) classification is highly
demanded, for the huge burden of disease control and prevention. However, the
WSI-based computer-aided VCC screening method is still vacant due to the scarce
labeled data and unique properties of candida. Candida in WSI is challenging to
be captured by conventional classification models due to its distinctive
elongated shape, the small proportion of their spatial distribution, and the
style gap from WSIs. To make the model focus on the candida easier, we propose
an attention-guided method, which can obtain a robust diagnosis classification
model. Specifically, we first use a pre-trained detection model as prior
instruction to initialize the classification model. Then we design a Skip
Self-Attention module to refine the attention onto the fined-grained features
of candida. Finally, we use a contrastive learning method to alleviate the
overfitting caused by the style gap of WSIs and suppress the attention to false
positive regions. Our experimental results demonstrate that our framework
achieves state-of-the-art performance. Code and example data are available at
https://github.com/cjdbehumble/MICCAI2023-VVC-Screening.Comment: Accepted in the main conference MICCAI 202
READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises
For many real-world applications, the user-generated inputs usually contain
various noises due to speech recognition errors caused by linguistic
variations1 or typographical errors (typos). Thus, it is crucial to test model
performance on data with realistic input noises to ensure robustness and
fairness. However, little study has been done to construct such benchmarks for
Chinese, where various language-specific input noises happen in the real world.
In order to fill this important gap, we construct READIN: a Chinese multi-task
benchmark with REalistic And Diverse Input Noises. READIN contains four diverse
tasks and requests annotators to re-enter the original test data with two
commonly used Chinese input methods: Pinyin input and speech input. We designed
our annotation pipeline to maximize diversity, for example by instructing the
annotators to use diverse input method editors (IMEs) for keyboard noises and
recruiting speakers from diverse dialectical groups for speech noises. We
experiment with a series of strong pretrained language models as well as robust
training methods, we find that these models often suffer significant
performance drops on READIN even with robustness methods like data
augmentation. As the first large-scale attempt in creating a benchmark with
noises geared towards user-generated inputs, we believe that READIN serves as
an important complement to existing Chinese NLP benchmarks. The source code and
dataset can be obtained from https://github.com/thunlp/READIN.Comment: Preprin
Privacy-Preserving Encrypted Low-Dose CT Denoising
Deep learning (DL) has made significant advancements in tomographic imaging,
particularly in low-dose computed tomography (LDCT) denoising. A recent trend
involves servers training powerful models with large amounts of self-collected
private data and providing application programming interfaces (APIs) for users,
such as Chat-GPT. To avoid model leakage, users are required to upload their
data to the server model, but this way raises public concerns about the
potential risk of privacy disclosure, especially for medical data. Hence, to
alleviate related concerns, in this paper, we propose to directly denoise LDCT
in the encrypted domain to achieve privacy-preserving cloud services without
exposing private data to the server. To this end, we employ homomorphic
encryption to encrypt private LDCT data, which is then transferred to the
server model trained with plaintext LDCT for further denoising. However, since
traditional operations, such as convolution and linear transformation, in DL
methods cannot be directly used in the encrypted domain, we transform the
fundamental mathematic operations in the plaintext domain into the operations
in the encrypted domain. In addition, we present two interactive frameworks for
linear and nonlinear models in this paper, both of which can achieve lossless
operating. In this way, the proposed methods can achieve two merits, the data
privacy is well protected and the server model is free from the risk of model
leakage. Moreover, we provide theoretical proof to validate the lossless
property of our framework. Finally, experiments were conducted to demonstrate
that the transferred contents are well protected and cannot be reconstructed.
The code will be released once the paper is accepted
- …