468 research outputs found
The Journey of the Potato Tuberworm Around the World
Potato (Solanum tuberosum L.) production is challenged by many factors including pests and diseases. Among insect pests, Phthorimaea operculella Zeller (Lepidoptera: Gelechiidae), known as the potato tuber worm or potato tuber moth, is considered one of the most important potato pests worldwide. Phthorimaea operculella is a cosmopolitan pest of solanaceous crops including potato, tomato (Solanum lycopersicum L.), and other important row crops. Adults oviposit in leaves, stems, and tubers; immature stage mines leaves causing foliar damage, but most importantly, burrows into tubers rendering them unmarketable. Currently, pest management practices are effective in controlling P. operculella, but the effectiveness depends on many factors that will be discussed later in this chapter. Each section includes up-to-date information related to P. operculella biology, ecology, and control, including origins, host range, life cycle, distribution, seasonal dynamics, and control methods
MIRACLE: Multi-task Learning based Interpretable Regulation of Autoimmune Diseases through Common Latent Epigenetics
DNA methylation is a crucial regulator of gene transcription and has been
linked to various diseases, including autoimmune diseases and cancers. However,
diagnostics based on DNA methylation face challenges due to large feature sets
and small sample sizes, resulting in overfitting and suboptimal performance. To
address these issues, we propose MIRACLE, a novel interpretable neural network
that leverages autoencoder-based multi-task learning to integrate multiple
datasets and jointly identify common patterns in DNA methylation.
MIRACLE's architecture reflects the relationships between methylation sites,
genes, and pathways, ensuring biological interpretability and meaningfulness.
The network comprises an encoder and a decoder, with a bottleneck layer
representing pathway information as the basic unit of heredity. Customized
defined MaskedLinear Layer is constrained by site-gene-pathway graph adjacency
matrix information, which provides explainability and expresses the
site-gene-pathway hierarchical structure explicitly. And from the embedding,
there are different multi-task classifiers to predict diseases.
Tested on six datasets, including rheumatoid arthritis, systemic lupus
erythematosus, multiple sclerosis, inflammatory bowel disease, psoriasis, and
type 1 diabetes, MIRACLE demonstrates robust performance in identifying common
functions of DNA methylation across different phenotypes, with higher accuracy
in prediction dieseases than baseline methods. By incorporating biological
prior knowledge, MIRACLE offers a meaningful and interpretable framework for
DNA methylation data analysis in the context of autoimmune diseases
Probabilistic Contrastive Learning for Long-Tailed Visual Recognition
Long-tailed distributions frequently emerge in real-world data, where a large
number of minority categories contain a limited number of samples. Such
imbalance issue considerably impairs the performance of standard supervised
learning algorithms, which are mainly designed for balanced training sets.
Recent investigations have revealed that supervised contrastive learning
exhibits promising potential in alleviating the data imbalance. However, the
performance of supervised contrastive learning is plagued by an inherent
challenge: it necessitates sufficiently large batches of training data to
construct contrastive pairs that cover all categories, yet this requirement is
difficult to meet in the context of class-imbalanced data. To overcome this
obstacle, we propose a novel probabilistic contrastive (ProCo) learning
algorithm that estimates the data distribution of the samples from each class
in the feature space, and samples contrastive pairs accordingly. In fact,
estimating the distributions of all classes using features in a small batch,
particularly for imbalanced data, is not feasible. Our key idea is to introduce
a reasonable and simple assumption that the normalized features in contrastive
learning follow a mixture of von Mises-Fisher (vMF) distributions on unit
space, which brings two-fold benefits. First, the distribution parameters can
be estimated using only the first sample moment, which can be efficiently
computed in an online manner across different batches. Second, based on the
estimated distribution, the vMF distribution allows us to sample an infinite
number of contrastive pairs and derive a closed form of the expected
contrastive loss for efficient optimization. Our code is available at
https://github.com/LeapLabTHU/ProCo.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence (T-PAMI
Fine-grained Recognition with Learnable Semantic Data Augmentation
Fine-grained image recognition is a longstanding computer vision challenge
that focuses on differentiating objects belonging to multiple subordinate
categories within the same meta-category. Since images belonging to the same
meta-category usually share similar visual appearances, mining discriminative
visual cues is the key to distinguishing fine-grained categories. Although
commonly used image-level data augmentation techniques have achieved great
success in generic image classification problems, they are rarely applied in
fine-grained scenarios, because their random editing-region behavior is prone
to destroy the discriminative visual cues residing in the subtle regions. In
this paper, we propose diversifying the training data at the feature-level to
alleviate the discriminative region loss problem. Specifically, we produce
diversified augmented samples by translating image features along semantically
meaningful directions. The semantic directions are estimated with a covariance
prediction network, which predicts a sample-wise covariance matrix to adapt to
the large intra-class variation inherent in fine-grained images. Furthermore,
the covariance prediction network is jointly optimized with the classification
network in a meta-learning manner to alleviate the degenerate solution problem.
Experiments on four competitive fine-grained recognition benchmarks
(CUB-200-2011, Stanford Cars, FGVC Aircrafts, NABirds) demonstrate that our
method significantly improves the generalization performance on several popular
classification networks (e.g., ResNets, DenseNets, EfficientNets, RegNets and
ViT). Combined with a recently proposed method, our semantic data augmentation
approach achieves state-of-the-art performance on the CUB-200-2011 dataset. The
source code will be released
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