205 research outputs found
A new approach of CMT seam welding deformation forecasting based on GA-BPNN
Welding deformation affects the quality of the welded parts. In this paper, by introducing improved back propagation neural network (BPNN), a cold metal transfer (CMT) welding deformation prediction model for aluminum-steel hybrid sheets is established. Before applying BPNN, important parameters affecting welding deformation were obtained by orthogonal test and gray relational grade theory. The accuracy of welding deformation prediction of BPNN is improved by genetic algorithm. The results show that compared with the prediction method based on traditional theory, the deformation prediction model based on GA-BPNN has higher accuracy. Predicted results were applied to the aluminum-steel CMT seam welding in the form of inverse deformation, and the deformation of the welded plate was significantly improved
Transmission of H7N9 influenza virus in mice by different infective routes.
BackgroundOn 19 February 2013, the first patient infected with a novel influenza A H7N9 virus from an avian source showed symptoms of sickness. More than 349 laboratory-confirmed cases and 109 deaths have been reported in mainland China since then. Laboratory-confirmed, human-to-human H7N9 virus transmission has not been documented between individuals having close contact; however, this transmission route could not be excluded for three families. To control the spread of the avian influenza H7N9 virus, we must better understand its pathogenesis, transmissibility, and transmission routes in mammals. Studies have shown that this particular virus is transmitted by aerosols among ferrets.MethodsTo study potential transmission routes in animals with direct or close contact to other animals, we investigated these factors in a murine model.ResultsViable H7N9 avian influenza virus was detected in the upper and lower respiratory tracts, intestine, and brain of model mice. The virus was transmissible between mice in close contact, with a higher concentration of virus found in pharyngeal and ocular secretions, and feces. All these biological materials were contagious for naïve mice.ConclusionsOur results suggest that the possible transmission routes for the H7N9 influenza virus were through mucosal secretions and feces
Zero-shot Composed Text-Image Retrieval
In this paper, we consider the problem of composed image retrieval (CIR), it
aims to train a model that can fuse multi-modal information, e.g., text and
images, to accurately retrieve images that match the query, extending the
user's expression ability. We make the following contributions: (i) we initiate
a scalable pipeline to automatically construct datasets for training CIR model,
by simply exploiting a large-scale dataset of image-text pairs, e.g., a subset
of LAION-5B; (ii) we introduce a transformer-based adaptive aggregation model,
TransAgg, which employs a simple yet efficient fusion mechanism, to adaptively
combine information from diverse modalities; (iii) we conduct extensive
ablation studies to investigate the usefulness of our proposed data
construction procedure, and the effectiveness of core components in TransAgg;
(iv) when evaluating on the publicly available benckmarks under the zero-shot
scenario, i.e., training on the automatically constructed datasets, then
directly conduct inference on target downstream datasets, e.g., CIRR and
FashionIQ, our proposed approach either performs on par with or significantly
outperforms the existing state-of-the-art (SOTA) models. Project page:
https://code-kunkun.github.io/ZS-CIR
Bag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays
Clinical classification of chest radiography is particularly challenging for
standard machine learning algorithms due to its inherent long-tailed and
multi-label nature. However, few attempts take into account the coupled
challenges posed by both the class imbalance and label co-occurrence, which
hinders their value to boost the diagnosis on chest X-rays (CXRs) in the
real-world scenarios. Besides, with the prevalence of pretraining techniques,
how to incorporate these new paradigms into the current framework lacks of the
systematical study. This technical report presents a brief description of our
solution in the ICCV CVAMD 2023 CXR-LT Competition. We empirically explored the
effectiveness for CXR diagnosis with the integration of several advanced
designs about data augmentation, feature extractor, classifier design, loss
function reweighting, exogenous data replenishment, etc. In addition, we
improve the performance through simple test-time data augmentation and
ensemble. Our framework finally achieves 0.349 mAP on the competition test set,
ranking in the top five.Comment: Accepted for the ICCV 2023 Workshop on Computer Vision for Automated
Medical Diagnosis (CVAMD
Combating Representation Learning Disparity with Geometric Harmonization
Self-supervised learning (SSL) as an effective paradigm of representation
learning has achieved tremendous success on various curated datasets in diverse
scenarios. Nevertheless, when facing the long-tailed distribution in real-world
applications, it is still hard for existing methods to capture transferable and
robust representation. Conventional SSL methods, pursuing sample-level
uniformity, easily leads to representation learning disparity where head
classes dominate the feature regime but tail classes passively collapse. To
address this problem, we propose a novel Geometric Harmonization (GH) method to
encourage category-level uniformity in representation learning, which is more
benign to the minority and almost does not hurt the majority under long-tailed
distribution. Specially, GH measures the population statistics of the embedding
space on top of self-supervised learning, and then infer an fine-grained
instance-wise calibration to constrain the space expansion of head classes and
avoid the passive collapse of tail classes. Our proposal does not alter the
setting of SSL and can be easily integrated into existing methods in a low-cost
manner. Extensive results on a range of benchmark datasets show the
effectiveness of GH with high tolerance to the distribution skewness. Our code
is available at https://github.com/MediaBrain-SJTU/Geometric-Harmonization.Comment: Accepted to NeurIPS 2023 (spotlight
Redundancy-Adaptive Multimodal Learning for Imperfect Data
Multimodal models trained on complete modality data often exhibit a
substantial decrease in performance when faced with imperfect data containing
corruptions or missing modalities. To address this robustness challenge, prior
methods have explored various approaches from aspects of augmentation,
consistency or uncertainty, but these approaches come with associated drawbacks
related to data complexity, representation, and learning, potentially
diminishing their overall effectiveness. In response to these challenges, this
study introduces a novel approach known as the Redundancy-Adaptive Multimodal
Learning (RAML). RAML efficiently harnesses information redundancy across
multiple modalities to combat the issues posed by imperfect data while
remaining compatible with the complete modality. Specifically, RAML achieves
redundancy-lossless information extraction through separate unimodal
discriminative tasks and enforces a proper norm constraint on each unimodal
feature representation. Furthermore, RAML explicitly enhances multimodal fusion
by leveraging fine-grained redundancy among unimodal features to learn
correspondences between corrupted and untainted information. Extensive
experiments on various benchmark datasets under diverse conditions have
consistently demonstrated that RAML outperforms state-of-the-art methods by a
significant margin
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