147 research outputs found
Scalar Invariant Networks with Zero Bias
Just like weights, bias terms are the learnable parameters of many popular
machine learning models, including neural networks. Biases are thought to
enhance the representational power of neural networks, enabling them to solve a
variety of tasks in computer vision. However, we argue that biases can be
disregarded for some image-related tasks such as image classification, by
considering the intrinsic distribution of images in the input space and desired
model properties from first principles. Our findings suggest that zero-bias
neural networks can perform comparably to biased networks for practical image
classification tasks. We demonstrate that zero-bias neural networks possess a
valuable property called scalar (multiplication) invariance. This means that
the prediction of the network remains unchanged when the contrast of the input
image is altered. We extend scalar invariance to more general cases, enabling
formal verification of certain convex regions of the input space. Additionally,
we prove that zero-bias neural networks are fair in predicting the zero image.
Unlike state-of-the-art models that may exhibit bias toward certain labels,
zero-bias networks have uniform belief in all labels. We believe dropping bias
terms can be considered as a geometric prior in designing neural network
architecture for image classification, which shares the spirit of adapting
convolutions as the transnational invariance prior. The robustness and fairness
advantages of zero-bias neural networks may also indicate a promising path
towards trustworthy and ethical AI.Comment: 22 pages, 8 figure
SDFE-LV: A Large-Scale, Multi-Source, and Unconstrained Database for Spotting Dynamic Facial Expressions in Long Videos
In this paper, we present a large-scale, multi-source, and unconstrained
database called SDFE-LV for spotting the onset and offset frames of a complete
dynamic facial expression from long videos, which is known as the topic of
dynamic facial expression spotting (DFES) and a vital prior step for lots of
facial expression analysis tasks. Specifically, SDFE-LV consists of 1,191 long
videos, each of which contains one or more complete dynamic facial expressions.
Moreover, each complete dynamic facial expression in its corresponding long
video was independently labeled for five times by 10 well-trained annotators.
To the best of our knowledge, SDFE-LV is the first unconstrained large-scale
database for the DFES task whose long videos are collected from multiple
real-world/closely real-world media sources, e.g., TV interviews,
documentaries, movies, and we-media short videos. Therefore, DFES tasks on
SDFE-LV database will encounter numerous difficulties in practice such as head
posture changes, occlusions, and illumination. We also provided a comprehensive
benchmark evaluation from different angles by using lots of recent
state-of-the-art deep spotting methods and hence researchers interested in DFES
can quickly and easily get started. Finally, with the deep discussions on the
experimental evaluation results, we attempt to point out several meaningful
directions to deal with DFES tasks and hope that DFES can be better advanced in
the future. In addition, SDFE-LV will be freely released for academic use only
as soon as possible
Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording
Relating speech to EEG holds considerable importance but is challenging. In
this study, a deep convolutional network was employed to extract spatiotemporal
features from EEG data. Self-supervised speech representation and contextual
text embedding were used as speech features. Contrastive learning was used to
relate EEG features to speech features. The experimental results demonstrate
the benefits of using self-supervised speech representation and contextual text
embedding. Through feature fusion and model ensemble, an accuracy of 60.29% was
achieved, and the performance was ranked as No.2 in Task 1 of the Auditory EEG
Challenge (ICASSP 2024). The code to implement our work is available on Github:
https://github.com/bobwangPKU/EEG-Stimulus-Match-Mismatch.Comment: 2 pages, 2 figures, accepted by ICASSP 202
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography
The amygdala plays a vital role in emotional processing and exhibits
structural diversity that necessitates fine-scale parcellation for a
comprehensive understanding of its anatomico-functional correlations. Diffusion
MRI tractography is an advanced imaging technique that can estimate the brain's
white matter structural connectivity to potentially reveal the topography of
the amygdala for studying its subdivisions. In this work, we present a deep
clustering pipeline to perform automated, fine-scale parcellation of the
amygdala using diffusion MRI tractography. First, we incorporate a newly
proposed deep learning approach to enable accurate segmentation of the amygdala
directly on the dMRI data. Next, we design a novel streamline clustering-based
structural connectivity feature for a robust representation of voxels within
the amygdala. Finally, we improve the popular joint dimensionality reduction
and k-means clustering approach to enable amygdala parcellation at a finer
scale. With the proposed method, we obtain nine unique amygdala parcels.
Experiments show that these parcels can be consistently identified across
subjects and have good correspondence to the widely used coarse-scale amygdala
parcellation
Expression of P450arom and Estrogen Receptor Alpha in the Oviduct of Chinese Brown Frog ( Rana dybowskii
One specific physiological phenomenon of Chinese brown frog (Rana dybowskii) is that its oviduct expands prior to hibernation instead of expanding during the breeding period. In this study, we investigated the expression of P450arom and estrogen receptors α and β (ERα and ERβ) in the oviduct of Rana dybowskii during the breeding period and prehibernation. The results of the present study showed that there were significant differences in both oviductal weight and size with values markedly higher in prehibernation than in the breeding period. P450arom was observed in stromal tissue in both the breeding period and prehibernation. ERα was expressed in stromal tissue and epithelial cells in both periods, whereas ERβ could not be detected. The mean protein and mRNA levels of P450arom and ERα were significantly higher in prehibernation as compared to the breeding period. Besides, oviductal content of 17β-estradiol was also higher in prehibernation than in the breeding period. These results suggested that estrogen may play autocrine/paracrine roles mediated by ERα in regulating the oviductal hypertrophy during prehibernation
Development of electronic nose for detection of micro-mechanical damages in strawberries
A self-developed portable electronic nose and its classification model were designed to detect and differentiate minor mechanical damage to strawberries. The electronic nose utilises four metal oxide sensors and four electrochemical sensors specifically calibrated for strawberry detection. The selected strawberries were subjected to simulated damage using an H2Q-C air bath oscillator at varying speeds and then stored at 4°C to mimic real-life mechanical damage scenarios. Multiple feature extraction methods have been proposed and combined with Principal Component Analysis (PCA) dimensionality reduction for comparative modelling. Following validation with various models such as SVM, KNN, LDA, naive Bayes, and subspace ensemble, the Grid Search-optimised SVM (GS-SVM) method achieved the highest classification accuracy of 0.84 for assessing the degree of strawberry damage. Additionally, the Feature Extraction ensemble classifier achieved the highest classification accuracy (0.89 in determining the time interval of strawberry damage). This experiment demonstrated the feasibility of the self-developed electronic nose for detecting minor mechanical damage in strawberries
Investigation of seasonal changes in lipid synthesis and metabolism-related genes in the oviduct of Chinese brown frog (<em>Rana dybowskii</em>)
A peculiar physiological characteristic of the Chinese brown frog (Rana dybowskii) is that its oviduct dilates during pre-brumation rather than during the breeding season. This research aimed to examine the expression of genes connected with lipid synthesis and metabolism in the oviduct of R. dybowskii during both the breeding season and pre-brumation. We observed significant changes in the weight and size of the oviduct between the breeding season and pre-brumation. Furthermore, compared to the breeding season, pre-brumation exhibited significantly lower triglyceride content and a marked increase in free fatty acid content. Immunohistochemical results revealed the spatial distribution of triglyceride synthase (Dgat1), triglyceride hydrolase (Lpl and Hsl), fatty acid synthase (Fasn), and fatty acid oxidases (Cpt1a, Acadl, and Hadh) in oviductal glandular cells and epithelial cells during both the breeding season and pre-brumation. While the mRNA levels of triglycerides and free fatty acid synthesis genes (dgat1 and fasn) did not show a significant difference between the breeding season and pre-brumation, the mRNA levels of genes involved in triglycerides and free fatty acid metabolism (lpl, cpt1a, acadl, acox and hadh) were considerably higher during pre-brumation. Furthermore, the R. dybowskii oviduct's transcriptomic and metabolomic data confirmed differential expression of genes and metabolites enriched in lipid metabolism signaling pathways during both the breeding season and pre-brumation. Overall, these results suggest that alterations in lipid synthesis and metabolism during pre-brumation may potentially influence the expanding size of the oviduct, contributing to the successful overwintering of R. dybowskii
Infrared Imaging of Magnetic Octupole Domains in Non-collinear Antiferromagnets
Magnetic structure plays a pivotal role in the functionality of
antiferromagnets (AFMs), which not only can be employed to encode digital data
but also yields novel phenomena. Despite its growing significance, visualizing
the antiferromagnetic domain structure remains a challenge, particularly for
non-collinear AFMs. Currently, the observation of magnetic domains in
non-collinear antiferromagnetic materials is feasible only in MnSn,
underscoring the limitations of existing techniques that necessitate distinct
methods for in-plane and out-of-plane magnetic domain imaging. In this study,
we present a versatile method for imaging the antiferromagnetic domain
structure in a series of non-collinear antiferromagnetic materials by utilizing
the anomalous Ettingshausen effect (AEE), which resolves both the magnetic
octupole moments parallel and perpendicular to the sample surface. Temperature
modulation due to the AEE originating from different magnetic domains is
measured by the lock-in thermography, revealing distinct behaviors of octupole
domains in different antiferromagnets. This work delivers an efficient
technique for the visualization of magnetic domains in non-collinear AFMs,
which enables comprehensive study of the magnetization process at the
microscopic level and paves the way for potential advancements in applications.Comment: National Science Review in pres
Liver fibrosis and MAFLD: the exploration of multi-drug combination therapy strategies
In recent years, the prevalence of metabolic-associated fatty liver disease (MAFLD) has reached pandemic proportions as a leading cause of liver fibrosis worldwide. However, the stage of liver fibrosis is associated with an increased risk of severe liver-related and cardiovascular events and is the strongest predictor of mortality in MAFLD patients. More and more people believe that MAFLD is a multifactorial disease with multiple pathways are involved in promoting the progression of liver fibrosis. Numerous drug targets and drugs have been explored for various anti-fibrosis pathways. The treatment of single medicines is brutal to obtain satisfactory results, so the strategies of multi-drug combination therapies have attracted increasing attention. In this review, we discuss the mechanism of MAFLD-related liver fibrosis and its regression, summarize the current intervention and treatment methods for this disease, and focus on the analysis of drug combination strategies for MAFLD and its subsequent liver fibrosis in recent years to explore safer and more effective multi-drug combination therapy strategies
Research progress and applications of epigenetic biomarkers in cancer
Epigenetic changes are heritable changes in gene expression without changes in the nucleotide sequence of genes. Epigenetic changes play an important role in the development of cancer and in the process of malignancy metastasis. Previous studies have shown that abnormal epigenetic changes can be used as biomarkers for disease status and disease prediction. The reversibility and controllability of epigenetic modification changes also provide new strategies for early disease prevention and treatment. In addition, corresponding drug development has also reached the clinical stage. In this paper, we will discuss the recent progress and application status of tumor epigenetic biomarkers from three perspectives: DNA methylation, non-coding RNA, and histone modification, in order to provide new opportunities for additional tumor research and applications
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