50 research outputs found
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional
sparse graph-structured data into low-dimensional dense vectors, which is a
fundamental task that has been widely studied in a range of fields, including
machine learning and data mining. Classic graph embedding methods follow the
basic idea that the embedding vectors of interconnected nodes in the graph can
still maintain a relatively close distance, thereby preserving the structural
information between the nodes in the graph. However, this is sub-optimal due
to: (i) traditional methods have limited model capacity which limits the
learning performance; (ii) existing techniques typically rely on unsupervised
learning strategies and fail to couple with the latest learning paradigms;
(iii) representation learning and downstream tasks are dependent on each other
which should be jointly enhanced. With the remarkable success of deep learning,
deep graph representation learning has shown great potential and advantages
over shallow (traditional) methods, there exist a large number of deep graph
representation learning techniques have been proposed in the past decade,
especially graph neural networks. In this survey, we conduct a comprehensive
survey on current deep graph representation learning algorithms by proposing a
new taxonomy of existing state-of-the-art literature. Specifically, we
systematically summarize the essential components of graph representation
learning and categorize existing approaches by the ways of graph neural network
architectures and the most recent advanced learning paradigms. Moreover, this
survey also provides the practical and promising applications of deep graph
representation learning. Last but not least, we state new perspectives and
suggest challenging directions which deserve further investigations in the
future
Non-invasive Amide Proton Transfer Imaging and ZOOM Diffusion-Weighted Imaging in Differentiating Benign and Malignant Thyroid Micronodules
Background: Pre-operative non-invasive differentiation of benign and malignant thyroid nodules is difficult for doctors. This study aims to determine whether amide proton transfer (APT) imaging and zonally oblique multi-slice (ZOOM) diffusion-weighted imaging (DWI) can provide increased accuracy in differentiating benign and malignant thyroid nodules.Methods: This retrospective study was approved by the institutional review board and included 60 thyroid nodules in 50 patients. All of the nodules were classified as malignant (n = 21) or benign (n = 39) based on pathology. It was meaningful to analyze the APT and apparent diffusion coefficient (ADC) values of the two groups by independent t-test to identify the benign and malignant thyroid nodules. The relationship between APT and ZOOM DWI was explored through Pearson correlation analysis. The diagnostic efficacy of APT and ZOOM DWI in determining if thyroid nodules were benign or malignant was compared using receiver operating characteristic (ROC) curve analysis.Results: The mean APTw value of the benign nodules was 2.99 ± 0.79, while that of the malignant nodules was 2.14 ± 0.73. Additionally, there was a significant difference in the APTw values of the two groups (P < 0.05). The mean ADC value of the benign nodules was 1.84 ± 0.41, and was significantly different from that of the malignant nodules, which was 1.21 ± 0.19 (P < 0.05). Scatter point and Pearson test showed a moderate positive correlation between the APT and ADC values (P < 0.05). The ROC curve showed that the area under the curve (AUC) value of ZOOM DWI (AUC = 0.937) was greater than that of APT (AUC = 0.783) (P = 0.028).Conclusion: APT and ZOOM DWI imaging improved the accuracy of distinguishing between benign and malignant thyroid nodules. ZOOM DWI is superior to APTw imaging (Z = 2.198, P < 0.05)
Skywork: A More Open Bilingual Foundation Model
In this technical report, we present Skywork-13B, a family of large language
models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both
English and Chinese texts. This bilingual foundation model is the most
extensively trained and openly published LLMs of comparable size to date. We
introduce a two-stage training methodology using a segmented corpus, targeting
general purpose training and then domain-specific enhancement training,
respectively. We show that our model not only excels on popular benchmarks, but
also achieves \emph{state of the art} performance in Chinese language modeling
on diverse domains. Furthermore, we propose a novel leakage detection method,
demonstrating that test data contamination is a pressing issue warranting
further investigation by the LLM community. To spur future research, we release
Skywork-13B along with checkpoints obtained during intermediate stages of the
training process. We are also releasing part of our SkyPile corpus, a
collection of over 150 billion tokens of web text, which is the largest high
quality open Chinese pre-training corpus to date. We hope Skywork-13B and our
open corpus will serve as a valuable open-source resource to democratize access
to high-quality LLMs
Dense Feature Aggregation and Pruning for RGBT Tracking
How to perform effective information fusion of different modalities is a core
factor in boosting the performance of RGBT tracking. This paper presents a
novel deep fusion algorithm based on the representations from an end-to-end
trained convolutional neural network. To deploy the complementarity of features
of all layers, we propose a recursive strategy to densely aggregate these
features that yield robust representations of target objects in each modality.
In different modalities, we propose to prune the densely aggregated features of
all modalities in a collaborative way. In a specific, we employ the operations
of global average pooling and weighted random selection to perform channel
scoring and selection, which could remove redundant and noisy features to
achieve more robust feature representation. Experimental results on two RGBT
tracking benchmark datasets suggest that our tracker achieves clear
state-of-the-art against other RGB and RGBT tracking methods.Comment: arXiv admin note: text overlap with arXiv:1811.0985
A tau fragment links depressive-like behaviors and cognitive declines in Alzheimer’s disease mouse models through attenuating mitochondrial function
IntroductionAlzheimer’s disease (AD) is the most prevalent neurodegenerative disease characterized by extracellular senile plaques including amyloid-β peptides and intracellular neurofibrillary tangles consisting of abnormal Tau. Depression is one of the most common neuropsychiatric symptoms in AD, and clinical evidence demonstrates that depressive symptoms accelerate the cognitive deficit of AD patients. However, the underlying molecular mechanisms of depressive symptoms present in the process of AD remain unclear.MethodsDepressive-like behaviors and cognitive decline in hTau mice were induced by chronic restraint stress (CRS). Computational prediction and molecular experiments supported that an asparagine endopeptidase (AEP)-derived Tau fragment, Tau N368 interacts with peroxisome proliferator-activated receptor delta (PPAR-δ). Further behavioral studies investigated the role of Tau N368-PPAR-δ interaction in depressive-like behaviors and cognitive declines of AD models exposed to CRS.ResultsWe found that mitochondrial dysfunction was positively associated with depressive-like behaviors and cognitive deficits in hTau mice. Chronic stress increased Tau N368 and promoted the interaction of Tau N368 with PPAR-δ, repressing PPAR-δ–mediated transactivation in the hippocampus of mice. Then we predicted and identified the binding sites of PPAR-δ. Finally, inhibition of AEP, clearance of Tau N368 and pharmacological activation of PPAR-δ effectively alleviated CRS-induced depressive-like behaviors and cognitive decline in mice.ConclusionThese results demonstrate that Tau N368 in the hippocampus impairs mitochondrial function by suppressing PPAR-δ, facilitating the occurrence of depressive-like behaviors and cognitive decline. Therefore, our findings may provide new mechanistic insight in the pathophysiology of depression-like phenotype in mouse models of Alzheimer’s disease
Microstructure and Shear Behaviour of Sn-3.0Ag-0.5Cu Composite Solder Pastes Enhanced by Epoxy Resin
With the rapid development of microelectronics packaging technology, the demand for high-performance packaging materials has further increased. This paper developed novel epoxy-containing Sn-3.0Ag-0.5Cu (SAC305-ER) composite solder pastes, and the effects of epoxy resin on their spreading performance, microstructure, and shear behaviour were analysed. The research results showed that with the addition of epoxy resin, SAC305 solder pastes exhibited exceptional spreadability on Cu substrates, which could be attributed to the reduction in the viscosity and the surface tension of the composite solder pastes. With the addition of epoxy resin, the solder matrix microstructure and interfacial morphology of SAC305-ER composite solder joints remained unchanged. However, continuous resin protective layers were observed on the surface of SAC305-ER composite solder joints after the reflow process. The shear properties of the composite solder joints were enhanced by the extra mechanical bonding effect provided by resin layers. When the epoxy resin content was 8 wt%, the shear forces of SAC305-ER composite solder joints reached the maximum value. Fracture analysis indicated that cracked epoxy resin was observed on the surface of SAC305-ER composite solder joints, indicating that the epoxy resin also underwent obvious deformation in the shear test
Morphophysiological Changes in <i>Staphylococcus aureus</i> Biofilms Treated with Plasma-Activated Hydrogen Peroxide Solution
Plasma-activated solution has attracted more attention in the food industry due to no chemical residue and good bacteriostatic properties. This study aimed to evaluate the effects of plasma-activated hydrogen peroxide solution (PAH) on the morphophysiology of Staphylococcus aureus biofilms. PAH was prepared using dielectric-barrier-discharge plasma and incubated with S. aureus biofilms for 0–40 min. Changes in biofilm morphophysiology were evaluated with laser scanning confocal microscopy, electron microscopic images, reactive oxygen species (ROS) content, metabolic capacity, and 1% agarose gel. Results indicated that the population of S. aureus in the biofilms was reduced by 4.04-log after incubation with PAH for 30 min. The thickness and metabolic capacity of biofilms were decreased, the ROS content and DNA fragments of bacteria increased after PAH treatments. Data suggested that PAH treatments significantly destroyed the morphophysiology of S. aureus (ATCC 6538) biofilms and could be considered as a valuable anti-biofilm technology to reduce foodborne pathogens on food and/or in food facilities