604 research outputs found
Occam learning
We discuss probabilistic neural network models for unsupervised learning
where the distribution of the hidden layer is fixed. We argue that learning
machines with this architecture enjoy a number of desirable properties. For
example, the model can be chosen as a simple and interpretable one, it does not
need to be over-parametrised and training is argued to be efficient in a
thermodynamic sense. When hidden units are binary variables, these models have
a natural interpretation in terms of features. We show that the featureless
state corresponds to a state of maximal ignorance about the features and that
learning the first feature depends on non-Gaussian statistical properties of
the data. We suggest that the distribution of hidden variables should be chosen
according to the principle of maximal relevance. We introduce the Hierarchical
Feature Model (HFM) as an example of a model that satisfies this principle, and
that encodes a neutral a priori organisation of the feature space. We present
extensive numerical experiments in order i) to test that the internal
representation of learning machines can indeed be independent of the data with
which they are trained and ii) that only a finite number of features are needed
to describe a number of datasets.Comment: 31 pages, 10 figure
Superiority of GNN over NN in generalizing bandlimited functions
Graph Neural Network (GNN) with its ability to integrate graph information
has been widely used for data analyses. However, the expressive power of GNN
has only been studied for graph-level tasks but not for node-level tasks, such
as node classification, where one tries to interpolate missing nodal labels
from the observed ones. In this paper, we study the expressive power of GNN for
the said classification task, which is in essence a function interpolation
problem. Explicitly, we derive the number of weights and layers needed for a
GNN to interpolate a band-limited function in . Our result shows
that, the number of weights needed to -approximate a bandlimited
function using the GNN architecture is much fewer than the best known one using
a fully connected neural network (NN) - in particular, one only needs weights using a GNN trained by samples to -approximate a discretized
bandlimited signal in . The result is obtained by drawing a
connection between the GNN structure and the classical sampling theorems,
making our work the first attempt in this direction
Long-Range Feature Propagating for Natural Image Matting
Natural image matting estimates the alpha values of unknown regions in the
trimap. Recently, deep learning based methods propagate the alpha values from
the known regions to unknown regions according to the similarity between them.
However, we find that more than 50\% pixels in the unknown regions cannot be
correlated to pixels in known regions due to the limitation of small effective
reception fields of common convolutional neural networks, which leads to
inaccurate estimation when the pixels in the unknown regions cannot be inferred
only with pixels in the reception fields. To solve this problem, we propose
Long-Range Feature Propagating Network (LFPNet), which learns the long-range
context features outside the reception fields for alpha matte estimation.
Specifically, we first design the propagating module which extracts the context
features from the downsampled image. Then, we present Center-Surround Pyramid
Pooling (CSPP) that explicitly propagates the context features from the
surrounding context image patch to the inner center image patch. Finally, we
use the matting module which takes the image, trimap and context features to
estimate the alpha matte. Experimental results demonstrate that the proposed
method performs favorably against the state-of-the-art methods on the
AlphaMatting and Adobe Image Matting datasets
Risk factors for falls among community-dwelling older adults: A systematic review and meta-analysis
Background and objectiveThe prevalence of falls among older adults living in the community is ~30% each year. The impacts of falls are not only confined to the individual but also affect families and the community. Injury from a fall also imposes a heavy financial burden on patients and their families. Currently, there are different reports on the risk factors for falls among older adults in the community. A retrospective analysis was used in this study to identify risk factors for falls in community-dwelling older adults. This research aimed to collect published studies to find risk factors for falls in community-dwelling older adults.MethodsWe searched for literature from the founding of PubMed, EMBASE, the Cochrane Library, the Web of Science, the China National Knowledge Infrastructure (CNKI), the China Science and Technology Periodicals Database (VIP), and the Wanfang database until September 2022. The studies were selected using inclusion and exclusion criteria. We collected information from relevant studies to compare the impact of potential risk factors such as age, female gender, fear of falling, history of falls, unclear vision, depression, and balance disorder on falls among community-dwelling older adults.ResultsA total of 31 studies were included with 70,868 community seniors. A significant risk factor for falls in the community of older adults was dementia (2.01, 95% CI: 1.41–2.86), age (1.15, 95% CI: 1.09–1.22), female gender (1.52, 95% CI: 1.27–1.81), fear of falling (2.82, 95% CI: 1.68–4.74), history of falls (3.22, 95% CI: 1.98–5.23), vision unclear (1.56, 95% CI: 1.29–1.89), depression (1.23, 95% CI: 1.10–1.37), and balance disorder (3.00, 95% CI: 2.05–4.39).ConclusionThis study provides preliminary evidence that falls among community-dwelling older adults are associated with factors such as age, female gender, fear of falling, history of falls, unclear vision, depression, and balance disorders. The results of this research may help improve clinician awareness, risk stratification, and fall prevention among community-dwelling older adults.Systematic review registrationidentifier INPLASY2022120080
Germline SDHB and SDHD Mutations in Pheochromocytoma and Paraganglioma Patients
Pheochromocytoma and paragangliomas (PCC/PGL) are neuroendocrine tumors that arise from chromaffin cells of the adrenal medulla and sympathetic/parasympathetic ganglia, respectively. Of clinical relevance regarding diagnosis is the highly variable presentation of symptoms in PCC/PGL patients. To date, the clear-cut correlations between the genotypes and phenotypes of PCC/PGL have not been entirely established. In this study, we reviewed the medical records of PCC/PGL patients with pertinent clinical, laboratory and genetic information. Next-generation sequencing (NGS) performed on patient samples revealed specific germline mutations in the SDHB (succinate dehydrogenase complex iron-sulfur subunit B) and SDHD(succinate dehydrogenase complex subunit D) genes and these mutations were validated by Sanger sequencing. Of the 119 patients, two were identified with SDHB mutation and one with SDHD mutation. Immunohistochemical (IHC) staining was used to analyze the expression of these mutated genes. The germline mutations identified in the SDH genes were: c343C\u3eT and c.541-542A\u3eG in the SDHB gene and c.334-337delACTG in the SDHD gene. IHC staining of tumors from the c.343C\u3eT and c.541-2A\u3eG carriers showed positive expression of SDHB. Tumors from the c.334-337delACTG carrier showed no expression of SDHD and a weak diffused staining pattern for SDHB. We strongly recommend genetic testing for suspected PCC/PGL patients with a positive family history, early onset of age, erratic hypertension, recurrence or multiple tumor sites and loss of SDHB and/or SDHD expression. Tailored personal management should be conducted once a patient is confirmed as an SDHB and/or SDHD mutation carrier or diagnosed with PCC/PGL
Comparison of Quality and Antioxidant Activity of Fu Brick Tea in Different Regions, and Its "Golden Flower" Fungi Morphological Characteristics
The chemical composition, quality characteristics, antioxidant activity, and "golden flower" fungi morphology of Fu brick tea from Hunan, Hubei, Shaanxi, Guizhou, and Zhejiang provinces of China had been researched in this study. The results revealed significant differences in the contents of chemical components in different Fu brick tea (P<0.05). Among them, Fu brick tea from Guizhou had the highest contents of free amino acids and total catechins (9.03, 63.12 mg/g, respectively), while Zhejiang Fu brick tea standed out with the most elevated content of tea polyphenols and flavonoids (132.93, 8.63 mg/g, respectively). According to the electronic taste evaluation, the Fu brick tea from Hunan and Shaanxi exhibited the strongest bitterness and astringency, respectively. Meanwhile,the Zhejiang sample had the most powerful aftertaste bitterness and saltiness, whereas the Guizhou sample demonstrated the strongest umami and richness. Different Fu brick teas had different antioxidant activities, with samples from Hubei and Zhejiang showing higher levels of antioxidant activity. Correlation analysis revealed a significant positive correlation between the main chemical components and taste attributes, and tea polyphenols contributed to the antioxidant activity of Fu brick tea. Additionally, morphology indicated that the five strains of "golden flower" fungi from different origins showed slight differences in the same medium, while the Hunan strain grew faster than the others. This study taked an important role in comprehending the chemical quality of Fu brick tea from different origins
Assessing the Carbon Emission Driven by the Consumption of Carbohydrate-Rich Foods: The Case of China
peer-reviewedBackground: Carbohydrate-rich (CR) foods are essential parts of the Chinese diet.
However, CR foods are often given less attention than animal-based foods. The objectives of this
study were to analyze the carbon emissions caused by CR foods and to generate sustainable diets with
low climate impact and adequate nutrients. Methods: Twelve common CR food consumption records
from 4857 individuals were analyzed using K-means clustering algorithms. Furthermore, linear
programming was used to generate optimized diets. Results: Total carbon emissions by CR foods was
683.38g CO2eq per day per capita, accounting for an annual total of 341.9Mt CO2eq. All individuals
were ultimately divided into eight clusters, and none of the popular clusters were low carbon or
nutrient sufficient. Optimized diets could reduce about 40% of carbon emissions compared to the
average current diet. However, significant structural differences exist between the current diet and
optimized diets. Conclusions: To reduce carbon emissions from the food chain, CR foods should be a
research focus. Current Chinese diets need a big change to achieve positive environmental and health
goals. The reduction of rice and wheat-based foods and an increase of bean foods were the focus of
structural dietary change in CR food consumption.Natural Science Foundation of Guangdong Provinc
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