Large-Scale Kernel Methods for Independence Testing

Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible framework for fully nonparametric hypothesis tests of independence, which can capture any type of departure from independence, including nonlinear associations and multivariate interactions. However, these approaches come with an at least quadratic computational cost in the number of observations, which can be prohibitive in many applications. Arguably, it is exactly in such large-scale datasets that capturing any type of dependence is of interest, so striking a favourable tradeoff between computational efficiency and test performance for kernel independence tests would have a direct impact on their applicability in practice. In this contribution, we provide an extensive study of the use of large-scale kernel approximations in the context of independence testing, contrasting block-based, Nystrom and random Fourier feature approaches. Through a variety of synthetic data experiments, it is demonstrated that our novel large scale methods give comparable performance with existing methods whilst using significantly less computation time and memory.Comment: 29 pages, 6 figure

Myocardial fibrosis in desmin-related hypertrophic cardiomyopathy

Desmin-related myopathy (DRM) is known to cause different types of cardiomyopathy. Late gadolinium enhancement cardiovascular magnetic resonance (CMR) has been shown to identify fibrosis in ischemic and non-ischemic cardiomyopathies. We present a rare case of desmin-related hypertrophic cardiomyopathy, CMR revealed fibrosis in the lateral wall of the left ventricle. CMR is superior to conventional echocardiography for the detection of myocardial fibrosis in desmin-related cardiomyopathy, which may be useful to detect early cardiac involvement and predict the patient prognosis

VS-TransGRU: A Novel Transformer-GRU-based Framework Enhanced by Visual-Semantic Fusion for Egocentric Action Anticipation

Egocentric action anticipation is a challenging task that aims to make advanced predictions of future actions from current and historical observations in the first-person view. Most existing methods focus on improving the model architecture and loss function based on the visual input and recurrent neural network to boost the anticipation performance. However, these methods, which merely consider visual information and rely on a single network architecture, gradually reach a performance plateau. In order to fully understand what has been observed and capture the dependencies between current observations and future actions well enough, we propose a novel visual-semantic fusion enhanced and Transformer GRU-based action anticipation framework in this paper. Firstly, high-level semantic information is introduced to improve the performance of action anticipation for the first time. We propose to use the semantic features generated based on the class labels or directly from the visual observations to augment the original visual features. Secondly, an effective visual-semantic fusion module is proposed to make up for the semantic gap and fully utilize the complementarity of different modalities. Thirdly, to take advantage of both the parallel and autoregressive models, we design a Transformer based encoder for long-term sequential modeling and a GRU-based decoder for flexible iteration decoding. Extensive experiments on two large-scale first-person view datasets, i.e., EPIC-Kitchens and EGTEA Gaze+, validate the effectiveness of our proposed method, which achieves new state-of-the-art performance, outperforming previous approaches by a large margin.Comment: 12 pages, 7 figure

UI Layout Generation with LLMs Guided by UI Grammar

The recent advances in Large Language Models (LLMs) have stimulated interest among researchers and industry professionals, particularly in their application to tasks concerning mobile user interfaces (UIs). This position paper investigates the use of LLMs for UI layout generation. Central to our exploration is the introduction of UI grammar -- a novel approach we proposed to represent the hierarchical structure inherent in UI screens. The aim of this approach is to guide the generative capacities of LLMs more effectively and improve the explainability and controllability of the process. Initial experiments conducted with GPT-4 showed the promising capability of LLMs to produce high-quality user interfaces via in-context learning. Furthermore, our preliminary comparative study suggested the potential of the grammar-based approach in improving the quality of generative results in specific aspects.Comment: ICML 2023 Workshop on AI and HC

Risk factors for lumbar disc herniation in adolescents and young adults: A case–control study

BackgroundThere is a limited understanding of the risk factors for lumbar disc herniation (LDH) in younger people, even though the evidence suggests that LDH is more prevalent in this population. This study aimed to comprehensively analyze the risk factors for LDH in adolescents and young adults.MethodsThe medical records of all patients were retrospectively reviewed with inclusion criteria of being younger than 25 years. Magnetic resonance imaging (MRI) was used to confirm LDH from September 2016 to September 2021. Furthermore, 104 healthy people in the same age range were enrolled as the control group from physical examination centers. Gender, BMI, smoking, drinking, genetic history, sitting posture, daily sitting time, traumatic history of the lower back, scoliosis, and daily exercise time were examined for all enrolled people. These factors were statistically analyzed to determine the high-risk factors.ResultsA total of 208 young individuals were enrolled in the present study. The mean age of the study group and the control group was 21.06 ± 3.27 years (range: 11–25 years) and 21.26 ± 2.23 years (range: 15–25 years), respectively. The result of the chi-squared test demonstrated that there was a significant difference in BMI of more than 30 (p < 0.001), genetic history (p = 0.004), sitting posture (p < 0.001), daily sitting time of more than 6 h (p < 0.001), and the history of low back trauma (p = 0.002). Additionally, multivariate logistic regression showed that these were high-risk factors for LDH, particularly the duration of daily sitting time (more than 6 h).ConclusionsBMI of more than 30, genetic history, sitting posture, daily sitting time of more than 6 h, and a history of low back trauma are the high-risk factors for adolescents and young adults with LDH. Therefore, providing them with the proper guidance and education, particularly about the protection of the lower back and the reduction of spinal load, could play a key role in preventing and reducing LDH

Optimization of pneumonia CT classification model using RepVGG and spatial attention features

IntroductionPneumonia is a common and widespread infectious disease that seriously affects the life and health of patients. Especially in recent years, the outbreak of COVID-19 has caused a sharp rise in the number of confirmed cases of epidemic spread. Therefore, early detection and treatment of pneumonia are very important. However, the uneven gray distribution and structural intricacy of pneumonia images substantially impair the classification accuracy of pneumonia. In this classification task of COVID-19 and other pneumonia, because there are some commonalities between this pneumonia, even a small gap will lead to the risk of prediction deviation, it is difficult to achieve high classification accuracy by directly using the current network model to optimize the classification model.MethodsConsequently, an optimization method for the CT classification model of COVID-19 based on RepVGG was proposed. In detail, it is made up of two essential modules, feature extraction backbone and spatial attention block, which allows it to extract spatial attention features while retaining the benefits of RepVGG.ResultsThe model’s inference time is significantly reduced, and it shows better learning ability than RepVGG on both the training and validation sets. Compared with the existing advanced network models VGG-16, ResNet-50, GoogleNet, ViT, AlexNet, MobileViT, ConvNeXt, ShuffleNet, and RepVGG_b0, our model has demonstrated the best performance in a lot of indicators. In testing, it achieved an accuracy of 0.951, an F1 score of 0.952, and a Youden index of 0.902.DiscussionOverall, multiple experiments on the large dataset of SARS-CoV-2 CT-scan dataset reveal that this method outperforms most basic models in terms of classification and screening of COVID-19 CT, and has a significant reference value. Simultaneously, in the inspection experiment, this method outperformed other networks with residual structures

Chemical interactions between ship-originated air pollutants and ocean-emitted halogens

Unidad de excelencia María de Maeztu CEX2019-000940-MOcean-going ships supply products from one region to another and contribute to the world's economy. Ship exhaust contains many air pollutants and results in significant changes in marine atmospheric composition. The role of reactive halogen species (RHS) in the troposphere has received increasing recognition and oceans are the largest contributors to their atmospheric burden. However, the impact of shipping emissions on RHS and that of RHS on ship-originated air pollutants have not been studied in detail. Here, an updated Weather Research Forecasting coupled with Chemistry model is utilized to explore the chemical interactions between ship emissions and oceanic RHS over the East Asia seas in summer. The emissions and resulting chemical transformations from shipping activities increase the level of NO and NO at the surface, increase O in the South China Sea, but decrease O in the East China Sea. Such changes in pollutants result in remarkable changes in the levels of RHS (>200% increase of chlorine; ∼30% and ∼5% decrease of bromine and iodine, respectively) as well as in their partitioning. The abundant RHS, in turn, reshape the loadings of air pollutants (∼20% decrease of NO and NO; ∼15% decrease of O) and those of the oxidants (>10% reduction of OH and HO; ∼40% decrease of NO) with marked patterns along the ship tracks. We, therefore, suggest that these important chemical interactions of ship-originated emissions with RHS should be considered in the environmental policy assessments of the role of shipping emissions in air quality and climate

Biosensors fabricated by laser-induced metallization on DLP composite resin

With the growing emphasis on medical testing, people are seeking more technologies to detect indexes of the human body quickly and at a low cost. The electrochemical biosensors became a research hotspot due to their excellent properties. In this study, dicopper hydroxide phosphate (Cu2(OH)PO4) was incorporated in resin, and the resin sheets were prepared by digital light processing (DLP). The copper base points were activated on the resin sheet surface by Nd: YAG laser and then covered by the electroless copper plating and the electroless silver plating. The laser could effectively activate copper base points on the resin surface. Furthermore, silver electrodes on the detection chips could distinguish glucose solutions of different concentrations well. Finally, a novel detection kit with a three-electrode chip was designed for rapid health testing at home or in medical institutions in the future

Evaluation of the Observational Associations and Shared Genetics Between Glaucoma With Depression and Anxiety

PURPOSE: Glaucoma, a leading cause of blindness worldwide, is suspected to exhibit a notable association with psychological disturbances. This study aimed to investigate epidemiological associations and explore shared genetic architecture between glaucoma and mental traits, including depression and anxiety.METHODS: Multivariable logistic regression and Cox proportional hazards regression models were employed to investigate longitudinal associations based on UK Biobank. A stepwise approach was used to explore the shared genetic architecture. First, linkage disequilibrium score regression inferred global genetic correlations. Second, MiXeR analysis quantified the number of shared causal variants. Third, specific shared loci were detected through conditional/conjunctional false discovery rate (condFDR/conjFDR) analysis and characterized for biological insights. Finally, two-sample Mendelian randomization (MR) was conducted to investigate bidirectional causal associations.RESULTS: Glaucoma was significantly associated with elevated risks of hospitalized depression (hazard ratio [HR] = 1.54; 95% confidence interval [CI], 1.01-2.34) and anxiety (HR = 2.61; 95% CI, 1.70-4.01) compared to healthy controls. Despite the absence of global genetic correlations, MiXeR analysis revealed 300 variants shared between glaucoma and depression, and 500 variants shared between glaucoma and anxiety. Subsequent condFDR/conjFDR analysis discovered 906 single-nucleotide polymorphisms (SNPs) jointly associated with glaucoma and depression and two associated with glaucoma and anxiety. The MR analysis did not support robust causal associations but indicated the existence of pleiotropic genetic variants influencing both glaucoma and depression.CONCLUSIONS: Our study enhances the existing epidemiological evidence and underscores the polygenic overlap between glaucoma and mental traits. This observation suggests a correlation shaped by pleiotropic genetic variants rather than being indicative of direct causal relationships.</p