An Integrated Approach for Mining Meta-Rules 1

Abstract: An integrated approach of mining association rules and meta-rules based on a hyper-structure is put forward. In this approach, time serial databases are partitioned according to time segments, and the total number of scanning database is only twice. In the first time, a set of 1-frequent itemsets and its projection database are formed at every partition. Then every projected database is scanned to construct a hyper-structure. Through mining the hyper-structure, various rules, for example, global association rules, meta-rules, stable association rules and trend rules etc. can be obtained. Compared with existing algorithms for mining association rule, our approach can mine and obtain more useful rules. Compared with existing algorithms for meta-mining or change mining, our approach has higher efficiency. The experimental results show that our approach is very promising

Choose your cell model wisely: The in vitro nanoneurotoxicity of differentially coated iron oxide nanoparticles for neural cell labeling

Currently, there is a large interest in the labeling of neural stem cells (NSCs) with iron oxide nanoparticles (IONPs) to allow MRI-guided detection after transplantation in regenerative medicine. For such biomedical applications, excluding nanotoxicity is key. Nanosafety is primarily evaluated in vitro where an immortalized or cancer cell line of murine origin is often applied, which is not necessarily an ideal cell model. Previous work revealed clear neurotoxic effects of PMA-coated IONPs in distinct cell types that could potentially be applied for nanosafety studies regarding neural cell labeling. Here, we aimed to assess if DMSA-coated IONPs could be regarded as a safer alternative for this purpose and how the cell model impacted our nanosafety optimization study. Hereto, we evaluated cytotoxicity, ROS production, calcium levels, mitochondrial homeostasis and cell morphology in six related neural cell types, namely neural stem cells, an immortalized cell line and a cancer cell line from human and murine origin. The cell lines mostly showed similar responses to both IONPs, which were frequently more pronounced for the PMA-IONPs. Of note, ROS and calcium levels showed opposite trends in the human and murine NSCs, indicating the importance of the species. Indeed, the human cell models were overall more sensitive than their murine counterpart. Despite the clear cell type-specific nanotoxicity profiles, our multiparametric approach revealed that the DMSA-IONPs outperformed the PMA-IONPs in terms of biocompatibility in each cell type. However, major cell type-dependent variations in the observed effects additionally warrant the use of relevant human cell models.status: publishe

Two-Stream Xception Structure Based on Feature Fusion for DeepFake Detection

Abstract DeepFake may have a crucial impact on people’s lives and reduce the trust in digital media, so DeepFake detection methods have developed rapidly. Most existing detection methods rely on single-space features (mostly RGB features), and there is still relatively little research on multi-space feature fusion. At the same time, a lot of existing methods used a single receptive field, which leads to models that cannot extract information of different scales. In order to solve the above problems, we propose a two-stream Xception network structure (Tception) that fused RGB spatial feature and noise-space feature. This network structure consists of two main parts. The first part is a feature fusion module, which can adaptively fuse RGB feature and noise-space feature generated by RGB images through SRM filters. The second part is the two-stream network structure, which utilizes a parallel structure of convolutional kernels of different sizes allowing the network to learn features of different scales. The experiments show that the proposed method improves performance compared to the Xception network. Compared to SSTNet, the detection accuracy of the Neural Textures is improved by nearly 8%

Survey on adversarial attacks and defense of face forgery and detection

Face forgery and detection has become a research hotspot.Face forgery methods can produce fake face images and videos.Some malicious videos, often targeting celebrities, are widely circulated on social networks, damaging the reputation of victims and causing significant social harm.As a result, it is crucial to develop effective detection methods to identify fake videos.In recent years, deep learning technology has made the task of face forgery and detection more accessible.Deep learning-based face forgery methods can generate highly realistic faces, while deep learning-based fake face detection methods demonstrate higher accuracy compared to traditional approaches.However, it has been shown that deep learning models are vulnerable to adversarial examples, which can lead to a degradation in performance.Consequently, games involving adversarial examples have emerged in the field of face forgery and detection, adding complexity to the original task.Both fakers and detectors now need to consider the adversarial security aspect of their methods.The combination of deep learning methods and adversarial examples is thus the future trend in this research field, particularly with a focus on adversarial attack and defense in face forgery and detection.The concept of face forgery and detection and the current mainstream methods were introduced.Classic adversarial attack and defense methods were reviewed.The application of adversarial attack and defense methods in face forgery and detection was described, and the current research trends were analyzed.Moreover, the challenges of adversarial attack and defense for face forgery and detection were summarized, and future development directions were discussed

Gender forgery of faces by fusing wavelet shortcut connection generative adversarial network

The mainstream methods in the field of facial attribute manipulation had the following two defects due to data and model architecture limitations.First, the bottleneck structure of the autoencoder model results in the loss of feature information, and the traditional method of continuously injected styles to the source domain features during the decoding process makes the generated image too referential to the target domain while losing the identity information and fine-grained details.Second, differences in facial attributes composition between images, such as gender, ethnicity, or age can cause variations in frequency domain information.And the current unsupervised training methods do not automatically adjust the proportion of source and target domain information in the style injection stage, resulting in artifacts in generated images.A facial gender forgery model based on generative adversarial networks and image-to-image translation techniques, namely fused wavelet shortcut connection generative adversarial network (WscGAN), was proposed to address the these issues.Shortcut connections were added to the autoencoder structure, and the outputs of different encoding stages were decomposed at the feature level by wavelet transform.Attention mechanism was employed to process them one by one, to dynamically change the proportion of source domain features at different frequencies in the decoding process.This model could complete forgery of facial images in terms of gender attributes.To verify the effectiveness of the model, it was conducted on the CelebA-HQ dataset and the FFHQ dataset.Compared with the existing optimal models, the method improves the FID and LPIPS indices by 5.4% and 11.2%, and by 1.8% and 6.7%, respectively.Furthermore, the effectiveness of the proposed method in improving the gender attribute conversion of facial images is fully demonstrated by the results based on qualitative visual comparisons

Creating consistent scene graphs using a probabilistic grammar

Growing numbers of 3D scenes in online repositories provide new opportunities for data-driven scene understanding, editing, and synthesis. Despite the plethora of data now available online, most of it cannot be effectively used for data-driven applications because it lacks consistent segmentations, category labels, and/or functional groupings required for co-analysis. In this paper, we develop algorithms that infer such information via parsing with a probabilistic grammar learned from examples. First, given a collection of scene graphs with consistent hierarchies and labels, we train a probabilistic hierarchical grammar to represent the distributions of shapes, cardinalities, and spatial relationships of semantic objects within the collection. Then, we use the learned grammar to parse new scenes to assign them segmentations, labels, and hierarchies consistent with the collection. During experiments with these algorithms, we find that: they work effectively for scene graphs for indoor scenes commonly found online (bedrooms, classrooms, and libraries); they outperform alternative approaches that consider only shape similarities and/or spatial relationships without hierarchy; they require relatively small sets of training data; they are robust to moderate over-segmentation in the inputs; and, they can robustly transfer labels from one data set to another. As a result, the proposed algorithms can be used to provide consistent hierarchies for large collections of scenes within the same semantic class

Hyperuricemia and Risk of Incident Hypertension: A Systematic Review and Meta-Analysis of Observational Studies

<div><p>Background</p><p>Observational studies of the relationship between hyperuricemia and the incidence of hypertension are controversial. We conducted a systematic review and meta-analysis to assess the association and consistency between uric acid levels and the risk of hypertension development.</p><p>Methods</p><p>We searched MEDLINE, EMBASE, CBM (Chinese Biomedicine Database) through September 2013 and reference lists of retrieved studies to identify cohort studies and nested case-control studies with uric acid levels as exposure and incident hypertension as outcome variables. Two reviewers independently extracted data and assessed study quality using Newcastle-Ottawa Scale. Extracted information included study design, population, definition of hyperuricemia and hypertension, number of incident hypertension, effect sizes, and adjusted confounders. Pooled relative risks (RRs) and corresponding 95% confidence intervals (CIs) for the association between hyperuricemia and risk of hypertension were calculated using a random-effects model.</p><p>Results</p><p>We included 25 studies with 97,824 participants assessing the association between uric acid and incident hypertension in our meta-analysis. The quality of included studies is moderate to high. Random-effects meta-analysis showed that hyperuricemia was associated with a higher risk of incident hypertension, regardless of whether the effect size was adjusted or not, whether the data were categorical or continuous as 1 SD/1 mg/dl increase in uric acid level (unadjusted: RR = 1.73, 95% CI 1.46∼2.06 for categorical data, RR = 1.22, 95% CI 1.03∼1.45 for a 1 SD increase; adjusted: RR = 1.48, 95% CI 1.33∼1.65 for categorical data, RR = 1.15, 95% CI 1.06∼1.26 for a 1 mg/dl increase), and the risk is consistent in subgroup analyses and have a dose-response relationship.</p><p>Conclusions</p><p>Hyperuricemia may modestly increase the risk of hypertension incidence, consistent with a dose-response relationship.</p></div

Flowchart of study selection.

<p>Flowchart of study selection.</p