Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness

In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks. Intuitively, robustness means that small perturbations to an input do not cause the network to perform misclassifications. In this paper, we present a novel algorithm for verifying robustness properties of neural networks. Our method synergistically combines gradient-based optimization methods for counterexample search with abstraction-based proof search to obtain a sound and ({\delta}-)complete decision procedure. Our method also employs a data-driven approach to learn a verification policy that guides abstract interpretation during proof search. We have implemented the proposed approach in a tool called Charon and experimentally evaluated it on hundreds of benchmarks. Our experiments show that the proposed approach significantly outperforms three state-of-the-art tools, namely AI^2 , Reluplex, and Reluval

Hypoxia-inducible factor-1α promotes macrophage functional activities in protecting hypoxia-tolerant large yellow croaker (Larimichthys crocea) against Aeromonas hydrophila infection

The immune system requires a high energy expenditure to resist pathogen invasion. Macrophages undergo metabolic reprogramming to meet these energy requirements and immunologic activity and polarize to M1-type macrophages. Understanding the metabolic pathway switching in large yellow croaker (Larimichthys crocea) macrophages in response to lipopolysaccharide (LPS) stimulation and whether this switching affects immunity is helpful in explaining the stronger immunity of hypoxia-tolerant L. crocea. In this study, transcript levels of glycolytic pathway genes (Glut1 and Pdk1), mRNA levels or enzyme activities of glycolytic enzymes [hexokinase (HK), phosphofructokinase (PFK), pyruvate kinase (PK), and lactate dehydrogenase A (LDHA)], aerobic respiratory enzymes [pyruvate dehydrogenase (PDH), isocitrate dehydrogenase (IDH), and succinate dehydrogenase (SDH)], metabolites [lactic acid (LA) and adenosine triphosphate (ATP)], levels of bactericidal products [reactive oxygen species (ROS) and nitric oxide (NO)], and transcripts and level changes of inflammatory factors [IL1β, TNFα, and interferon (IFN) γ] were detected in LPS-stimulated L. crocea head kidney macrophages. We showed that glycolysis was significantly induced, the tricarboxylic acid (TCA) cycle was inhibited, and metabolic reprogramming occurred, showing the Warburg effect when immune cells were activated. To determine the potential regulatory mechanism behind these changes, LcHIF-1α was detected and found to be significantly induced and transferred to the nucleus after LPS stimulation. LcHif-1α interference led to a significant reduction in glycolytic pathway gene transcript expression, enzyme activity, metabolites, bactericidal substances, and inflammatory factor levels; a significant increase in the aerobic respiration enzymes; and decreased migration, invasion, and phagocytosis. Further ultrastructural observation by electron microscopy showed that fewer microspheres contained phagocytes and that more cells were damaged after LcHif-1α interference. LcHif-1α overexpression L. crocea head kidney macrophages showed the opposite trend, and promoter activities of Ldha and Il1β were significantly enhanced after LcHif-1α overexpression in HEK293T cells. Our data showed that LcHIF-1α acted as a metabolic switch in L. crocea macrophages and was important in polarization. Hypoxia-tolerant L. crocea head kidney showed a stronger Warburg effect and inhibited the TCA cycle, higher metabolites, and bactericidal substance levels. These results collectively revealed that LcHif-1α may promote the functional activities of head kidney macrophages in protecting hypoxia-tolerant L. crocea from Aeromonas hydrophila infection

Predicting brain age using Tri-UNet and various MRI scale features

Abstract In the process of human aging, significant age-related changes occur in brain tissue. To assist individuals in assessing the degree of brain aging, screening for disease risks, and further diagnosing age-related diseases, it is crucial to develop an accurate method for predicting brain age. This paper proposes a multi-scale feature fusion method called Tri-UNet based on the U-Net network structure, as well as a brain region information fusion method based on multi-channel input networks. These methods address the issue of insufficient image feature learning in brain neuroimaging data. They can effectively utilize features at different scales of MRI and fully leverage feature information from different regions of the brain. In the end, experiments were conducted on the Cam-CAN dataset, resulting in a minimum Mean Absolute Error (MAE) of 7.46. The results demonstrate that this method provides a new approach to feature learning at different scales in brain age prediction tasks, contributing to the advancement of the field and holding significance for practical applications in the context of elderly education

Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network

According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN) is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient

Extraction of Gallium from Brown Corundum Dust by Roasting—Acid Leaching Process

Publisher Copyright: © 2023 by the authors.Brown corundum dust is a solid waste produced during the preparation of brown corundum with bauxite as the raw material. The dust has a relatively high gallium content; therefore, it is of great value to recover the gallium from this kind of dust. In this paper, a range of analysis and characterization methods, including XRD, XRF, SEM-EDS, and EPMA, were used to determine the occurrence of gallium. It was found that gallium was mainly present in the potassium-rich phase, wrapped by amorphous silicate and the corundum phase. Roasting activation followed by an acid leaching process was proposed to extract gallium from brown corundum dust. An investigation was carried out on the effects of roasting temperature, roasting time, and additive dosage on the recovery of gallium and the evolution of the phase composition of the dust. The results show that the roasting activation of sodium carbonate was better than that of calcium oxide. After roasting at 1073 K for 40 min with a sodium carbonate dosage of 0.5 (mass ratio of sodium carbonate to dust), the phase composition changed completely to mainly consist of sodium silicate, sodium aluminosilicate, and potassium aluminosilicate. In that case, around 93% of Ga could be recovered from the roasted dust through H2SO4 (4.6 mol/L) leaching for 90 min. The leaching process was described well by the kinetic equation of k3t = 1/(1 − α)1/3 − 1, with an apparent activation energy of 16.81 kJ/mol, suggesting that the leaching rate was limited by the transfer of leaching agent across the contacting interface of the dust particles.Peer reviewe

A Linear Strain-Free Matching Algorithm for Twisted Two-Dimensional Materials

As nano-electronic technology makes electronic devices gradually microscopic in size and diversified in function, obtaining new materials with superior performance is the main goal at this stage. Interfaces formed by adjacent layers of material in electronic devices affect their performance, as does the strain caused by lattice mismatch, which can be simulated and analyzed by theoretical calculations. The common period of the cell changes when the van der Waals (vdW) material is twisted. Therefore, it is a significant challenge to determine the common supercell of two crystals constituting the interface. Here. we present a novel cell matching algorithm for twisted bilayer vdW materials with orthogonal unit cells, where the resulting common supercell remains orthogonal and only angular strains exist without linear strains, facilitating accuracy control. We apply this method to 2-Pmmn twisted bilayer borophene. It can automatically find the resource-allowed common supercell at multiple rotation angles or fix the rotation angle to find the proper accuracy

Identification of hub genes with prognostic values in gastric cancer by bioinformatics analysis

Abstract Background Gastric cancer (GC) is a prevalent malignant cancer of digestive system. To identify key genes in GC, mRNA microarray GSE27342, GSE29272, and GSE33335 were downloaded from GEO database. Methods Differentially expressed genes (DEGs) were obtained using GEO2R. DAVID database was used to analyze function and pathways enrichment of DEGs. Protein-protein interaction (PPI) network was established by STRING and visualized by Cytoscape software. Then, the influence of hub genes on overall survival (OS) was performed by the Kaplan-Meier plotter online tool. Module analysis of the PPI network was performed using MCODE. Additionally, potential stem loop miRNAs of hub genes were predicted by miRecords and screened by TCGA dataset. Transcription factors (TFs) of hub genes were detected by NetworkAnalyst. Results In total, 67 DEGs were identified; upregulated DEGs were mainly enriched in biological process (BP) related to angiogenesis and extracellular matrix organization and the downregulated DEGs were mainly enriched in BP related to ion transport and response to bacterium. KEGG pathways analysis showed that the upregulated DEGs were enriched in ECM-receptor interaction and the downregulated DEGs were enriched in gastric acid secretion. A PPI network of DEGs was constructed, consisting of 43 nodes and 87 edges. Twelve genes were considered as hub genes owing to high degrees in the network. Hsa-miR-29c, hsa-miR-30c, hsa-miR-335, hsa-miR-33b, and hsa-miR-101 might play a crucial role in hub genes regulation. In addition, the transcription factors-hub genes pairs were displayed with 182 edges and 102 nodes. The high expression of 7 out of 12 hub genes was associated with worse OS, including COL4A1, VCAN, THBS2, TIMP1, COL1A2, SERPINH1, and COL6A3. Conclusions The miRNA and TFs regulation network of hub genes in GC may promote understanding of the molecular mechanisms underlying the development of gastric cancer and provide potential targets for GC diagnosis and treatment

Efficacy and Adverse Events Associated With Use of OnabotulinumtoxinA for Treatment of Neurogenic Detrusor Overactivity: A Meta-Analysis

Purpose OnabotulinumtoxinA is used widely for the treatment of neurogenic detrusor overactivity. We conducted a systematic review and meta-analysis to assess its efficacy and safety for neurogenic detrusor overactivity treatment. Methods A systematic literature review was performed to identify all published randomized double-blind, placebo-controlled trials of onabotulinumtoxinA for neurogenic detrusor overactivity treatment. MEDLINE, Embase, and the CENTRAL were employed. Reference lists of retrieved studies were reviewed carefully. Results Six publications involving 871 patients, which compared onabotulinumtoxinA with a placebo were analyzed. Efficacy of onabotulinumtoxinA treatment was shown as a reduction of the mean number of urinary incontinence episodes per day (mean difference, -1.41; 95% confidence interval [CI], -1.70 to -1.12; P<0.00001), maximum cystometric capacity (135.48; 95% CI, 118.22–152.75; P<0.00001), and maximum detrusor pressure (-32.98; 95% CI, -37.33 to -28.62; P<0.00001). Assessment of adverse events revealed that complications due to onabotulinumtoxinA injection were localized primarily to the urinary tract. Conclusions This meta-analysis suggests that onabotulinumtoxinA is an effective treatment for neurogenic detrusor overactivity with localized advent events

Timetable synchronization optimization in a subway-bus network

The subway and bus together contribute to the structure of public transport network in a metropolis. However, integrated service of public transport is not satisfied due to the lower timetable synchronization between these two modes at transfer stations. In this paper, a double-layer public transport network composed of subway/bus lines and stations is first created. Then, two stages are included in the timetable optimization process. In the first stage, a user equilibrium assignment model and a capacity restriction iterative algorithm are employed to obtain the passenger distribution in the subway-bus public transport transfer network. Based on this distribution, with the consideration of the synchronization and section service level, a mixed-integer linear programming (MILP) model is applied to optimize the timetables of the subway and bus networks in the second stage. Computational results using real Automatic Fare Collection System (AFC) data from the public transport system in Beijing are reported, and these results show that the suggested model is efficient in improving passenger service levels

Bearing Fault Diagnosis Based on Randomized Fisher Discriminant Analysis

In this paper, a novel randomized Fisher discriminant analysis (RFDA) based bearing fault diagnosis method is proposed. First, several representative time-domain features are extracted from the raw vibration signals. Second, linear Fisher discriminant analysis (FDA) is extended to nonlinear FDA named RFDA by introducing the random feature map to deal with the non-linearity issue. Specifically, the extracted time-domain features data are mapped onto a high-dimensional space using the random feature map function rather than kernel functions. Third, the time-domain features are fed into the built RFDA model to extract the discriminant features for diagnosis. Moreover, a Bayesian inference is employed to identify the class of the collected vibration signals to diagnose the bearing status. The proposed method uses random Fourier features to approximate the kernel matrix in the kernel Fisher discriminant analysis. Through employing randomized Fisher discriminant analysis, the nonlinearity issue is dealt with, and the computational burden is remarkably reduced compared to the kernel Fisher discriminant analysis (KFDA). To illustrate the superior performance of the proposed RFDA-based bearing fault diagnosis method, comparative experiments are conducted on two widely used datasets, the Case Western Reserve University (CWRU) bearing dataset and the Paderborn University (PU) bearing dataset. For the CWRU dataset, the computation time of RFDA is much shorter than KFDA, while the accuracy rate reaches the same level of KFDA. For the PU dataset, the accuracy rate of RFDA is slightly higher than KFDA, and the computation time is only 44.14% of KFDA