Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of U-shaped pure transformer medical image segmentation network

In recent years, neural networks have made pioneering achievements in the field of medical imaging. In particular, deep neural networks based on U-shaped structures are widely used in different medical image segmentation tasks. In order to improve the early diagnosis and clinical decision-making system of lung diseases, it has become a key step to use the neural network for lung segmentation to assist in positioning and observing the shape. There is still the problem of low precision. For the sake of achieving better segmentation accuracy, an optimized pure Transformer U-shaped segmentation is proposed in this article. The optimization segmentation network adopts the method of adding skip connections and performing special splicing processing, which reduces the information loss in the encoding process and increases the information in the decoding process, so as to achieve the purpose of improving the segmentation accuracy. The final experiment shows that our improved network achieves 97.86% accuracy in segmentation of the “Chest Xray Masks and Labels” dataset, which is better than the full convolutional network or the combination of Transformer and convolution.
Stabilization and Finite-Time Stabilization of Probabilistic Boolean Control Networks

In this paper, we study the stabilization and finite-time stabilization of probabilistic Boolean control networks (PBCNs). A complete family of reachable sets is defined first, based on which, feedback stabilization conditions are obtained. Then a way to find all possible state feedback controllers are presented for the stabilization of PBCNs accordingly. Moreover, it has been stated that the approach in this paper can also be applied to finite-time stabilization via some changes in the construction of set sequence. Finally, an evolutionary networked game is given as an example to illustrate the efficiency of the obtained results.
Characterization of the Tongue Worm, Linguatula serrata (Pentastomida), Identified from Hares (Lepus europaeus) in Romania

Linguatula serrata (Frölich, 1789) is a widespread parasite known as the tongue worm belonging to the family Linguatulidae. The adult form of the parasite is usually located in the upper respiratory tract of domestic and wild carnivores while the larval forms are located in the visceral organs of intermediate hosts (various herbivorous mammals). Twenty-four European brown hares (Lepus europaeus) were examined in this study, of which two were positive with L. serrata nymphs. The collected nymphs were examined morphologically using electron-microscopic analysis and molecularly by amplification of 18S rRNA and COX1 genes. Lung tissue samples were also collected and histopathological examination was performed. Histopathological examination revealed the following lesions: generalized inflammatory oedema, granulomas with necrosis, calcification and fibrosis in the bronchial tree. The results of molecular sequencing for L. serrata specimens collected from the European brown hares are deposited in GenBank. This study presents the first report on Linguatula serrata nymphs collected from L. europaeus in Romania, using molecular and morphological characterization simultaneously.
Nonlinear Lifting-Line Model using a Vector Formulation of the Unsteady Kutta-Joukowski Theorem

In this paper, a vector form of the unsteady Kutta-Joukowski theorem is derived and then used in the formulation of a general Lifting-Line Model capable of analysing a wide range of engineering problems of interest. The model is applicable to investigating lifting surfaces having low to moderate sweep, dihedral, out-of-plane features such as winglets, in both steady-state and unsteady cases. It features corrections of the span-wise circulation distribution based on available two-dimensional aerofoil experimental data, and stable wake relaxation through fictitious time marching. Potential applications include the conceptual and initial design of low-speed Unmanned Aerial Vehicles, the study of flapping flight or Wind Turbine blade design and analysis. Several verification and validation cases are presented, showing good agreement with experimental data and widely-used computational methods.
Single and Combined Effects of Chlorpyrifos and Glyphosate on the Brain of Common Carp: Based on Biochemical and Molecular Perspective

Chlorpyrifos (CPF) and glyphosate (GLY) are the most widely used organophosphate insecticide and herbicide worldwide, respectively; co-occurrence of CPF and GLY in aquatic environments occurs where they inevitably have potential hazards to fish. However, the potential mechanisms of CPF and GLY to induce toxicity have not been fully explored. To identify the adverse impacts of CPF and GLY on fish, either alone or in combination (MIX), CPF (25 μg/L) and GLY (3.5 mg/L) were set up according to an environmentally relevant concentration to expose to common carp for 21 days. After exposure, CPF and GLY decreased the activities of acetylcholinesterase and Na+/K+-ATPase, altered monoamine oxidase levels, decreased antioxidant enzyme activities (superoxide dismutase, catalase, glutathione S-transferase and glutamic reductase), and induced the accumulation of malondialdehyde in the carp brain. The parameters in the MIX groups had a greater impact compared to that in the CPF or GLY group, suggesting that both single and combined exposure could affect neurological signaling systems and cause oxidative stress and lipid peroxidation damage in carp brains, and that MIX exposure increases the impact of each pollutant. RNA-seq results showed that single or combined exposure to CPF and GLY induced global transcriptomic changes in fish brains, and the number of differentially expressed genes in MIX-treated carp brains were globally increased compared to either the CPF or GLY groups, suggesting that the effects of co-exposure were greater than single exposure. Further analysis results revealed that the global transcriptomic changes participated in oxidative stress, immune dysfunction, and apoptosis of fish brains, and identified that the P13k-Akt signaling pathway participates in both single and combined exposure of CPF- and GLY-induced toxicity. Taken together, our results demonstrated that the interaction of CPF and GLY might be synergic and provided novel insights into the molecular mechanisms of fish brains coping with CPF and GLY.
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks

Graph representation learning (GRL) has become a prominent tool for furthering the understanding of complex networks providing tools for network embedding, link prediction, and node classification. In this paper, we propose the Hybrid Membership-Latent Distance Model (HM-LDM) by exploring how a Latent Distance Model (LDM) can be constrained to a latent simplex. By controlling the edge lengths of the corners of the simplex, the volume of the latent space can be systematically controlled. Thereby communities are revealed as the space becomes more constrained, with hard memberships being recovered as the simplex volume goes to zero. We further explore a recent likelihood formulation for signed networks utilizing the Skellam distribution to account for signed weighted networks and extend the HM-LDM to the signed Hybrid Membership-Latent Distance Model (sHM-LDM). Importantly, the induced likelihood function explicitly attracts nodes with positive links and deters nodes from having negative interactions. We demonstrate the utility of HM-LDM and sHM-LDM on several real networks. We find that the procedures successfully identify prominent distinct structures, as well as how nodes relate to the extracted aspects providing favorable performances in terms of link prediction when compared to prominent baselines. Furthermore, the learned soft memberships enable easily interpretable network visualizations highlighting distinct patterns.
UvA-DARE ( Digital Academic Repository ) Top-Down Beta Enhances Bottom-Up Gamma

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Construction of stemness gene score by bulk and single-cell transcriptome to characterize the prognosis of breast cancer

Breast cancer (BC) is a heterogeneous disease characterized by significant differences in prognosis and therapy response. Numerous prognostic tools have been developed for breast cancer. Usually these tools are based on bulk RNA-sequencing (RNA-Seq) and ignore tumor heterogeneity. Consequently, the goal of this study was to construct a single-cell level tool for predicting the prognosis of BC patients. In this study, we constructed a stemness-risk gene score (SGS) model based on single-sample gene set enrichment analysis (ssGSEA). Patients were divided into two groups based on the median SGS. Patients with a high SGS scores had a significantly worse prognosis than those with a low SGS, and these groups exhibited differences in several tumor characteristics, such as immune infiltration, gene mutations, and copy number variants. Our results indicate that the SGS is a reliable tool for predicting prognosis and response to immunotherapy in BC patients.
The Improved Stochastic Fractional Order Gradient Descent Algorithm

This paper mainly proposes some improved stochastic gradient descent (SGD) algorithms with a fractional order gradient for the online optimization problem. For three scenarios, including standard learning rate, adaptive gradient learning rate, and momentum learning rate, three new SGD algorithms are designed combining a fractional order gradient and it is shown that the corresponding regret functions are convergent at a sub-linear rate. Then we discuss the impact of the fractional order on the convergence and monotonicity and prove that the better performance can be obtained by adjusting the order of the fractional gradient. Finally, several practical examples are given to verify the superiority and validity of the proposed algorithm.
1st Place in ICCV 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning for Computer Vision: Budgeted Model Training Challenge

The budgeted model training challenge aims to train an efficient classification model under resource limitations. To tackle this task in ImageNet-100, we describe a simple yet effective resource-aware backbone search framework composed of profile and instantiation phases. In addition, we employ multi-resolution ensembles to boost inference accuracy on limited resources. The profile phase obeys time and memory constraints to determine the models' optimal batch-size, max epochs, and automatic mixed precision (AMP). And the instantiation phase trains models with the determined parameters from the profile phase. For improving intra-domain generalizations, the multi-resolution ensembles are formed by two-resolution images with randomly applied flips. We present a comprehensive analysis with expensive experiments. Based on our approach, we win first place in International Conference on Computer Vision (ICCV) 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning for Computer Vision (RCV).
Steel Surface Defect Detection Based on SSAM-YOLO

The defect inspection of the steel surface is crucial to modern manufacturing and highly depends on inefficient manual work. The emergence of deep learning has prompted the development of automated defect detection methods, but the current methods perform badly in the detection of the crazing and rolled-in scale-two types of defects on steel surfaces. The difficulty in the detection of crazing and rolled-in scale is mainly due to the similarity between object regions and background regions. Based on this, the authors propose a supervised spatial-attention module (SSAM). It introduces a priori knowledge compared to the traditional spatial attention mechanism, which can enhance the supervision of relevant parameters in the attention mechanism module during network training. Finally, they introduced the SSAM to the YOLOv5 and got the SSAM-YOLO. The test result on the NEU-DET dataset shows that the proposed method has better detection accuracy, achieving improvements of 7.3% and 3.02% on the AP@0.5 for the crazing and rolled-in scale. The method also outperforms the comparative main stream algorithms for steel surface defect detection, verifying the effectiveness of our algorithm.
Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS

Due to the development of communication technology and the rise of user network demand, a reasonable resource allocation for wireless networks is the key to guaranteeing regular operation and improving system performance. Various frequency bands exist in the natural network environment, and heterogeneous cellular network (HCN) has become a hot topic for current research. Meanwhile, Reconfigurable Intelligent Surface (RIS) has become a key technology for developing next-generation wireless networks. By modifying the phase of the incident signal arriving at the RIS surface, RIS can improve the signal quality at the receiver and reduce co-channel interference. In this article, we develop a RIS-assisted HCN model for a multi-base station (BS) multi-frequency network, which includes 4G, 5G, millimeter wave (mmWave), and terahertz networks, and considers the case of multiple network coverage users, which is more in line with the realistic network characteristics and the concept of 6G networks. We propose the optimization objective of maximizing the system sum rate, which is decomposed into two subproblems, i.e., the user resource allocation and the phase shift optimization problem of RIS components. Due to the NP-hard and coupling relationship, we use the block coordinate descent (BCD) method to alternately optimize the local solutions of the coalition game and the local discrete phase search algorithm to obtain the global solution. In contrast, most previous studies have used the coalition game algorithm to solve the resource allocation problem alone. Simulation results show that the algorithm performs better than the rest of the algorithms, effectively improves the system sum rate, and achieves performance close to the optimal solution of the traversal algorithm with low complexity.
Impact of COVID-19 on Mothers Raising Children with Special Needs: Insights from a Survey Study

Home confinement during the COVID-19 outbreak had psychological effects that continue to be explored by researchers. This study investigated factors influencing the mental health of mothers caring for special needs children in Italy’s first lockdown. Specifically, we investigated the relationships between emotional states of depression, anxiety, stress, perceived distress related to home confinement, coping strategies, and other contextual variables (such as opportunities for distance learning and remote working) in a group of 68 mothers of children with special needs and 68 matched mothers of typically developing children. Data from an online survey showed no significant difference between the two groups. However, the research revealed that being a remote worker was a significant predictor of reduced stress in mothers of children with special needs, while distance learning was a significant predictor of reduced stress in mothers of typically developing children. In addition, the study found that hyperarousal symptoms were predictive of stress in mothers of children with special needs, while intrusive thoughts and avoidance coping were predictive of stress in mothers of typically developing children. In conclusion, further research is needed to develop effective support and intervention strategies for families with children with special needs and to deeply investigate the impact of flexible work arrangements and social aid on the mental health of mothers in non-emergency settings.
Identification of Hub Genes Associated with Progression and Prognosis of Bladder Cancer by Integrated Bioinformatics Analysis.

BACKGROUND Bladder cancer (BLCA) is an extremely common carcinoma of the urinary system that has a high incidence of relapse. Although intensive studies have investigated its pathology in the past decades, there are significant knowledge gaps regarding the characterization of the molecular processes underlying the progression of disease and consequently its prognosis. The purpose of current research was to identify significant genes that could serve as prognostic and progression biomarkers. METHODS Gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Differential gene expression analysis (DGEA) and weighted gene co-expression network analysis (WGCNA) were conducted to recognize differential co-expression genes. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to explore gene function. Moreover, protein-protein interactions (PPI) network, overall survival (OS) and disease-free survival (DFS) were used to identify survival-related hub genes. Additionally, associations between these gene's expression and clinical parameters were determined. Finally, the Human Protein Atlas (HPA) database and qRT-PCR were used to validate gene's expression. RESULTS About 124 differential co-expression genes were identified. These genes were mainly enriched in muscle system process and muscle contraction (biological process, BP), contractile fiber, myofibril, sarcomere, focal adhesion and cell-substrate junction (cellular component, CC) and actin binding (molecular function, MF) in GO enrichment analysis, while enriched in vascular smooth muscle contraction, focal adhesion, cardiac muscle contraction, hypertrophic cardiomyopathy, dilated cardiomyopathy and regulation of actin cytoskeleton in KEGG analysis. Furthermore, five survival-related hub genes (MYH11, ACTA2, CALD1, TPM1, MYLK) were identified via OS and DFS. In addition, these survival-related gene's expression was correlated with grade, stage and TNM stage. Finally, all survival-related hub genes were determined to be down-regulated in BLCA tissues by qRT-PCR and HPA databases. CONCLUSIONS Our current study verified five new key genes in BLCA, which may participate in the prognosis and progression and serve as novel biomarkers of BLCA.
Effect of Fluorosilicone Rubber on Mechanical Properties, Dielectric Breakdown Strength and Hydrophobicity of Methyl Vinyl Silicone Rubber

Silicone rubber (SIR) is used in high-voltage insulators because of its insulation, and excellent hydrophobicity is very important in harsh outdoor environments. To enhance the hydrophobicity and low-temperature resistance of silicone rubber, methyl vinyl silicone rubber and fluorosilicone rubber (FSIR) blend composites with different ratios were prepared. The samples were characterized and analyzed using scanning electron microscopy, tensile testing, dynamic mechanical analysis and static contact angle testing. The results showed that after blending, SIR and FSIR were well compatible. FSIR had higher elastic modulus and reduced the tensile strength to some extent in SIR/FSIR composites. The addition of a small amount of FSIR made its crystallization temperature decrease from −30 to −45 °C, meaning that the low-temperature resistance was significantly improved. The breakdown strength of SIR/FSIR composites can still be maintained at a high level when a small amount of FSIR is added. The contact angle of the composites increased from 108.9 to 115.8° with the increase in FSIR content, indicating the enhanced hydrophobicity. When the samples were immersed in water for 96 h, the hydrophobicity migration phenomenon occurred. The static contact angle of the samples with less FSIR content had a weaker decreasing trend, which illustrated that the hydrophobicity was maintained at a high level.
Research Division Federal Reserve Bank of St . Louis Working Paper Series Accounting for Changes in the Homeownership Rate

After three decades of being relatively constant, the homeownership rate increased over the period 1994 to 2005 to attain record highs. The objective of this paper is to account for the observed boom in ownership by examining the role played changes in demographic factors and innovations in the mortgage market which lessened downpayment requirements. To measure the aggregate and distributional impact of these factors, we construct a quantitative general equilibrium overlapping generation model with housing. We nd that the long-run importance of the introduction of new mortgage products for the aggregate homeownership rate ranges from 56 and 70 percent. Demographic factors account for between 16 and 31 percent of the change. However, demographic factors alone are not able to account for the age distributional changes in homeownership. Transitional analysis suggests that demographic factors play a more important, but not dominant, role the further away from the long-run equilibrium. Distributionally, mortgage market innovations have a larger impact explaining participation rate changes of younger households, while demographic factors seem to be the key to understanding the participation rate changes of older households. Our analysis suggests that the key to understanding the increase in the homeownership rate is the expansion in the set of mortgage contracts. We test the robustness of this result by considering changes in mortgage nancing after 1940. We nd that the introduction of the conventional xed rate mortgage, which replaced balloon contracts, accounts for at least fty percent of the observed increase in homeownership during that period. We acknowledge the useful comments of three anonymous referees, Dirk Krueger, David Marshall, Ed Prescott, Victor Ríos-Rull and Eric Young. A version of this paper was presented at the 2008 NBER Economic Fluctuations and Growth Research Meeting, 2004 Annual Meetings of the Society for Economic Dynamics, Universitat Autònoma de Barcelona, Universitat de Barcelona, Iowa State University, University of Virginia, and SUNY at Stony Brook. We are grateful to the nancial support of the National Science Foundation for Grant SES-0649374. Carlos Garriga also acknowledges support from the Spanish Ministerio de Ciencia y Tecnología through grant SEJ2006-02879. The views expressed herein do not necessarily reect those of the Federal Reserve Bank of St. Louis nor those of the Federal Reserve System. Corresponding author: Don Schlagenhauf, Department of Economics, Florida State University, 246 Bellamy Building, Tallahassee, FL 32306-2180. E-mail: dschlage@mailer.fsu. edu. Tel. : 850-644-3817. Fax: 850-644-4535.