Miracle-dz/UniFAR-FTUs huggingface dataset:
Translation-aware semantic segmentation via conditional least-square generative adversarial networks

Abstract. Semantic segmentation has recently made rapid progress in the field of remote sensing and computer vision. However, many leading approaches cannot simultaneously translate label maps to possible source images with a limited number of training images. The core issue is insufficient adversarial information to interpret the inverse process and proper objective loss function to overcome the vanishing gradient problem. We propose the use of conditional least squares generative adversarial networks (CLS-GAN) to delineate visual objects and solve these problems. We trained the CLS-GAN network for semantic segmentation to discriminate dense prediction information either from training images or generative networks. We show that the optimal objective function of CLS-GAN is a special class of f-divergence and yields a generator that lies on the decision boundary of discriminator that reduces possible vanished gradient. We also demonstrate the effectiveness of the proposed architecture at translating images from label maps in the learning process. Experiments on a limited number of high resolution images, including close-range and remote sensing datasets, indicate that the proposed method leads to the improved semantic segmentation accuracy and can simultaneously generate high quality images from label maps.
Is the Random Tree Puzzle process the same as the Yule-Harding process?

It has been suggested that a Random Tree Puzzle (RTP) process leads to a Yule-Harding (YH) distribution, when the number of taxa becomes large. In this study, we formalize this conjecture, and we prove that the two tree distributions converge for two particular properties, which suggests that the conjecture may be true. However, we present evidence that, while the two distributions are close, the RTP appears to converge on a different distribution than does the YH.
A fully explicit fluid-structure interaction approach based on the PFEM

Abstract. The efficient numerical simulation of Fluid-Structure Interaction (FSI) problems is of growing interest in many engineering fields. Staggered approaches are particularly interesting because they allow for the reuse of existing softwares. In this work we propose a staggered scheme based on the weakly compressible PFEM for the fluid domain and SIMULIA Abaqus/Explicit for the solid domain. The coupling is treated with a domain decomposition approach based on the Gravouil-Combescure algorithm. The main goal is to show the possibility of a fully explicit coupling with different time step size on the two phases (fluid and solid) and incompatible mesh at the interfaces. 2D test-cases will be presented to validate the proposed coupling technique. The explicit time integration scheme for both the fluid and solid subdomains, together with the explicit treatment of the coupling, makes this method appealing for applications in a variety of engineering problems with fast dynamics and/or a high degree of non-linearity.
Characterization of Brucella abortus Mutant A19mut2, a Potential DIVA Vaccine Candidate with a Modification on Lipopolysaccharide

Background: Brucella abortus is the main causative agent for bovine brucellosis. B. abortus A19 is a widely used vaccine strain to protect cows from Brucella infection in China. However, A19 has a similar lipopolysaccharide (LPS) antigen to that of the field virulent Brucella strain, whose immunization interferes with the serodiagnosis of vaccinated and infected animals. [Aim] To develop a novel Brucella DIVA vaccine candidate. Study design and methods: The B. abortus mutant A19mut2 with the formyltransferase gene wbkC is replaced by an acetyltransferase gene wbdR from E. coli O157 using the bacterial homologous recombination technique, generating a modified O-polysaccharide that cannot induce antibodies in mice against wild-type Brucella LPS. The biological phenotypes of the A19mut2 were assessed using a growth curve analysis, agglutination tests, Western blotting, and stress resistance assays. Histopathological changes and bacterial colonization in the spleens of vaccinated mice were investigated to assess the residual virulence and protection of the A19mut2. Humoral and cellular immunity was evaluated by measuring the levels of IgG, IgG subtypes, and the release of cytokines IFN-γ and IL10 in the splenocytes of the vaccinated mice. ELISA coated with wild-type LPS can distinguish mouse antibodies induced by A19 and A19mut2 immunization. Results: The A19mut2 showed a decreased residual virulence in mice, compared to the A19 strain, but induced significant humoral and cellular immune responses, as the A19 immunization did. The protection efficacy of A19mut2 immunization against B. abortus S2308 NalR infection was similar to that of A19 immunization. Conclusion: The A19mut2 has potential as a novel DIVA vaccine candidate in the future.
Analisis Sentimen dengan Metode Klasifikasi Naïve Bayes dan Seleksi Fitur Chi-Square

Analisis sentimen merupakan metode komputasi untuk mengevaluasi teks dengan tujuan menentukan emosi yang terkandung di dalamnya. Analisis sentimen dapat dilakukan dengan pendekatan pembelajaran mesin. Salah satu metode pembelajaran mesin yang sering digunakan untuk analisis sentimen adalah Klasifikasi Naïve Bayes, yaitu metode yang menggunakan teorema Bayes sebagai dasar untuk melakukan klasifikasi. Penggunaan Klasifikasi Naïve Bayes juga dapat dikombinasikan dengan seleksi fitur. Studi sebelumnya menunjukkan bahwa seleksi fitur Chi-Square merupakan satu metode seleksi fitur yang efektif, yang mana merupakan proses untuk memilih kata yang paling merepresentasikan data berdasarkan nilai Chi-Square untuk mempercepat proses komputasi. Penelitian ini bertujuan untuk memperoleh model Klasifikasi Naïve Bayes dari data teks yang telah melalui tahap seleksi fitur Chi-Square.
The Characteristics and Contributing Factors of Air Pollution in Nanjing: A Case Study Based on an Unmanned Aerial Vehicle Experiment and Multiple Datasets

Unmanned aerial vehicle (UAV) experiments, multiple datasets from ground-based stations and satellite remote sensing platforms, and backward trajectory models were combined to investigate the characteristics and influential mechanisms of the air pollution episode that occurred in Nanjing during 3–4 December 2017. Before the experiments, the position of the detector mounted on a UAV that was minimally disturbed by the rotation of the rotors was analyzed based on computational fluid dynamics (CFD) simulations. The combined analysis indicated that the surface meteorological conditions—high relative humidity, low wind speed, and low temperature—were conducive to the accumulation of PM2.5. Strongly intense temperature inversion layers and the low thickness of the atmospheric mixed layer could have resulted in elevated PM2.5 mass concentrations. In the early stage, air pollution was affected by the synoptic circulation of the homogenous pressure field and low wind speeds, and the pollutants mainly originated from emissions from surrounding areas. The aggravated pollution was mainly attributed to the cold front and strong northwesterly winds above 850 hPa, and the pollutants mostly originated from the long-distance transport of emissions with northwesterly winds, mainly from the Beijing‒Tianjin‒Hebei (BTH) region and its surrounding areas. This long-distance transport predominated during this event. The air pollution level and aerosol optical depth (AOD) were positively correlated with respect to their spatial distributions; they could reflect shifts in areas of serious pollution. Pollution was concentrated in Anhui Province when it was alleviated in Nanjing. Polluted dust, polluted continental and smoke aerosols were primarily observed during this process. In particular, polluted dust aerosols accounted for a major part of the transport stage, and existed between the surface and 4 km. Moreover, the average extinction coefficient at lower altitudes (<1 km) was higher for aerosol deposition.
Validation of LBM based on BGK on Poiseuille s Flow and Vortex Street in a Channel Flow

The aim of research is to simulate a large scale bioreactor using Lattice Boltzmann method (LBM). Design and scale up a bioreactor is a highly complex process since it involves various physical phenomena occurring together. Computational Fluid Dynamics (CFD) has been developed over the years to simulate complex phenomenon occurring in a bioreactor and can make up for the shortcomings associated with traditional scale-up methods. It allows the simulation of all the relevant physics like species and thermal transport along with millions of bubbles. Due to various recent advances in numerical techniques and computer hardware (particularly the Graphical Processing Units-GPUs, aimed at number crunching applications), the simulation of an industrial scale reactor is now possible in reasonable time. While conventional pressure based solvers, such as the SIMPLE algorithm and its variants, do not scale well with large number of processors and may not utilize the GPUs cores effectively, Lattice Boltzmann Methods are highly parallelizable and scale almost linearly with number of cores. In this paper we present the development and validation of a basic LBM code which will be used for the bioreactor simulation. The Bhatnagar-GrossKrook (BGK) model is used and is validated by theoretically computed parameters for Poiseuille’s flow and with experimentally determined parameters for Vortex Street in a channel flow. Velocity profile, Strouhal number and vortex distance are compared. Reynolds numbers (Re) of 30, 60, 120, and 140 have been considered. Keywords—Lattice Boltzmann Method, Computational Fluid Dynamic, Bhatnagar-Gross-Krook, Von karman vortex street
Development of a Compact Divertor Plasma Simulator for Plasma-Wall Interaction Studies on Neutron-Irradiated Materials

Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan 1)National Institute for Fusion Science, Oroshi-cho, Toki, Gifu 509-5292, Japan 2)Institute for Materials Research, Tohoku University, Oarai-machi, Ibaraki 311-1313, Japan 3)Hydrogen Isotope Research Center, University of Toyama, Toyama 930-8555, Japan 4)Research Institute for Applied Mechanics, Kyushu University, Kasuga-kouen, Kasuga, Fukuoka 816-8580, Japan
An Elementary Proof of the Equivalence between 2D and 3D Classical Snakes and Geodesic Active Contours

Recently, Caselles et al. have shown in the equivalence between a classical snake problem of Kass et al. and a geodesic active contour model. The PDE derived from the geodesic problem gives an evolution equation for active contours which is very powerfull for image segmentation since changes of topology are allowed using the level set implementation. However in Caselles' paper the equivalence with classical snake is only shown for 2D images with 1D curves, by using concepts of Hamiltonian theory which have no meanings for active contours. This paper propose a proof using only elementary calculus of mathematical analysis. This proof is also valid in the 3D case for active surfaces.
Research on Two Algorithms of Solving Large-scale Tridiagonal Linear Equations

Based on the analysis of the two kinds of algorithms in solving large-scale tridiagonal linear equations, which are linear interpolation method and the method of double parameters, it is shown that the principle of the linear interpolation method and double parameter method is consistent and it points out that in this principle, the solutions to certain types of tridiagonal equations in the two methods are not stable. But in the case of not so sick, their relative errors of solution are very small, and the situation is very stable.
Double-diffusive instabilities at a horizontal boundary after the sudden onset of heating

When a deep body of fluid with a stable salinity gradient is heated from below at a horizontal boundary a destabilizing temperature gradient develops and can lead to instabilities. We will focus on two variants of this problem: the sudden increase in the boundary temperature at the initial time and the sudden turning on of a constant heat flux. These generate time-dependent temperature profiles. We look at the growing phase of the linear instabilities as an initial value problem where the initial time for the instabilities is a parameter to be determined. We determine numerically the optimal initial conditions and the optimal starting time for the instabilities to ensure that the maximum growth occurs at some given later time. The method that is used is an extension of the method developed by Kerr & Gumm (J. Fluid Mech., vol. 825, 2017, pp. 1002–1034) in their investigation of the stability of developing temperature boundary layers at horizontal and vertical boundaries. This requires the use of an appropriate measure of the amplitude of the disturbances which is identified. The effectiveness of this approach is verified by looking at the classic problem of double-diffusive convection in a horizontal layer, where we look at both the salt-finger regime and the diffusive regime. We show that this approach is an effective way of investigating instabilities where the background gradients time dependent. For the problem of heating a salinity gradient from below, as the heat diffuses into the fluid the effective thermal Rayleigh number based on the instantaneous diffusion length scale grows. For the case of a sudden increase in the temperature by a fixed amount the effective thermal Rayleigh number is proportional to $t^{3/2}$ , and for a constant heat flux it is proportional to $t^{2}$ , where $t$ is the time since the onset of heating. However, the effective salt Rayleigh number also grows as $t^{2}$ . We will show that for the constant temperature case the thermal Rayleigh number initially dominates and the instabilities undergo a phase where the convection is essentially thermal, and the onset is essentially instantaneous. As the salt Rayleigh number becomes more significant the instability undergoes a transition to oscillatory double-diffusive convection. For the constant heat flux the ratio of the thermal and salt Rayleigh numbers is constant, and the instabilities are always double diffusive in their nature. These instabilities initially decay. Hence, to achieve the largest growth at some given fixed time, there is an optimal time after the onset of heating for the instabilities to be initiated. These instabilities are essentially double diffusive throughout their growth.
Effective Feature-Based Downward-Facing Monocular Visual Odometry

To achieve accurate pose estimation for robots in industrial applications and services, this brief proposes an effective feature-based downward-facing monocular visual odometry technology that uses an affordable sensor system and a systematic optimization approach. To extract more effective features simply and efficiently from images of the ground, even for small mobile systems, the proposed visual odometry system is designed in a lightweight and cost-effective manner; we used an easily available LED, a single-channel time-of-flight (ToF) sensor, and a monocular camera. From the extracted features, the potentially irrelevant ones are removed in advance, using a masking algorithm and measured velocity. This enhances feature efficiency and reduces the computational burden. Finally, the optimal pose estimate is explicitly obtained by solving a nonconvex optimization problem, to make the best use of the features. The experiments’ results show that our proposed method improves feature tracking ability and pose estimation accuracy.
A Highly Precise On-State Voltage Measurement Circuit With Dual Current Sources for Online Operation of SiC MOSFETs

This letter proposes an on-state drain-source voltage (${V}_{{\rm{ds,on}}}$) measurement circuit for SiC MOSFETs. The proposed circuit effectively solves the problem of inconsistent diode volt-ampere characteristics through an additional current source. The voltage difference between the two diodes is compensated by adjusting the auxiliary current under the condition of a fixed first current. This circuit does not require diode pre-selection and significantly improves measurement accuracy. The circuit is implemented in a double-pulse test (DPT) of a SiC MOSFET (900 V/23 A, 25 °C). The results show that the circuit has a fast response speed with a delay time of 0.4 μs. Even at the same current, the forward voltage difference between the two diodes reaches 3.32 mV. Compared to measurement results from a six-bit semi-digital multimeter, the measurement error of the proposed circuit is less than 1.5% at different currents and temperatures.
Bedhaya Ketawang: A Yogic Perspective on Human Function and Expression as a means to comply with the physical distancing

The study finds that the Movement Patterns performed in Bedhaya Ketawang are fundamental and universal. Hence, the monotony of the dance in indicating the universality of human function and expression for higher mental performance and concentration is justified through the Yogic Approach. This finding implies further studies on specifying ancient cultural and civilisational influence in Javanese Culture at large across eras. The study explains Bhedaya's phenomenon which indicates that the practice could be designed as a social and virtual space for educational purposes. This implies further extensive studies on exploring, structuring and organising the practical application of the recommendations and findings.
Environmental cadmium exposure alters the internal microbiota and metabolome of Sprague–Dawley rats

Cadmium (Cd) is a toxic element that can negatively affect both humans and animals. It enters the human and animal bodies through the respiratory and digestive tracts, following which it tends to accumulate in different organs, thereby seriously affecting human and animal health, as well as hampering social and economic development. Cd exposure can alter the composition of intestinal microbiota. In addition, it can damage the peripheral organs by causing the translocation of intestinal microbiota. However, the relationship between translocation-induced changes in the composition of microbiome in the blood and metabolic changes remains unclear. In the present study, we investigated the effects of Cd exposure on microbiota and serum metabolism in rats by omics analysis. The results demonstrated that Cd exposure disrupted the balance between the blood and intestinal flora in Sprague–Dawley (SD) rats, with a significant increase in gut microbiota (Clostridia_UCG_014, NK4A214_group) and blood microbiome (Corynebacterium, Muribaculaceae). However, Cd exposure caused the translocation of Corynebacterium and Muribaculaceae from the gut into the blood. In addition, Cd exposure was associated with the up-regulation of serum indoxyl sulfate, phenyl sulfate, and p-cresol sulfate; down-regulation of δ-tocopherol and L-glutamine; and changes in blood microbiome and metabolites. In conclusion, we identified novel metabolic biomarkers for Cd toxicity, which will also expand our understanding of the role of blood microbiome in Cd-induced injury.
Heart Disease Diagnosis via Nonparametric Mixture Models

Aims/Objectives: E(cid:11)ective and e(cid:14)cient heart disease prediction via nonparametric mixture regression models. Data Source: Data used in this paper is from the UCI database of the Cleveland Clinic Foundation for heart disease. The original data source contains 76 raw attributes with 303 observations each. For the purpose of this paper only 14 attributes were used as explained in section 4. Methodology: Cluster analysis was applied via mixture models in the form of Nonparametric Density-based models. The clusters were identi(cid:12)ed using a graph theory based technique. Voronoi diagrams were used and and their distributions were estimated nonparametrically
GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems

Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques.
Production of Graft Copolymers of Cod Collagen with Butyl Acrylate and Vinyl Butyl Ether in the Presence of Triethylborane—Prospects for Use in Regenerative Medicine

Collagen is a suitable material for regenerative medicine because it is characterized by its good biocompatibility. However, due to its fibrillar structure, it cannot organize itself into three-dimensional porous structures without additional modification. The introduction of synthetic monomer elements into the collagen macromolecules is a technique used to form three-dimensional, collagen-based, branched, and crosslinked structures. New types of graft copolymers made from cod collagen with a butyl acrylate and vinyl butyl ether copolymer in aqueous dispersion were obtained in the presence of triethylborane by a radical mechanism. The process of graft copolymer formation proceeded as usual by radical initiation, through radicals formed during triethylborane oxidation by oxygen residues, collagen borination, and reversible inhibition with the participation of a boroxyl radical. The characteristics of the graft copolymers were determined using methods of physical and chemical analysis (GPC, SEM, IR spectroscopy, etc.), while the cytotoxicity was assessed using the MTT assay method. It is shown that the grafting of alternating blocks of butyl acrylate and vinyl butyl ether to the protein macromolecules results in changes in the morphological pattern of the graft co-polymer in comparison with native collagen. This is manifested in the development of consolidations around the collagen fibers of the structural matrices, with the co-polymer cellular structure consisting of interpenetrating pores of unequal size. Additionally, it is important that the graft co-polymer solutions are not toxic at a certain concentration. The above properties confirm the promising nature of the technique’s application as the basis for producing new materials for regenerative medicine.