Miracle-dz/UniFAR-FTUs huggingface dataset:
Maternal Magnolol Supplementation during Pregnancy and Lactation Promotes Antioxidant Capacity, Improves Gut Health, and Alters Gut Microbiota and Metabolites of Weanling Piglets

Maternal nutrition exerts a profound effect on the postnatal performance of offspring, especially during the weaning period. The multifunctional bioactive component magnolol (MAG) has shown promise as a dietary supplement. This study aimed to explore the effects of maternal MAG supplementation on the antioxidant capacity, gut health, gut microbiome, and metabolome composition of weanling piglets. Fifty pregnant sows were randomly divided into two equally sized groups, the control group and the group supplemented with 100 g/t MAG during the gestation and lactation periods, and 7 days postweaning, the pups were euthanized. The microbiome and metabolome features of weanling piglet colons were compared. Our results revealed that maternal MAG supplementation modified the serum redox status of weanling piglets by decreasing malondialdehyde concentration and increasing superoxide dismutase activity and total antioxidant capacity. Moreover, the decreased indicators of diarrhea were accompanied by improved gut barrier function, in which serum diamine oxidase concentration was decreased, and expressions of zona occludens-1, claudin-1, and intestinal alkaline phosphatase were increased in the colon of weanling piglets from sows supplemented with MAG. Further analysis of the gut microbiota indicated that maternal MAG supplementation significantly increased the relative abundance of beneficial bacteria in the colon of weanling piglets, including Faecalibacterium prausnitzii and Oscillospira. Metabolome analysis identified 540 differential metabolites in the colon of piglets from MAG-fed dams, of which glycerophospholipid classes were highly correlated with progeny gut health and key beneficial bacteria. Our findings indicated that maternal MAG supplementation can improve the oxidative status and gut health of weanling piglets, possibly due to alterations in the gut microbiota and metabolites.
Modeling of individual egg weights of Lohmann-Brown layer hens

: This study was carried out to determine the most suitable model for egg weights of the Cubic, Gompertz, Logistics, Gamma, Richard, Piecewise Quadratic, Orscov, and Sigmoaidal models, which are widely used in estimation. Lohmann-Brown Classic laying hens raised in Ondokuz Mayıs University Research and Application farm were used as animal material. In the modeling of egg weights of 351 layer hens raised in 3-storey cages, individual egg weight data measured at weekly intervals, 32 on the 1st floor, 17 on the 2nd floor, and 27 on the 3rd floor, were taken into account, and a total of 76 hens’ individual egg weight modeling was carried out. Coefficient of determination, mean square error, Durbin-Watson, and Akaike Information Criteria values were taken into account in modeling egg weights and comparing the compatibility of models with point distribution. According to the comparison criteria, it was determined that the best estimation model was Richard. It was determined that the closest predictions to the Richard prediction model were obtained from the Logistics and Gompertz models. In addition, it was concluded that Orskov, Sigmoidal, and Quadratic piecewise regression models had the worst fit.
FastAL: Fast Evaluation Module for Efficient Dynamic Deep Active Learning Using Broad Learning System

State-of-the-art Active Learning (AL) methods often encounter challenges associated with a hysteretic learning process and an expensive data sampling mechanism. The former implies that data selection in the ( $i+1$ )-th round is solely based on the learned model’s results in the $i$ -th round. The latter involves using model inference to calculate data value (e.g., uncertainty estimation based on model inference), which can be cumbersome, particularly when working with large datasets or Deep Neural Networks (DNNs). To address these challenges, we propose FastAL, an efficient and dynamic deep AL framework. Our approach includes an efficient method for calculating data value from the frequency domain perspective, generating multiple candidates. Then, we introduce the Fast Evaluation Module, which directly calculates each candidate’s contribution to future model training and selects the best options. In addition, current AL methods, particularly those based on uncertainty, are susceptible to data bias, which implies that selected data may not represent the original unlabeled data adequately. To alleviate this issue, we propose the De-similar Module, which removes partially similar data. The above three modules are model-agnostic and thus can be seamlessly integrated into any Active Learning framework. We conducted rigorous experiments on various benchmark datasets to validate our approach’s effectiveness. Our results demonstrate that FastAL outperforms other state-of-the-art methods by a significant margin, including those based on uncertainty, diversity, and expected model change.
The Dynamics of the Unitary Fermi Gas

The Dynamics of the Unitary Fermi Gas Yuan Lung Luo Chair of the Supervisory Committee: Professor Aurel Bulgac Department of Physics This dissertation is concerned with the dynamics of dilute Fermi gases in the unitary regime with short-range interaction and infinite scattering length. This work represents the first abinitio simulation of quantized vortex generation and domain walls generation. In addition, the interaction of quantized vortices and domain walls are investigated.
A time-averaged neutral model with environmental stochasticity

Competition is the main driver of population dynamics, which shapes the genetic composition of populations and the assembly of ecological communities. Neutral models neglect the effects of selection and niche partitioning, such that all individuals are equivalent and the dynamics is governed by demographic (shot) noise. Despite their great success in explaining static properties of communities, like species abundance distributions (SADs), the existing neutral models were criticized for making unrealistic predictions of fundamental dynamic patterns. A time-averaged neutral model, in which environmental variations affect coherently the relative fitness of entire populations, has been suggested to account for this problem. Here we solve for the SAD of a well-mixed, time-averaged neutral model in a community of size $N$. A self-consistent mean-field approach is used to reduce the multi-species problem to two-species dynamics, and the large-$N$ asymptotics of the emerging set of Fokker-Planck equations is obtained. Our analytic expressions are shown to fit the SADs obtained from extensive Monte-Carlo simulations and from numerical solutions of the corresponding master equations.
Changing gender role attitudes and the changing gender gap in labour force participation

This paper uses micro-data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey to examine the relationship between gender role attitudes and the labour supply of men and women. Using the Wellington decomposition technique, the paper also considers how much of the change in the gender gap in labour force participation (LFP) between 2001/5 and 2015/19 may be explained by changes in the gender role attitudes of adult women and men. The results show a 6.5 percentage point convergence in the gender gap in LFP between the two periods. Nearly half the convergence arises from a change in the schooling attainment of men and women. Just over one-third is due to changes in gender role attitudes (faster adoption of egalitarian gender role attitudes by women).
Antioxidant Performance of Borago officinalis Leaf Essential Oil and Protective Effect on Thermal Oxidation of Fish Oil

This study aimed to determine the antioxidant activity of Borago officinalis essential oil in the thermal oxidation of fish oil. The volatile compound profile of B. officinalis essential oil (BEO) was determined using gas chromatography and mass spectrometry. As a result of the analysis, 97.27 percent of the volatile components of the product were characterized. The product’s major components were benzene acetaldehyde (28.59 percent) and linalool (13.60 percent). As a result of the free radical scavenging activity determined using 2-diphenyl-1-picrylhydrazyl (DPPH) analysis, its antioxidant activity was determined, and a 50 percent inhibitory concentration value was calculated as 736.06 ppm. In order to determine the protective effect of the BEO on fish oil oxidation, 0% (BEO0), 0.1% (BEO0.1), 0.5% (BEO0.5), 1% (BEO1), and 3% (BEO3) ratios of BEO were added to the fish oil, and the experimental groups were kept at 70 °C for 24 h with continuous ventilation for the thermal oxidation process. As a result of the oxidation study, it was determined that the addition of BEO suppressed fish oil oxidation, and the oxidation radicals in the product decreased significantly (p < 0.05) depending on the BEO concentration. In conclusion, it was determined that 1 percent BEO supplementation minimized the oxidation of fish oil under various temperature and ventilation conditions.
Matter effects study in very long baseline neutrino oscillation experiments

We examine the physics prospects of a neutrino oscillation experiment to be performed at a Neutrino Factory with a very long baseline in the range of 6500 km. We consider three family mixing and make the approximation that only one mass scale is relevant in our framework. We study the effect of the neutrino propagation through matter (MSW effect). Given the density of the Earth, the existence of the MSW resonance can be experimentally proven by comparing the oscillated spectra of neutrinos obtained from the decays of muons of positive and negative charges. A precision study of the oscillations of all three flavors could be performed since neutrinos are above the tau production threshold (appearance searches).
Asymptotic results for a stationary random process generated by the stochastic EM algorithm in mixture analyses

Abstract Implementing the stochastic EM (or SEM) algorithm for parameter estimation in finite mixture models yields a random process which is normally distributed AR(1) as the sample size tends to infinity. The normal distribution underlying this process follows from a regularity assumption pertaining to theory concerning SEM. In the present paper we consider a centred version {Xi} of this process and focus on the particular case where this is generated from the stationary state of SEM, denoted as process {X(i)}. We study distribution properties of as n tends to infinity without assuming a normal distribution for {X(i)}. To this end a multivariate central limit theorem is shown to apply and relationship between the variance of as n tends to infinity and that of for r sufficiently large is established.
Flux qubits with neutral currents in optical lattices

Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543 andNational Institute of Education and Institute of Advanced Studies,Nanyang Technological University, 1 Nanyang Walk, Singapore 637616(Dated: January 11, 2014)We study an experimentally feasible qubit system employing neutral currents. Our system is based on bosoniccold atoms trapped in ring-shaped optical lattice potentials. The lattice makes the system strictly one dimen-sional and it provides the infrastructure to realize a tunable ring-ring interaction. By breaking the Galileaninvariance we demonstrate how atomic currents through the lattice provide a realization of a qubit. We breakthe Galilean invariance either by artiﬁcially creating a phase slip in a single ring, or by considering two ho-mogeneous ring lattices, coupled by tunneling interaction. The Hamiltonian of the system effectively leads toa washboard potential in the phase representation, tilted by the applied ’ﬂux’. The single qubit infrastructureis experimentally investigated with tailored optical potentials. An experimentally feasible scheme of the two-ring-qubit is discussed. In this case, the dynamics is demonstrated to show macroscopic quantum self trapping.Time-of-ﬂight expansion maps the pattern of atomic currents into detectable atomic density distributions. Basedon our analysis, we provide viable protocols to initialize, address, and read-out the qubit.
Environmentally sensitive photosensitizers enable targeted photodynamic ablation of Gram-positive antibiotic resistant bacteria

Bacterial infections remain among the biggest challenges to human health, leading to high antibiotic usage, morbidity, hospitalizations, and accounting for approximately 8 million deaths worldwide every year. The overuse of antibiotics and paucity of antimicrobial innovation has led to antimicrobial resistant pathogens that threaten to reverse key advances of modern medicine. Photodynamic therapeutics can kill bacteria but there are few agents that can ablate pathogens with minimal off-target effects. Methods: We describe nitrobenzoselenadiazoles as some of the first environmentally sensitive organic photosensitizers, and their adaptation to produce theranostics with optical detection and light-controlled antimicrobial activity. We combined nitrobenzoselenadiazoles with bacteria-targeting moieties (i.e., glucose-6-phosphate, amoxicillin, vancomycin) producing environmentally sensitive photodynamic agents. Results: The labelled vancomycin conjugate was able to both visualize and eradicate multidrug resistant Gram-positive ESKAPE pathogens at nanomolar concentrations, including clinical isolates and those that form biofilms. Conclusion: Nitrobenzoselenadiazole conjugates are easily synthesized and display strong environment dependent ROS production. Due to their small size and non-invasive character, they unobtrusively label antimicrobial targeting moieties. We envisage that the simplicity and modularity of this chemical strategy will accelerate the rational design of new antimicrobial therapies for refractory bacterial infections.
Autonomous Adaptive Control System for Airplane Landing

The paper presents a new autonomous adaptive system for the control of airplanes during landing in longitudinal plane. For the first stage of landing in longitudinal plane, the main variables to be controlled are the glide slope angle and longitudinal velocity; during the second main stage of landing in longitudinal plane, the vertical velocity error and the airplane's longitudinal velocity are controlled. The new robust control architecture has linear subsystems, for which the relative degrees are calculated; the new architecture will also consists of a dynamic compensator, a linear observer, and two reference models, their design being accomplished with respect to the calculated relative degrees. The signal estimated by the observer is useful for training a neural network – an adaptive subsystem of the architecture that provides the adaptive component of the control law. In the case of actuators having nonlinear dynamics, pseudo control hedging blocks are used to cancel the adapting difficulties of the neural networks. The new adaptive architecture is software implemented and validated by complex numerical simulations.
A study of the generalized eigenvalue decomposition in discriminant analysis

The well-known Linear Discriminant Analysis (LDA) approac h to feature extraction in classification problems is typically formulated using a gen eralized eigenvalue decomposition, S1V = S2VΛ, whereS1 andS2 are two symmetric, positive-semidefinite matrices defining the measure to be maximized and that to be minimized. Most of the LDA algorithms developed to date are based on tuning one of these two m atrices to solve a specific problem. However, the search for a set of metrics that can be a pplied to a large number of problems has met difficulty. In this thesis, we take the view t hat most of these problem are caused by the use of the generalized eigenvalue decompositi on equation described above. Further, we argue that many of these problems can be solved by stud ing and modifying this basic equation. At the core of this thesis lays a new fact orization ofS−1 2 S1 that can be used to resolve several of the problems of LDA. Three novel algorithms are derived, each based on our propos ed factorization. In the first algorithm, we define a criterion to prune noisy bases in L DA. This is possible thanks to the flexibility of our factorization, which allows the sup pression of a set of vectors of any metric. The second algorithm is called Subclass Discrimina nt Analysis (SDA). SDA can be applied to a large variety of distribution types because i t approximates the underlying distribution of each class with a mixture of Gaussians. The m ost convenient number of Gaussians can be readily selected thanks to our proposed fac toriz tion. The third algorithm is aimed to address the over-fitting issue in LDA. A direct app lication of this algorithm is
Applications of ultracompact aperture-coupled plasmonic slot cavity with spectrally splitting capability

Abstract. Theoretical analysis and numerical simulations are applied to propose an improved spectrally splitting method in a filter structure, based on aperture-coupled plasmonic slot cavity. By considering the field distribution inside the cavity slot, this structure is benefited from the position of output aperture to split the resonance modes of the cavity. According to the proposed narrow bandpass filter and spectrally splitting mechanism, a two- and three-port power splitters and an ultracompact four-channel multi/demultiplexer are proposed. Our structures are numerically analyzed using the two-dimensional finite-difference time-domain method. Our results can be utilized for construction of nanoplasmonic multiwavelength multi/demultiplexers to be used in nanoplasmonic integrated circuits.
Numerical solution of second law analysis for MHD Casson nanofluid past a wedge with activation energy and binary chemical reaction

Purpose This paper aims to peruse the influence of second law analysis for electrically conducting fluid of a Casson nanofluid over a wedge. For activation energy, a modified Arrhenius function is used. Design/methodology/approach The highly non-linear governing equations are developed using similarity transformations and then computed numerically via Keller–Box method. Findings The influences of emerging parameters on velocity, temperature distribution and concentration of nanoparticle are explained and presented via graphs and tables. Also, the behavior of fluid flow is investigated through the coefficient of skin friction, Nusselt and Sherwood numbers. Results reveal that the velocity profile enhances due to increasing Casson parameter and magnetic parameter, whereas the temperature distribution and concentration of nanoparticle decrease with larger vales of Casson parameter. It is inspected that the concentration boundary layer increases due to activation energy and decreases due to reaction rate and temperature differences. Originality/value The authors believe that all the numerical results are original and significant which are used in biomedicine, industrial, electronics and transportation. The results have not been considered elsewhere.
The Hypothesis of a “Living Pulse” in Cells

Motility is a great biosignature and its pattern is characteristic for specific microbes. However, motion does also occur within the cell by the myriads of ongoing processes within the cell and the exchange of gases and nutrients with the outside environment. Here, we propose that the sum of these processes in a microbial cell is equivalent to a pulse in complex organisms and suggest a first approach to measure the “living pulse” in microorganisms. We emphasize that if a “living pulse” can be shown to exist, it would have far-reaching applications, such as for finding life in extreme environments on Earth and in extraterrestrial locations, as well as making sure that life is not present where it should not be, such as during medical procedures and in the food processing industry.
Chromatic add-on to improve depth from defocus with a conventional camera

We propose to add an optical component in front of a conventional camera to improve depth estimation performance of Depth from Defocus (DFD), an approach based on the relation between defocus blur and depth. The add-on is an afocal doublet, which adds chromatic aberration to the global system. This overcomes ambiguity and dead zone, which are the fundamental limitations of DFD with a conventional camera since the blur become unambiguous and measurable for each depth. In this paper we present the principle of the add-on, a theoretical performance model and experiments on real prototype to illustrate the improvement of depth estimation performance with the proposed add-on.
An Alternative Estimation Method of a Time-Varying Parameter Model

A non-Bayesian, regression-based or generalized least squares (GLS)-based approach is formally proposed to estimate a class of time-varying AR parameter models. This approach has partly been used by Ito et al. (2014, 2016a,b), and is proven to be efficient because, unlike conventional methods, it does not require Kalman filtering and smoothing procedures, but yields a smoothed estimate that is identical to the Kalman-smoothed estimate. Unlike the maximum likelihood estimator, the possibility of the pile-up problem is negligible. In addition, this approach enables us to deal with stochastic volatility models, models with a time-dependent variance-covariance matrix, and models with non-Gaussian errors that allow us to deal with abrupt changes or structural breaks in time-varying parameters.
Slimmer CNNs Through Feature Approximation and Kernel Size Reduction

Convolutional Neural Networks (CNNs) have been shown to achieve state of the art results on several image processing tasks such as classification, localization, and segmentation. Convolutional and fully connected layers form the building blocks of these networks. The convolution layers are responsible for the majority of the computations even though they have fewer parameters. As inference is used much more than training (which happens only once), it is important to reduce the computations of the network for this phase. This work presents a systematic procedure to trim CNNs by identifying the least important features in the convolution layers and replacing them either with approximations or kernels of reduced size. We also propose an algorithm to integrate the lower kernel approximation technique for a given accuracy budget. We show that using the linear approximation method can achieve a 15% – 80% savings with a median of 52% reduction while the lower kernel method can achieve 33% – 95% reduction with a median of 65% in the required number of computations with only a marginal 1% loss in accuracy across several benchmark datasets. We have also demonstrated the proposed methods on VGG-16 architecture for various datasets. On VGG-16 we have achieved 4.2% - 45% savings in MAC computations (with a median of 18.5%) with only a marginal 0.5% loss in accuracy. We also show how an existing hardware accelerator for DNNs (DianNao) can be modified with low added complexity to take advantage of the kernel approximations, and estimate the speedups that can be obtained in such a way on custom embedded hardware.