Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Approximate IsoRank for Scalable Global Alignment of Biological Networks

A well-studied approximate version of the graph matching problem is directly relevant for the study of protein-protein interaction networks. Called by the computational biology community Global Network Alignment, the two networks to be matched are derived from the protein-protein interaction (PPI) networks from organisms of two different species. If these two species evolved recently from a common ancestor, we can view the two PPI networks as a single network that evolved over time. It is the two versions of this network that we want to align using approximate graph matching. The first spectral method for the PPI global alignment problem proposed by the biological community was the IsoRank method of Singh et al. This method for global biological network alignment is still used today. However, with the advent of many more experiments, the size of the networks available to match has grown considerably, making running IsoRank unfeasible on these networks without access to state of the art computational resources. In this paper, we develop a new IsoRank approximation, which exploits the mathematical properties of IsoRank’s linear system to solve the problem in quadratic time with respect to the maximum size of the two PPI networks. We further propose a computationally cheaper refinement to this initial approximation so that the updated result is even closer to the original IsoRank formulation. In experiments on synthetic and real PPI networks, we find that the results of our approximate IsoRank are not only nearly as accurate as the original IsoRank results but are also much faster, which makes the global alignment of large-scale biological networks feasible and scalable.
A brief real-time fNIRS-informed neurofeedback training of the prefrontal cortex changes brain activity and connectivity during subsequent working memory challenge

Working memory (WM) represents a building-block of higher cognitive functions and a wide range of mental disorders are associated with WM impairments. Initial studies have shown that several sessions of functional nearinfrared spectroscopy (fNIRS) informed real-time neurofeedback (NF) allow healthy individuals to volitionally increase activity in the dorsolateral prefrontal cortex (DLPFC), a region critically involved in WM. For the translation to therapeutic or neuroenhancement applications, however, it is critical to assess whether fNIRS-NF success transfers into neural and behavioral WM enhancement in the absence of feedback. We therefore combined single-session fNIRS-NF of the left DLPFC with a randomized sham-controlled design (N = 62 participants) and a subsequent WM challenge with concomitant functional MRI. Over four runs of fNIRS-NF, the left DLPFC NF training group demonstrated enhanced neural activity in this region, reflecting successful acquisition of neural selfregulation. During the subsequent WM challenge, we observed no evidence for performance differences between the training and the sham group. Importantly, however, examination of the fMRI data revealed that - compared to the sham group - the training group exhibited significantly increased regional activity in the bilateral DLPFC and decreased left DLPFC - left anterior insula functional connectivity during the WM challenge. Exploratory analyses revealed a negative association between DLPFC activity and WM reaction times in the NF group. Together, these findings indicate that healthy individuals can learn to volitionally increase left DLPFC activity in a single training session and that the training success translates into WM-related neural activation and connectivity changes in the absence of feedback. This renders fNIRS-NF as a promising and scalable WM intervention approach that could be applied to various mental disorders.
Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees

General linear models have been the foundational statistical framework used to discover the ecological processes that explain the distribution and abundance of natural populations. Analyses of the rapidly expanding cache of environmental and ecological data, however, require advanced statistical methods to contend with complexities inherent to extremely large natural data sets. Modern machine learning frameworks such as gradient boosted trees efficiently identify complex ecological relationships in massive data sets, which are expected to result in accurate predictions of the distribution and abundance of organisms in nature. However, rigorous assessments of the theoretical advantages of these methodologies on natural data sets are rare. Here we compare the abilities of gradient boosted and linear models to identify environmental features that explain observed variations in the distribution and abundance of blacklegged tick (Ixodes scapularis) populations in a data set collected across New York State over a ten-year period. The gradient boosted and linear models use similar environmental features to explain tick demography, although the gradient boosted models found non-linear relationships and interactions that are difficult to anticipate and often impractical to identify with a linear modeling framework. Further, the gradient boosted models predicted the distribution and abundance of ticks in years and areas beyond the training data with much greater accuracy than their linear model counterparts. The flexible gradient boosting framework also permitted additional model types that provide practical advantages for tick surveillance and public health. The results highlight the potential of gradient boosted models to discover novel ecological phenomena affecting pathogen demography and as a powerful public health tool to mitigate disease risks.
Age-related epithelial defects limit thymic function and regeneration

The thymus is essential for establishing adaptive immunity yet undergoes age-related atrophy leading to compromised immune responsiveness. The thymus is also extremely sensitive to acute insult and although capable of regeneration, this capacity declines with age. Focusing on non-hematopoietic stromal cells, and using single-cell and spatial transcriptomics, lineage-tracing, and advanced imaging, we discovered two atypical thymic epithelial cell (TEC) states that emerged with age. Age-associated (aa)TECs formed atypical high-density epithelial clusters that were devoid of thymocytes, an accretion of non-functional thymic tissue that worsened with age and exhibited features of partial epithelial-to-mesenchymal transition (EMT). In silico interaction analysis revealed that aaTEC emergence drew tonic signals from other TEC populations at baseline, acting as a sink for TEC growth factors. Following damage, aaTEC expanded substantially, further perturbing trophic pathways, and correlating with defective regeneration of the involuted thymus. These findings define a unique feature of thymic involution linked to immune aging.
MHD Three Dimensional Couette Flow past a Porous Plate with Heat Transfer

In this paper a steady three dimensional flow of a viscous - incompressible fluid through highly porous medium bounded by a vertical infinite porous plate under the influence of a transverse applied magnetic field, with periodic suction velocity is considered. Governing equations are solved by using perturbation technique and obtained the expressions for velocity and temperature. The above flow quantities are discussed through the graphs for different physical parameters. Also the expressions for shear stress and the rate of heat transfer co-efficient are derived and discussed.
Differential Inequalities and Starlikeness

We study some differential inequalities in the unit disc which imply starlikeness: for example if ƒ (z) = z + Σ∞ n=2 an (ƒ)zn is analytic in D = {z | |z| < 1} and |z(ƒ′′(z) − 1)| ≤ 1 − α, z∈D for some α ] [0, 1), then ƒ is one-to-one on D and ƒ (D) is a starlike domain with respect to the origin.
An analytical study of the Kelvin–Helmholtz instabilities of compressible, magnetized, anisotropic, and isotropic tangential velocity discontinuities

An analytical study is made of the linear stability of tangential velocity discontinuities in anisotropic and isotropic compressible, magnetized plasmas. A new analytical technique leads to entirely new results for both cases, and recovers previously obtained numerical results for the isotropic case. For both the anisotropic and isotropic vortex sheets, the structure of unstable, standing wave modes is mapped out in the (inverse plasma beta, Mach number) plane for modes at arbitrary angles to the flow and the magnetic field. Solutions are obtained for unstable standing and traveling modes of the anisotropic vortex sheet propagating parallel or antiparallel to the magnetic field, and at small angles to these directions. Numerically computed unstable standing modes obtained previously for the isotropic case are recovered analytically and extended to more general configurations. Also, it is shown that modes propagating along or opposite to the magnetic field direction, and at a general angle to the flow, do .
A Note on Bootstrapping the Local Time of the Empirical Process

In this note we prove that Efron's bootstrap is asymptotically consistent in estimating the distribution of the local time of the empirical process, provided the underlying distribution of the observations is continuous. We employ the classical method of moments. It appears that our result is not easy to obtain from the general theory of bootstrapping (functionals of) empirical processes. l. INTRODUCTION AND MAIN RESULT Let X 1 , X2, . be independent random variables, defined on a single probability space (n, A, P), with common distribution function (dj)F on the real line. Let Fn denote the empirical df based on X1, . ,Xn; i.e. Fn(x) = n1 z::7= 1 l(X;:::; x),-oo < x < oo. Conditionally given X 1 , . •. ,Xn, let Xi, . , X~ denote a random sample of size n, drawn from Fn. Let F~ denote the empirical dj based on Xi, . , X~. Define the empirical process Un(x) = n~(Fn(x) F(x)), -oo < x < 00 (1.1) and the bootstrapped empirical process U~(x) = nt(F~(x) Fn(x)), -oo < x < oo ( 1.2) It is well-known that Un 7 B(F) (cf. Billingsley [2]) in the space (D,d), and, in addition, that U~ 7 B(F), with P-probability 1 (i.e., for almost all sequences X 1,X2, . ) (cf. Bickel and Freedman [1]). Here B denotes the Brownian bridge process, and 7indicates convergence in distribution. A beautiful and farreaching extension of this result was obtained by Gine and Zinn [4]. The local time of Un at zero up to 'time' XF = sup{x: F(x) < 1} is given by L~F(Un) = n-t I: J{o}(Un(x)) (1.3) x:SxF Note that, if F is strictly increasing, then L~,. (Un) is nothing but n-t times the number of zerocrossings of the Un-process. If Un(x) happens to be zero for all x, which belong to a 'flat part' of F, then we simply count this as a single zero crossing. It is well-known that L~F(Un) is distributionfree when Fis continuous. In this case, we may as well take F equal to the uniform df on (0, 1) and write L? (Un) for the local time of the uniform empirical process. AMS Subject Classification {1991}: 62G09, 60J55, 62G30, 62G20.
Topologies on lattice ordered groups, separation from closed downward sets and conjugations of type Lau

We extend the results of Rubinov and Glover on the separation of normal subsets of , and those of Martínez-Legaz, Rubinov and Singer on the separation of downward subsets of , to the unifying framework of subsets of An , where A is a continuous conditionally complete lattice ordered group. We also extend earlier results of the second author on the characterizations of conjugations of type Lau of functions , to the framework of functions , where is the canonical enlargement of A. These extensions are based on the notions and results of the theory of continuous lattices, generalized in an earlier work of the first author and here, to the conditionally complete case, in particular the way below order relation ≪, and the Scott and Lawson topologies. We also introduce the “way above order relation ≫”, and consider the “bi-Scott topology” on A, introduced previously by the first author, which under our continuity assumption on A, turns out to be equal to the order topology; moreover, we show that for this topology, (A, ⊗) is a topological group and (A, ≤) is a topological lattice.
Surface Dynamics of Observed Maritime Fronts

Abstract The problem of establishing the important mechanisms and processes in surface fronts is examined. It is shown that boundary layer processes may modify the details of frontal dynamics near the surface and that at the same time fronts may alter boundary layer solutions. A similarity model that assumes a horizontally homogeneous steady-state planetary boundary layer is used to test the effects of baroclinity and stratification on the ageostrophic vorticity. Model response to baroclinity and stratification is tested. A boundary layer positive feedback mechanism that stems from the variation in stratification and thermal advection across a front and favors the creation of stronger temperature gradients in cold fronts is proposed. A compatible model that includes the effects of horizontal inhomogeneity is derived and used in conjunction with scatterometer data to observe the behavior of five surface fronts. Observations are compared to results of numerical models by several authors and it is concluded .
ON PRIME AND SEMIPRIME MODULES AND COMODULES

In this paper we describe the structure of prime and semiprime R-modules M such that R/AnnR(M) is artinian. The obtained results are then applied to describe the structure of prime and semiprime right comodules over a coring C under some assumption, where a right comodule M is said to be prime (semiprime) if the corresponding left *C-module is prime (semiprime). Finally we apply the results to coalgebras over commutative rings.
The conormal type function for CR manifolds

We introduce the total type function Φ(p, δ), and its microlocalization, the conormal type function Φ(p, ξ, δ), for a CR manifold M . These functions generalize important aspects of the classical Levi form, as well as higher Levi forms. We show how these type functions have direct applications to important questions about CR manifolds.
Two-Dimensional Spin Density Wave State in LaFeAsO

The parent compound of the Fe oxypnictide superconductor, LaFeAsO, has been studied by pulsed neutron powder inelastic scattering measurement. The inelastic scattering intensity along the Q -axis at T =140 K exhibits a prominent asymmetric peak ascribed to the (π,π, l ) magnetic rod, suggesting the two-dimensionality of the spin density wave above the magnetic transition temperature. It persists even in the tetragonal phase up to room temperature, which is well above the magnetic transition temperature, corresponding to two antiferromagnetic sublattices or domains with a strong effective antiferromagnetic interaction J 2 between next-nearest-neighbour Fe magnetic moments in a single Fe square lattice.
Consensus Protocol for Discrete-Time Linear Multiagent Systems With Channel Fadings: A Dynamic Event-Based Approach

This article investigates the consensus problem of discrete-time linear multiagent systems with channel fadings over wireless sensor networks. The phenomena of channel fadings are described as the stochastic Rice fading model with random time-varying channel coefficients. By utilizing the local state information and constructing a dynamic auxiliary system, a novel control protocol with the dynamic discrete-time event-triggered scheme has been proposed. Sufficient conditions are obtained to guarantee the asymptotic consensus of the multiagent systems under the proposed protocol. Compared with the existing works related to discrete-time event-triggered consensus, the interevent time between any two successive events can be prolonged, and thus, the transmission data packages can be decreased immensely. Finally, the effectiveness of the proposed consensus protocol with a dynamic discrete-time event-triggered scheme is testified by numerical simulations.
Sensitivity of Temperature Perturbation to Precipitation: A Parametric Study

Global warming and unusual rainfall are significant consequences of anthropogenic activities and have been a hot research topic since the last century. Urban heat island (UHI) causes serious environmental hazards that occur mainly due to urbanisation and anthropogenic activity, which may change the weather pattern. UHI effect is referred to the temperature of the air or the land surface of an urban area being more than the surrounding non-urban region. This temperature difference, referred to as urban heat island intensity, is generated mainly due to an increase in anthropogenic heat. This intensity of UHI may cause an uplift of local heat flux and spread to the atmospheric planetary boundary layer (PBL), which leads to instability in the thermodynamic and dynamic structure of the urban environment. Together with ground surface heating, atmosphere instability indirectly influences the intensity of rainfall in the urbanised area. Most of the earlier UHI-related study has shown the increase in temperature around the major city ranging from $2 ^{\circ}\mathrm{C}$ to $10 ^{\circ}\mathrm{C}$. In the present work, an attempt is made to quantify the effect of temperature perturbation on precipitation rate. A numerical simulation is carried out by perturbing temperature at the surface level by 2 K, 4K, 6 K, 8 K and 10 K and exponentially decreasing height up to the PBL. The enhanced temperature increases in near-surface mixing and convection resulted in a significant change in the local weather leading to a shift in rainfall pattern. Due to temperature perturbation from 2 K to 10 K, the precipitation rate increases continuously; however precipitation rate is more sensitive to the degree of perturbation. These enhancement characteristics are observed locally at the downwind side of perturbation.
Stock Price Prediction using Hybrid Deep Learning Technique for Accurate Performance

Deep learning and intelligent systems are constantly growing in popularity in the modern world. Artificial intelligence has several uses, all of which relate to human activities. Projection analysis is one of the general uses of neural networks and artificial intelligence. The authors of this work also carried out an artificial intelligence-based comparison investigation. Using various models, authors have made stock market predictions. Since stock markets are inherently unpredictable, accurate prediction analysis is crucial for assessing stock values and their downs and ups throughout time. Using algorithms for machine learning on data from financial news, which can also modify investors' interests, the stock values can be readily anticipated. Traditional prediction techniques, on the other hand, are no longer effective when applied to non-stationary time series information. With the development of deep learning technologies, this research suggests a way for accurately predicting stock prices.
Air Quality Prediction Model Based on Spatio-temporal Graph Convolution Neural Networks

With the rapidly development of economy and technology, the problem of air pollution is becoming more and more serious. A large number of studies show that air pollution not only causes huge economic losses, but also seriously endangers human life and health. Therefore, the control and prevention of air pollution has become very important, and air quality prediction is an important part of the control and prevention. In the era of big data, prediction models based on deep learning show superior performance in the field of air quality prediction. In this paper, the prediction model based on Spatio-Temporal Graph Convolutional Neural Networks(STGCN) is used to predict the air quality. The prediction results show that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the Spatio-Temporal Graph Convolutional Neural Networks(STGCN) model are better than the traditional algorithm models such as RNN,LSTM,LSVR,GRU and ARIMA.
Hybrid Model Convolutional Stage-Positive Definite Metric Operator-Infinity Laplacian Applied to Depth Completion

Depth completion is a fundamental task for many applications such as autonomous vehicles, 3D cinema, 3D reconstruction, and others. Many approaches have been proposed to tackle this problem, from classical models (variational models, morphological models, etc.) to convolutional networks. Hybrid models consider the advantages of the convolutional networks to select features and the generalization capacities of the classical models to extrapolate data. A hybrid model that considers convolutional stages and an interpolator has been proposed in the literature. This model assumes an anisotropic metric gij and the image domain (Ω), embedding the data in a Riemannian manifold $M$ = (gij, Ω). The interpolation task is performed by solving a degenerated partial differential equation in this manifold M. The proposed metric gij used in this manifold is fundamental to correctly estimate distances between points in the sparse depth data. In this paper, our contributions are two-fold: first, an empirical evaluation of a metric based on a Positive Definite Operator Metric to compare color pixels applied to the depth completion task, and second, a variation of the infinity Laplacian (also the biased infinity Laplacian), namely unbalanced infinity Laplacian (unbalanced biased infinity Laplacian). Both interpolators include a weight map that balances the contribution of different models in the interpolation process. Experimental results in the KITTI Depth Completion Suite dataset, which is publicly available, show that the use of the Positive Definite Metric Operator performs better than the other two models and also performs better than similar models in this dataset.
Self-similarity Tests for Internet Traffic

The paper aims to demonstrate the fractal nature of the Internet traffic using self-similarity tests. First, it presents the self-similarity of the Internet traffic and proposes a fractal model of this traffic. Secondly, it offers an analysis of experimental results that prove the similarity in real traffic and a comparison with similar tests performed by simulation on a network model with scale-free topology. Furthermore, the authors demonstrate that the fractal structure of the topology influences the fractal behaviour of the Internet traffic.
On Potential Risks of “Natural” Hybrid Load Balancing in Large-Scale Clouds: Work in Progress

Realization of economic, reliability, and low latency benefits of the Cloud computing model will probably require flexible hybrid static/dynamic load balancing which combine good queuing performance under stable exogenous conditions with timely response to varying/uncertain exogenous conditions. This paper reports on work in progress on potential risks/benefits of natural two-tier hybrid load balancing strategies. At the “slow” time scale, this strategy employs quasi static load balancing which adjusts to the expected/normal exogenous conditions. Once divergence between the actual and expected exogenous conditions is indicated by grows of the real-time queues, a “natural” dynamic load balancing is employed. Our main, and somewhat unexpected, finding is that while this “natural” hybrid strategy inherits benefits of static load balancing, it also inherits local instability risk of dynamic load balancing.