Miracle-dz/UniFAR-FTUs huggingface dataset:
A Neurally Guided Patch-Based Style Transfer for Mobile Devices

Style transfer is an application that has increased interest, primarily because of the impressive results obtained using neural networks. However, this application demands a lot of computational resources, thus preventing its use in low-end mobiles. The patch-based approach is an interesting alternative that consumes less memory. This work uses two methods to generate high-resolution stylized images. Gated convolution in the neural network and the Halide language in the patch-based implementation are used for optimization. As a result, we were able to apply the style transfer method on a mobile device with 4 GB of memory RAM running under 6 seconds for $1920\times 1080$ image size preserving the high-frequency details.
A Robust Scheduling Algorithm for Overload-Tolerant Real-Time Systems

A real-time system is overloaded when all the tasks in a workload cannot meet their deadlines, and hence a robust algorithm is essential to maximize the number of tasks that meet their deadlines with the minimum number of miss rates and context switching. Although the Rate Monotonic (RM), Earliest Deadline First (EDF), and Least Laxity First (LLF) algorithms optimally perform and schedule tasks on a non-overloaded system, they have deficient performance when the system is overloaded. Therefore, we propose a new scheduling algorithm for uniprocessor and partitioned multiprocessor systems to address the overload situation. Since the proposed scheduling algorithm operates like EDF non-overloaded conditions, the proposed algorithm is optimal for non-overloaded systems. In addition, the proposed algorithm is robust against overloading situations as it executes the maximum possible tasks in the overload situation instead of missing deadlines of many tasks or burdening context switching to the system. The proposed algorithm allocates a processor to tasks based on the possibility of executing the task. The experimental results demonstrate that the proposed scheduling algorithm maximizes the number of tasks that meet their deadlines in overload conditions without a domino effect and context switching. In addition, the proposed algorithm achieves the lowest miss rate without context switching and the highest efficiency and processor utilization in the overloaded system compared with RM, EDF, and LLF.
A Preliminary Study on Random Walk based Similar Contents Discovery

The present paper focuses on similar contents discovery in which an agent discovers similar contents while visiting nodes on a network, and investigates how quickly the random walk on a graph can discover similar contents. To this end, we introduce a performance metric for the similar contents discovery, the s-content discovery time, which is defined as the time taken to discover a content whose similarity is larger than or equal to given similarity s; we also present preliminary simulation results. As a consequence, we reveal that the s-content discovery time is significantly affected by similarity s and also give an observation concerning the cause of increase in the s-content discovery time.
Tilted incompressible Coriolis modes in spheroids

The incompressible flow of a uniform fluid, which fills a rigid spheroid rotating about an arbitrary axis fixed in an inertial frame, is dominated at small Rossby and Ekman numbers by the rotation through the Coriolis force. The effects of rotation on the flow can be found by treating the Coriolis force modified by a pressure gradient as a skew-symmetric bounded linear operator $\boldsymbol{{\mathcal{C}}}$ acting on smooth inviscid incompressible flows in the spheroid. It is shown that the space of incompressible polynomial flows of degree $N$ or less in the spheroid is invariant under $\boldsymbol{{\mathcal{C}}}$ for any $N$ . The skew symmetry of $\boldsymbol{{\mathcal{C}}}$ implies the Coriolis operator $\boldsymbol{{\mathcal{C}}}$ is non-defective for such flows with an orthogonal set of eigenmodes (inertial and geostrophic modes) which form a basis for the finite-dimensional space of spheroidal polynomial flows. The eigenmodes are tilted if the rotation axis is not aligned with the symmetry axis of the spheroid. The non-defective property of $\boldsymbol{{\mathcal{C}}}$ enables enumeration of the modes and proof of their completeness using the Weierstrass polynomial approximation theorem. The fundamental tool, which is required to establish invariance of spheroidal polynomial flows under $\boldsymbol{{\mathcal{C}}}$ and completeness of the Coriolis modes, is that the solution of the polynomial Poisson–Neumann problem, i.e. Poisson’s equation with Neumann boundary condition and polynomial data, in a spheroid is a polynomial. The Coriolis modes of degree one and all geostrophic modes are explicitly constructed. Only the modes of degree one have non-zero angular momentum in the boundary frame.
Lifted Curls

We present an isotropic, hyperelastic model specifically designed for the efficient simulation of tightly coiled hairs whose curl radii approach 5 mm. Our model is robust to large bends and torsions, even when they appear at the scale of the strand discretization. The terms of our model are consistently quadratic with respect to their primary variables, do not require per-edge frames or any parallel transport operators, and can efficiently take large timesteps on the order of ~1/30 of a second. Additionally, we show that it is possible to obtain fast, closed-form eigensystems for all the terms in the energy. Our eigenanalysis is sufficiently generic that it generalizes to other models. Our entirely vertex-based formulation integrates naturally with existing finite element codes, and we demonstrate its efficiency and robustness in a variety of scenarios.
Prevalence and incidence of stroke among people with HIV

Objective: We aimed to obtain more precise estimates of stroke to address the wide variation of stroke burden among people with HIV (PWH) in different clinical settings. Design: Systematic review and meta-analysis. Methods: We systematically searched PubMed, EMBASE, and Web of Science for original articles reporting the prevalence and incidence of stroke among PWH up to November 23, 2022. Der Simonian-Laird random effects were used to obtain pooled estimates and 95% confidence intervals (CIs). Results: We included 17 observational studies covering 1 749 003 PWH on estimation of prevalence, and 17 cohort studies covering 249 606 PWH on estimation of incidence. The pooled prevalence of stroke was 1.30% (95% CI: 1.01%, 1.59%) for PWH aged at least 15 years, and 3.98% (95% CI: 2.45%, 5.51%) for PWH aged at least 50 years. The pooled incidence of stroke was 17.86 per 10 000 person-years (95% CI: 15.96, 19.76), meanwhile, the pooled incidence of ischemic stroke (31.50 per 10 000 person-years; 95% CI: 11.11, 51.89) was higher than hemorrhagic stroke (4.43 per 10 000 person-years; 95% CI: 1.95, 6.91) among PWH aged at least 15 years. Conclusion: The prevalence of stroke was nearly one per hundred among PWH aged ≥15 years, and reached four per hundred for those aged ≥50 years. The occurrence of stroke is common during HIV progression, particularly ischemic stroke. Therefore, more efforts should be made on implementing policies, strategies, and programs aimed at identifying the risk factors, improving treatment, and facilitating rehabilitation for stroke to reduce the disease burden among PWH.
Analysis and Comparison of Peak-to-Peak Current Ripple in Two-Level and Multilevel PWM Inverters

—Three-phase multilevel inverters are used in many medium-and high-power applications such as motor drives and grid-connected systems. Despite numerous PWM techniques for multilevel inverters have been developed, the impact of these modulation schemes on the peak-to-peak output current ripple amplitude has not been addressed yet. In this paper the analysis and the comparison of current ripple for two-and three-level voltage source inverters are given. Reference is made to optimal and popular modulation, so-called centered PWM, easily obtained by both carrier-based modulation (phase disposition, with proper common-mode voltage injection) and space vector modulation (nearest three vectors). It is shown that the peak-to-peak current ripple amplitude in three-level inverters can be determined on the basis of the ripple in two-level inverters, obtaining the same re-sults as by directly analyzing the output voltage waveforms of the three-level inverters. This procedure can be readily extended to higher level numbers. The proposed analytical developments are verified by both numerical simulations and experimental tests.
UvA-DARE (Digital Academic Repository) Non-abelian vortices on compact Riemann surfaces

: We consider the vortex equations for a U ( n ) gauge ﬁeld A coupled to a Higgs ﬁeld φ with values on the n × n matrices. It is known that when these equations are deﬁned on a compact Riemann surface (cid:2) , their moduli space of solutions is closely related to a moduli space of τ -stable holomorphic n -pairs on that surface. Using this fact and a local factorization result for the matrix φ , we show that the vortex solutions are entirely characterized by the location in (cid:2) of the zeros of det φ and by the choice of a vortex internal structure at each of these zeros. We describe explicitly the vortex internal spaces and show that they are compact and connected spaces.
Analysis of Nyström method with sequential ridge leverage scores

Large-scale kernel ridge regression (KRR) is limited by the need to store a large kernel matrix Kt. To avoid storing the entire matrix Kt, Nystrom methods subsample a subset of columns of the kernel matrix, and efficiently find anapproximate KRR solution on the reconstructed Kt. The chosen subsampling distribution in turn affects the statistical and computational tradeoffs. For KRR problems, [16, 1] show that a sampling distribution proportional to the ridge leverage scores (RLSs) provides strong reconstruction guarantees for Kt. While exact RLSs are as difficult to compute as a KRR solution, we may be able to approximate them well enough. In this paper, we study KRR problems in a sequential setting and introduce the INK-ESTIMATE algorithm, that incrementally computes the RLSs estimates. INK-ESTIMATE maintains a small sketch of Kt, that at each step is used to compute an intermediate estimate of the RLSs. First, our sketch update does not require access to previously seen columns, and therefore a single pass over the kernel matrix is sufficient. Second, the algorithm requires a fixed, small space budget to run dependent only on the effective dimension of the kernel matrix. Finally, our sketch provides strong approximation guarantees on the distance ‖Kt - Kt‖2, and on the statistical risk of the approximate KRR solution at any time, because all our guarantees hold at any intermediate step.
Spatial Wage Inequality in North America and Western Europe: Changes Between and Within Local Labour

in advanced economies has been often linked with the growth of spatial inequalities within countries, yet there is limited comparative research that studies the relationship between national and subnational economic inequality. This paper presents the first systematic attempt to create internationally comparable evidence showing how different countries perform in terms of geographic wage inequalities. We create cross-country comparable measures of spatial wage disparities between and within similarly-defined local labour market areas (LLMAs) for Canada, France, (West) Germany, the UK and the US since the 1970s, and assess their contribution to national inequality. By the end of the 2010s, spatial inequalities in LLMA mean wages are similar in Canada, France, Germany and the UK; the US exhibits the highest degree of spatial inequality. Over the study period, spatial inequalities have nearly doubled in all countries, except for France where spatial inequalities have fallen back to 1970s levels. Due to a concomitant increase in within-place inequality, the contribution of places in explaining national wage inequality has remained fairly constant over the 40-year study period, except in the UK where we document a significant increase. Whilst common global social, economic and technological shocks are important drivers of spatial inequality, this variation in levels and trends of spatial inequality opens the way to comparative research exploring the role of national institutions in mediating how global shocks translate into economic disparities between places.
of Gene

The expression of genes necessary for reverse cholesterol transport (RCT) and cholesterol metabolism is controlled by the liver x receptors (LXR) alpha and beta, members of the nuclear hormone receptor superfamily. Previously, we reported an acute induction of RCT component expression occurs (mRNA and protein) coinciding with the fall in progesterone (P4) synthesis at the end of the luteal phase (i.e., functional regression) in the rhesus macaque corpus luteum (CL). Additionally, we identified three endogenous LXR ligands (22R-hydroxycholesterol and 27-hydroxycholesterol, or 22ROH and 27OH, respectively; and desmosterol) that are present in the CL during the luteal phase. Therefore, LXR regulation of gene expression may be a key determinant of functional regression in the primate CL. To test this hypothesis, we used dispersed primate luteal cell cultures obtained from mid-luteal stage CL (days 7-8 post LH-surge, peak P4 production) to determine whether synthetic and endogenous LXR ligands: 1) increase expression of known LXR target genes, 2) inhibit expression of cholesterol uptake and steroidogenic genes, and 3) increase cholesterol efflux. The well characterized synthetic LXR agonist T0901317 (T09, 1 l M) as well as the endogenous ligands 22ROH, 27OH, and desmosterol (1 or 5 l M, physiologic range) were incubated with luteal cells for 24 hr. To facilitate uptake of sterols, 0.5% (w:v) beta-cyclodextrin (beta-CDX) was added to the culture media. T09 caused significant (p , 0.05) increases compared to vehicle in mRNA levels of known LXR target genes including the cholesterol efflux proteins ATP binding cassette subfamily A1
Comparison of the Relationship Between Inflammatory Markers and Atrial Fibrillation Burden

Background: Atrial fibrillation is a complex disease with irregular ventricular response and tachycardia as a result of irregular and rapid contraction of the atria, with poor cardiovascular outcomes unless treated. Various mechanisms are involved in its pathophysiology. Inflammation has an important place among these mechanisms. Many cardiovascular events accompany inflammation. Understanding and correct evaluation of inflammation in current situations contribute to the diagnosis and severity of the disease. The aim of our study was to understand the role of inflammatory biomarkers in patients with atrial fibrillation and to evaluate the difference between whether the disease is paroxysmal and persistent (atrial fibrillation burden). Methods: The study was done retrospectively, and a total of 752 patients who were admitted to the cardiology outpatient clinic were recruited. The normal sinus rhythm group of the study consisted of 140 patients, and the atrial fibrillation group consisted of 351 [permanent atrial fibrillation (n = 206) and paroxysmal atrial fibrillation (n = 145)] patients. Inflammation markers were evaluated by dividing the patients into 3 groups. Results: Higher permanent atrial fibrillation [209.71 (40.73-604.0)], paroxysmal atrial fibrillation [188.51 (53.95-617.46)], normal sinus rhythm [629.47 (104-4695)]; permanent atrial fibrillation [4.53 (0.27-17.94)], paroxysmal atrial fibrillation [3.09 (0.40-11.0)], normal sinus rhythm [2.34 (0.61-13.51)] (P < .05); and permanent atrial fibrillation [1569.54 (139-6069)], paroxysmal atrial fibrillation [1035.09 (133-4013)], normal sinus rhythm [130.40 (26.42-680.39)] (P < .05) were detected in the systemic immune inflammation index, neutrophil–lymphocyte ratio, and platelet/lymphocyte ratio atrial fibrillation groups compared to normal sinus rhythm group. Correlation between C-reactive protein and systemic immune inflammation index (r = 0.679, r = 0.483 P < .05, respectively) was found in the permanent atrial fibrillation and paroxysmal atrial fibrillation groups. Conclusion: Systemic immune inflammation index, neutrophil–lymphocyte ratio, and platelet–lymphocyte ratio were found to be higher in permanent atrial fibrillation compared to paroxysmal atrial fibrillation and in the whole atrial fibrillation group compared to the normal sinus rhythm group. This indicates that inflammation is associated with AF burden and the SII index is successful in reflecting this.
The Effect of L-Ascorbic Acid-2-Phosphate on Human Adipose-Derived Stem Cells Population Doubling Time and Angiogenic Potency

Adipose-derived stem cells (ADSCs) have been developed as substitute cells for tissue engineering. However, in order to get a high number of cells, over-repassage leads the cells to become big in spread size indicating that senescence occurred. This study aimed to evaluate the potency of ascorbic acid to induce the proliferation and angiogenic potency of human ADSCs and inhibit the aging process of the cells. Human ADSCs were isolated by an enzymatic process. ADSCs were cultured in 10% fetal bovine serum (FBS) supplemented by ascorbic acid in various concentrations (1, 3, 6 µ g/ml). The morphology, proliferation, population doubling time (PDT), and angiogenic potency were evaluated. Angiogenic potency analysis was performed using gene expression of vascular endothelial growth factor (VEGF) and its receptor (VEGFR). Ascorbic acid on 6 µ g/mL suppressed the senescence proved by cell spread areas, growth curves, viability, PDT, and percentage of scratch width closure. No signiﬁcant difference in vascular endothelial growth factor and its receptor expression to the control. However, the high concentration of ascorbic acid (6 µ g/ml) expressed the highest VEGF and VEGFR. Our study showed that ADSCs with supplementation of ascorbic acid (6 µ g/ml) promoted proliferation and angiogenic potency.
Unleashing the Potential of Artificial Intelligence in Architecture and Development of the Built Environment: From Misconceptions to Productive Prospects

-Artificial intelligence invades our lives and professions at an ever-increasing pace and intensity. Architecture, the built environment, and real estate have been joining the trend only timidly and belatedly. The record of some of the most recent "famous achievements" in the field set straight, the paper challenges the state-of-the-art concerning these fields, debunks the idea of the (truly) creative potential of the technology, and puts forward a sketch roadmap to a realistic - and significant - deployment of artificial intelligence in architecture and the built environment. The attention turns to open-source patterns-platforms, generative patterns processing, generative pre-design, parametric evaluation, and optimization. Leveraging the objectivity of assessments and streamlining workflows, artificial intelligence promises to unleash true architectural creativity and leverage the productivity and efficiency of the design-and planning processes.
Fintech Agents: Technologies and Theories

Many financial technology (fintech) applications have incorporated interactive computer agents to act as mediators between the user and the fintech system. This paper provides a comprehensive review of interactive fintech agents from technological and social science perspectives. First, we explain the general fintech landscape and define interactive fintech agents. Next, we review the major technologies involved in creating fintech: (1) artificial intelligence and machine learning, (2) big data, (3) cloud computing, and (4) blockchain; as well as the specific key technologies enabling the following aspects of interactive fintech agents: (1) intelligence, (2) understanding of users, and (3) manifestation as social actors. Following the technology review, we examine issues and theories related to human-fintech agent interaction in the following areas: (1) agents’ understanding of users, (2) agents’ manifestation as social actors (via embodiment, emotion, and personality), and (3) users’ social interaction with agents. Finally, we suggest directions for future research on fintech agents.
Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel

Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient’s heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.
A Decision‐Relevant Factor‐Fixing Framework: Application to Uncertainty Analysis of a High‐Dimensional Water Quality Model

Factor Fixing (FF) is a common method for reducing the number of model parameters to lower computational cost. FF typically starts with distinguishing the insensitive parameters from the sensitive and pursues uncertainty quantification (UQ) on the resulting reduced‐order model, fixing each insensitive parameter at a fixed value. There is a need, however, to expand such a common approach to consider the effects of decision choices in the FF‐UQ procedure on metrics of interest. Therefore, to guide the use of FF and increase confidence in the resulting dimension‐reduced model, we propose a new adaptive framework consisting of four principles: (a) re‐parameterize the model first to reduce obvious non‐identifiable parameter combinations, (b) focus on decision relevance especially with respect to errors in quantities of interest (QoI), (c) conduct adaptive evaluation and robustness assessment of errors in the QoI across FF choices as sample size increases, and (d) reconsider whether fixing is warranted. The framework is demonstrated on a spatially‐distributed water quality model. The error in estimates of QoI caused by FF can be estimated using a Polynomial Chaos Expansion (PCE) surrogate model. Built with 70 model runs, the surrogate is computationally inexpensive to evaluate and can provide global sensitivity indices for free. For the selected catchment, just two factors may provide an acceptably accurate estimate of model uncertainty in the average annual load of Total Suspended Solids (TSS), suggesting that reducing the uncertainty in these two parameters is a priority for future work before undertaking further formal uncertainty quantification.