Miracle-dz/UniFAR-FTUs huggingface dataset:
Cluster-Specific Predictions with Multi-Task Gaussian Processes-compilation: November 18, 2020

A model involving Gaussian processes (GPs) is introduced to simultaneously handle multi-task learning, clustering, and prediction for multiple functional data. This procedure acts as a model-based clustering method for functional data as well as a learning step for subsequent predictions for new tasks. The model is instantiated as a mixture of multi-task GPs with common mean processes. A variational EM algorithm is derived for dealing with the optimisation of the hyper-parameters along with the hyper-posteriors’ estimation of latent
Periodic Non-uniform Sampling for Sparse Signals in shift- invariant Spaces*

Periodic Non-uniform sampling in shift-invariant spaces is an effective method for lowrate sampling of continuous time sparse signals, but, recovery of sampled time sparse signals must be unstable in general. In the paper, the possibility of stable recovery under a combination of sufficient sparsity is proposed. The sampling and reconstruction of sparse signals were transformed into matrix and vector operations by using minimum L1 normal. Necessary condition of reconstruction is analyzed. Finally, we are taking multiband sinusoidal signals as an example to prove that the method can achieve the sampling and reconstruction of sparse signals from recovery successful rate and integer of system.
Research on Image Segmentation Technology and Intelligent Language Recognition Based on Ant Colony Algorithm

—Aiming at the problems of large image segmentation error and low intelligent language recognition accuracy in existing image segmentation and intelligent language recognition, an image segmentation technology and intelligent language recognition method based on ant colony algorithm are designed. First, the internal structure of the image is regarded as a network, and its mapping relationship is determined. Then, the Gaussian function is used to determine the gray, color, texture and other characteristic values of the image, set the constitutive matrix, calculate the weight value of the segmented image, and use the histogram method to complete the effective segmentation of the image. Finally, the advantages of the ant colony algorithm are used to determine the spatial position of the intelligent language in the image, and Fisher's law is used to judge the language recognition results, and the discrete degree within the language class represents the classification density, thus completing the intelligent language recognition. The experimental results show that the image segmentation error is small and the intelligent language recognition accuracy is high.
Extracting process hierarchies by multi-sequence alignment adaptations

ABSTRACT Process mining is an active research area that provides a wide range of automated process discovery, conformance checking and process enhancement solutions by extracting process information from event logs. With the emerge of new shared economy models and system architectures, monolithic perspective of process mining, i.e. a single process within a single organisation, is evolved towards cross-organisational business processes. The results can be used to form new collaboration among the organisations and share best practices, business knowledge or a common infrastructure. However, the variability across the cross-organisational process variants constitutes a challenge to deal with, especially at flexible environments. This paper demonstrates a quantitative approach to measure how similar organisations execute the same process. The approach is based on the multi-sequence alignment of dominant behaviour, which is a typical sequence of behaviour recurring at the process cases. Then process clusters are extracted in the form of process hierarchy according to the degree of process similarities. A novel feature of the proposed approach is that branching in process hierarchy reflects how organisations’ behaviours differ from each other. Additionally, common fragments at the aligned process variants within the same cluster pinpoint a functional inheritance or a shared process behaviour for the corresponding organisations. The proposed approach is applied on a real-life case and the comparison with previous alignment techniques reveals the advantages of the approach.
An axiom system for the line geometry

IntTQdtfction The set of lines of three-dimensional projective geometry PGg usually called the line geometry (cf.[2]) is always considered in the projective space which is treated as the theory of the universe of points and planes, with e.g. an incidence relation. The set of lines has a well-known interpretation in five-dimensional projective geonetry (so-called the geometry on Plucker' quadric). It would be interesting to define the line geometry as the axiomatic theory of the lines with certain relation. An attempt of the axiomatic approach of such a theory was shown below and this paper stands for the first part of research of axiomatic theory (designed by LGg in this paper) for the set lines mentioned above. Next it was shown that in LGg can be built the projective geometry PG^Irepresentation theorems) and. that set of lines built in such a way geometry with the properly defined relation is isomorphic with LG^. This isomorphism allows to treat LG~ as axiomatics of geometry on PIucker'quadric.
Tuning the classical spin-liquid behaviour in a triangular-lattice, dipolar, Ising antiferromagnet

We study the triangular-lattice Ising model with dipolar interactions, inspired by its realisation in artificial arrays of nanomagnets. We show that a classical spin liquid forms at intermediate temperatures, and that its behaviour can be tuned by temperature and/or a small lattice distortion between a "weakly-coupled" string Luttinger liquid and a "strongly-coupled" domain-wall-network state. We also study the transition out of the spin liquid, and show that the critical physics can be tuned between first-order, Kasteleyn and 2D-Ising transitions, with an intermediate Pokrovsky-Talapov tricritical point. The dipolar model is analysed using Monte Carlo simulations, guided by exact and perturbative solutions of related toy models and a phenomenological theory written in terms of spinless fermions.
Lane detection based on color probability model and fuzzy clustering

In the vehicle driver assistance systems, the accuracy and speed of lane line detection are the most important. This paper is based on color probability model and Fuzzy Local Information C-Means (FLICM) clustering algorithm. The Hough transform and the constraints of structural road are used to detect the lane line accurately. The global map of the lane line is drawn by the lane curve fitting equation. The experimental results show that the algorithm has good robustness.
Institutional Trading Patterns and Price Impact Around Decimalization Sugato Chakravarty

We examine the effect of decimalization on institutional investors using proprietary data. In particular, we examine the time and the number of trades it takes to execute a given trading decision, as well as the price impact of these trades. We use three different benchmarks to determine the price impact of a trade. Unlike the transition of the minimum tick size from eighths to sixteenths, we find no significant changes in the implicit costs of trading for institutional investors following decimalization. Our results survive extensive partitioning of the data and are surprising in light of an oft-repeated complaint among professional traders that liquidity is hard and expensive to find in a post-decimal trading milieu. These findings have important regulatory implications.
of orbital convergence in primates: A test of the snake detection hypothesis of primate

22 Traditional explanations for the evolution of high orbital convergence and stereoscopic vision in 23 primates have focused on how stereopsis might have aided early primates in foraging or 24 locomoting in an arboreal environment. It has recently been suggested that predation risk by 25 constricting snakes was the selective force which favored the evolution of orbital convergence in 26 early primates, and that later exposure to venomous snakes favored further degrees of 27 convergence in anthropoid primates. Our study tests this snake detection hypothesis (SDH) by 28 examining whether orbital convergence among extant primates is indeed associated with the 29 shared evolutionary history with snakes or the risk that snakes pose for a given species. We 30 predicted that orbital convergence would be higher in species that: 1) have a longer history of 31 sympatry with venomous snakes, 2) are likely to encounter snakes more frequently, 3) are less 32 able to detect or deter snakes due to group size effects, and 4) are more likely to be preyed upon 33 by snakes. Results based on phylogenetically independent contrasts do not support the SDH. 34 Orbital convergence shows no relationship to the shared history with venomous snakes, 35 likelihood of encountering snakes, or group size. Moreover, those species less likely to be 36 targeted as prey by snakes show significantly higher values of orbital convergence. Although an 37 improved ability to detect camouflaged snakes, along with other cryptic stimuli, is likely a 38 consequence of increased orbital convergence, this was unlikely to have been the primary 39 selective force favoring the evolution of stereoscopic vision in primates. 40
ZoomNAS: Searching for Whole-Body Human Pose Estimation in the Wild

This paper investigates the task of 2D whole-body human pose estimation, which aims to localize dense landmarks on the entire human body including body, feet, face, and hands. We propose a single-network approach, termed ZoomNet, to take into account the hierarchical structure of the full human body and solve the scale variation of different body parts. We further propose a neural architecture search framework, termed ZoomNAS, to promote both the accuracy and efficiency of whole-body pose estimation. ZoomNAS jointly searches the model architecture and the connections between different sub-modules, and automatically allocates computational complexity for searched sub-modules. To train and evaluate ZoomNAS, we introduce the first large-scale 2D human whole-body dataset, namely COCO-WholeBody V1.0, which annotates 133 keypoints for in-the-wild images. Extensive experiments demonstrate the effectiveness of ZoomNAS and the significance of COCO-WholeBody V1.0.
Functional Characterization of the Antibiotic Resistance Reservoir in the Human Microflora

Hidden Pockets of Resistance Groups of bacteria indulge in gene swapping at frequencies correlated with prevailing selection pressures and phylogenetic relatedness. When assaulted by antibiotics, antibiotic resistance genes become favored currency for exchange among bacteria. During sequencing of human gut microflora, Sommer et al. (p. 1128) found a very large reservoir of distinct genes that, when put into Escherichia coli, conferred resistance to a wide range of drugs. By contrast, analysis of the culturable aerobic gut microbiome, which constitutes a tiny fraction of the entire gut flora, revealed resistance genes highly similar to those harbored by human pathogens. Although there is a risk of novel modes of antibiotic resistance emerging from this reservoir, because they are evolutionarily distant, gene transfer between pathogens and the poorly known majority of the microbiome might actually be quite restricted. Large numbers of previously unidentified antibiotic resistance genes occur in gut bacteria. To understand the process by which antibiotic resistance genes are acquired by human pathogens, we functionally characterized the resistance reservoir in the microbial flora of healthy individuals. Most of the resistance genes we identified using culture-independent sampling have not been previously identified and are evolutionarily distant from known resistance genes. By contrast, nearly half of the resistance genes we identified in cultured aerobic gut isolates (a small subset of the gut microbiome) are identical to resistance genes harbored by major pathogens. The immense diversity of resistance genes in the human microbiome could contribute to future emergence of antibiotic resistance in human pathogens.
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

During the COVID-19 pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images, and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an AUC of 0.786 (95% CI: 0.742-0.827) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions, and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at NYU Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.
effects of a high-intensity intermittent training program on aerobic capacity and lipid profile in trained subjects

Background: Data regarding the effect of training on plasma lipids are controversial. Most studies have addressed continuous or long intermittent training programs. The present study evaluated the effect of short-short high-intensity intermittent training (HIIT) on aerobic capacity and plasma lipids in soccer players. Methods: The study included 24 male subjects aged 21–26 years, divided into three groups: experimental group 1 (EG1, n = 8) comprising soccer players who exercised in addition to regular short-short HIIT twice a week for 12 weeks; experimental group 2 (EG2, n = 8) comprising soccer players who exercised in a regular football training program; and a control group (CG, n = 8) comprising untrained subjects who did not practice regular physical activity. Maximal aerobic velocity and maximal oxygen uptake along with plasma lipids were measured before and after 6 weeks and 12 weeks of the respective training program. Results: Compared with basal values, maximal oxygen uptake had significantly increased in EG1 (from 53.3 ± 4.0 mL/min/kg to 54.8 ± 3.0 mL/min/kg at 6 weeks [ P , 0.05] and to 57.0 ± 3.2 mL/min/kg at 12 weeks [ P , 0.001]). Maximal oxygen uptake was increased only after 12 weeks in EG2 (from 52.8 ± 2.7 mL/min/kg to 54.2 ± 2.6 mL/min/kg, [ P , 0.05]), but remain unchanged in CG. After 12 weeks of training, maximal oxygen uptake was significantly higher in EG1 than in EG2 ( P , 0.05). During training, no significant changes in plasma lipids occurred. However, after 12 weeks, total and low-density lipoprotein cholesterol levels had decreased (by about 2%) in EG1 but increased in CG. High-density lipoprotein cholesterol levels increased in EG1 and EG2, but decreased in CG. Plasma triglycerides decreased by 8% in EG1 and increased by about 4% in CG. Conclusion: Twelve weeks of short-short HIIT improves aerobic capacity. Although changes in the lipid profile were not significant after this training program, they may have a beneficial impact on health.
Factors affecting the regulation of pacing: current perspectives

During prolonged dynamic and rhythmic exercise, muscular pain and discomfort arises as a result of an increased concentration of deleterious metabolites. Sensed by peripheral nociceptors and transmitted via afferent feedback to the brain, this provides important information regarding the physiological state of the muscle. These sensations ultimately contribute to what is termed “exercise-induced pain”. Despite being well recognized by athletes and coaches, and suggested to be integral to exercise performance, this construct has largely escaped attention in experimental work. This perspective article highlights the current understanding of pacing in endurance performance, and the causes of exercise-induced pain. A new perspective is described, which proposes how exercise-induced pain may be a contributing factor in helping individuals to regulate their work rate during exercise and thus provides an important construct in pacing.
Projection Sparse Principal Component Analysis: an efficient method for improving the interpretation of principal components

Sparse principal components analysis (SPCA) methods approximate principal components with combinations of few of the observed variables. Sparse components are more interpretable than standard principal components as they identify few key features of a dataset. We propose a practical SPCA method in which sparse components are computed by projecting the full principal components onto a subset of the variables. We show that these components explain more than a predetermined percentage of the variance explained by the principal components. We also show that this approach is strictly related to least squares SPCA by providing a novel interpretation for the latter. We propose a simple and efficient algorithm that uses simple forward selection to select variables and the power method to compute eigenvectors. We illustrate the method with the analysis of a real dataset containing socioeconomic data and the computational results for nine datasets of increasing dimension with up to 16,000 variables.
measles epidemics Decreasing stochasticity through enhanced seasonality in Supplementary data

References http://rsif.royalsocietypublishing. org/content/7/46/727.full. html#ref-list-1 This article cites 61 articles, 18 of which can be accessed free Rapid response http://rsif.royalsocietypublishing. org/letters/submit/royinterface;7/46/727 Respond to this article Subject collections (296 articles) environmental science  (108 articles) biomathematics  Articles on similar topics can be found in the following collections
A Comparison of Elastic Contact Modeling Utilizing Statistical and Fractal Approaches

Statistical and fractal approaches for characterizing surface topography have been used widely in contact mechanics. In the present study, an attempt is made to compare between contact mechanics results obtained with statistical and fractal approaches to characterize surface topography. Specifically, a three-dimensional fractal surface was generated and statistical surface parameters were extracted using different sampling resolutions. Contact mechanics simulations were performed using the simulated fractal surface and statistical surfaces represented by the extracted statistical surface parameters. Purely elastic contact is studied in order to eliminate the influence of the individual asperity mechanical response on the obtained results. Therefore, differences in the simulated contact area and load can be related solely to the different approach employed for surface characterization. The influence of surface characterization approaches on the contact parameters and the resulting predictions is discussed in the contents of the presented results.Copyright © 2005 by ASME
Discrete Optimization A new approach to solving the multiple traveling salesperson problem using genetic algorithms

AbstractThe multiple traveling salesperson problem (MTSP) involves scheduling m > 1 salespersons to visit a set of n > mlocations so that each location is visited exactly once while minimizing the total (or maximum) distance traveled by thesalespersons. The MTSP is similar to the notoriously diﬃcult traveling salesperson problem (TSP) with the added com-plication that each location may be visited by any one of the salespersons. Previous studies investigated solving theMTSP with genetic algorithms (GAs) using standard TSP chromosomes and operators. This paper proposes a newGA chromosome and related operators for the MTSP and compares the theoretical properties and computational per-formance of the proposed technique to previous work. Computational testing shows the new approach results in a smal-ler search space and, in many cases, produces better solutions than previous techniques. 2005 Elsevier B.V. All rights reserved. Keywords: Genetic algorithms; Scheduling; Traveling salesman
Title of The Paper: Role of Technology And Innovation in Enhancing Customer Experience.

: Due to the current advances in technology and innovation, consumers’ increasing interest in entertaining and interactive retail environments, the sector of retailing is forced to pursuit innovation to maintain existing consumers and attract new ones. Especially the use of virtual techniques offers tools for supporting the design of innovative systems capable of enhancing this process. The aim of this paper is to integrate the consumers’ experience in the development of technology and innovation process for retailing. In particular, our findings highlight how the technologies can be an efficient tool for pushing innovation in retailing.
A Cutting-Plane Algorithm for Learning from Ambiguous Examples

We present a novel algorithm developed within the risk minimization framework for learning from ambiguous and unlabeled examples as encountered in multi-instance, transductive and semi-supervised learning. For these problems, risk minimization involves optimization over a non-convex feasible region defined by the ambiguous examples. Our method generalizes 1-norm SVM. We adapts lift-and-project cuttingplanes from integer-programming to approximate the convex hull of the feasible region to arbitrary precision. The resulting classifier is competitive on several benchmarks, and in particular, outperforms existing algorithms on text categorization tasks due to its feature selectivity. This algorithm demonstrates that risk minimization can be used to effectively disambiguate and learn from ambiguous examples.
Max-Linear Competing Factor Models

Models incorporating “latent” variables have been commonplace in financial, social, and behavioral sciences. Factor model, the most popular latent model, explains the continuous observed variables in a smaller set of latent variables (factors) in a matter of linear relationship. However, complex data often simultaneously display asymmetric dependence, asymptotic dependence, and positive (negative) dependence between random variables, which linearity and Gaussian distributions and many other extant distributions are not capable of modeling. This article proposes a nonlinear factor model that can model the above-mentioned variable dependence features but still possesses a simple form of factor structure. The random variables, marginally distributed as unit Fréchet distributions, are decomposed into max linear functions of underlying Fréchet idiosyncratic risks, transformed from Gaussian copula, and independent shared external Fréchet risks. By allowing the random variables to share underlying (latent) pervasive risks with random impact parameters, various dependence structures are created. This innovates a new promising technique to generate families of distributions with simple interpretations. We dive in the multivariate extreme value properties of the proposed model and investigate maximum composite likelihood methods for the impact parameters of the latent risks. The estimates are shown to be consistent. The estimation schemes are illustrated on several sets of simulated data, where comparisons of performance are addressed. We employ a bootstrap method to obtain standard errors in real data analysis. Real application to financial data reveals inherent dependencies that previous work has not disclosed and demonstrates the model’s interpretability to real data. Supplementary materials for this article are available online.
INVESTIGATING OFF-RESONANCE FAT MODULATIONS IN THE TURBOSPI SIGNAL TO IMPROVE R2* MAPPING FOR QUANTITATIVE CELL TRACKING

xv List of Abbreviations and Symbols Used . xvi Acknowledgements . xviii Chapter