Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECTS OF 10-WEEK IN-SEASON PLYOMETRIC TRAINING PROGRAM ON AGILITY PERFORMANCE IN MALE AMATEUR FOOTBALLERS

This randomized controlled study aimed to investigate the effects of a 10-week in-season plyometric training program on agility performance. Thirty-one male soccer players (age = 28.8 ± 4.1 years; body mass = 73.7 ± 7.2 kg; height = 175.2 ± 6.0 cm) from an amateur soccer team were assigned into two groups, an experimental (EG; n=15) or a control (CG; n=16) group. The EG group performed a 10-week plyometric training program and the CG did not perform any plyometric training techniques. The program included one training session per week in place of a weekly usual soccer training session and lower limbs plyometric exercises at maximal intensity (total of 90 - 140-foot contacts/session) were executed. All subjects participated in two agility tests before and after the experiment: T-test and Illinois Agility Test. The EG group showed significantly ( p < 0.05, d = 0.7) improvement than CG in the T-test (-0.5 vs. 0.5 sec., -4.3 vs. 4.4%, respectively) and Illinois Agility test (-0.6 vs. 0.5 sec., -3.7 vs. 3.1%, respectively) after 10-week. Results suggest that amateur adult male soccer players that are participating in a plyometric training program can enhance their agility performance by replacing a part of their standard regimen with an in-season 10-week one-weekly program of plyometric training for the lower limbs. This study provided the first objective demonstration of plyometric training value in amateur soccer players. Article visualizations:
Waterfowl Nesting in Fall-Seeded and Spring-Seeded Cropland in Saskatchewan

Abstract Waterfowl nesting in annual croplands has remained a little-known aspect of waterfowl nesting ecology because of the inability of many studies to systematically search this habitat through the nesting season. Where searches have been conducted, they are generally restricted to the period prior to seeding, and many nests found are destroyed by the seeding operation. Consequently, fall-seeded crops have been promoted as an alternative cropping practice that could increase nest survival of waterfowl nesting in croplands. During 1996–1999, we conducted 3–4 complete nest searches on 4,274 ha of cropland, including spring-seeded wheat and barley, winter wheat, and fall rye in southern Saskatchewan, Canada. Using suites of predictive models, we tested hypotheses regarding relative nest abundance and nest survival among crop types and tested the influence of several landscape-scale covariates on these metrics. Apparent nest densities were higher in fall-seeded crops (winter wheat: 0.39 nests/ha, fall rye: 0.25 nests/ha) than in spring-seeded crops (0.03 nests/ha), and nest density in spring-seeded croplands increased with percent cropland and percent wetland habitat in the surrounding landscape. Nest survival was higher in winter wheat (38%) than in either fall rye (18%) or spring-seeded crops (12%), and nest survival in spring-seeded crops increased with relative nest initiation date. Nest survival was unaffected by surrounding landscape characteristics but tended to be higher in years of average wetness. Based on our findings, winter wheat and fall rye have the potential to provide productive nesting habitat for ≥7 species of upland nesting ducks and fall-seeded crops are a conservation tool well suited to highly cropped landscapes.
Optical counterpart to Swift J0243.6+6124

Context. Swift J0243.6+6124 is a unique system. It is the first and only ultra-luminous X-ray source in our Galaxy. It is the first and only high-mass Be X-ray pulsar showing radio jet emission. It was discovered during a giant X-ray outburst in October 2017. While there are numerous studies in the X-ray band, very little is known about the optical counterpart. Aims. Our aim is to characterize the variability timescales in the optical and infrared bands in order to understand the nature of this intriguing system. Methods. We performed optical spectroscopic observations to determine the spectral type. Long-term photometric light curves together with the equivalent width of the Hα line were used to monitor the state of the circumstellar disk. We used BVRI photometry to estimate the interstellar absorption and distance to the source. Continuous photometric monitoring in the B and V bands allowed us to search for intra-night variability. Results. The optical counterpart to Swift J0243.6+6124 is a V = 12.9, O9.5Ve star, located at a distance of ∼5 kpc. The optical extinction in the direction of the source is AV = 3.6 mag. The rotational velocity of the O-type star is 210 km s−1. The long-term optical variability agrees with the growth and subsequent dissipation of the Be circumstellar disk after the giant X-ray outburst. The optical and X-ray luminosity are strongly correlated during the outburst, suggesting a common origin. We did not detect short-term periodic variability that could be associated with nonradial pulsations from the Be star photosphere. Conclusions. The long-term optical and infrared pattern of variability of Swift J0243.6+6124 is typical of Be/X-ray binaries. However, the absence of nonradial pulsations is unusual and adds another peculiar trait to this unique source.
Multivariate spline bases, oriented matroids and zonotopal tilings

In the first part of this work, we uncover a connection between polynomial-reproducing bases of simplex splines and certain single-element liftings of oriented matroids associated to point configurations. We use this correspondence to construct a broad family of spline bases on generic point multisets, generalizing a known result on Delaunay configurations. Our spline bases are naturally defined on finite knot sets with affine dependencies and higher multiplicities, without need for special treatment of degenerate cases. We reformulate our bases in the language of zonotopal tilings, via the Bohne-Dress theorem, obtaining a link to a known construction algorithm for bivariate spline bases based on centroid triangulations. In the second part of this work, we restrict again our attention to weighted Delaunay configurations and the associated spline bases, and we capitalize on our combinatorial viewpoint to extend, in this restricted case, the well-known bivariate construction algorithm to higher dimensions and generic point multisets. Finally, we employ this machinery to propose algorithms for the determination and evaluation of all multivariate basis spline functions supported on a given point.
Towards Recurrent Autoregressive Flow Models

Stochastic processes generated by non-stationary distributions are difficult to represent with conventional models such as Gaussian processes. This work presents Recurrent Autoregressive Flows as a method toward general stochastic process modeling with normalizing flows. The proposed method defines a conditional distribution for each variable in a sequential process by conditioning the parameters of a normalizing flow with recurrent neural connections. Complex conditional relationships are learned through the recurrent network parameters. In this work, we present an initial design for a recurrent flow cell and a method to train the model to match observed empirical distributions. We demonstrate the effectiveness of this class of models through a series of experiments in which models are trained on three complex stochastic processes. We highlight the shortcomings of our current formulation and suggest some potential solutions.
Fefferman--Graham and Bondi Gauges in the Fluid/Gravity Correspondence

In three-dimensional gravity, we discuss the relation between the Fefferman--Graham gauge, the Bondi gauge and the Eddington--Finkelstein type of gauge, often referred to as the derivative expansion, involved in the fluid/gravity correspondence. Starting with a negative cosmological constant, for each gauge, we derive the solution space and the residual gauge diffeomorphisms. We construct explicitly the diffeomorphisms that relate the various gauges, and establish the precise matching of their boundary data. We show that Bondi and Fefferman--Graham gauges are equivalent, while the fluid/gravity derivative expansion, originating from a partial gauge fixing, exhibits an extra unspecified function that encodes the boundary fluid velocity. The Bondi gauge turns out to describe a subspace of the derivative expansion's solution space, featuring a fluid in a specific hydrodynamic frame. We pursue our analysis with the Ricci-flat limit of the Bondi gauge and of the fluid/gravity derivative expansion. The relations between them persist in this limit, which is well-defined and non-trivial. Moreover, the flat limit of the derivative expansion maps to the ultra-relativistic limit on the boundary. This procedure allows to unravel the holographic properties of the Bondi gauge for vanishing cosmological constant, in terms of its boundary Carrollian dual fluid.
Riemannian Continuous Normalizing Flows

Normalizing flows have shown great promise for modelling flexible probability distributions in a computationally tractable way. However, whilst data is often naturally described on Riemannian manifolds such as spheres, torii, and hyperbolic spaces, most normalizing flows implicitly assume a flat geometry, making them either misspecified or ill-suited in these situations. To overcome this problem, we introduce Riemannian continuous normalizing flows, a model which admits the parametrization of flexible probability measures on smooth manifolds by defining flows as the solution to ordinary differential equations. We show that this approach can lead to substantial improvements on both synthetic and real-world data when compared to standard flows or previously introduced projected flows.
Qualitative Indicator Functions for Imaging Crack Networks Using Acoustic Waves

We consider the problem of imaging a crack network embedded in some homogeneous background from measured multi-static far field data generated by acoustic plane waves. We propose two novel approaches that can be seen as extensions of linear sampling-type methods and that provide indicator functions which are sensitive to local cracks densities. The first approach uses multiple frequencies data to compute spectral signatures associated with artificially embedded localized obstacles. The second approach also exploits the idea of incorporating an artificial background but uses data for a single frequency. The indicator function is built using a similar concept as for differential sampling methods: compare the solution of the interior transmission problem for healthy inclusion with the one with embedded cracks. The performance of the methods is tested and discussed on synthetic examples and the numerical results are compared with the ones obtained using the classical factorization method.
Self-and-Mixed Attention Decoder with Deep Acoustic Structure for Transformer-based LVCSR

The Transformer has shown impressive performance in automatic speech recognition. It uses the encoder-decoder structure with self-attention to learn the relationship between the high-level representation of the source inputs and embedding of the target outputs. In this paper, we propose a novel decoder structure that features a self-and-mixed attention decoder (SMAD) with a deep acoustic structure (DAS) to improve the acoustic representation of Transformer-based LVCSR. Specifically, we introduce a self-attention mechanism to learn a multi-layer deep acoustic structure for multiple levels of acoustic abstraction. We also design a mixed attention mechanism that learns the alignment between different levels of acoustic abstraction and its corresponding linguistic information simultaneously in a shared embedding space. The ASR experiments on Aishell-1 shown that the proposed structure achieves CERs of 4.8% on the dev set and 5.1% on the test set, which are the best results obtained on this task to the best of our knowledge.
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss

We consider online learning for episodic stochastically constrained Markov decision processes (CMDP), which plays a central role in ensuring the safety of reinforcement learning. Here the loss function can vary arbitrarily across the episodes, whereas both the loss received and the budget consumption are revealed at the end of each episode. Previous works solve this problem under the restrictive assumption that the transition model of the Markov decision processes (MDP) is known a priori and establish regret bounds that depend polynomially on the cardinalities of the state space $\mathcal{S}$ and the action space $\mathcal{A}$. In this work, we propose a new \emph{upper confidence primal-dual} algorithm, which only requires the trajectories sampled from the transition model. In particular, we prove that the proposed algorithm achieves $\widetilde{\mathcal{O}}(L|\mathcal{S}|\sqrt{|\mathcal{A}|T})$ upper bounds of both the regret and the constraint violation, where $L$ is the length of each episode. Our analysis incorporates a new high-probability drift analysis of Lagrange multiplier processes into the celebrated regret analysis of upper confidence reinforcement learning, which demonstrates the power of "optimism in the face of uncertainty" in constrained online learning.
(Non)-neutrality of science and algorithms: Machine Learning between fundamental physics and society

The impact of Machine Learning (ML) algorithms in the age of big data and platform capitalism has not spared scientific research in academia. In this work, we will analyse the use of ML in fundamental physics and its relationship to other cases that directly affect society. We will deal with different aspects of the issue, from a bibliometric analysis of the publications, to a detailed discussion of the literature, to an overview on the productive and working context inside and outside academia. The analysis will be conducted on the basis of three key elements: the non-neutrality of science, understood as its intrinsic relationship with history and society; the non-neutrality of the algorithms, in the sense of the presence of elements that depend on the choices of the programmer, which cannot be eliminated whatever the technological progress is; the problematic nature of a paradigm shift in favour of a data-driven science (and society). The deconstruction of the presumed universality of scientific thought from the inside becomes in this perspective a necessary first step also for any social and political discussion. This is the subject of this work in the case study of ML.
Real-Time Monocular 4D Face Reconstruction using the LSFM models

4D face reconstruction from a single camera is a challenging task, especially when it is required to be performed in real time. We demonstrate a system of our own implementation that solves this task accurately and runs in real time on a commodity laptop, using a webcam as the only input. Our system is interactive, allowing the user to freely move their head and show various expressions while standing in front of the camera. As a result, the put forward system both reconstructs and visualises the identity of the subject in the correct pose along with the acted facial expressions in real-time. The 4D reconstruction in our framework is based on the recently-released Large-Scale Facial Models (LSFM) \cite{LSFM1, LSFM2}, which are the largest-scale 3D Morphable Models of facial shapes ever constructed, based on a dataset of more than 10,000 facial identities from a wide range of gender, age and ethnicity combinations. This is the first real-time demo that gives users the opportunity to test in practice the capabilities of the recently-released Large-Scale Facial Models (LSFM)
Allozymic Divergence in North American Polystichum (Dryopteridaceae)

Allozymic comparisons of 69 populations of the six North American diploid species of Polystichum resulted in new hypotheses of interspecific relationships in this poorly understood species complex. Polystichum lonchitis, circumboreal in distribution, is highly divergent allozymically from the other five species; genetic identities between P. lonchitis and the other species range from 0.187 with P. acrostichoides to 0.378 with P. munitum. Among the strictly North American species, two groups emerged from the analyses, one consisting of P. acrostichoides, P. dudleyi, and P. lemmonii and the other comprising P. imbricans and P. munitum. Genetic identities among these five species range from 0.524 between P. acrostichoides and P. imbricans to 0.924 between P. acrostichoides and P. lemmonii, with a mean of 0.666. This mean value is much higher than those obtained in other analyses of genetic divergence among congeneric fern species and suggests relatively recent radiation in North American Polystichum. Because genetically similar species of Polystichum are allopatric or at least allotopic, a model of geographic speciation, perhaps with an edaphic component, is proposed for North American Polystichum.
Computing linear response statistics using orthogonal polynomial based estimators: An RKHS formulation

We study the problem of estimating linear response statistics under external perturbations using time series of unperturbed dynamics. A standard approach to this estimation problem is to employ the Fluctuation-Dissipation Theory, which requires the knowledge of the functional form of the underlying unperturbed density that is not available in general. To overcome this issue, we consider a nonparametric density estimator formulated by the kernel embedding of distributions. To avoid the computational expense associated with using radial type kernels, we consider the "Mercer-type" kernels constructed based on the classical orthogonal bases defined on non-compact domains. While the resulting representation is analogous to Polynomial Chaos Expansion(PCE), by studying in the reproducing kernel Hilbert space(RKHS) setting, we establish the uniform convergence of the estimator. More importantly, the RKHS formulation allows one to systematically address a practical question of identifying the PCE basis for a consistent estimation through the decay property of the target functions that can be quantified using the available data. In terms of the linear response estimation, our study provides practical conditions for the well-posedness of not only the estimator but also the well-posedness of the underlying response statistics. We provide a theoretical guarantee for the convergence of the estimator to the underlying linear response statistics. Finally, we offer an error bound for the density estimation that accounts for the Monte-Carlo averaging over non-i.i. d time series and the biases due to truncation. This error bound helps understand the feasibility as well as limitation of the kernel embedding with Mercer-type kernels. Numerically, we verify the effectiveness of the kernel embedding linear response estimator on two stochastic dynamics with known, yet, non-trivial equilibrium densities.
Effect of carrot on quality improvement of sweet syrupy cheese ball (Rasgulla)

To evaluate the effect of carrot on the quality improvement of the sweet syrupy cheese ball known as Rasgulla, six types of Rasgulla samples containing carrot paste in the proportions of 0, 10, 20, 30, 40 and 50% were prepared and stored in closed containers at 25°C for 40 h. With increasing storage time the acidity of syrup, free fatty acid formation, absorptivity value and colour of all Rasgulla samples increased. The addition of carrot to chhana (analogous to cottage cheese) decreased acid and free fatty acid formation in Rasgulla syrup, decreased absorptivity, and also gave colour stability. Carrot concentration up to 30% improves the quality of Rasgulla. More than a 30% concentration of carrot reduced the oxidative stability of Rasgulla, while a more than 40% carrot concentration reduced its colour stability. Thus, the use of carrot up a concentration of 30% is suggested to increase the oxidative stability as well as the colour stability of Rasgulla.
Robust Unsupervised Learning of Temporal Dynamic Interactions

Robust representation learning of temporal dynamic interactions is an important problem in robotic learning in general and automated unsupervised learning in particular. Temporal dynamic interactions can be described by (multiple) geometric trajectories in a suitable space over which unsupervised learning techniques may be applied to extract useful features from raw and high-dimensional data measurements. Taking a geometric approach to robust representation learning for temporal dynamic interactions, it is necessary to develop suitable metrics and a systematic methodology for comparison and for assessing the stability of an unsupervised learning method with respect to its tuning parameters. Such metrics must account for the (geometric) constraints in the physical world as well as the uncertainty associated with the learned patterns. In this paper we introduce a model-free metric based on the Procrustes distance for robust representation learning of interactions, and an optimal transport based distance metric for comparing between distributions of interaction primitives. These distance metrics can serve as an objective for assessing the stability of an interaction learning algorithm. They are also used for comparing the outcomes produced by different algorithms. Moreover, they may also be adopted as an objective function to obtain clusters and representative interaction primitives. These concepts and techniques will be introduced, along with mathematical properties, while their usefulness will be demonstrated in unsupervised learning of vehicle-to-vechicle interactions extracted from the Safety Pilot database, the world's largest database for connected vehicles.
Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. We build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors’ data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.