Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactions between the Field Vole Microtus agrestis and Its Microtine Competitors in Central-Scandinavian Populations

The main competitors of Microtus agrestis (L.) in Central-Scandinavian populations are Microtus arvalis (Pall.), Clethrionomys glareolus (Schreb.) and Arvicola terrestris (L.); of these three species M. arvalis is restricted to Southern Finland. The habitat preferences of M. agrestis and its main competitors show some overlap. Interactions of M. agrestis on the one hand andM. arvalis orA. terrestris on the other are most likely to occur in permanent grassland habitats on mineral soil, i.e., favourite production habitats of M. agrestis. The ecological ranges of M. agrestis and C. glareolus are most likely to overlap outside the breeding season, e.g., in forest regeneration lots. The food niches of M. agrestis are rather similar to those of its sympatric competitors, M. arvalis and A. terrestris, during the reproductive season. Outside the breeding season its food niches overlap with those of M. arvalis and C. glareolus, while A. terrestris has clearly different food preferences. As to direct behavioural interactions, the competitors represent a clear gradient in regard to the severity of the encounters with M. agrestis. In the relationship between M. agrestis and C. glareolus, the former is considered physically dominant, but competition is mainly manifested in exploitation of common food resources. Between M. agrestis and M. arvalis strict territorial defence occurs during the breeding season but aggressiveness is much less pronounced in the non-breeding subadult-dominated populations. A. terrestris is physically dominant over M. agrestis, and sympatric coexistence generally leads to exclusion of M. agrestis from theArvicola-dominated area. Owing to exploitation of different strata, coexistence of these two species is possible during the winter months. The different types of species interactions are illustrated by a series of graphical models and the "leaky vessel" model by Grant is recommended as a starting-point for the quantification of the effects of interspecific competition on the population dynamics of M. agrestis.
Evaluating Nonlinear Decision Trees for Binary Classification Tasks with Other Existing Methods

Classification of datasets into two or more distinct classes is an important machine learning task. Many methods are able to classify binary classification tasks with a very high accuracy on test data, but cannot provide any easily interpretable explanation for users to have a deeper understanding of reasons for the split of data into two classes. In this paper, we highlight and evaluate a recently proposed nonlinear decision tree approach with a number of commonly used classification methods on a number of datasets involving a few to a large number of features. The study reveals key issues such as effect of classification on the method’s parameter values, complexity of the classifier versus achieved accuracy, and interpretability of resulting classifiers.
Aphasic Speech Recognition using a Mixture of Speech Intelligibility Experts

Robust speech recognition is a key prerequisite for semantic feature extraction in automatic aphasic speech analysis. However, standard one-size-fits-all automatic speech recognition models perform poorly when applied to aphasic speech. One reason for this is the wide range of speech intelligibility due to different levels of severity (i.e., higher severity lends itself to less intelligible speech). To address this, we propose a novel acoustic model based on a mixture of experts (MoE), which handles the varying intelligibility stages present in aphasic speech by explicitly defining severity-based experts. At test time, the contribution of each expert is decided by estimating speech intelligibility with a speech intelligibility detector (SID). We show that our proposed approach significantly reduces phone error rates across all severity stages in aphasic speech compared to a baseline approach that does not incorporate severity information into the modeling process.
Modeling the Transport of Nonthermal Particles in Flares Using Fokker–Planck Kinetic Theory

We describe a new approach for modeling the transport of high-energy particles accelerated during flares from the acceleration region in the solar corona until their eventual thermalization in the flare footpoint. Our technique numerically solves the Fokker–Planck equation and includes forces corresponding to Coulomb collisions in a flux loop with nonuniform ionization, synchrotron emission reaction, magnetic mirroring, and a return current electric field. Our solution to the Fokker–Planck equation includes second-order pitch angle and momentum diffusion. It is applicable to particles of arbitrary mass and charge. By tracking the collisions, we predict the bremsstrahlung produced as these particles interact with the ambient stellar atmosphere. This can be compared directly with observations and used to constrain the accelerated particle energy distribution. We have named our numerical code FP and distributed it for general use. We demonstrate its effectiveness in several test cases.
Ensuring Monotonic Policy Improvement in Entropy-regularized Value-based Reinforcement Learning

This paper aims to establish an entropy-regularized value-based reinforcement learning method that can ensure the monotonic improvement of policies at each policy update. Unlike previously proposed lower-bounds on policy improvement in general infinite-horizon MDPs, we derive an entropy-regularization aware lower bound. Since our bound only requires the expected policy advantage function to be estimated, it is scalable to large-scale (continuous) state-space problems. We propose a novel reinforcement learning algorithm that exploits this lower-bound as a criterion for adjusting the degree of a policy update for alleviating policy oscillation. We demonstrate the effectiveness of our approach in both discrete-state maze and continuous-state inverted pendulum tasks using a linear function approximator for value estimation.
Smart Weather Forecasting Using Machine Learning: A Case Study in Tennessee

Traditionally, weather predictions are performed with the help of large complex models of physics, which utilize different atmospheric conditions over a long period of time. These conditions are often unstable because of perturbations of the weather system, causing the models to provide inaccurate forecasts. The models are generally run on hundreds of nodes in a large High Performance Computing (HPC) environment which consumes a large amount of energy. In this paper, we present a weather prediction technique that utilizes historical data from multiple weather stations to train simple machine learning models, which can provide usable forecasts about certain weather conditions for the near future within a very short period of time. The models can be run on much less resource intensive environments. The evaluation results show that the accuracy of the models is good enough to be used alongside the current state-of-the-art techniques. Furthermore, we show that it is beneficial to leverage the weather station data from multiple neighboring areas over the data of only the area for which weather forecasting is being performed.
Variational Image Restoration Network

Deep neural networks (DNNs) have achieved significant success in image restoration tasks by directly learning a powerful non-linear mapping from corrupted images to their latent clean ones. However, there still exist two major limitations for these deep learning (DL)-based methods. Firstly, the noises contained in real corrupted images are very complex, usually neglected and largely under-estimated in most current methods. Secondly, existing DL methods are mostly trained on one pre-assumed degradation process for all of the training image pairs, such as the widely used bicubic downsampling assumption in the image super-resolution task, inevitably leading to poor generalization performance when the true degradation does not match with such assumed one. To address these issues, we propose a unified generative model for the image restoration, which elaborately configures the degradation process from the latent clean image to the observed corrupted one. Specifically, different from most of current methods, the pixel-wisely non-i.i. d. Gaussian distribution, being with more flexibility, is adopted in our method to fit the complex real noises. Furthermore, the method is built on the general image degradation process, making it capable of adapting diverse degradations under one single model. Besides, we design a variational inference algorithm to learn all parameters involved in the proposed model with explicit form of objective loss. Specifically, beyond traditional variational methodology, two DNNs are employed to parameterize the posteriori distributions, one to infer the distribution of the latent clean image, and another to infer the distribution of the image noise. Extensive experiments demonstrate the superiority of the proposed method on three classical image restoration tasks, including image denoising, image super-resolution and JPEG image deblocking.
Near-infrared Characterization of Four Massive Stars in Transition Phases

Massive stars typically undergo short-lived post-main-sequence evolutionary phases with strong mass loss and occasional mass eruptions. Many of such massive stars in transition phases have been identified based on their dusty envelopes. The ejected material often veils the stellar photospheres so that the central stars cannot be assigned proper spectral types and evolutionary stages. The infrared spectral range has proved to be ideal for the classification of evolved massive stars and for the characterization of their environments. To improve our knowledge on the central stars of four such dust-enshrouded objects, [GKF 2010] MN 83, [GKF 2010] MN 108, [GKF 2010] MN 109, and [GKF 2010] MN 112, we collect and present their first medium-resolution K-band spectra in the 2.3–2.47 μm region and discuss the location of the stars in the JHK color–color diagram. We find that the emission-line spectra of both MN 83 and MN 112 show characteristics typically seen in luminous blue variable (LBV) stars. In addition, we propose that the presence and strength of the newly reported Mg ii lines might be used as a new complementary criterion to identify LBV candidates. The spectra of the other two objects imply that MN 108 is an O-type supergiant, whereas MN 109 could be an LBV candidate in its active phase. We derive lower limits for the reddening toward the stars and find that three of all dereddened fall into the region of confirmed LBVs.
Few Shot Text-Independent speaker verification using 3D-CNN

Facial recognition system is one of the major successes of Artificial intelligence and has been used a lot over the last years. But, images are not the only biometric present: audio is another possible biometric that can be used as an alternative to the existing recognition systems. However, the text-independent audio data is not always available for tasks like speaker verification and also no work has been done in the past for text-independent speaker verification assuming very little training data. Therefore, In this paper, we have proposed a novel method to verify the identity of the claimed speaker using very few training data. To achieve this we are using a Siamese neural network with center loss and speaker bias loss. Experiments conducted on the VoxCeleb1 dataset show that the proposed model accuracy even on training with very few data is near to the state of the art model on text-independent speaker verification
Evaluation of Cardinal Temperature for three Species of Medicinal Plants, Thymus Transcaspicus, Foeniculum Vulgare and Calligonum Junceum

In order to evaluate cardinal temperatures and optimum thermal range of germination of three medicinal plants including Thymus transcaspicus, Foeniculum vulgare and Calligonumjunceum an experiment was conducted in a Completely Randomized Design with three replications. Therefore, this experiment was conducted to study of the seed germination characteristics to temperature. The we used three medicinal plants with 9 fixed temperatures ( 5 0 C, 10 0 C, 15 0 C, 20 0 C, 25 0 C ,30 0 C, 35 0 C, 40 0 C and 45 0 C) as factorial experiment in the growth chamber. The results showed that the effect of these crops, temperature and their interactions on germination characteristics were significant. The basic, optimum and maximum temperatures were 1.4, 29.93, and 44.66 for Thymus transcaspicus, 3.33, 21.35 and 42.54 for Foeniculum vulgare and 5.23, 33.60 and 45 for Calligonum junceum.
Energetic constraints and spawning interval in the anadromous Arctic charr (Salvelinus alpinus)

DEPARTMENT OF BIOLOGY, MEMPHIS STATE UNIVERSITY, MEMPHIS, TENNESSEE 38152 (PKK AND MLK); DEPARTMENT OF BIOLOGICAL SCIENCES, NORTH TEXAS STATE UNIVERSITY, DENTON, TEXAS 76203; DEPARTMENT OF BIOLOGICAL SCIENCES, TEXAS TECH UNIVERSITY, BOX 4149, LUBBOCK, TEXAS 79409; SAVANNAH RIVER ECOLOGY LABORATORY, DRAWER E, AIKEN, SOUTH CAROLINA 29801. PRESENT ADDRESS (PKK): SAVANNAH RIVER ECOLOGY LABORATORY, DRAWER E, AIKEN, SOUTH CAROLINA 29801.
Transitions in loopy random graphs with fixed degrees and arbitrary degree distributions

We analyze maximum entropy random graph ensembles with constrained degrees, drawn from arbitrary degree distributions, and a tuneable number of 3-loops (triangles). We find that such ensembles generally exhibit two transitions, a clustering and a shattering transition, separating three distinct regimes. At the clustering transition, the graphs change from typically having only isolated loops to forming loop clusters. At the shattering transition the graphs break up into extensively many small cliques to achieve the desired loop density. The locations of both transitions depend nontrivially on the system size. We derive a general formula for the loop density in the regime of isolated loops, for graphs with degree distributions that have finite and second moments. For bounded degree distributions we present further analytical results on loop densities and phase transition locations, which, while non-rigorous, are all validated via MCMC sampling simulations. We show that the shattering transition is of an entropic nature, occurring for all loop density values, provided the system is large enough.
A Robotic Line Scan System with Adaptive ROI for Inspection of Defects over Convex Free-form Specular Surfaces

In this paper, we present a new robotic system to perform defect inspection tasks over free-form specular surfaces. The autonomous procedure is achieved by a six-DOF manipulator, equipped with a line scan camera and a high-intensity lighting system. Our method first uses the object's CAD mesh model to implement a K-means unsupervised learning algorithm that segments the object's surface into areas with similar curvature. Then, the scanning path is computed by using an adaptive algorithm that adjusts the camera's ROI to observe regions with irregular shapes properly. A novel iterative closest point-based projection registration method that robustly localizes the object in the robot's coordinate frame system is proposed to deal with the blind spot problem of specular objects captured by depth sensors. Finally, an image processing pipeline automatically detects surface defects in the captured high-resolution images. A detailed experimental study with a vision-guided robotic scanning system is reported to validate the proposed methodology.
Efficient computation of global resolvent modes

Abstract Resolvent analysis of the linearized Navier–Stokes equations provides useful insight into the dynamics of transitional and turbulent flows and can provide a model for the dominant coherent structures within the flow, particularly for flows where the linear operator selectively amplifies one particular force component, known as the optimal force mode. Force and response modes are typically obtained from a singular-value decomposition of the resolvent operator. Despite recent progress, the cost of resolvent analysis for complex flows remains considerable, and explicit construction of the resolvent operator is feasible only for simplified problems with a small number of degrees of freedom. In this paper we propose two new matrix-free methods for computing resolvent modes based on the integration of the linearized equations and the corresponding adjoint system in the time domain. Our approach achieves an order of magnitude speedup when compared with previous matrix-free time-stepping methods by enabling all frequencies of interest to be computed simultaneously. Two different methods are presented: one based on analysis of the transient response, providing leading modes with fine frequency discretization; and another based on the steady-state response to periodic forcing, providing optimal and suboptimal modes for a discrete set of frequencies. The methods are validated using a linearized Ginzburg–Landau equation and applied to the three-dimensional flow around a parabolic body.
Adaptive Fast Smooth Second-Order Sliding Mode Control for Attitude Tracking of a 3-DOF Helicopter

This paper presents a novel adaptive fast smooth second-order sliding mode control for the attitude tracking of the three degree-of-freedom (3-DOF) helicopter system with lumped disturbances. Combining with a non-singular integral sliding mode surface, we propose a novel adaptive fast smooth second-order sliding mode control method to enable elevation and pitch angles to track given desired trajectories respectively with the features of non-singularity, adaptation to disturbances, chattering suppression and fast finite-time convergence. In addition, a novel adaptive-gain smooth second-order sliding mode observer is proposed to compensate time-varying lumped disturbances with the smoother output compared with the adaptive-gain second-order sliding mode observer. The fast finite-time convergence of the closed-loop system with constant disturbances and the fast finite-time uniformly ultimately boundedness of the closed-loop system with the time-varying lumped disturbances are proved with the finite-time Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed control methods are verified by comparative simulation experiments.
Inhibition of Growth and Sclerotium Formation in Rhizoctonia Solani by Garlic Oil

Oil of garlic (Allium sativum L.) in natural and synthetic forms has been reported to suppress the activity of many air and soil-borne fungi (Murthy and Amonkar, 1974). Several other workers have observed the antimicrobial activity of extracts of garlic (Agrawal, 1978; Fliermans, 1973; Rao et al., 1947; Tansey and Apleton, 1975). Singh et al. (1979) have recently shown that even garlic-leaf extract significantly reduced the growth of Sclerotinia sclerotiorum (Lib.) deBary and Fusarium oxysporum f. sp. ciceri Padwick causing wilts in gram (Cicer arietinum L.) We report here the inhibition of growth and sclerotium formation in Rhizoctonia solani Kuhn, incitant of root rot of gram, by garlic oil. Rhizoctonia solani was isolated from infected roots of gram and grown on potato-dextrose-agar (peeled potato 250 g, dextrose 20 g, distilled water 1 liter) slants. Index of antifungal effect of garlic oil on growth of the pathogen was obtained by measuring the growth in the form of dry weight in SP3 liquid medium (glucose 50 g, yeast extract 1 g, peptone 1 g, KH2PO4 1.5 g, (NH4)2SO4 1.5 g, MgSO4 1 g, agar 20 g, distilled water 1,000 ml). A 25-ml aliquot of the medium was poured in 150-ml flasks containing 50, 100, 150, 200 and 250 ppm of sterilized oil. Garlic oil was sterilized by passing through a millipore filter (0.22 gm) using a Swinny filter adapter. Each flask was inoculated with a 2-mm mycelial disc of the fungus. Control flasks were inoculated with equal-sized mycelial discs, but without garlic oil. The flasks were incubated at 25 ± 2 C for 21 da. Growth was recorded after 7, 14 and 21 da by harvesting through filtration on oven-dried Whatman filter paper No. 42. The filters containing fungal growth were
Learning Obstacle Representations for Neural Motion Planning

Motion planning and obstacle avoidance is a key challenge in robotics applications. While previous work succeeds to provide excellent solutions for known environments, sensor-based motion planning in new and dynamic environments remains difficult. In this work we address sensor-based motion planning from a learning perspective. Motivated by recent advances in visual recognition, we argue the importance of learning appropriate representations for motion planning. We propose a new obstacle representation based on the PointNet architecture [1] and train it jointly with policies for obstacle avoidance. We experimentally evaluate our approach for rigid body motion planning in challenging environments and demonstrate significant improvements of the state of the art in terms of accuracy and efficiency.
On the complexity of query containment and computing certain answers in the presence of ACs

We often add arithmetic to extend the expressiveness of query languages and study the complexity of problems such as testing query containment and finding certain answers in the framework of answering queries using views. When adding arithmetic comparisons, the complexity of such problems is higher than the complexity of their counterparts without them. It has been observed that we can achieve lower complexity if we restrict some of the comparisons in the containing query to be closed or open semi-interval comparisons. Here, focusing a) on the problem of containment for conjunctive queries with arithmetic comparisons (CQAC queries, for short), we prove upper bounds on its computational complexity and b) on the problem of computing certain answers, we find large classes of CQAC queries and views where this problem is polynomial.
An anisotropic viscoplasticity model for shale based on layered microstructure homogenization

Viscoplastic deformation of shale is frequently observed in many subsurface applications. Many studies have suggested that this viscoplastic behavior is anisotropic—specifically, transversely isotropic—and closely linked to the layered composite structure at the microscale. In this work, we develop a two‐scale constitutive model for shale in which anisotropic viscoplastic behavior naturally emerges from semianalytical homogenization of a bilayer microstructure. The microstructure is modeled as a composite of soft layers, representing a ductile matrix formed by clay and organics, and hard layers, corresponding to a brittle matrix composed of stiff minerals. This layered microstructure renders the macroscopic behavior anisotropic, even when the individual layers are modeled with isotropic constitutive laws. Using a common correlation between clay and organic content and magnitude of creep, we apply a viscoplastic modified Cam‐Clay plasticity model to the soft layers, while treating the hard layers as a linear elastic material to minimize the number of calibration parameters. We then describe the implementation of the proposed model in a standard material update subroutine. The model is validated with laboratory creep data on samples from three gas shale formations. We also demonstrate the computational behavior of the proposed model through simulation of time‐dependent borehole closure in a shale formation with different bedding plane directions.