Miracle-dz/UniFAR-FTUs huggingface dataset:
Diferencias observadas entre estándares internacionales de la gallina ponedora Isa Brown y los resultados obtenidos en el Cantón Bolívar, Ecuador

espanolResumen Se comparo diversos parametros productivos obtenidos en el canton Bolivar con los estandares internacionales de la ISA Brown y modelar las funciones de produccion. Se estudiaron a 300 gallinas ponedoras en la fase I de produccion. Se midieron: peso ave (g), consumo alimenticio (g), peso huevo (g), porcentaje de postura (%), huevo acumulado alojado, masa huevo acumulado (kg), conversion alimenticia (g/g), grosor del cascaron (mm). Se empleo la prueba de Lord y modelos no lineales. Obteniendose los siguientes resultados: masa de huevo, huevo acumulado, peso de huevo y porcentaje de postura del Mocochal, fueron estadisticamente inferiores a los estandares internacionales. Se debe priorizar el fomento de buenas condiciones de crianza a fin de conseguir mejor peso vivo y una buena uniformidad a la quinta semana de edad e insistir en la division de raciones, particulas grandes para las horas de la tarde y las particulas finas para la manana. EnglishAbstract The present research tries to compare productive parameters in Mocochal, canton Bolivar with the international standards of ISA Brown as well as the fitting of different growth models. A total of 300 hens were studied. Following parameter were investigated: weigh of hens, food consumption, egg weigh, % posture, egg amount, egg mass, feed conversion and thingness of shell. Lord test and Non lineal regression were applied. Obtained the following results: the productive parameters were significantly below to the international standards. The development growth of the hens should be prioritized instead of appealing the sexual maturity of the same ones. It can be obtained with good conditions of rearing in order to obtain better weight and a good uniformity to the 5 weeks of age. After the week 6, it is necessary to insist that the distribution of the food have to be divided in two portions, big particles for the hours of the afternoon and the fine particles for the morning.
European Journal of Foreign Language Teaching-ISSN 2501-1754

Research on the use of Flipgrid in the language classrooms has focused on its effects of online video-mediated communication tools, learners’ attitudes, and perceptions; nonetheless, not much research has recently been conducted about the influences of the Flipgrid app on EFL learners’ speaking anxiety. Therefore, this research aimed at investigating whether Flipgrid helps the EFL high school learners reduce their anxiety in learning English speaking and determine the learners’ attitudes towards its usages. The research followed a combination of a quasi-experimental method and a mixed-method with the participation of 60 EFL tenth-grade learners in a high school in the Mekong Delta. Three research instruments namely the modified Foreign Language Classroom Anxiety Scale (FLCAS), the questionnaire, and the interviews were employed in this study. The findings showed that there was a decrease in EFL high school learners’ level of anxiety in learning English speaking after experiencing Flipgrid. Moreover, the majority of the learners also showed their positive attitudes towards the use of Flipgrid in learning English speaking and expected that Flipgrid should be employed frequently to make learning English speaking become more motivating.
Assessing the viability of A 4 , S 4 , and A 5 flavor symmetries for description of neutrino mixing

We consider the A 4 , S 4 , and A 5 discrete lepton flavor symmetries in the case of 3-neutrino mixing, broken down to nontrivial residual symmetries in the charged lepton and neutrino sectors in such a way that at least one of them is a Z 2 . Such symmetry breaking patterns lead to predictions for some of the three neutrino mixing angles and/or the leptonic Dirac CP violation phase δ of the neutrino mixing matrix. We assess the viability of these predictions by performing a statistical analysis which uses as an input the latest global data on the neutrino mixing parameters. We find 14 phenomenologically viable cases providing distinct predictions for some of the mixing angles and/or the Dirac phase δ . Employing the current best fit values of the three neutrino mixing angles, we perform a statistical analysis of these cases taking into account the prospective uncertainties in the determination of the mixing angles, planned to be achieved in currently running (Daya Bay) and the next generation (JUNO, T2HK, DUNE) of neutrino oscillation experiments. We find that only six cases would be compatible with these prospective data. We show that this number is likely to be further reduced by a precision measurement of δ . DOI: 10.1103/PhysRevD.97. 115045
M ETA -L EARNING THE I NDUCTIVE B IASES OF S IMPLE N EURAL C IRCUITS

Animals receive noisy and incomplete information, from which we must learn how to react in novel situations. A fundamental problem is that training data is always ﬁnite, making it unclear how to generalise to unseen data. But, animals do react appropriately to unseen data, wielding Occam’s razor to select a parsimo-nious explanation of the observations. How they do this is called their inductive bias, and it is implicitly built into the operation of animals’ neural circuits. This relationship between an observed circuit and its inductive bias is a useful explanatory window for neuroscience, allowing design choices to be understood normatively. However, it is generally very difﬁcult to map circuit structure to inductive bias. In this work we present a neural network tool to bridge this gap. The tool allows us to meta-learn the inductive bias of neural circuits by learning functions that a neural circuit ﬁnds easy to generalise, since easy-to-generalise functions are exactly those the circuit chooses to explain incomplete data. We show that in systems where the inductive bias is known analytically, i.e. linear and kernel regression, our tool recovers it. Then, we show it is able to ﬂexibly extract inductive biases from differentiable circuits, including spiking neural networks, and use it to interpret recent connectomic data through their effect on generalisation. This illustrates the intended use of our tool: understanding the role of otherwise opaque pieces of neural functionality through the inductive bias they induce.
Effect of the Grip Angle on Off-Spin Bowling Performance Parameters, Analysed with a Smart Cricket Ball

In the off-spin bowling grip, the ball is clamped between index and middle fingers. Spin bowlers attempt to select a spread angle between these two fingers that achieves comfort and optimises performance. The aim of this paper was to investigate whether the standard grip is superior to narrow and wide grips. The bowling performance parameters were obtained from a smart cricket ball. Smart ball data revealed that the performance parameters varied with grip type. The following parameters were optimum at the standard grip: spin rate, resultant torque, spin torque, peak angular acceleration, and peak power. The following parameters were optimum at standard and wide grips: efficiency. The following parameters were optimum at standard and narrow grips: pitch angle of spin axis. The following parameters were optimum at the wide grip: precession and the precession torque. In general, the data tended to show that the standard grip is most effective for spin bowling. However, more research is needed to confirm this result, because the precession and precession torque were optimum at the wide grip, suggesting that this may have a superior performance over the standard and narrow grips.
Residual translation compensations in radar target narrowband imaging based on trajectory information

High velocity translation will result in defocusing scattering centers in radar imaging. In this paper, we propose a Residual Translation Compensations (RTC) method based on target trajectory information to eliminate the translation effects in radar imaging. Translation could not be simply regarded as a uniformly accelerated motion in reality. So the prior knowledge of the target trajectory is introduced to enhance compensation precision. First we use the two-body orbit model to figure out the radial distance. Then, stepwise compensations are applied to eliminate residual propagation delay based on conjugate multiplication method. Finally, tomography is used to confirm the validity of the method. Compare with translation parameters estimation method based on the spectral peak of the conjugate multiplied signal, RTC method in this paper enjoys a better tomography result. When the Signal Noise Ratio (SNR) of the radar echo signal is 4dB, the scattering centers can also be extracted clearly.
Angular dependence of 12‐kHz seafloor acoustic backscatter

The angular dependence of seafloor acoustic backscatter, measured with a 12‐kHz multi narrow‐beam echo‐sounder at two sites in the central North Pacific with water depths of 1500 and 3100 m, respectively, has been determined for incidence angles between 0° and 20°. The acoustic data consist of quadrature samples of the beamformed echoes received on each of the 16 2.66° beams of a Sea Beam echo‐sounder. These data are subjected to adaptive noise cancelling for sidelobe interference rejection, and the centroid of each echo is determined. After corrections for the ship’s roll and raybending effects through the water column, the angles of arrival are converted to angles of incidence by taking athwartships apparent bottom slopes into account. For each beam, the mean echo power received is normalized by the corresponding insonified area that depends on the transmit and receive beam patterns, the ship’s roll angle and the local bottom slope. For lack of system calibration, the data are presented as relative mean.
Predicting Task and Subject Differences with Functional Connectivity and Blood-Oxygen-Level-Dependent Variability

Previous research has found that functional connectivity (FC) can accurately predict the identity of a subject performing a task and the type of task being performed. These results are replicated using a large data set collected at the Ohio State University Center for Cognitive and Behavioral Brain Imaging. This work introduces a novel perspective on task and subject identity prediction: blood-oxygen-level-dependent variability (BV). Conceptually, BV is a region-specific measure based on the variance within each brain region. BV is simple to compute, interpret, and visualize. This work shows that both FC and BV are predictive of task and subject, even across scanning sessions separated by multiple years. Subject differences rather than task differences account for the majority of changes in BV and FC. Similar to results in FC, BV is reduced during cognitive tasks relative to rest.
Distributed Cache Enabled V2X Networks: Proposals, Research Trends and Challenging Issues

Nowadays, the internet of vehicles (IoV) has been evolved into the stage of vehicle to everything (V2X). However, the majority of existing work focuses on the motor-vehicles. In contrast, the sharing bicycle system is vastly and rapidly deployed as a feasible internet of things (IoT) application scene for the last mile problem (e.g., from station to home/office). Moreover, the internet access of current V2X is relied on the back-haul to roadside unit (RSU) connections. In this paper, other than prior work, we propose a versatile V2X system with a distributed framework and heterogeneous caching method. All the vehicles and devices on-the-road (motor-vehicle, non-motor-vehicle, pedestrian, etc.) are comprehensively included in the proposed networks. We further introduce a heterogeneous cache method for effective wireless transmission while utilizing the massive connected devices. The potential research trends on achieving high speed transmission, deep programming dedicated network slicing are highlighted as well as the big data, machine learning (ML), fog computing based image recognition and reconstruction, to provide some insights for future studies. Finally, the challenging issues, i.e., urban street canyon path loss and channel models, ultra reliable communication and low latency requirements, are discussed.
DeepSAGE Reveals Genetic Variants Associated with Alternative Polyadenylation and Expression of Coding and Non-coding Transcripts

Many disease-associated variants affect gene expression levels (expression quantitative trait loci, eQTLs) and expression profiling using next generation sequencing (NGS) technology is a powerful way to detect these eQTLs. We analyzed 94 total blood samples from healthy volunteers with DeepSAGE to gain specific insight into how genetic variants affect the expression of genes and lengths of 3 9 -untranslated regions (3 9 -UTRs). We detected previously unknown cis -eQTL effects for GWAS hits in disease- and physiology-associated traits. Apart from cis -eQTLs that are typically easily identifiable using microarrays or RNA-sequencing, DeepSAGE also revealed many cis -eQTLs for antisense and other non-coding transcripts, often in genomic regions containing retrotransposon-derived elements. We also identified and confirmed SNPs that affect the usage of alternative polyadenylation sites, thereby potentially influencing the stability of messenger RNAs (mRNA). We then combined the power of RNA-sequencing with DeepSAGE by performing a meta-analysis of three datasets, leading to the identification of many more cis -eQTLs. Our results indicate that DeepSAGE data is useful for eQTL mapping of known and unknown transcripts, and for identifying SNPs that affect alternative polyadenylation. Because of the inherent differences between DeepSAGE and RNA-sequencing, our complementary, integrative approach leads to greater insight into the molecular consequences of many disease-associated variants. The research leading to these European Community’s Health Seventh Framework Programme (FP7/2007–2013) under grant agreement n u 259867. This study was financed in part SIA-raakPRO project BioCOMP. The funders had no role in study design, data collection and analysis, decision to or preparation of the manuscript. Interests:
Auditory Spatial Attention Representations in the Human Cerebral Cortex

Lingqiang Kong1,5, Samantha W. Michalka2,5, Maya L. Rosen3,5, Summer L. Sheremata6, Jascha D. Swisher7, Barbara G. Shinn-Cunningham2,4,5,8 and David C. Somers2,3,5,8 Cognitive and Neural Systems, Neuroscience Program, Department of Psychology, Biomedical Engineering, Boston University, Boston, USA Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, USA Department of Psychology, Vanderbilt University, Nashville, TN, USA and Center for Computational Neuroscience and Neural Technology, Boston, USA
The Synchronization within and Interaction between the Default and Dorsal Attention Networks in Early Infancy

An anticorrelated interaction between the dorsal attention and the default-mode networks has been observed, although how these 2 networks establish such relationship remains elusive. Behavioral studies have reported the emergence of attention and default network--related functions and a preliminary competing relationship between them at early infancy. This study attempted to test the hypothesis—resting-state functional magnetic resonance imaging will demonstrate not only improved network synchronization of the dorsal attention and the default networks, respectively, during the first 2 years of life but also an anticorrelated network interaction pattern between the 2 networks at 1 year which will be further enhanced at 2 years old. Our results demonstrate that both networks start from an isolated region in neonates but evolve to highly synchronized networks at 1 year old. Paralleling the individual network maturation process, the anticorrelated behaviors are absent at birth but become apparent at 1 year and are further enhanced during the second year of life. Our studies elucidate not only the individual maturation process of the dorsal attention and default networks but also offer evidence that the maturation of the individual networks may be needed prior exhibiting the adult-like interaction patterns between the 2 networks.
Magnetic Resonance Images Reconstruction using Uniform Discrete Curvelet Transform Sparse Prior based Compressed Sensing

Compressed sensing(CS) has shown great potential in speeding up magnetic resonance imaging(MRI) without degrading images quality. In CS MRI, sparsity (compressibility) is a crucial premise to reconstruct high-quality images from non-uniformly undersampled k-space measurements. In this paper, a novel multi-scale geometric analysis method (uniform discrete curvelet transform) is introduced as sparse prior to sparsify magnetic resonance images. The generated CS MRI reconstruction formulation is solved via variable splitting and alternating direction method of multipliers, involving revising sparse coefficients via optimizing penalty term and measurements via constraining k-space data fidelity term. The reconstructed result is the weighted average of the two terms. Simulated results on in vivo data are evaluated by objective indices and visual perception, which indicate that the proposed method outperforms earlier methods and can obtain lower reconstruction error.
Topological Data Analysis Made Easy with the Topology ToolKit, A Sequel

Figure 1: TTK is a software platform for topological data analysis in scientific visualization. It is both easily accessible to end users (ParaView plugins (a), VTK-based generic GUIs (b) or command-line programs (c)) and flexible for developers (Python (d), VTK/C++ (e) or dependence-free C++ (f) bindings). TTK provides an efficient and unified approach to topological data representation and simplification, which enables in this example a discrete Morse-Smale complex (a) to comply to the level of simplification dictated by a piecewise linear persistence diagram (bottom-right linked view, a). Code snippets are provided (d-f) to reproduce this pipeline.
The Improvement of first Iteration Log Periodic Fractal Koch Antenna with Slot Implementation

In this paper, a slotted is introduced at each of the radiating elements on the 1 st iteration log periodic fractal Koch antenna (LPFKA). The antenna is designed to testify the appropriate performance at UHF Digital television which operates from 4.0 GHz to 1.0 GHz. The dimension of the conventional 0 th iteration LPKFA is successfully reduced by 17% with the implementation of slotted. The results show a good agreement with a stable radiation pattern across the operating bandwidth, stable gain more than 5 dBi and reflection coefficient of below -10 dB over the desired frequency range.
Expert Systems With Applications

E -communication represents a major threat to users who are exposed to a number of risks and potential attacks. Detecting these risks with as much anticipation as possible is crucial for prevention. However, much research so far has focused on forensic tools that can be applied only when an attack has been performed. This paper proposes a novel and effective methodology for the early detection of threats in written social media. The goal is to recognize a potential attack before it is consummated
Numerical Solution of ordinary Differential Equations Based on Semi-Taylor by Neural Network improvement

Abstract. In this paper, a new approach is proposed in order to solve the differential equations of ordinary initial value based on the feed-forward neural network and Semi-Taylor series ordinary differential equation is first replaced by a system of ordinary differential equations. A trial Solution of this System consists of two parts. The first part, that is, Semi-Taylor series contains no adjustable parameters. And the second part includes the neural network and adjustable parameters (the weights). Using modified neural network requires that training points be selected over the open interval (a, b) without training the network in the range of the first and end points. Therefore, the calculating volume containing computational error is reduced. In fact, the training points depending on the distance [a, b] selected for training neural networks are converted into similar points in the open interval (a, b) using the new approach, then the network is trained in these similar areas.In comparison with existing similar neural networks, the proposed model provides solutions with high accuracy. Numerical examples with simulation results illustrate the effectiveness of the proposed model.
A Denial of Service Detector based on Bayesian Classifiers and the Random Neural Network

In spite of extensive research in defence against Denial of Service (DoS), such attacks remain a predominant threat in today’s networks. Due to the simplicity of the concept and the availability of the relevant attack tools, launching a DoS attack is relatively easy, while defending a network resource against it is disproportionately difficult. The first step of any comprehensive protection scheme against DoS is the detection of its existence, ideally long before the destructive traffic build-up. In this paper we propose a generic approach for DoS detection which uses multiple Bayesian classifiers and random neural networks (RNN). Our method is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood estimation and fusing the information gathered from the individual input features using likelihood averaging and different architectures of RNNs. We present and compare seven different implementations of it and evaluate our experimental results obtained in a large networking testbed.