Miracle-dz/UniFAR-FTUs huggingface dataset:
1 Features of Classification Model Learning Algorithms

A supervised learning task involves constructing a mapping from an input data space (normally described by several features) to an output space. A set of training examples—examples with known output values—is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output consists of one or more classes to which the corresponding input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate’s measurements. The generalization performance of a learned model (how closely the target outputs and the model’s predicted outputs agree for examples that have not been presented to the learning algorithm) would provide an indication of how well the model has learned the desired mapping. More formally, a classification learning algorithm L takes a training set T as its input. The training set consists of |T | examples or instances. It is assumed that there is a probability distribution D from which all training examples are drawn independently—that is, all the training examples are independently and identically distributed (i.i.d). The ith training example is of the form (xi, yi), where xi is a vector of values of several features and yi represents the class to be predicted. In the sunspot classification example given above, each training example would represent one sunspot’s classification (yi) and the corresponding set of measurements (xi). The output of a supervised learning algorithm is a model h that approximates the unknown mapping from the inputs to the outputs. In our example, h would map from the sunspot measurements to the
Hyperspectral remote sensing applications for early stress detection of young plants

Hyperspectral remote sensing applications for early stress detection of young plants D. Krezhova1∗, S. Maneva2, I. Moskova3, K. Krezhov4 1 Space Research and Technology Institute, Bulgarian Academy of Sciences, Acad G. Bonchev Str. , Bl. 1, 1113 Sofia, Bulgaria 2 Institute of Soil Science, Agrotechnology and Plant Protection, Bulgarian Academy of Agriculture, 3B Panayot Volov Str. , 2230 Kostinbrod, Bulgaria 3 Institute of Plant Physiology and Genetics, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, Bl. 21, 1113 Sofia, Bulgaria 4 Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, 72 Tsarigradsko Chaussee Blvd., 1784 Sofia, Bulgaria
Evaluating FAVAR with Time-Varying Parameters and Stochastic Volatility ∗

This paper investigates the performance of FAVAR (Factor Augmented Vector Autoregression) with time-varying parameters and stochastic volatility (TVP/SV-FAVAR) in capturing time variation in monetary policy transmission, in comparison with that of small-scale TVP/SV-VAR. The analysis is conducted through Monte Carlo (MC)-based experiments using open-economy Dynamic Stochastic General Equilibrium (DSGE) and Smets-Wouters model (2007) as the data-generating process. The experiments show that although TVP/SVVAR does not adequately detect the time variation, TVP/SV-FAVAR does. This result is subsequently interpreted in terms of the information amounts of these two empirical models. Using the concept of informational deficiency and a technique to compute it, both of which were proposed in recent literature, I reveal that small-scale VAR easily suffers from two kinds of informational problems in identifying the time variation, whereas FAVAR can avoid them by containing a sufficient number of latent factors. Moreover, a reliable algorithm to estimate factors can give a further help to TVP/SV-FAVAR in terms of overcoming the informational problems, even if the model possesses only a limited number of factors.
A comparative study of word embedding methods for early risk prediction on the Internet

We built a system to participate in the eRisk 2019 T1 Shared Task. The aim of the task was to evaluate systems for early risk prediction on the internet, in particular to identify users suffering from eating disorders as accurately and quickly as possible given their history of Reddit posts in chronological order. In the controlled settings of this task, we also evaluated the performance of three different word representation methods: random indexing, GloVe, and ELMo. We discuss our system’s performance, also in the light of the scores obtained by other teams in the shared task. Our results show that our two-step learning approach was quite successful, and we obtained good scores on the early risk prediction metric ERDE across the board. Contrary to our expectations, we did not observe a clear-cut advantage of contextualized ELMo vectors over the commonly used and much more light-weight GloVe vectors. Our best model in terms of F1 score turned out to be a model with GloVe vectors as input to the text classifier and a multi-layer perceptron as user classifier. The best ERDE scores were obtained by the model with ELMo vectors and a multi-layer perceptron. The model with random indexing vectors hit a good balance between precision and recall in the early processing stages but was eventually surpassed by the models with GloVe and ELMo vectors. We put forward some possible explanations for the observed results, as well as proposing some improvements to our system.
Probabilistic Relational Concept Extraction in Ontology Learning

Progresses in ranges, for example, characteristic dialect preparing, data recovery, machine learning, information mining, and information representation to understand a constantly developing assemblage of literary data in electronic structures, referred to just as data. The intermixing of systems from these regions has empowered us to remove and speak to realities and examples for enhancing the administration, access and interpretability of data. Notwithstanding, it was not until the turn of the thousand years with the Semantic Web dream and the blast of data due to the "Read/Write" Web that the requirement for a deliberate group of study in extensive scale extraction and representation of certainties and examples turned out to be more self-evident. Cosmology Learning (OL) which expects to turn realities and examples from a regularly developing assortment of data into shareable, abnormal state builds for improving ordinary applications (e.g. Web look) and empowering insightful frameworks. Ontologies are successfully formal and unequivocal determinations, as ideas and relations, of shared conceptualizations. Ontologies may contain sayings for acceptance and authorizing requirements. Probabilistic Relational Of Concept Extraction in Ontology Learning (PROCEOL) depicts the techniques for idea extraction.
Bis(propane-1,2-diammonium) hexa­iodo­plumbate(II) trihydrate

The title compound, [NH3CH2CH(NH3)CH3]2[PbI6]·3H2O, crystallizes as an organic–inorganic hybrid, consisting of alternating inorganic and organic layers. The ionic layer consists of isolated [PbI6]4− octa­hedra. The hydro­carbon layer has one propane-1,2-diammonium cation in the asymmetric unit which links to the ionic layer via hydrogen bonding. Two solvent water mol­ecules lie between the anions and cations. The Pb atom lies on a centre of inversion.
Model- and Full-Scale URANS/DES Simulations for Athena R/V Resistance, Powering, and Motions

This study develops and implements a two-layer, twopoint wall-function model in the general-purpose URANS/DES solver CFDSHIP-IOWA-V.4 to routinely simulate modeland full-scale ship flows for resistance, propulsion, seakeeping, and maneuvering. The wall-function is validated for smooth flat-plate flows for high Reynolds numbers, and applied to study high speed transom ship flows, Athena R/V. Resistance predictions for Athena barehull with skeg at modelscale compare well with the near-wall turbulence model results and EFD data. For full-scale simulations frictional resistance predictions are in good agreement with the IITC line. No significant Reynolds number effect is found for free surface wave elevation and sinkage and trim (smooth wall). Full-scale boundary layer is thinner than that of the model-scale. Rough wall simulations show higher frictional and total resistances and relative large and unexpected changes are observed for sinkage and trim, which may be due to the coarse grid used and needs to be confirmed by further study. Resistance and powering computations are performed at full-scale for self-propelled fully appended Athena free to sink and trim with smoothand rough-walls, and results are compared with fullscale data extrapolated from model scale measurements using ITTC ship-model correlation line including a correlation allowance. Rough wall conditions predict higher frictional and total resistances leading to better Froude number and resistance without significant effects on sinkage and trim. Sinkage and trim are not significantly affected by Reynolds number. Full-scale computations are performed for the towed fully appended Athena free to sink and trim and the boundary layer and wake profiles are compared with full-scale EFD data. Rough-wall results are found to be in better agreement with the EFD data than the smoothwall results. Transom flow instability is studied for the fully appended Athena free to sink and trim at fullscale using DES with the wall-function. Karman-like vortex shedding and associated instabilities are analyzed and compared with the previous URANS/DES for the Athena barehull at model-scale. Seakeeping capability of the wall-function is also demonstrated at both modeland full-scale. Finally, limitations of the current wall-function approach and future work are discussed.
A Compressed Depth Cache

We propose a depth cache that keeps the depth data in compressed format, when possible. Compared to previous work, this requires a more flexible cache implementation, where a tile may occupy a variable number of cache lines depending on whether it can be compressed or not. The advantage of this is that the effective cache size increases proportionally to the compression ratio. We show that the depth-buffer bandwidth can be reduced, on average, by 17%, compared to a system compressing the data after the cache. Alternatively, and perhaps more interestingly, we show that pre-cache compression in all cases increases the effective cache size by a factor of two or more, compared to a post-cache compressor, at equal or higher performance.
The use of colours to support diversity

Diversity is a way to prevent that different connections are affected by the same failure. As networks are constructed in different layers and are structured into separate levels, planning for diversity becomes difficult. In Telia's network this has been efficiently solved by structuring the network with colours; the red core network is completely separate from the blue one. Each core network can then be optimised independently. This colouring concept is in the contribution presented in relation to the notion of shared risk link groups that has appeared in standardisation. SRLGs are proper to define diversity, but our judgement is that colours are needed for network structuring and to get simple routing rules. Therefore we want to see colours implemented in the control plane of an automatically switched optical network (ASON).
Epidemiología del Dengue en Jamaica Genotipos del Virus del Dengue en Jamaica , 2007

The genotypes of dengue viruses (DENV) isolated from patients with dengue in Jamaica during 2007 were determined using DNA sequencing and phylogenetic analysis of the C-prM gene junction. The 17 DENV analysed included strains of DENV serotypes 1 (DENV-1, n = 3), DENV-2 (n = 7) and DENV-4 (n = 7). All strains of DENV-1 were classified as genotype III, while 1 of 7 strains of DENV-2 belonged to the Asian American/Asian genotype, genotype I/ III (Jamaica genotype), 2 were genotype V, the American genotype and 4 strains clustered with reference strains belonging to genotype IV. The 6 DENV-4 strains from Jamaica and the control strain clustered together in a separate clade from Caribbean/ American reference strains, which belong to genotype II and Asian strains, classified as genotypes I and III. There has been little evolution in the DENV-1 strains circulating in Jamaica over the years and this might reduce the risk of outbreaks due to this serotype. In contrast, the high genetic diversity in strains of DENV-2 viruses in circulation, the presence of more recently introduced genotypes and a new clade of DENV-4 might contribute to the epidemic potential of these DENV serotypes. These preliminary data clearly indicate the need to maintain laboratory surveillance, and other control measures against hyperendemicity of dengue in Jamaica.
Output Final Prediction Policy Output Modal 1 Modal 2 Final Prediction Policy Fused Representation Vector Representation Vector Representation Modal 1 Modal 2

In this study, we investigated multi-modal approaches using images, descriptions, and title to categorize e-commerce products on Amazon.com. Specifically, we examined late fusion models, where the modalities are fused at the decision level. Products were each assigned multiple labels, and the hierarchy in the labels were flattened and filtered. For our individual baseline models, we modified a CNN architecture to classify the description and title, and then modified Keras’ ResNet-50 to classify the images, achieving F1 scores of 77.0%, 82.7%, and 61.0%, respectively. In comparison, our tri-modal late fusion model can classify products more accurately than single modal models can, improving the F1 score to 88.2%. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the accuracy of multi-label classification problems.
Triple Material Surrounding Gate (TMSG) Nanoscale Tunnel FET-Analytical Modeling and Simulation

In the nanoscale regime, many multigate devices are explored to reduce their size further and to enhance their performance. In this paper, design of a novel device called, Triple Material Surrounding Gate Tunnel Field effect transistor (TMSGTFET) has been developed and proposed. The advantages of surrounding gate and tunnel FET are combined to form a new structure. The gate material surrounding the device is replaced by three gate materials of different work functions in order to curb the short channel effects. A 2-D analytical modeling of the surface potential, lateral electric field, vertical electric field and drain current of the device is done, and the results are discussed. A step up potential profile is obtained which screens the drain potential, thus reducing the drain control over the channel. This results in appreciable diminishing of short channel effects and hot carrier effects. The proposed model also shows improved ON current. The excellent device characteristics predicted by the model are validated using TCAD simulation, thus ensuring the accuracy of our model.
Numerical Solution to Strong Cylindrical Shock Wave in the Presence of Magnetic Field

One-dimensional flow of converging shock waves in cylindrical symmetry in MHD, which is propagating into plasma, is analyzed. Due to implosion (by impulse method), plasma is assumed to be generated and is assumed to be non-ideal gas whose equation of state is of Mie–Gruneisen type. Suitable transformations reduce the governing equations into ordinary differential equations of Poincare type. Shock is assumed to be strong and propagating into a medium according to a power law. In the present work, two different equations of state (EOS) of Mie–Gruneisen type have been considered with suitable material constants and the cylindrical case is worked out in detail. The complete set of governing equations is formulated as finite difference problem and solved numerically using MATLAB. The numerical technique applied in this paper provides a global solution to the implosion problem for the flow variables, the similarity exponent for different Gruneisen parameters. It is shown that increase in measure of shock strength ( ) has effect on the shock front i.e., the velocity and pressure behind the shock front increases quickly in the presence of the magnetic field and decreases gradually. This phenomenon was found to exist irrespective of the piston is accelerated, is moving at constant speed or is decelerated. In particular we found that numerical solution is shown to exist in the range of cowling number These results are presented through the illustrative graphs and tables.
A 15-year climatology of stratosphere-troposphere exchange and its link to potential vorticity streamers and cutoffs

Trans-tropopause mass exchange gained a lot of interest in the last few years, not at least due to its chemical and climatological impact. Many approaches have been adopted in order to quantify the exchange rate and to identify the most prone regions for mass exchange. The review of Holton et al. (1995) primarily focused on the global aspects of cross-tropopause mass exchange. They calculated the zonally-mean mass flux by use of the residual mean diabatic circulation. On the other hand, mesoscale dynamical processes can be important for localized cross-tropopause exchange, for instance through the formation of stratospheric streamers, their breakup into cutoff lows, and diabatic decay (Appenzeller et al. (1996). Several case studies have been performed to study the exchange for such events. In the present study, high-resolution data which cover the northern hemisphere are used to provide a 15-year climatology of cross-tropopause exchange in the extratropics. It builds upon the work of Bourqui (2001) and Wernli and Bourqui (2001) where a fully Lagrangian approach was adopted to identify (for a selected year) a distinct seasonal cycle and preferred regions for exchange. Furthermore, short-lived and long-lasting exchange events were discussed separately, since such a distinction is of major importance for the chemical impact of cross-tropopause exchange. Here, in addition to the study by Bourqui (2001), an objective analysis is performed in order to quantify the link between crosstropopause exchange and near-tropopause streamers and cutoffs.
On Mixed Conditional FFJORD with Large-Batch Training

We introduce a new conditioning method for the Continuous Normalizing Flows and their improved version, the Free-form Jacobian of Reversible Dynamics (FFJORD) model. We name our new conditional model, the Mixed Conditional FFJORD (MC-FFJORD). In the MCFFJORD, the latent code is partitioned into the supervised and unsupervised codes. The supervised code is used for conditioning the model while the unsupervised code accounts for other latent variations in the data. Our partitioning approach fosters the learning of disentangled representation by the model. Our empirical study corroborates the advantage of the MC-FFJORD over the baseline conditional FFJORD (C-FFJORD) in both classification and density estimation. We show that the MC-FFJORD requires less number of function evaluations and is more efficient than the C-FFJORD when training with large batches. We also explore the possibility of scaling up the MC-FFJORD using large-batch training with large learning rates and high error tolerances for the ODE solvers in the model. While the MCFFJORD trained with this approach outperforms the MC-FFJORD trained with large batches only, its performance still lags behind that of the same model trained with small batches.
A NEW VERSION OF FIRST RETURN TIME TEST OF PSEUDORANDOMNESS

We present a new version of the first return time test for pseudorandomness. Let R n be the first return time of initial n-block with overlapping. An algorithm to calculate the probability distribution of the first return time R n for each starting block is presented and used to test pseudorandom number generators. The standard Z-test for log R n is applied to test the pseudorandom number generators.
Knowledge-Aware Execution of Programs in IndiGolog

Agents that act in dynamic environments often face situations where their belief about the world is in contradiction with reality. The lack of secured knowledge may let the agents perform wrong or even dangerous actions. In order to deal with this two different approaches can be used. One approach limits the reasoning to that knowledge that is secure while the other approach actively diagnose and repair problems in the knowledge. In this paper we briefly discuss how the two approaches can be combined in order to improve the robustness of execution of high-level IndiGolog programs controlling an agent.
RISK FACTORS OF ALLERGIC DISEASES

Objective: The aim of presented paper was to determine relationship of the development of allergic diseases and child's age, gender, family history of allergy, breastfeeding, supplementation or mother’s nutrition during breastfeeding. Methods: The research method of questionnaires was designed for healthy and allergic children. The sample consisted of 228 healthy children aged 0-15 years and 183 allergic children aged 0-15 years. Results: The research results indicated that the development of allergic diseases was significantly related to age, genetic predisposition, and duration of exclusive breastfeeding. The risk of allergic diseases increased with early supplementation with milk substitute formulas and citrus consumption during lactation. Conclusion: The results can be applied in the care of children and adolescents.
Fuzzy logic based ATM policing

We compare the performance of a fuzzy logic (FL) based policer with leaky bucket (LB) based policer. The FL based policer continuously computes the instantaneous drop-rate based on a collective evaluation of the compliance/violation level of the two parameters: (a) the ratio of the up-to-date mean burst length to the negotiated burst length, and (b) the ratio of the up-to-date mean bit rate to the negotiated mean bit rate. The compliance/violation of either of the two parameters results in an increase/decrease of the strength of the rules with consequence pass/drop. The throughput performance for the policing mean bit rate and mean burst length of the FL based policers is compared with that based on LB based policers of different token pool sizes. The throughput performance curve of the FL based policer for the policing mean bit rate is very close to the optimal performance curve of the LB based policers, but significantly outperform the LB based policers for the policing mean burst length. <<ETX>>