Miracle-dz/UniFAR-FTUs huggingface dataset:
DOI: 10

The improved elastic recovery and a wider range of linear elastic behavior of multi-scale hybrid polyamide 6 composites are demonstrated in this study. For the composites basalt fiber was used as micro-sized, and multi-walled carbon nanotubes and surface-treated montmorillonite as nano-sized reinforcement. The materials were melt compounded and then specimens were injection molded. To prove increased elastic behavior, a relatively simple and fast cyclic measurement method was applied, where the load was increased in each cycle and 30 seconds of relaxation time was allowed after unloading. The results showed that both micro and nanoparticles can broaden the range of the elastic behavior of the polyamide 6 significantly, which is reflected in increased elastic recovery and decreased plastic deformation compliance. Nanoand micro-sized reinforcing materials also produce a synergistic effect, which can be explained with the physical crosslinking of the nanoparticles.
Whole genome molecular characterization of Infectious Pancreatic Necrosis Viruses isolated in Turkey

Infectious pancreatic necrosis virus (IPNV; Birnaviridae, Aquabirnavirus ) causes infectious pancreatic necrosis (IPN) in fish. IPN disease was first found in 2002 in Turkey. In this study, 10 IPNV isolates were isolated between 2005 and 2013 and propagated in RTG-2 cell cultures. RNAs obtained from cell lysates were used as template and VP1, VP2, VP3, VP4 and VP5 genes of the virus were amplified in full length. Sequence analyses of the genes were made. The obtained sequences were compared with international reference strains from GenBank and phylogenetic analyses and genogrouping of the viruses were conducted. Turkish isolates were found to show a genetic similarity of between 93.5% and 99.8% in terms of molecules. When segment A sequences were compared with each other, a similarity between 97.8% and 99.8% was found; when they were compared with international reference strains, they were found to have the highest similarity (99.3%) with France AJ622822 isolate and the lowest (65.7%) similarity with Canada NC001915 isolate. When segment B sequences of the isolates were compared with each other, a similarity between 93.5% and 99.5% was found; when they were compared with international reference strains, they were found to have the highest similarity (98.9%) with Canada M58757 isolate and the lowest (52.7%) similarity with Finland KY548519 strain. As a result of phylogenetic analyses conducted, Turkish isolates were found to be closely related with France, Spain and Iran strains in serotype A2 and genogroup 5 they were placed in.
A NIMATION OF TWO-DIMENSIONAL PICTORIAL WORKS INTO MULTIPURPOSE THREE-DIMENSIONAL OBJECTS

This paper reports the preliminary results of an ongoing interdisciplinary research in digital humanities and animation that the authors are undertaking to explore how a new generation of three-dimensional (3D) non-photorealistic animated visualisations can improve upon two-dimensional (2D) visualisation methods. The atlas of the world’s ships depicted in the Fra Mauro’s mappa mundi (Venice, Marciana National Library, dated 1460) has been used to: 1) showcase the design process and techniques of an innovative method through the prototyping of a 3D non-photorealistic-rendering (NPR) model of one ship, and 2) demonstrate the effectiveness of this method through the 3D NPR animation of all Fra Mauro’s ships as a contribution to both the advancement of learning in pre-modern maritime history (with a focus on shipping), and the implementation of NPR for the creative industry.
The Quadruple System HIP 45734

HIP 45734 is a quadruple system of 2 + 2 architecture located at 68 pc from the Sun. The outer 9″ system A,B has a period of ∼104 yr. The subsystem Aa,Ab is a visual binary with a period of 20.1 yr and an eccentricity of 0.78. Its periastron in 2019.1 was observed spectroscopically, yielding masses (1.10 ± 0.04 and 0.98 ± 0.03 ) and orbital parallax, 14.90 ± 0.37 mas. The masses, luminosities, and colors approximately agree with evolutionary models of main-sequence stars. Component Aa has a detectable lithium line, whereas in Ab it is absent. The pair Ba,Bb is a single-lined spectroscopic binary with a period of 0.55552 day and an orbital inclination of ∼45° derived by modeling the rotationally broadened line profile with flat bottom. The mass of Bb is ∼0.4 . Star B is chromospherically active (an X-ray source); its flux is modulated with the orbital period by starspots, in addition to occasional flares. The system is probably older than ∼600 Myr; it does not belong to any known moving group.
The impact of overbooking on a pre-trial risk assessment tool

Pre-trial risk assessment tools are used to make recommendations to judges about appropriate conditions of pre-trial supervision for people who have been arrested. Increasingly, there is concern about whether these models are operating fairly, including concerns about whether the models' input factors are fair measures of one's criminal activity. In this paper, we assess the impact of booking charges that do not result in a conviction on a popular risk assessment tool, the Arnold Public Safety Assessment. Using data from a pilot run of the tool in San Francisco, CA, we find that booking charges that do not result in a conviction (i.e. charges that are dropped or end in an acquittal) increased the recommended level of pre-trial supervision in around 27% of cases evaluated by the tool.
London penetration depth at zero temperature and near the superconducting transition

A simple relation is established between the zero-$T$ penetration depth $\lambda (0)$ and the slope of $\lambda^{-2}(T)$ near $T_c$, similar to Helfand-Werthamer's relation for $H_{c2}(0)$ and the slope of $H_{c2}(T)$ at $T_c$ for the isotropic s-wave case with non-magnetic scattering. \cite{HW} When the scattering parameter $\rho=\hbar v/2\pi T_c\ell$ ($v$ is the Fermi velocity and $\ell$ is the mean-free path) varies from 1 to 10, the coefficient of proportionality between $\lambda^{-2}(0)$ and $T_c (d\lambda^{-2}/dT)_{T_c}$ changes from 0.43 to 0.38. Combining this relation with the Rutgers thermodynamic identity, one can express $\lambda (0)$ in terms of the slope $(dH_{c2}/dT)_{T_c}$ and the density of states.
Predictions for the 21cm-galaxy cross-power spectrum observable with SKA and future galaxy surveys

In this paper we use radiative transfer + N-body simulations to explore the feasibility of measurements of cross-correlations between the 21 cm field observed by the Square Kilometer Array (SKA) and high-z Lyman Alpha Emitters (LAEs) detected in galaxy surveys with the Subaru Hyper Supreme Cam (HSC), Subaru Prime Focus Spectrograph (PFS) and Wide Field Infrared Survey Telescope (WFIRST). 21cm-LAE cross-correlations are in fact a powerful probe of the epoch of reionization as they are expected to provide precious information on the progress of reionization and the typical scale of ionized regions at different redshifts. The next generation observations with SKA will have a noise level much lower than those with its precursor radio facilities, introducing a significant improvement in the measurement of the cross-correlations. We find that an SKA-HSC/PFS observation will allow to investigate scales below ∼10 h−1 Mpc and ∼60 h−1 Mpc at z = 7.3 and 6.6, respectively. WFIRST will allow to access also higher redshifts, as it is expected to observe spectroscopically ∼900 LAEs per square degree and unit redshift in the range 7.5 ≤ z ≤ 8.5. Because of the reduction of the shot noise compared to HSC and PFS, observations with WFIRST will result in more precise cross-correlations and increased observable scales.
Informing the Design of Privacy-Empowering Tools for the Connected Home

Connected devices in the home represent a potentially grave new privacy threat due to their unfettered access to the most personal spaces in people's lives. Prior work has shown that despite concerns about such devices, people often lack sufficient awareness, understanding, or means of taking effective action. To explore the potential for new tools that support such needs directly we developed Aretha, a privacy assistant technology probe that combines a network disaggregator, personal tutor, and firewall, to empower end-users with both the knowledge and mechanisms to control disclosures from their homes. We deployed Aretha in three households over six weeks, with the aim of understanding how this combination of capabilities might enable users to gain awareness of data disclosures by their devices, form educated privacy preferences, and to block unwanted data flows. The probe, with its novel affordances-and its limitations-prompted users to co-adapt, finding new control mechanisms and suggesting new approaches to address the challenge of regaining privacy in the connected home.
On the subring of special cycles

Abstract By old results with Millson, the generating series for the cohomology classes of special cycles on orthogonal Shimura varieties over a totally real field are Hilbert–Siegel modular forms. These forms arise via theta series. Using this result and the Siegel–Weil formula, we show that the products in the subring of cohomology generated by the special cycles are controlled by the Fourier coefficients of triple pullbacks of certain Siegel–Eisenstein series. As a consequence, there are comparison isomorphisms between special subrings for different Shimura varieties. In the case in which the signature of the quadratic space V is (m,2)(m,2) at an even number d+d_{+} of archimedean places, the comparison gives a “combinatorial model” for the special cycle ring in terms of the associated totally positive definite space.
Progressive Local Filter Pruning for Image Retrieval Acceleration

Most image retrieval works aim at learning discriminative visual features, while little attention is paid to the retrieval efficiency. The speed of feature extraction is key to the real-world system. Therefore, in this article, we focus on network pruning for image retrieval acceleration. Different from the classification models predicting discrete categories, image retrieval models usually extract continuous features for retrieval, which are more sensitive to network pruning. Such different characteristics of the retrieval and classification models make the traditional pruning method sub-optimal for image retrieval acceleration. Two points are critical for pruning image retrieval models: preserving the local geometry structure of filters and maintaining the model capacity during pruning. In view of the above considerations, we propose a Progressive Local Filter Pruning (PLFP) method. Specifically, we analyze the local geometry of filter distribution in every layer and select redundant filters according to one new criterion that the filter can be replaced locally by other similar filters. Furthermore, to preserve the model capacity of the original model, the proposed method progressively prune the filter by decreasing the scale of filter weights gradually. We evaluate our method on four scene retrieval datasets, i.e., Oxford5K, Oxford105K, Paris6K, and Paris106K, and one person re-identification dataset, i.e., Market-1501. Extensive experiments show that the proposed method (1) preserves the original model capacity while pruning (2) and achieves superior performance to other widely-used pruning methods.
Fundamental bounds on qubit reset

Qubit reset is a basic prerequisite for operating quantum devices, requiring the export of entropy. The fastest and most accurate way to reset a qubit is obtained by coupling the qubit to an ancilla on demand. Here, we derive fundamental bounds on qubit reset in terms of maximum fidelity and minimum time, assuming control over the qubit and no control over the ancilla. Using the Cartan decomposition of the Lie algebra of qubit plus two-level ancilla, we identify the types of interaction and controls for which the qubit can be purified. For these configurations, we show that a time-optimal protocol consists of purity exchange between qubit and ancilla brought into resonance, where the maximum fidelity is identical for all cases but the minimum time depends on the type of interaction and control. Furthermore, we find the maximally achievable fidelity to increase with the size of the ancilla Hilbert space, whereas the reset time remains constant.
Teaching Performance Modeling in the era of millennials

Performance Modeling (PM) teaching started in the early 70s and reached its peak in the 80s. From those years and until today computing systems have deeply changed. Moreover, in the last two decades an economical crisis has involved the educational system, while the new generations show new learning modes. In this time, new literature has developed about learning and teaching. Rarely highlighting the critical issues. Higher learning is changing its role maybe unawares. In this paper, the author starts from a close examination of the state of the art of PM courses in Universities around the world and tries to highlight the main critical issues of teaching nowadays. The paper has not the aim to give answers but mainly that to open the way to reflections and discussions.
Enhancing OBDA Query Translation over Tabular Data with Morph-CSV

Ontology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets (e.g., relational database, CSV, JSON), either by materializing integrated data into RDF or by performing on-the-fly integration via SPARQL-to-SQL query translation. In the specific case of tabular datasets comprised of several CSV or Excel files, query translation approaches have been applied taking as input a lightweight schema with table and column names, and considering each source as a single table that can be loaded into a relational database system (RDB). This naive approach does not consider implicit constraints in this type of data, e.g., referential integrity among data sources, datatypes, or data integrity; We propose Morph-CSV, a framework that enforces constraints and can be used together with any SPARQL-to-SQL OBDA engine. Morph-CSV resorts to both a Constraints component and a set of operators that apply each type of constraint to the input with the aim of enhancing query completeness and performance. We evaluate Morph-CSV against a set of real-world open tabular datasets in the domain of the public transport; Morph-CSV is compared with existing approaches in terms of query result completeness and performance.
Peculiar velocity cosmology with type Ia supernovae

Type Ia Supernovae have yet again the opportunity to revolutionize the field of cosmology as the new generation of surveys are acquiring thousands of nearby SNeIa opening a new era in cosmology: the direct measurement of the growth of structure parametrized by $fD$. This method is based on the SNeIa peculiar velocities derived from the residual to the Hubble law as direct tracers of the full gravitational potential caused by large scale structure. With this technique, we could probe not only the properties of dark energy, but also the laws of gravity. In this paper we present the analytical framework and forecasts. We show that ZTF and LSST will be able to reach 5\% precision on $fD$ by 2027. Our analysis is not significantly sensitive to photo-typing, but known selection functions and spectroscopic redshifts are mandatory. We finally introduce an idea of a dedicated spectrograph that would get all the required information in addition to boost the efficiency to each SNeIa so that we could reach the 5\% precision within the first two years of LSST operation and the few percent level by the end of the survey.
What's a Good Prediction? Issues in Evaluating General Value Functions Through Error

Constructing and maintaining knowledge of the world is a central problem for artificial intelligence research. Approaches to constructing an agent's knowledge using predictions have received increased amounts of interest in recent years. A particularly promising collection of research centres itself around architectures that formulate predictions as General Value Functions (GVFs), an approach commonly referred to as \textit{predictive knowledge}. A pernicious challenge for predictive knowledge architectures is determining what to predict. In this paper, we argue that evaluation methods---i.e., return error and RUPEE---are not well suited for the challenges of determining what to predict. As a primary contribution, we provide extended examples that evaluate predictions in terms of how they are used in further prediction tasks: a key motivation of predictive knowledge systems. We demonstrate that simply because a GVF's error is low, it does not necessarily follow the prediction is useful as a cumulant. We suggest evaluating 1) the relevance of a GVF's features to the prediction task at hand, and 2) evaluation of GVFs by \textit{how} they are used. To determine feature relevance, we generalize AutoStep to GTD, producing a step-size learning method suited to the life-long continual learning settings that predictive knowledge architectures are commonly deployed in. This paper contributes a first look into evaluation of predictions through their use, an integral component of predictive knowledge which is as of yet explored.
A universal formula for the relativistic correction to the mutual friction coupling time-scale in neutron stars

Vortex-mediated mutual friction governs the coupling between the superfluid and normal components in neutron star interiors. By, for example, comparing precise timing observations of pulsar glitches with theoretical predictions it is possible to constrain the physics in the interior of the star, but to do so an accurate model of the mutual friction coupling in general relativity is needed. We derive such a model directly from Carter's multifluid formalism, and study the vortex structure and coupling time-scale between the components in a relativistic star. We calculate how general relativity modifies the shape and the density of the quantized vortices and show that, in the quasi-Schwarzschild coordinates, they can be approximated as straight lines for realistic neutron star configurations. Finally, we present a simple universal formula (given as a function of the stellar compactness alone) for the relativistic correction to the glitch rise-time, which is valid under the assumption that the superfluid reservoir is in a thin shell in the crust or in the outer core. This universal relation can be easily employed to correct, a posteriori, any Newtonian estimate for the coupling time-scale, without any additional computational expense.
Forecasting Corn Yield With Machine Learning Ensembles

The emergence of new technologies to synthesize and analyze big data with high-performance computing has increased our capacity to more accurately predict crop yields. Recent research has shown that machine learning (ML) can provide reasonable predictions faster and with higher flexibility compared to simulation crop modeling. However, a single machine learning model can be outperformed by a “committee” of models (machine learning ensembles) that can reduce prediction bias, variance, or both and is able to better capture the underlying distribution of the data. Yet, there are many aspects to be investigated with regard to prediction accuracy, time of the prediction, and scale. The earlier the prediction during the growing season the better, but this has not been thoroughly investigated as previous studies considered all data available to predict yields. This paper provides a machine leaning based framework to forecast corn yields in three US Corn Belt states (Illinois, Indiana, and Iowa) considering complete and partial in-season weather knowledge. Several ensemble models are designed using blocked sequential procedure to generate out-of-bag predictions. The forecasts are made in county-level scale and aggregated for agricultural district and state level scales. Results show that the proposed optimized weighted ensemble and the average ensemble are the most precise models with RRMSE of 9.5%. Stacked LASSO makes the least biased predictions (MBE of 53 kg/ha), while other ensemble models also outperformed the base learners in terms of bias. On the contrary, although random k-fold cross-validation is replaced by blocked sequential procedure, it is shown that stacked ensembles perform not as good as weighted ensemble models for time series data sets as they require the data to be non-IID to perform favorably. Comparing our proposed model forecasts with the literature demonstrates the acceptable performance of forecasts made by our proposed ensemble model. Results from the scenario of having partial in-season weather knowledge reveals that decent yield forecasts with RRMSE of 9.2% can be made as early as June 1st. Moreover, it was shown that the proposed model performed better than individual models and benchmark ensembles at agricultural district and state-level scales as well as county-level scale. To find the marginal effect of each input feature on the forecasts made by the proposed ensemble model, a methodology is suggested that is the basis for finding feature importance for the ensemble model. The findings suggest that weather features corresponding to weather in weeks 18–24 (May 1st to June 1st) are the most important input features.
PairNets: Novel Fast Shallow Artificial Neural Networks on Partitioned Subspaces

Traditionally, an artificial neural network (ANN) is trained slowly by a gradient descent algorithm such as the backpropagation algorithm since a large number of hyperparameters of the ANN need to be fine-tuned with many training epochs. To highly speed up training, we created a novel shallow 4-layer ANN called "Pairwise Neural Network" ("PairNet") with high-speed hyperparameter optimization. In addition, a value of each input is partitioned into multiple intervals, and then an n-dimensional space is partitioned into M n-dimensional subspaces. M local PairNets are built in M partitioned local n-dimensional subspaces. A local PairNet is trained very quickly with only one epoch since its hyperparameters are directly optimized one-time via simply solving a system of linear equations by using the multivariate least squares fitting method. Simulation results for three regression problems indicated that the PairNet achieved much higher speeds and lower average testing mean squared errors (MSEs) for the three cases, and lower average training MSEs for two cases than the traditional ANNs. A significant future work is to develop better and faster optimization algorithms based on intelligent methods and parallel computing methods to optimize both partitioned subspaces and hyperparameters to build the fast and effective PairNets for applications in big data mining and real-time machine learning.
Large-Scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction

Reservoir computing is a relatively recent computational paradigm that originates from a recurrent neural network, and is known for its wide-range of implementations using different physical technologies. Large reservoirs are very hard to obtain in conventional computers as both the computation complexity and memory usage grows quadratically. We propose an optical scheme performing reservoir computing over very large networks of up to $10^6$ fully connected photonic nodes thanks to its intrinsic properties of parallelism. Our experimental studies confirm that in contrast to conventional computers, the computation time of our optical scheme is only linearly dependent on the number of photonic nodes of the network, which is due to electronic overheads, while the optical part of computation remains fully parallel and independent of the reservoir size. To demonstrate the scalability of our optical scheme, we perform for the first time predictions on large multidimensional chaotic datasets using the Kuramoto-Sivashinsky equation as an example of a spatiotemporal chaotic system. Our results are extremely challenging for conventional Turing-von Neumann machines, and they significantly advance the state-of-the-art of unconventional reservoir computing approaches in general.