Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of the Morphological Types in Grinding of Iron-Ore Products

The iron-ore products are characterized in order to investigate the effect of the mineralogical features on the grinding results into pelletizing plant. The studied materials are from four mines of the different regions of Iron Quadrangle, processed by flotation, magnetic separator and produced from screening with P80 in 8 mm. In this study, we apply for the first time the novelty of electron backscatter diffraction technique for the iron ore phase identification and quantification of the microstructural parameters. The mineralogical and microstructural parameters were introduced as a base in the specific grinding test applied in the industrial routine. Each sample was characterized based on the mineral shape, pores, fractures, polycrystalline hematite, and aggregate of hematite with goethite. The test results demonstrate that there is a strong relationship between the gain of the specific surface area, represented by the K-factor, and the morphological types.
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs

This article unveils a new relation between the Nishimori temperature parametrizing a distribution P and the Bethe free energy on random Erdős–Rényi graphs with edge weights distributed according to P. Estimating the Nishimori temperature being a task of major importance in Bayesian inference problems, as a practical corollary of this new relation, a numerical method is proposed to accurately estimate the Nishimori temperature from the eigenvalues of the Bethe Hessian matrix of the weighted graph. The algorithm, in turn, is used to propose a new spectral method for node classification in weighted (possibly sparse) graphs. The superiority of the method over competing state-of-the-art approaches is demonstrated both through theoretical arguments and real-world data experiments.
Probing Inflation with Large-Scale Structure Data: The Contribution of Information at Small Scales

Upcoming full-sky large-scale structure surveys such as Euclid can probe the primordial Universe. Using the specifications for the Euclid survey, we estimate the constraints on the inflation potential beyond slow-roll. We use mock Euclid and Planck data from fiducial cosmological models using the Wiggly Whipped Inflation (WWI) framework, which generates features in the primordial power spectrum. We include Euclid cosmic shear and galaxy clustering, with two setups (Conservative and Realistic) for the non-linear cut-off. We find that the addition of Euclid data gives an improvement in constraints in the WWI potential, with the Realistic setup providing marginal improvement over the Conservative for most models. This shows that Euclid may allow us to identify oscillations in the primordial spectrum present at intermediate to small scales. ar X iv :2 10 3. 03 29 1v 1 [ as tr oph . C O ] 4 M ar 2 02 1
Value distribution and uniqueness for q-difference of meromorphic functions Sharing Two Sets

In this paper, we investigate the value distribution for linear qdifference polynomials of transcendental meromorphic functions of zero order which improves the results of Xu, Liu and Cao ([26]). We also investigate the uniqueness of zero order meromorphic function with its q-difference operator sharing two sets with finite weight. Some examples have been exhibited which are relevant to the content of the paper.
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation

Sequential recommender systems aim to model users’ evolving interests from their historical behaviors, and hence make customized time-relevant recommendations. Compared with traditional models, deep learning approaches such as CNN and RNN have achieved remarkable advancements in recommendation tasks. Recently, the BERT framework also emerges as a promising method, benefited from its self-attention mechanism in processing sequential data. However, one limitation of the original BERT framework is that it only considers one input source of the natural language tokens. It is still an open question to leverage various types of information under the BERT framework. Nonetheless, it is intuitively appealing to utilize other side information, such as item category or tag, for more comprehensive depictions and better recommendations. In our pilot experiments, we found naive approaches, which directly fuse types of side information into the item embeddings, usually bring very little or even negative effects. Therefore, in this paper, we propose the NOn-inVasive self-Attention mechanism (NOVA) to leverage side information effectively under the BERT framework. NOVA makes use of side information to generate better attention distribution, rather than directly altering the item embeddings, which may cause information overwhelming. We validate the NOVA-BERT model on both public and commercial datasets, and our method can stably outperform the state-of-the-art models with negligible computational overheads.
The response of laser interferometric gravitational wave detectors beyond the eikonal equation

The response of Michelson interferometers to weak plane gravitational waves is computed at one order of accuracy beyond the eikonal equation. The modulation of the electromagnetic field amplitude and polarisation are taken into account by solving the transport equations of geometrical optics with boundary conditions adapted to laser interferometry. Considering both DC and balanced homodyne readout schemes, explicit formulae for the interferometer output signals are derived. These signals comprise perturbations of the optical path length, frequency and amplitude, and are shown to be insensitive to polarisation perturbations.
A Multi-Platform Analysis of Political News Discussion and Sharing on Web Communities

The news ecosystem encompasses a wide range of sources with varying levels of trustworthiness, and with public commentary giving different spins to the same stories. In this paper, we present a measurement pipeline able to identify news articles that discuss the same story and trace how they are shared on multiple online communities. We compile a list of 1,073 news websites and extract posts from four Web communities (Twitter, Reddit, 4chan, and Gab) that contain URLs from these sources. This yields a dataset of 38M posts containing 15.6M unique news URLs, spanning almost three years. We study the data along several axes, assessing the trustworthiness of shared news stories, analyzing how they are discussed, and measuring the influence various Web communities have in that. Our analysis shows that different communities discuss different types of news, with polarized communities like Gab and /r/The_Donald subreddit disproportionately referencing untrustworthy sources. We also find t hat f ringe c ommunities o ften h ave a disproportionate influence o n o ther p latforms w . r.t. p ushing n arratives around certain news, for example, about political elections, immigration, or foreign policy. In fact, fringe communities are seemingly successful in influencing the discussion on false narratives about news events on mainstream social networks.
PCP: Preemptive Circuit Padding against Tor circuit fingerprinting

Online anonymity and privacy has been based on confusing the adversary by creating indistinguishable network elements. Tor is the largest and most-used deployed anonymity system, designed against realistic modern adversaries. Recently, researchers have managed to fingerprint Tor's circuits - and hence the type of underlying traffic - simply by capturing and analyzing traffic traces. In this work, we study the circuit fingerprinting problem, isolating it from website fingerprinting, and revisit previous findings in this model, showing that accurate attacks are possible even when the application-layer traffic is identical. We then proceed to incrementally create defenses against circuit fingerprinting, using a generic adaptive padding framework for Tor based on WTF-PAD. We present a simple but high-latency defense, as well as a more advanced low-latency one which can effectively hide onion service circuits with no additional delays. We thoroughly evaluate both defenses, both analytically and experimentally, discovering new subtle fingerprints, but also showing the effectiveness of our defenses.
Learning to Extend Molecular Scaffolds with Structural Motifs

Recent advancements in deep learning-based modeling of molecules promise to accelerate in silico drug discovery. A plethora of generative models is available, building molecules either atom-by-atom and bond-by-bond or fragment-by-fragment. However, many drug discovery projects require a fixed scaffold to be present in the generated molecule, and incorporating that constraint has only recently been explored. Here, we propose MoLeR, a graph-based model that naturally supports scaffolds as initial seed of the generative procedure, which is possible because it is not conditioned on the generation history. Our experiments show that MoLeR performs comparably to state-of-the-art methods on unconstrained molecular optimization tasks, and outperforms them on scaffold-based tasks, while being an order of magnitude faster to train and sample from than existing approaches. Furthermore, we show the influence of a number of seemingly minor design choices on the overall performance.
Rissanen Data Analysis: Examining Dataset Characteristics via Description Length

We introduce a method to determine if a certain capability helps to achieve an accurate model of given data. We view labels as being generated from the inputs by a program composed of subroutines with different capabilities, and we posit that a subroutine is useful if and only if the minimal program that invokes it is shorter than the one that does not. Since minimum program length is uncomputable, we instead estimate the labels' minimum description length (MDL) as a proxy, giving us a theoretically-grounded method for analyzing dataset characteristics. We call the method Rissanen Data Analysis (RDA) after the father of MDL, and we showcase its applicability on a wide variety of settings in NLP, ranging from evaluating the utility of generating subquestions before answering a question, to analyzing the value of rationales and explanations, to investigating the importance of different parts of speech, and uncovering dataset gender bias.
GROWTH, YIELD AND YIELD COMPONENTS OF LOWLAND RICE AS INFLUENCED BY AMMONIUM SULFATE AND UREA FERTILIZATION

Nitrogen (N) is one of the most important nutrients in increasing lowland rice yield. Two greenhouse experiments were conducted to evaluate influence of ammonium sulfate and urea fertilization on growth, yield and yield components of lowland rice. The nitrogen rates used were 0, 50, 100, 150, 300 and 400 mg N kg−1 of soil. Shoot dry weight and grain yield were significantly (P < 0.01) increased in a quadratic fashion when N rate increased from 0 to 400 mg kg−1 by ammonium sulfate as well as urea fertilization. Maximum grain yield was obtained at 168 mg N kg−1 soil by ammonium sulfate and at 152 mg N kg−1 soil by urea. Maximum grain yield at average N rate (160 mg kg−1) was 22% higher with the application of ammonium sulfate compared to urea, indicating superiority of ammonium sulfate compared to urea. Rice yield components, N uptake and use efficiency were significantly influenced with the increasing N rate from 0 to 400 mg kg−1 of soil by both the sources of N. Plant height, shoot dry weight, grain harvest index, 1000 grain weight and N uptake and use efficiency in shoot and grain had significant positive association with grain yield. However, spikelet sterility was negatively associated with grain yield. Soil pH, soil calcium, phosphorus, and potassium contents were significantly influenced by N treatments with urea fertilization. These soil properties were not influenced significantly by ammonium sulfate treatment, except P content.
M ar 2 02 1 The deflection angle of light by black holes in bumblebee gravity

Í. D. D. Carvalho , G. Alencar , W.M. Mendes , and R. R. Landim 1§ Universidade Federal do Ceará (UFC), Departamento de F́ısica, Campus do Pici, Fortaleza CE, C.P. 6030, 60455-760 Brazil International Institute of Physics, Federal University of Rio Grande do Norte, Campus Universitário, Lagoa Nova, Natal, RN 59078-970, Brazil. and Departamento de Ensino, Instituto Federal de Educação, Ciência e Tecnologia do Ceará (IFCE), Campus Crateús, 63700-000, Crateús, CE, Brasil. (Dated: March 8, 2021)
M ar 2 02 1 Electromagnetic shock waves in the Quantum Vacuum in Born Electrodynamics

We study two counter–propagating electromagnetic waves in the vacuum within the framework of the Born–Infeld theory in quantum electrodynamics. By choosing the crossed field case E ·B = 0, i.e. G = 0, we present the solution of a statical problem which is identical for both the Born and the Born–Infeld electrodynamics. The Born theory exhibits birefringence effect contrary to the Born–Infeld theory, therefore we are able to investigate shock wave development. In this paper, we show that the non–linear field equations decouple for ordinary wave case using self-similar solutions and we demonstrate the wave steepening, shock wave formation and higher-harmonic generation. We discuss the phase shift and the cross section of the process. This work serves as a preliminary study towards analyzing the shock waves in the nonstatical case in the Born–Infeld theory.
Testing for a Random Walk Structure in the Frequency Evolution of a Tone in Noise

Inference and hypothesis testing are typically constructed on the basis that a specific model holds for the data. To determine the veracity of conclusions drawn from such data analyses, one must be able to identify the presence of the assumed structure within the data. In this paper, a model verification test is developed for the presence of a random walk-like structure in the variations in the frequency of complex-valued sinusoidal signals measured in additive Gaussian noise. This test evaluates the joint inference of the random walk hypothesis tests found in economics literature that seek random walk behaviours in time series data, with an additional test to account for how the random walk behaves in frequency space.
Experimental hierarchy and optimal robustness of quantum correlations of two-qubit states with controllable white noise

Kateřina Jiráková ,1,* Antonín Černoch ,2,† Karel Lemr ,1,‡ Karol Bartkiewicz ,1,3,§ and Adam Miranowicz 3,‖ 1Joint Laboratory of Optics of Palacký University and Institute of Physics of Czech Academy of Sciences, 17. listopadu 12, 779 00 Olomouc, Czech Republic 2Institute of Physics of the Czech Academy of Sciences, Joint Laboratory of Optics of PU and IP AS CR, 17. listopadu 50A, 772 07 Olomouc, Czech Republic 3Institute of Spintronics and Quantum Information, Faculty of Physics, Adam Mickiewicz University, 61-614 Poznań, Poland
Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness

We propose a variational autoencoder architecture to model both ignorable and nonignorable missing data using pattern-set mixtures as proposed by Little (1993). Our model explicitly learns to cluster the missing data into missingness pattern sets based on the observed data and missingness masks. Underpinning our approach is the assumption that the data distribution under missingness is probabilistically semi-supervised by samples from the observed data distribution. Our setup trades off the characteristics of ignorable and nonignorable missingness and can thus be applied to data of both types. We evaluate our method on a wide range of data sets with different types of missingness and achieve state-of-the-art imputation performance. Our model outperforms many common imputation algorithms, especially when the amount of missing data is high and the missingness mechanism is nonignorable.
Hybrid Point Cloud Semantic Compression for Automotive Sensors: A Performance Evaluation

In a fully autonomous driving framework, where vehicles operate without human intervention, information sharing plays a fundamental role. In this context, new network solutions have to be designed to handle the large volumes of data generated by the rich sensor suite of the cars in a reliable and efficient way. Among all the possible sensors, Light Detection and Ranging (LiDAR) can produce an accurate 3D point cloud representation of the surrounding environment, which in turn generates high data rates. For this reason, efficient point cloud compression is paramount to alleviate the burden of data transmission over bandwidth-constrained channels and to facilitate real-time communications. In this paper, we propose a pipeline to efficiently compress LiDAR observations in an automotive scenario. First, we leverage the capabilities of RangeNet++, a Deep Neural Network (DNN) used to semantically infer point labels, to reduce the channel load by selecting the most valuable environmental data to be disseminated. Second, we compress the selected points using Draco, a 3D compression algorithm which is able to obtain compression up to the quantization error. Our experiments, validated on the Semantic KITTI dataset, demonstrate that it is possible to compress and send the information at the frame rate of the LiDAR, thus achieving real-time performance.
Hierarchical Transformer for Multilingual Machine Translation

The choice of parameter sharing strategy in multilingual machine translation models determines how optimally parameter space is used and hence, directly influences ultimate translation quality. Inspired by linguistic trees that show the degree of relatedness between different languages, the new general approach to parameter sharing in multilingual machine translation was suggested recently. The main idea is to use these expert language hierarchies as a basis for multilingual architecture: the closer two languages are, the more parameters they share. In this work, we test this idea using the Transformer architecture and show that despite the success in previous work there are problems inherent to training such hierarchical models. We demonstrate that in case of carefully chosen training strategy the hierarchical architecture can outperform bilingual models and multilingual models with full parameter sharing.
Global overview on grid‐parity

Grid‐parity is a very important milestone for further photovoltaic (PV) diffusion. A grid‐parity model is presented, which is based on levelized cost of electricity (LCOE) coupled with the experience curve approach. Relevant assumptions for the model are given, and its key driving forces are discussed in detail. Results of the analysis are shown for more than 150 countries and a total of 305 market segments all over the world, representing 98.0% of world population and 99.7% of global gross domestic product. High PV industry growth rates enable a fast reduction of LCOE. Depletion of fossil fuel resources and climate change mitigation forces societies to internalize these effects and pave the way for sustainable energy technologies. First grid‐parity events occur right now. The 2010s are characterized by ongoing grid‐parity events throughout the most regions in the world, reaching an addressable market of about 75–90% of total global electricity market. In consequence, new political frameworks for maximizing social benefits will be required. In parallel, PV industry tackle its next milestone, fuel‐parity. In conclusion, PV is on the pathway to become a highly competitive energy technology. Copyright © 2012 John Wiley & Sons, Ltd.
Experiments on the structure and scaling of hypersonic turbulent boundary layers

Particle image velocimetry and filtered Rayleigh scattering experiments were performed over a range of Reynolds numbers to study the scaling and structure of a smooth, flat-plate turbulent boundary layer with a free stream Mach number of 7.5. The measurements indicate few, if any, dynamic differences due to Mach number. Mean and fluctuating streamwise velocities in the outer layer show strong similarity to incompressible flows at comparable Reynolds numbers when scaled according to van Driest and Morkovin. In addition, correlation lengths and structure angles based on velocity statistics were found to be less sensitive to compressibility than indicated by previous studies based on density fields or mass-weighted statistics, suggesting that the density and velocity fields obey different scaling. Finally, the boundary layer displays uniform momentum zones, with the number of these zones similar to incompressible boundary layers at comparable Reynolds numbers.
Sorption Of Molybdenum In Soils Field-equilibrated With Biosolids

Molybdenum (Mo) adsorption by soil is a function of several chemical factors, including added Mo load, solution pH, competing anion concentration, and iron (Fe) and aluminum (Al) oxide concentration in the soil, all of which can be changed by biosolids additions. The objective of this study was to determine the effects of biosolids on Mo sorption in two sandy Florida soils field-equilibrated with various biosolids. Molybdenum sorption was determined on surface samples of soils taken for up to 4 yr after biosolids amendment. Various parameters (soil pH, oxalate extractable-Fe and Al, and complementary ion concentrations) were measured and related to Mo sorption to explain biosolids effects. Biosolids application had only a small effect on Mo sorption as a result of added Fe and Al oxides in both soils studied. Molybdenum sorption was strongly related to soil pH and was maximized at soil pH values <4 to 5 for both soils studied. Although biosolids-additions had only a small effect on Mo sorption as a result of added Fe and Al oxides, biosolids (and other amendments) can change the pH of the soil environment, which can dramatically impact Mo retention and release. #Contribution of the Florida Agric. Exp. Stn. Journal Series No. R-08345.
Current-density distribution on a plane cathode in DC glow and streamer corona regimes in air

The variation of current densities on a plane cathode in air has been investigated in a positive point-plane gap configurations. This work is concerned with the effects of gap spacing, point diameter and humidity variations on the current density profiles, according to the type of corona regime produced at the point anode during predischarge development. Cases are considered where the point electrode is energized by a positive DC voltage producing either a pure glow or streamers, according to given particular experimental conditions such as point-plane gap spacing, point diameter and ambient absolute humidity. The results obtained show that the current density variation for positive corona in air does not always follow Warburg's law but depends upon the type of corona regime generated at the HV point anode. Comparisons are made with negative point-plane gaps. >