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Multifactorial experimental designs allow us to assess the contribution of several factors, and potentially their interactions, to one or several responses of interests. Following the principles of the partition of the variance advocated by Sir R.A. Fisher, the experimental responses are factored into the quantitative contribution of main factors and interactions. A popular approach to perform this factorization in both ANOVA and ASCA(+) is through General Linear Models. Subsequently, different inferential approaches can be used to identify whether the contributions are statistically significant or not. Unfortunately, the performance of inferential approaches in terms of Type I and Type II errors can be heavily affected by missing data, outliers and/or the departure from normality of the distribution of the responses, which are commonplace problems in modern analytical experiments. In this paper, we study these problem and suggest good practices of application., Comment: Submitted to Chemometrics and Intelligent Laboratory Systems

Topological defects, or singularities, play a key role in the statics and dynamics of complex systems. In magnetism, Bloch point singularities represent point defects that mediate the nucleation of textures such as skyrmions and hopfions. However, while the textures are typically stabilised in chiral magnets, the influence of chirality on the Bloch point singularities remains relatively unexplored. Here we harness advanced three-dimensional nanofabrication to explore the influence of chirality on Bloch point singularities by introducing curvature-induced symmetry breaking in a ferromagnetic nanowire. Combining X-ray magnetic microscopy with the application of in situ magnetic fields, we demonstrate that Bloch point singularity-containing domain walls are stabilised in straight regions of the sample, and determine that curvature can be used to tune the energy landscape of the Bloch points. Not only are we able to pattern pinning points but, by controlling the gradient of curvature, we define asymmetric potential wells to realise a robust Bloch point shift-register with non-reciprocal behaviour. These insights into the influence of symmetry and chirality on singularities offers a route to the controlled nucleation and propagation of topological textures, providing opportunities for logic and computing devices.

The Extreme Universe Space Observatory on a Super Pressure Balloon 1 (EUSO-SPB1) was launched in 2017 April from Wanaka, New Zealand. The plan of this mission of opportunity on a NASA super pressure balloon test flight was to circle the southern hemisphere. The primary scientific goal was to make the first observations of ultra-high-energy cosmic-ray extensive air showers (EASs) by looking down on the atmosphere with an ultraviolet (UV) fluorescence telescope from suborbital altitude (33~km). After 12~days and 4~hours aloft, the flight was terminated prematurely in the Pacific Ocean. Before the flight, the instrument was tested extensively in the West Desert of Utah, USA, with UV point sources and lasers. The test results indicated that the instrument had sensitivity to EASs of approximately 3 EeV. Simulations of the telescope system, telescope on time, and realized flight trajectory predicted an observation of about 1 event assuming clear sky conditions. The effects of high clouds were estimated to reduce this value by approximately a factor of 2. A manual search and a machine-learning-based search did not find any EAS signals in these data. Here we review the EUSO-SPB1 instrument and flight and the EAS search., Comment: 18 pages, 19 figures

This paper presents a non-zoned discrete dielectric lens comprising two or three matching layers to reduce the 50-110 GHz frequency range reflections. Based on Chebyshev and binomial multi-section transformers, the designed models use matching layers at the top and bottom. In addition, the presented designs use pins instead of the conventional slots for the matching layers, thus easing the manufacturing process. The results show that the broadband realized gain obtained using the proposed design is higher for both the two- and three-layer design than the commonly used quarter-wave transformer. A Binomial lens with two matchings layers using 38 unit cells is fabricated and illuminated by an open-ended waveguide to validate the simulation results obtained using CST Microwave Studio. The fabrication process uses stereolithography additive manufacturing.

We have grown high-quality magnetite micrometric islands on ruthenium stripes on sapphire through a combination of magnetron sputtering (Ru film), high-temperature molecular beam epitaxy (oxide islands), and optical lithography. The samples have been characterized by atomic force microscopy, Raman spectroscopy, X-ray absorption and magnetic circular dichroism in a photoemission microscope. The magnetic domains on the magnetite islands can be modified by the application of current pulses through the Ru stripes in combination with magnetic fields. The modification of the magnetic domains is explained by the Oersted field generated by the electrical current flowing through the stripes underneath the magnetite nanostructures. The fabrication method is applicable to a wide variety of rock salt and spinel oxides.

Due to the issues associated with rare-earth elements, there arises a strong need for magnets with properties between those of ferrites and rare-earth magnets that could substitute the latter in selected applications. Here, we produce a high remanent magnetization composite bonded magnet by mixing FeCo nanowire powders with hexaferrite particles. In the first step, metallic nanowires with diameters between 30 and 100 nm and length of at least 2 {\mu}m are fabricated by electrodeposition. The oriented as-synthesized nanowires show remanence ratios above 0.76 and coercivities above 199 kA/m and resist core oxidation up to 300 {\deg}C due to the existence of a > 8 nm thin oxide passivating shell. In the second step, a composite powder is fabricated by mixing the nanowires with hexaferrite particles. After the optimal nanowire diameter and composite composition are selected, a bonded magnet is produced. The resulting magnet presents a 20% increase in remanence and an enhancement of the energy product of 48% with respect to a pure hexaferrite (strontium ferrite) magnet. These results put nanowire-ferrite composites at the forefront as candidate materials for alternative magnets for substitution of rare earths in applications that operate with moderate magnet performance.

Structured sentiment analysis (SSA) aims to automatically extract people's opinions from a text in natural language and adequately represent that information in a graph structure. One of the most accurate methods for performing SSA was recently proposed and consists of approaching it as a dependency parsing task. Although we can find in the literature how transition-based algorithms excel in dependency parsing in terms of accuracy and efficiency, all proposed attempts to tackle SSA following that approach were based on graph-based models. In this article, we present the first transition-based method to address SSA as dependency parsing. Specifically, we design a transition system that processes the input text in a left-to-right pass, incrementally generating the graph structure containing all identified opinions. To effectively implement our final transition-based model, we resort to a Pointer Network architecture as a backbone. From an extensive evaluation, we demonstrate that our model offers the best performance to date in practically all cases among prior dependency-based methods, and surpass recent task-specific techniques on the most challenging datasets. We additionally include an in-depth analysis and empirically prove that the overall time-complexity cost of our approach is quadratic in the sentence length, being more efficient than top-performing graph-based parsers.

Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely used nowadays. These task-oriented dialogue systems require a semantic parsing module in order to process user utterances and understand the action to be performed. This semantic parsing component was initially implemented by rule-based or statistical slot-filling approaches for processing simple queries; however, the appearance of more complex utterances demanded the application of shift-reduce parsers or sequence-to-sequence models. Although shift-reduce approaches were initially considered the most promising option, the emergence of sequence-to-sequence neural systems has propelled them to the forefront as the highest-performing method for this particular task. In this article, we advance the research on shift-reduce semantic parsing for task-oriented dialogue. We implement novel shift-reduce parsers that rely on Stack-Transformers. This framework allows to adequately model transition systems on the Transformer neural architecture, notably boosting shift-reduce parsing performance. Furthermore, our approach goes beyond the conventional top-down algorithm: we incorporate alternative bottom-up and in-order transition systems derived from constituency parsing into the realm of task-oriented parsing. We extensively test our approach on multiple domains from the Facebook TOP benchmark, improving over existing shift-reduce parsers and state-of-the-art sequence-to-sequence models in both high-resource and low-resource settings. We also empirically prove that the in-order algorithm substantially outperforms the commonly-used top-down strategy. Through the creation of innovative transition systems and harnessing the capabilities of a robust neural architecture, our study showcases the superiority of shift-reduce parsers over leading sequence-to-sequence methods on the main benchmark., Comment: Final peer-reviewed manuscript accepted for publication in Cognitive Computation

Semantic role labeling (SRL) focuses on recognizing the predicate-argument structure of a sentence and plays a critical role in many natural language processing tasks such as machine translation and question answering. Practically all available methods do not perform full SRL, since they rely on pre-identified predicates, and most of them follow a pipeline strategy, using specific models for undertaking one or several SRL subtasks. In addition, previous approaches have a strong dependence on syntactic information to achieve state-of-the-art performance, despite being syntactic trees equally hard to produce. These simplifications and requirements make the majority of SRL systems impractical for real-world applications. In this article, we propose the first transition-based SRL approach that is capable of completely processing an input sentence in a single left-to-right pass, with neither leveraging syntactic information nor resorting to additional modules. Thanks to our implementation based on Pointer Networks, full SRL can be accurately and efficiently done in $O(n^2)$, achieving the best performance to date on the majority of languages from the CoNLL-2009 shared task., Comment: Final peer-reviewed manuscript accepted for publication in Knowledge-Based Systems

We analyze the micromagnetics of short longitudinal modulations of a high-magnetization material in cylindrical nanowires made of a soft-magnetic material of lower magnetization such as permalloy, combining magnetic microscopy, analytical modeling, and micromagnetic simulations. The mismatch of magnetization induces curling of magnetization around the axis in the modulations, in an attempt to screen the interfacial magnetic charges. The curling angle increases with modulation length, until a plateau is reached with nearly full charge screening for a specific length scale~$\Delta_\mathrm{mod}$, larger than the dipolar exchange length of any of the two materials. The curling circulation can be switched by the Oersted field arising from a charge current with typical magnitude $10^{12} A/m^{2}$ for a diameter of $\sim$100 nm, and reaching a maximum for $\Delta_\mathrm{mod}$.

Compilation of papers presented by the JEM-EUSO Collaboration at the 37th International Cosmic Ray Conference (ICRC), held on July 12-23, 2021 (online) in Berlin, Germany., Comment: html page with links to the JEM-EUSO Collaboration papers presented at ICRC-2021, Berlin, Germany

In order to achieve deep natural language understanding, syntactic constituent parsing is a vital step, highly demanded by many artificial intelligence systems to process both text and speech. One of the most recent proposals is the use of standard sequence-to-sequence models to perform constituent parsing as a machine translation task, instead of applying task-specific parsers. While they show a competitive performance, these text-to-parse transducers are still lagging behind classic techniques in terms of accuracy, coverage and speed. To close the gap, we here extend the framework of sequence-to-sequence models for constituent parsing, not only by providing a more powerful neural architecture for improving their performance, but also by enlarging their coverage to handle the most complex syntactic phenomena: discontinuous structures. To that end, we design several novel linearizations that can fully produce discontinuities and, for the first time, we test a sequence-to-sequence model on the main discontinuous benchmarks, obtaining competitive results on par with task-specific discontinuous constituent parsers and achieving state-of-the-art scores on the (discontinuous) English Penn Treebank., Comment: Final peer-reviewed manuscript accepted for publication in Neurocomputing

The spin Hall effect and its inverse are important spin-charge conversion mechanisms. The direct spin Hall effect induces a surface spin accumulation from a transverse charge current due to spin orbit coupling even in non-magnetic conductors. However, most detection schemes involve additional interfaces, leading to large scattering in reported data. Here we perform interface free x-ray spectroscopy measurements at the Cu L_{3,2} absorption edges of highly Bi-doped Cu (Cu_{95}Bi_{5}). The detected X-ray magnetic circular dichroism (XMCD) signal corresponds to an induced magnetic moment of (2.7 +/- 0.5) x 10-12 {\mu}_{B} A^{-1} cm^{2} per Cu atom averaged over the probing depth, which is of the same order as for Pt measured by magneto-optics. The results highlight the importance of interface free measurements to assess material parameters and the potential of CuBi for spin-charge conversion applications.

Dependency parsing is a crucial step towards deep language understanding and, therefore, widely demanded by numerous Natural Language Processing applications. In particular, left-to-right and top-down transition-based algorithms that rely on Pointer Networks are among the most accurate approaches for performing dependency parsing. Additionally, it has been observed for the top-down algorithm that Pointer Networks' sequential decoding can be improved by implementing a hierarchical variant, more adequate to model dependency structures. Considering all this, we develop a bottom-up-oriented Hierarchical Pointer Network for the left-to-right parser and propose two novel transition-based alternatives: an approach that parses a sentence in right-to-left order and a variant that does it from the outside in. We empirically test the proposed neural architecture with the different algorithms on a wide variety of languages, outperforming the original approach in practically all of them and setting new state-of-the-art results on the English and Chinese Penn Treebanks for non-contextualized and BERT-based embeddings., Comment: Final peer-reviewed manuscript accepted for publication in Information Fusion

This letter presents a closed-form integral-equation formulation that models the director tilting in nematic liquid crystals. The proposed formulation is computationally efficient compared to numerical methods and provides a physical insight on the matter. Our previous work is generalized from the one-constant approach to all the possible cases that cover the elastic constants. These constants determine in great extent the electrical properties of the liquid crystal, and subsequently, the response of the reconfigurable devices that take use of it. Therefore, a more precise but still simple modeling of their influence is pursued here. Simulations show good agreement with numerical implementations. In comparison, the error in the estimation is considerably reduced when using the integral-equation formulation, specially as the polarization voltage or the dielectric anisotropy decreases., Comment: IEEE Microwave and Wireless Components Letters

Discontinuous constituent parsers have always lagged behind continuous approaches in terms of accuracy and speed, as the presence of constituents with discontinuous yield introduces extra complexity to the task. However, a discontinuous tree can be converted into a continuous variant by reordering tokens. Based on that, we propose to reduce discontinuous parsing to a continuous problem, which can then be directly solved by any off-the-shelf continuous parser. To that end, we develop a Pointer Network capable of accurately generating the continuous token arrangement for a given input sentence and define a bijective function to recover the original order. Experiments on the main benchmarks with two continuous parsers prove that our approach is on par in accuracy with purely discontinuous state-of-the-art algorithms, but considerably faster., Comment: Camera-ready version to appear at proceedings of EMNLP 2021. 9 pages (incl. appendices)

In this paper, we study the wave propagation in a metallic parallel-plate structure with glide-symmetric elliptical holes. To perform this study, we derived a mode-matching technique based on the generalized Floquet theorem for glide-symmetric structures. This mode-matching technique benefits from a lower computational cost since it takes advantage of the glide symmetry in the structure. It also provides physical insight on the specific properties of Floquet modes propagating in these specific structures. With our analysis, we demonstrate that glide-symmetric structures with periodic elliptical holes exhibit an anisotropic refractive index over a wide range of frequencies. The equivalent refractive index can be controlled by tuning the dimensions of the holes. Finally, by combining the anisotropy related to the elliptical holes and transformation optics, a Maxwell fish-eye lens with a 33.33% size compression is designed. This lens operates in a wideband frequency range from 2.5 GHz to 10 GHz., Comment: 12 pages, 19 figures

We propose a transition-based approach that, by training a single model, can efficiently parse any input sentence with both constituent and dependency trees, supporting both continuous/projective and discontinuous/non-projective syntactic structures. To that end, we develop a Pointer Network architecture with two separate task-specific decoders and a common encoder, and follow a multitask learning strategy to jointly train them. The resulting quadratic system, not only becomes the first parser that can jointly produce both unrestricted constituent and dependency trees from a single model, but also proves that both syntactic formalisms can benefit from each other during training, achieving state-of-the-art accuracies in several widely-used benchmarks such as the continuous English and Chinese Penn Treebanks, as well as the discontinuous German NEGRA and TIGER datasets., Comment: Final peer-reviewed manuscript accepted for publication in Knowledge-Based Systems

Sequence-to-sequence constituent parsing requires a linearization to represent trees as sequences. Top-down tree linearizations, which can be based on brackets or shift-reduce actions, have achieved the best accuracy to date. In this paper, we show that these results can be improved by using an in-order linearization instead. Based on this observation, we implement an enriched in-order shift-reduce linearization inspired by Vinyals et al. (2015)'s approach, achieving the best accuracy to date on the English PTB dataset among fully-supervised single-model sequence-to-sequence constituent parsers. Finally, we apply deterministic attention mechanisms to match the speed of state-of-the-art transition-based parsers, thus showing that sequence-to-sequence models can match them, not only in accuracy, but also in speed., Comment: Proceedings of ACL 2020. 8 pages

Transition-based parsers implemented with Pointer Networks have become the new state of the art in dependency parsing, excelling in producing labelled syntactic trees and outperforming graph-based models in this task. In order to further test the capabilities of these powerful neural networks on a harder NLP problem, we propose a transition system that, thanks to Pointer Networks, can straightforwardly produce labelled directed acyclic graphs and perform semantic dependency parsing. In addition, we enhance our approach with deep contextualized word embeddings extracted from BERT. The resulting system not only outperforms all existing transition-based models, but also matches the best fully-supervised accuracy to date on the SemEval 2015 Task 18 English datasets among previous state-of-the-art graph-based parsers., Comment: Proceedings of ACL 2020. 12 pages

One of the most complex syntactic representations used in computational linguistics and NLP are discontinuous constituent trees, crucial for representing all grammatical phenomena of languages such as German. Recent advances in dependency parsing have shown that Pointer Networks excel in efficiently parsing syntactic relations between words in a sentence. This kind of sequence-to-sequence models achieve outstanding accuracies in building non-projective dependency trees, but its potential has not been proved yet on a more difficult task. We propose a novel neural network architecture that, by means of Pointer Networks, is able to generate the most accurate discontinuous constituent representations to date, even without the need of Part-of-Speech tagging information. To do so, we internally model discontinuous constituent structures as augmented non-projective dependency structures. The proposed approach achieves state-of-the-art results on the two widely-used NEGRA and TIGER benchmarks, outperforming previous work by a wide margin., Comment: Proceedings of AAAI 2020. 8 pages

Compilation of papers presented by the JEM-EUSO Collaboration at the 36th International Cosmic Ray Conference (ICRC), held July 24 through August 1, 2019 in Madison, Wisconsin., Comment: links to the 24 papers published in arXiv

For a numerical semigroup, we encode the set of primitive elements that are larger than its Frobenius number and show how to produce in a fast way the corresponding sets for its children in the semigroup tree. This allows us to present an efficient algorithm for exploring the tree up to a given genus. The algorithm exploits the second nonzero element of a numerical semigroup and the particular pseudo-ordinary case in which this element is the conductor., Comment: Accepted at Mathematics of Computation (AMS)

We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building $n$ attachments, with $n$ being the length of the input sentence. Similarly to the recent stack-pointer parser by Ma et al. (2018), we use the pointer network framework that, given a word, can directly point to a position from the sentence. However, our left-to-right approach is simpler than the original top-down stack-pointer parser (not requiring a stack) and reduces transition sequence length in half, from 2$n$-1 actions to $n$. This results in a quadratic non-projective parser that runs twice as fast as the original while achieving the best accuracy to date on the English PTB dataset (96.04% UAS, 94.43% LAS) among fully-supervised single-model dependency parsers, and improves over the former top-down transition system in the majority of languages tested., Comment: Proceedings of NAACL 2019. 7 pages

We introduce novel dynamic oracles for training two of the most accurate known shift-reduce algorithms for constituent parsing: the top-down and in-order transition-based parsers. In both cases, the dynamic oracles manage to notably increase their accuracy, in comparison to that obtained by performing classic static training. In addition, by improving the performance of the state-of-the-art in-order shift-reduce parser, we achieve the best accuracy to date (92.0 F1) obtained by a fully-supervised single-model greedy shift-reduce constituent parser on the WSJ benchmark., Comment: Proceedings of EMNLP 2018. 11 pages

EUSO-Balloon is a pathfinder mission for the Extreme Universe Space Observatory onboard the Japanese Experiment Module (JEM-EUSO). It was launched on the moonless night of the 25$^{th}$ of August 2014 from Timmins, Canada. The flight ended successfully after maintaining the target altitude of 38 km for five hours. One part of the mission was a 2.5 hour underflight using a helicopter equipped with three UV light sources (LED, xenon flasher and laser) to perform an inflight calibration and examine the detectors capability to measure tracks moving at the speed of light. We describe the helicopter laser system and details of the underflight as well as how the laser tracks were recorded and found in the data. These are the first recorded laser tracks measured from a fluorescence detector looking down on the atmosphere. Finally, we present a first reconstruction of the direction of the laser tracks relative to the detector., Comment: 18 pages, 14 figures

We propose an efficient dynamic oracle for training the 2-Planar transition-based parser, a linear-time parser with over 99% coverage on non-projective syntactic corpora. This novel approach outperforms the static training strategy in the vast majority of languages tested and scored better on most datasets than the arc-hybrid parser enhanced with the SWAP transition, which can handle unrestricted non-projectivity., Comment: Proceedings of NAACL-HLT 2018. 7 pages