bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated generation of consistent, diverse and structurally realistic graph models
Authors: Semeráth Oszkár , Babikian Aren A. , Chen Boqi , Li Chuning , Marussy Kristóf , Szárnyas Gábor , Varró Dániel 
Published: May 2021

AbstractIn this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios.
MKPM: Multi keyword-pair matching for natural language sentences
Authors: Lu Xin , Deng Yao , Sun Ting , Gao Yi , Feng Jun , Sun Xia , Sutcliffe Richard 
Published: May 2021

AbstractSentence matching is widely used in various natural language tasks, such as natural language inference, paraphrase identification and question answering. For these tasks, we need to understand the logical and semantic relationship between two sentences. Most current methods use all information within a sentence to build a model and hence determine its relationship to another sentence. However, the information contained in some sentences may cause redundancy or introduce noise, impeding the performance of the model. Therefore, we propose a sentence matching method based on multi keyword-pair matching (MKPM), which uses keyword pairs in two sentences to represent the semantic relationship between them, avoiding the interference of redundancy and noise. Specifically, we first propose a sentence-pair-based attention mechanismsp-attentionto select the most important word pair from the two sentences as a keyword pair, and then propose a Bi-task architecture to model the semantic information of these keyword pairs. The Bi-task architecture is as follows: 1. In order to understand the semantic relationship at the word level between two sentences, we design a word-pair task (WP-Task), which uses these keyword pairs to complete sentence matching independently. 2. We design a sentence-pair task (SP-Task) to understand the sentence level semantic relationship between the two sentences by sentence denoising. Through the integration of the two tasks, our model can understand sentences more accurately from the two granularities of word and sentence. Experimental results show that our model can achieve state-of-the-art performance in several tasks. Our source code is publicly available1.
Model risk in mean-variance portfolio selection: an analytic solution to the worst-case approach
Authors: Baviera Roberto , Bianchi Giulia 
Published: May 2021

AbstractIn this paper we consider the worst-case model risk approach described in Glasserman and Xu (Quant Finance 14(1):29–58, 2014). Portfolio selection with model risk can be a challenging operational research problem. In particular, it presents an additional optimisation compared to the classical one. We find the analytical solution for the optimal mean-variance portfolio selection in the worst-case scenario approach and for the special case with the additional constraint of a constant mean vector considered in Glasserman and Xu (Quant Finance 14(1):29–58, 2014). Moreover, we prove in two relevant cases—the minimum-variance case and the symmetric case, i.e. when all assets have the same mean—that the analytical solutions in the alternative model and in the nominal one are equal; we show that this corresponds to the situation when model risk reduces to estimation risk.
A Neural Network Based System for Efficient Semantic Segmentation of Radar Point Clouds
Authors: Cennamo Alessandro , Kaestner Florian , Kummert Anton 
Published: May 2021

AbstractThe last decade has witnessed important advancements in the field of computer vision and scene understanding, enabling applications such us autonomous vehicles. Radar is a commonly adopted sensor in automotive industry, but its suitability to machine learning techniques still remains an open question. In this work, we propose a neural network (NN) based solution to efficiently process radar data. We introduce RadarPCNN, an architecture specifically designed for performing semantic segmentation on radar point clouds. It uses PointNet$$++$$

+
+

as a building-block—enhancing the sampling stage with mean-shift—and an attention mechanism to fuse information. Additionally, we propose a machine learning radar pre-processing module that confers the network the ability to learn from radar features. We show that our solutions are effective, yielding superior performance than the state-of-the-art.
The Influence of Gas–Liquid Interfacial Transport Theory on Numerical Modelling of Plasma Activation of Water
Authors: Silsby J. A. , Simon S. , Walsh J. L. , Hasan M. I. 
Published: May 2021

AbstractPlasma activated water has shown great promise in a number of emerging application domains; yet the interaction between non-equilibrium plasma and liquid represents a complex multiphase process that is difficult to probe experimentally, necessitating the development of accurate numerical models. In this work, a global computational model was developed to follow the concentrations of aqueous reactive species in water treated using a surface barrier discharge in ambient air. While the two-film theory has long superseded other methods of modelling mass transfer in such areas of research as environmental and aerosol science, plasma modelling studies continue to use equilibrium and one-film theories. The transport of reactive species across the gas–liquid interface was therefore treated using the one-film and two-film theories, with the results compared to ascertain which is most appropriate for PAW modelling studies. Comparing the model-predicted concentrations to those measured, it was shown that concentrations of aqueous H+ and NO3− ions were better represented by the two-film theory, more closely fitting experimental measurements in trend and in magnitude by a factor of ten, while HNO2 and NO2− showed a slightly worse fit using this theory. This is attributed to the assumption in two-film theory of a gas-phase stagnant film which provides additional resistance to the absorption of hydrophilic species, which is absent in the one-film theory, which could be improved with a more accurate value of the Sherwood number for each species.
Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media
Authors: Santos Javier E. , Yin Ying , Jo Honggeun , Pan Wen , Kang Qinjun , Viswanathan Hari S. , Prodanović Maša , Pyrcz Michael J. , Lubbers Nicholas 
Published: May 2021

AbstractThe permeability of complex porous materials is of interest to many engineering disciplines. This quantity can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particular, the simulation convergence time scales poorly as the simulation domains become less porous or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e., porosity and tortuosity) have been proposed, but these features only partly summarize the domain, resulting in limited applicability. On the other hand, data-driven machine learning approaches have shown great promise for building more general models by virtue of accounting for the spatial arrangement of the domains’ solid boundaries. However, prior approaches building on the convolutional neural network (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a representative elementary volume (REV). As such, most prior work focused on homogeneous samples, where a small REV entails that the global nature of fluid flow could be mostly neglected, and accordingly, the memory bottleneck of addressing 3D domains with ConvNets was side-stepped. Therefore, important geometries such as fractures and vuggy domains could not be modeled properly. In this work, we address this limitation with a general multiscale deep learning model that is able to learn from porous media simulation data. By using a coupled set of neural networks that view the domain on different scales, we enable the evaluation of large ($$>512^3$$

>

512
3

) images in approximately one second on a single graphics processing unit. This model architecture opens up the possibility of modeling domain sizes that would not be feasible using traditional direct simulation tools on a desktop computer. We validate our method with a laminar fluid flow case using vuggy samples and fractures. As a result of viewing the entire domain at once, our model is able to perform accurate prediction on domains exhibiting a large degree of heterogeneity. We expect the methodology to be applicable to many other transport problems where complex geometries play a central role.
Micro productive failure and the acquisition of algebraic procedural knowledge
Authors: Ziegler Esther , Trninic Dragan , Kapur Manu 
Published: May 2021

AbstractProductive failure has shown positive effects on conceptual and transfer measures, but no clear effects on procedural measures. It is therefore an open question whether, and to what extent, productive failure methods may be used to enhance the learning of procedural skills. A typical productive failure study focuses on a single, complex concept; in contrast, procedural knowledge generally consists of a series of less-complex procedural steps. In this study, failure occasions were adapted to specifically fit procedural knowledge by introducing procedural problems prior to the formal instruction of relevant principles. These procedural problems offered brief but multiple occasions for failure, which we call micro productive failure. A total of 85 sixth-graders were introduced to algebraic expression simplification by providing problem-solving prior to instruction (PS-I condition), compared to providing problem-solving after instruction (I-PS condition). Findings reveal a stable effect of offering micro productive failure occasions for procedural learning; however, as anticipated, there were no effects on conceptual or transfer measures.
Labor clauses in trade agreements: Hidden protectionism?
Authors: Carrère Céline , Olarreaga Marcelo , Raess Damian 
Published: May 2021

AbstractWe explore the impact of the introduction and design of labor clauses (LCs) in preferential trade agreements (PTAs) on bilateral trade flows over the period 1990–2014. While it is not a priori clear if the inclusion of LCs in PTAs will decrease or increase bilateral trade, we expect the direction of trade to matter, that is, we expect to observe the (negative or positive) impact of LCs in the South-North trade configuration. We also expect, in that configuration, stronger LCs to yield stronger (negative or positive) effects on bilateral trade flows. Using a novel dataset on the content of labor provisions in PTAs, we find in line with our first expectation that while the introduction of LCs has on average no impact on bilateral trade flows, it increases exports of low and middle-income countries with weaker labor standards in North–South trade agreements. Consistent with our second expectation, this positive impact is mostly driven by LCs with institutionalized cooperation provisions. In contrast, LCs with strong enforcement mechanisms do not have a statistically significant impact on exports of developing countries in North–South PTAs. The results are inconsistent with the ideas that LCs are set for protectionist reasons or have protectionist effects, casting doubt on the logic for the reluctance of many developing countries to include LCs in their trade agreements.
Equal chances, unequal outcomes? Network-based evolutionary learning and the industrial dynamics of superstar firms
Authors: Schulz Jan , Mayerhoffer Daniel M. 
Published: May 2021

AbstractWith the advent of platform economies and the increasing availability of online price comparisons, many empirical markets now select on relative rather than absolute performance. This feature might give rise to the ‘winner takes all/most’ phenomenon, where tiny initial productivity differences amount to large differences in market shares. We study the effect of heterogeneous initial productivities arising from locally segregated markets on aggregate outcomes, e.g., regarding revenue distributions. Several of those firm-level characteristics follow distributional regularities or ‘scaling laws’ (Brock in Ind Corp Change 8(3):409–446, 1999). Among the most prominent are Zipf’s law describing the largest firms‘ extremely concentrated size distribution and the robustly fat-tailed nature of firm size growth rates, indicating a high frequency of extreme growth events. Dosi et al. (Ind Corp Change 26(2):187–210, 2017b) recently proposed a model of evolutionary learning that can simultaneously explain many of these regularities. We propose a parsimonious extension to their model to examine the effect for deviations in market structure from global competition, implicitly assumed in Dosi et al. (2017b). This extension makes it possible to disentangle the effects of two modes of competition: the global competition for sales and the localised competition for market power, giving rise to industry-specific entry productivity. We find that the empirically well-established combination of ‘superstar firms’ and Zipf tail is consistent only with a knife-edge scenario in the neighbourhood of most intensive local competition. Our model also contests the conventional wisdom derived from a general equilibrium setting that maximum competition leads to minimum concentration of revenue (Silvestre in J Econ Lit 31(1):105–141, 1993). We find that most intensive local competition leads to the highest concentration, whilst the lowest concentration appears for a mild degree of (local) oligopoly. Paradoxically, a level playing field in initial conditions might induce extreme concentration in market outcomes.
Magnetic resonance cisternography imaging findings related to the leakage of Gadolinium into the subarachnoid space
Authors: Nakamichi Rei , Taoka Toshiaki , Kawai Hisashi , Yoshida Tadao , Sone Michihiko , Naganawa Shinji 
Published: May 2021

Abstract
Purpose
To identify magnetic resonance cisternography (MRC) imaging findings related to Gadolinium-based contrast agent (GBCA) leakage into the subarachnoid space.

Materials and methods
The number of voxels of GBCA leakage (V-leak) on 3D-real inversion recovery images was measured in 56 patients scanned 4 h post-intravenous GBCA injection. Bridging veins (BVs) were identified on MRC. The numbers of BVs with surrounding cystic structures (BV-cyst), with arachnoid granulations protruding into the superior sagittal sinus (BV-AG-SSS) and the skull (BV-AG-skull), and including any of these factors (BV-incl) were recorded. Correlations between these variables and V-leak were examined based on the﻿ Spearman’s rank correlation coefficient. Receiver-operating characteristic (ROC) curves were generated to investigate the predictive performance of GBCA leakage.

Results
V-leak and the number of BV-incl were strongly correlated (r = 0.609, p < 0.0001). The numbers of BV-cyst and BV-AG-skull had weaker correlations with V-leak (r = 0.364, p = 0.006; r = 0.311, p = 0.020, respectively). The number of BV-AG-SSS was not correlated with V-leak. The ROC curve for contrast leakage exceeding 1000 voxels and the number of BV-incl had moderate accuracy, with an area under the curve of 0.871.

Conclusion
The number of BV-incl may be a predictor of GBCA leakage and a biomarker for waste drainage function without using GBCA.
Dynamics of a discrete-time pioneer–climax model
Authors: Gilbertson Nora M. , Kot Mark 
Published: May 2021

AbstractWe present a simple mathematical model for the dynamics of a successional pioneer–climax system using difference equations. Each population is subject to inter- and intraspecific competition; population growth is dependent on the combined densities of both species. Nine different geometric cases, corresponding to different orientations of the zero-growth isoclines, are possible for this system. We fully characterize the long-term dynamics of the model for each of the nine cases, uncovering diverse sets of potential behaviors. Competitive exclusion of the pioneer species and of the climax species are both possible depending on the relative strength of competition. Stable coexistence of both species may also occur; in two cases, a coexistence state is destabilized through a Neimark–Sacker bifurcation, and an attracting invariant circle is born. The invariant circle eventually disappears into thin air in a heteroclinic or homoclinic bifurcation, leading to the sudden transition of the system to an exclusion state. Neither global bifurcation has been observed in a discrete-time pioneer–climax model before. The homoclinic bifurcation is novel to all pioneer–climax models. We conclude by discussing the ecological implications of our results.
Comprehensive Management of Daily Living Activities, behavioral and Psychological Symptoms, and Cognitive Function in Patients with Alzheimer's Disease: A Chinese Consensus on the Comprehensive Management of Alzheimer's Disease
Authors: Jia Jianjun , Xu Jun , Liu Jun , Wang Yongjun , Wang Yanjiang , Cao Yunpeng , Guo Qihao , Qu Qiuming , Wei Cuibai , Wei Wenshi , Zhang Junjian , Yu Enyan 
Published: May 2021

AbstractAlzheimer's disease (AD) is the most common cognitive disorder in the elderly. Its main clinical manifestations are cognitive decline (C), behavioral and psychological symptoms (B), and a decline in the activities of daily living (A), also known as ABC symptoms. Early identification and evaluation of ABC symptoms are helpful for establishing the accurate diagnosis, comprehensive treatment, and prognosis of AD. To guide Chinese clinical practice for optimization of the comprehensive management of AD, in 2018, The Academy of Cognitive Disorder of China gathered 22 neurologists and gerontologists in China to build a consensus on the comprehensive management of AD. Based on a review of the evidence, the consensus summarizes the pathogenesis, pathological changes, clinical manifestations, evaluation, diagnosis, drug and non-drug treatment, and patient care for AD. Focus group discussion was used to establish a flowchart of comprehensive ABC management for AD patients. The new consensus provides a feasible AD management process for clinicians.
ScOSA system software: the reliable and scalable middleware for a heterogeneous and distributed on-board computer architecture
Authors: Lund Andreas , Haj Hammadeh Zain Alabedin , Kenny Patrick , Vishav Vishav , Kovalov Andrii , Watolla Hannes , Gerndt Andreas , Lüdtke Daniel 
Published: May 2021

AbstractDesigning on-board computers (OBC) for future space missions is determined by the trade-off between reliability and performance. Space applications with higher computational demands are not supported by currently available, state-of-the-art, space-qualified computing hardware, since their requirements exceed the capabilities of these components. Such space applications include Earth observation with high-resolution cameras, on-orbit real-time servicing, as well as autonomous spacecraft and rover missions on distant celestial bodies. An alternative to state-of-the-art space-qualified computing hardware is the use of commercial-off-the-shelf (COTS) components for the OBC. Not only are these components cheap and widely available, but they also achieve high performance. Unfortunately, they are also significantly more vulnerable to errors induced by radiation than space-qualified components. TheScOSA (Scalable On-board Computing for Space Avionics) Flight Experimentproject aims to develop an OBC architecture which avoids this trade-off by combining space-qualified radiation-hardened components (the reliable computing nodes, RCNs) together with COTS components (the high performance nodes, HPNs) into a single distributed system. To abstract this heterogeneous architecture for the application developers, we are developing a middleware for the aforementioned OBC architecture. Besides providing an monolithic abstraction of the distributed system, the middleware shall also enhance the architecture by providing additional reliability and fault tolerance. In this paper, we present the individual components comprising the middleware, alongside the features the middleware offers. Since theScOSA Flight Experimentproject is a successor of theOBC-NGand theScOSAprojects, its middleware is also a further development of the existing middleware. Therefore, we will present and discuss our contributions and plans for enhancement of the middleware in the course of the current project. Finally, we will present first results for the scalability of the middleware, which we obtained by conducting software-in-the-loop experiments of different sized scenarios.
Dual-purpose production of eggs and meat — Part 1: cockerels of crosses between layer and meat breeds achieve moderate growth rates while showing unimpaired animal welfare
Authors: Baldinger Lisa , Bussemas Ralf 
Published: May 2021

AbstractThe use of modern chicken genotypes with high egg or meat performance results in the ethically unacceptable practice of culling day-old male layer chicks because of their inefficient fattening performance. Dual-purpose genotypes with a balanced performance profile for both eggs and meat are one option to avoid this practice. In this study, four chicken crosses of a layer breed (White Rock or New Hampshire) and the meat breed Bresse Gauloise were compared under the conditions of organic agriculture. Purebred Bresse Gauloise and the layer hybrid Lohmann Sandy served as controls. Part 1 of this study focused on the fattening performance of the cockerels, which were reared together with the pullets. The birds were housed in a floor system (9.9–20.7 kg live weight per m2 at the end of week 15) with access to a green outdoor run. Live weight of the crosses before slaughter at the age of 15 weeks ranged between 2355 and 2447 g and did not differ significantly between the genotypes. With average daily gains of 22.1–22.8 g, the crosses grew slower than Bresse Gauloise males (26.1 g) but faster than Lohmann Sandy males (15.9 g). Welfare assessment indicated a generally high level of welfare with no foot pad lesions or hock burns on any of the cockerels. Fattening dual-purpose cockerels can therefore be an ethically desirable option with a high level of animal welfare and a better fattening performance than in male layer chicks, but still requires more resources than the fattening of specialised broilers.
Some q-supercongruences modulo the square and cube of a cyclotomic polynomial
Authors: Guo Victor J. W. , Schlosser Michael J. 
Published: May 2021

AbstractTwoq-supercongruences of truncated basic hypergeometric series containing two free parameters are established by employing specific identities for basic hypergeometric series. The results partly extend twoq-supercongruences that were earlier conjectured by the same authors and involveq-supercongruences modulo the square and the cube of a cyclotomic polynomial. One of the newly provedq-supercongruences is even conjectured to hold modulo the fourth power of a cyclotomic polynomial.