Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the Substrate Complexity and Parameter Choice on the Reproducibility of Results

Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodological pitfalls in the design of the biomimicking geometrical configurations and the simulation parameters can lead to approximation biases. Such pitfalls affect the reliability of the estimated signal, as well as its validity and reproducibility as ground truth data. In this work, we first present a set of experiments in order to study three critical pitfalls encountered in the design of MCDS in the literature, namely, the number of simulated particles and time steps, simplifications in the intra-axonal substrate representation, and the impact of the substrate's size on the signal stemming from the extra-axonal space. The results obtained show important changes in the simulated signals and the recovered microstructure features when changes in those parameters are introduced. Thereupon, driven by our findings from the first studies, we outline a general framework able to generate complex substrates. We show the framework's capability to overcome the aforementioned simplifications by generating a complex crossing substrate, which preserves the volume in the crossing area and achieves a high packing density. The results presented in this work, along with the simulator developed, pave the way toward more realistic and reproducible Monte-Carlo simulations for Diffusion-Weighted MRI.
On the relation between Binomial Theorem and Discrete Convolution of piecewise defined Power Function

In this manuscript we establish a relation between Binomial theorem and discrete convolution of piecewise defined power function. We show that Binomial expansion of s-powered sum (a + b)s, s ≥ 1, a + b ≥ 1 is equivalent to the sum of consequent discrete convolutions of piecewise defined power function multiplied by the certain real coefficients. In addition, we generalise relation between Binomial theorem and discrete convolution of piecewise defined power function to Multinomial case.
Precision requirements for interferometric gridding in the analysis of a 21 cm power spectrum

Context. Experiments that try to observe the 21 cm redshifted signals from the epoch of reionisation (EoR) using interferometric low-frequency instruments have stringent requirements on the processing accuracy. Aims. We analyse the accuracy of radio interferometric gridding of visibilities with the aim to quantify the power spectrum bias caused by gridding. We do this ultimately to determine the suitability of different imaging algorithms and gridding settings for an analysis of a 21 cm power spectrum. Methods. We simulated realistic Low-Frequency Array (LOFAR) data and constructed power spectra with convolutional gridding and w stacking, w projection, image-domain gridding, and without w correction. These were compared against data that were directly Fourier transformed. The influence of oversampling, kernel size, w-quantization, kernel windowing function, and image padding were quantified. The gridding excess power was measured with a foreground subtraction strategy, for which foregrounds were subtracted using Gaussian progress regression, as well as with a foreground avoidance strategy. Results. Constructing a power spectrum with a significantly lower bias than the expected EoR signals is possible with the methods we tested, but requires a kernel oversampling factor of at least 4000, and when w-correction is used, at least 500 w-quantization levels. These values are higher than typically used values for imaging, but they are computationally feasible. The kernel size and padding factor parameters are less crucial. Of the tested methods, image-domain gridding shows the highest accuracy with the lowest imaging time. Conclusions. LOFAR 21 cm power spectrum results are not affected by gridding. Image-domain gridding is overall the most suitable algorithm for 21 cm EoR power spectrum experiments, including for future analyses of data from the Square Kilometre Array (SKA) EoR. Nevertheless, convolutional gridding with tuned parameters results in sufficient accuracy for interferometric 21 cm EoR experiments. This also holds for w stacking for wide-field imaging. The w-projection algorithm is less suitable because of the requirements for kernel oversampling, and a faceting approach is unsuitable because it causes spatial discontinuities.
Combinatorics of the geometry of Wilson loop diagrams I: equivalence classes via matroids and polytopes

Abstract Wilson loop diagrams are an important tool in studying scattering amplitudes of SYM $N=4$ theory and are known by previous work to be associated to positroids. We characterize the conditions under which two Wilson loop diagrams give the same positroid, prove that an important subclass of subdiagrams (exact subdiagrams) corresponds to uniform matroids, and enumerate the number of different Wilson loop diagrams that correspond to each positroid cell. We also give a correspondence between those positroids which can arise from Wilson loop diagrams and directions in associahedra.
Redistribution of stars and gas in the star formation deserts of barred galaxies

Bars strongly influence the distribution of gas and stars within the central regions of their host galaxies. This is particularly pronounced in the star formation desert (SFD) which is defined as two symmetrical regions either side of the bar that show a deficit in young stars. Previous studies proposed that, if star formation is truncated because of the influence of the bar, then the age distribution of stars within the SFD could be used to determine the epoch of bar formation. To test this, we study the properties of SFDs in six galaxies from zoom-in cosmological re-simulations. Age maps reveal old regions on both sides of the bars, with a lack of stars younger than 10 Myr, confirming the SFD phenomenon. Local star formation is truncated in the SFDs because after the bar forms, gas in these regions is removed on 1 Gyr time-scales. However, the overall age distribution of stars in the SFD does not show a sharp truncation after bar formation but rather a gradual downturn in comparison to that of the bar. This more subtle signature may still give information on bar formation epochs in observed galaxies, but the interpretation will be more difficult than originally hoped. The gradual drop in the SFD age distribution, instead of a truncation, is due to radial migration of stars born in the disc. The SFD is thus one of the only regions where an uncontaminated sample of stars only affected by radial migration can be studied.
On Efficient Data Transfers Across Geographically Dispersed Datacenters

As applications become more distributed to improve user experience and offer higher availability, businesses rely on geographically dispersed datacenters that host such applications more than ever. Dedicated inter-datacenter networks have been built that provide high visibility into the network status and flexible control over traffic forwarding to offer quality communication across the instances of applications hosted on many datacenters. These networks are relatively small, with tens to hundreds of nodes and are managed by the same organization that operates the datacenters which make centralized traffic engineering feasible. Using coordinated data transmission from the services and routing over the inter-datacenter network, one can optimize the network performance according to a variety of utility functions that take into account data transfer deadlines, network capacity consumption, and transfer completion times. In this dissertation, we study techniques and algorithms for fast and efficient data transfers across geographically dispersed datacenters over the inter-datacenter networks. We discuss different forms and properties of inter-datacenter transfers and present a generalized optimization framework to maximize an operator selected utility function. Next, in the several chapters that follow, we study, in detail, the problems of admission control for transfers with deadlines and inter-datacenter multicast transfers. For the admission control problem, our solutions offer significant speed up in the admission control process while offering almost identical performance in the total traffic admitted into the network. For the bulk multicasting problem, our techniques enable significant performance gain in receiver completion times with low computational complexity, which makes them highly applicable to inter-datacenter networks.
Optimized Face Recognition Technique based on PCA and RBF Neural Network

In the field of machine recognition and computer vision, face recognition using optimized Radial Basis Function (RBF) Network is a very efficient solution for the researchers working in this field. In face recognition, the main challenge is to obtain high recognition efficiency. In the present work, principal component method is used for feature extraction and feature reduction. The eigenvalues obtained are passed to the Radial Basis Function Network for classification. In this study, particle swarm optimization technique is used to optimize the centre and the width of the Radial Basis Function Network. The work is tested on three datasets: AT & T, Yale and CMU PIE. The results obtained show that the efficiency of the investigated work, in terms of recognition rate and the training time is better than existing neural Network based in Back Propagation.
Ruthenium(III)triphosphide RuP3: Preparation, crystal structure and properties

Rutheniumtriphosphide RuP3 has been prepared from the elements in a tin phosphide melt at 1270 Κ in sealed quartz ampoules. RuP3 is a semiconductor of «-type with a band gap of 1.67 eV. The compound is stable on air and to non-oxidizing acids. In the crystal structure (P i , a = 592.4(3) pm; b = 821.3(6) pm; c = 586.6(3) pm; α = 112.35(4)°; β = 107.41(4)°; 7 = 98.19(5)°, Ζ = 4) the catenation of Ρ atoms result in a novel Jo[P ]polyanion which differs by branching (charge disproportionation) from the hitherto known kinds. The RuP6 octahedra are condensed to double octahedra with common edges. This entities form cis chains with an e-arrangement (Ru2) linked by corner sharing double octahedra (Rul). In agreement with the diamagnetism (xmol = —81.10 emu/mol) the Ru atoms form Ru2 pairs with low spin d states, where the unpaired electrons are coupled to metal-metal bonds. The formal charges of the Ρ anions allow the differentiation of the two crystallographically independent metal-metal bond lengths </(Rul R u l ' ) = 286.9 pm and </(Ru2-Ru2') = 279.5 pm. The mean bond distances <7(P-P) = 219.7 pm and <7(Ru-P) = 236.6 pm are within known ranges and correspond to covalent 2e bonds.
Operationalizing Regulatory Focus in the Digital Age: Evidence from an E-Commerce Context

Regulatory focus theory (RFT) has been regarded as an important theory for understanding customer behavior in e-commerce; however, there is a significant gap between theoretical analysis with RFT and its practical applications. In particular, there is little research on how to identify the chronic regulatory focus of customers; as such, it is difficult to apply RFT in e-commerce operations. To fill this research gap, we propose an innovative method to operationalize customer regulatory focus from the affective dimension, leading to operationalized regulatory focus (ORF). In this regard, our study spearheads a new avenue of research on how social theories can be operationalized and applied in e-commerce operations. We first identify customers’ chronic regulatory focus (i.e., promotion focus or prevention focus) based on online review data using text mining, leading to an innovative method we refer to as “regulatory focus discovery. ” Then, we validate the computed results on regulatory focus by surveying corresponding customers included in the same dataset. Finally, we evaluate the applicability of ORF via an econometric analysis. In this article, we demonstrate that it is possible to compute regulatory focus of specific customers for the purpose of assessing their purchasing tendency. The theoretical and practical implications of ORF are discussed.
Developing Scene Understanding Neural Software for Realistic Autonomous Outdoor Missions

Abstract : We present a deep learning neural network model software implementation for improving scene understanding of realistic autonomous outdoor missions in complex and changing environments. Scene understanding for realistic outdoor missions has been considered an unsolved problem due to the uncertainty of inferring the mutual context of detected objects and the changing weather, terrain, and environmental surroundings. We report proof-of-principle progress in autonomously searching for and recognizing key activities or scenarios by identifying both salient objects and relevant environmental settings depicted in outdoor scenes. Importantly, we demonstrate autonomous detection of targeted scenarios using neural network models separately trained on both objects and places image databases. In addition, using instructive analysis of 5 representative real-world mission scenarios, we show that adding dynamic environmental data and physics-based modeling could minimize unpredictably by constraining neural predictions to physically realizable solutions.
REPtX compounds with structures related to AIB2- and MgAgAs-type (RE=Y, Rare earth element;Χ = Ρ, As, Sb)

Several new compounds REPtX (RE = Y, rare earth element except Eu; X = Ρ, As, Sb) have been synthesized and examined by Xray methods. The phosphides crystallize with AlB2-type structure. The arsenides possess a new AlB2-type derivative structure with puckered PtAs layers and cREPtA, » 4CAJB2. For the compounds Ä£PtSb we found a modified Caln2-type structure in case of the light rare earth elements, whereas the MgAgAs-type structure was assigned to YPtSb. The structures have been determined by single-crystal diffraction data for YbPtP (PEm2; a = 407.7 pm, c = 377.7 pm), Y PtAs (P63/mmc; a = 425.0 pm, c = 1516.5 pm), NdPtSb (/>63mc; a = 453.4 pm, c = 786.6 pm) and YPtSb (F43m; a = 653.8 pm). In all structures Pt and the pnicogen are arranged in ordered positions.
Latent Modeling of Unexpectedness for Recommendations

Unexpectedness constitutes an important factor for recommender system to improve user satisfaction and avoid filter bubble issues. Previous methods model unexpectedness in the feature space, making them difficult to capture the latent, complex and heterogeneous interactions between users and items. In this paper, we propose to model unexpectedness in the latent space and utilize a latent convex hull structure to provide unexpected recommendations, as illustrated in Figure 1. Extensive experiments on two real-world datasets demonstrate effectiveness of latent unexpectedness over explicit unexpectedness and show that the proposed model significantly outperforms baseline models in terms of unexpectedness measures while achieving the same level of accuracy.
Crystallographic aspects of the linear electro-optic effect

Aspects of symmetry and crystal chemistry of the linear electrooptic effect are analyzed on the basis of new experimental results. Relations between the linear electro-optic effect [r? ,*] (undamped) and the linear electrostrictive effect [diJk] are investigated by comparison of the longitudinal effects r¡ j j and The relation \d[ ! t |V|'"i 11Γ" è 1 is valid for 41 of the hitherto available data of 49 crystals. Guidelines for the search of crystals with high electro-optic effects are reviewed. The most important keywords for a "chemical engineering" of new electro-optic materials are : pyroelectric symmetry groups ; "polar" structural units ; structures with strongly asymmetric bonds ; molecules with high hyperpolarizabilities ; structures tending towards a dielectric instability.
Ferromagnetic soft continuum robots

A submillimeter-scale, soft continuum robot navigates through highly constrained environments by steering based on magnetic actuation. Small-scale soft continuum robots capable of active steering and navigation in a remotely controllable manner hold great promise in diverse areas, particularly in medical applications. Existing continuum robots, however, are often limited to millimeter or centimeter scales due to miniaturization challenges inherent in conventional actuation mechanisms, such as pulling mechanical wires, inflating pneumatic or hydraulic chambers, or embedding rigid magnets for manipulation. In addition, the friction experienced by the continuum robots during navigation poses another challenge for their applications. Here, we present a submillimeter-scale, self-lubricating soft continuum robot with omnidirectional steering and navigating capabilities based on magnetic actuation, which are enabled by programming ferromagnetic domains in its soft body while growing hydrogel skin on its surface. The robot’s body, composed of a homogeneous continuum of a soft polymer matrix with uniformly dispersed ferromagnetic microparticles, can be miniaturized below a few hundreds of micrometers in diameter, and the hydrogel skin reduces the friction by more than 10 times. We demonstrate the capability of navigating through complex and constrained environments, such as a tortuous cerebrovascular phantom with multiple aneurysms. We further demonstrate additional functionalities, such as steerable laser delivery through a functional core incorporated in the robot’s body. Given their compact, self-contained actuation and intuitive manipulation, our ferromagnetic soft continuum robots may open avenues to minimally invasive robotic surgery for previously inaccessible lesions, thereby addressing challenges and unmet needs in healthcare.
A nuclear role for the DEAD-box protein Dbp5 in tRNA export

Dbp5 is an essential DEAD-box protein that mediates nuclear mRNP export. Dbp5 also shuttles between nuclear and cytoplasmic compartments with reported roles in transcription, ribosomal subunit export, and translation; however, the mechanism(s) by which nucleocytoplasmic transport occurs and how Dbp5 specifically contributes to each of these processes remains unclear. Towards understanding the functions and transport of Dbp5 in Saccharomyces cerevisiae, alanine scanning mutagenesis was used to generate point mutants at all possible residues within a GFP-Dbp5 reporter. Characterization of the 456 viable mutants led to the identification of an N-terminal Xpo1-dependent nuclear export signal in Dbp5, in addition to other separation-of-function alleles, which together provide evidence that Dbp5 nuclear shuttling is not essential for mRNP export. Rather, disruptions in Dbp5 nucleocytoplasmic transport result in tRNA export defects, including changes in tRNA shuttling dynamics during recovery from nutrient stress.
Event Structures for Mixed Choice

In the context of models with mixed nondeterministic and probabilistic choice, we present a concurrent model based on partial orders, more precisely Winskel’s event structures. We study its relationship with the interleaving-based model of Segala’s probabilistic automata. Lastly, we use this model to give a truly concurrent semantics to an extension of CCS with probabilistic choice, and relate this concurrent semantics to the usual interleaving semantics, thus generalising existing results on CCS, event structures and labelled transition systems.
Protecting elections from social media manipulation

Rigorous causal analysis could help harden democracy against future attacks To what extent are democratic elections vulnerable to social media manipulation? The fractured state of research and evidence on this most important question facing democracy is reflected in the range of disagreement among experts. Facebook chief executive officer Mark Zuckerberg has repeatedly called on the U.S. government to regulate election manipulation through social media. But we cannot manage what we do not measure. Without an organized research agenda that informs policy, democracies will remain vulnerable to foreign and domestic attacks. Thankfully, social media's effects are, in our view, eminently measurable. Here, we advocate a research agenda for measuring social media manipulation of elections, highlight underutilized approaches to rigorous causal inference, and discuss political, legal, and ethical implications of undertaking such analysis. Consideration of this research agenda illuminates the need to overcome important trade-offs for public and corporate policy—for example, between election integrity and privacy. We have promising research tools, but they have not been applied to election manipulation, mainly because of a lack of access to data and lack of cooperation from the platforms (driven in part by public policy and political constraints).
Correcting Crowdsourced Annotations to Improve Detection of Outcome Types in Evidence Based Medicine

© 2019 for this paper by its authors. The validity and authenticity of annotations in datasets massively influences the performance of Natural Language Processing (NLP) systems. In other words, poorly annotated datasets are likely to produce fatal results in at-least most NLP problems hence misinforming consumers of these models, systems or applications. This is a bottleneck in most domains, especially in healthcare where crowdsourcing is a popular strategy in obtaining annotations. In this paper, we present a framework that automatically corrects incorrectly captured annotations of outcomes, thereby improving the quality of the crowdsourced annotations. We investigate a publicly available dataset called EBM-NLP, built to power NLP tasks in support of Evidence based Medicine (EBM) primarily focusing on health outcomes.
PQ-VAE: Efficient Recommendation Using Quantized Embeddings

Large neural recommendation models can be a challenge to deploy at scale. For recommendation services with a large number of users, the most powerful models may require an impractical amount of space to store the large dense vectors encoding each of the users’ tastes. Combining ideas from auto-encoder-based recommender systems, neural discrete representation learning (VQ-VAE), and product quantization (PQ), we propose PQ-VAE, a recommendation model that learns compact, discrete embeddings at only a small cost in accuracy.