Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the economic pro ﬁ tability of fodder legume production for Green Biore ﬁ neries e A cost-bene ﬁ t analysis to evaluate farmers pro ﬁ tability Cleaner

Fodder legumes play a major role in developing sustainable agricultural production systems and contain a range of compounds, which can be utilized to produce a wide spectrum of materials currently man-ufactured from petroleum-based sources. Hence, if associated with Green Biore ﬁ nery technology, the use of fodder legumes brings about signi ﬁ cant advantages in terms of overall environmental sustainability. Since fodder legume production in Europe is currently very low, the objective of this study is to assess if a new value chain generated by Green Biore ﬁ neries can make fodder legume production pro ﬁ table for farmers, and therewith increase cultivation numbers. We conducted a ﬁ nancial cost-bene ﬁ t analysis of producing biomass from agricultural land in the federal state of Brandenburg (Germany) in three different production scenarios at two farm size levels. Costs, bene ﬁ ts, expected pro ﬁ ts and risks between the scenarios were quanti ﬁ ed. Fodder legume production for traditional fodder production was already able to increase the internal rate of return, while the production of feedstocks for Green Biore ﬁ neries, depending on prices paid for the legume juice showed an even higher pro ﬁ t potential. Therefore, in future agricultural production systems, fodder legumes should be part of crop rotations again. © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Immune Regulation src-Family Kinases in FCRL4-Mediated Involvement of the HCK and FGR

FCRL4 is an immunoregulatory receptor expressed by a subpopulation of memory B cells. These tissue-based cells express increased levels of the src-family kinases HCK and FGR. In this study, we investigate the roles of these src-family kinases in FCRL4-mediated immunoregulation of B cells in the context of previously unrecognized palmitoylation of the receptor. We observed enhanced phosphorylation of FCRL4 on tyrosine residues in the presence of the HCK p59 or FGR. This phosphorylation was markedly reduced in assays using a palmitoylation-defective mutant of FCRL4. In reporter gene studies, we observe that FCRL4 expression enhances CpG-mediated activation of NF- k B signaling. Surprisingly, using a reporter gene linked to activation of the MAPK substrate Elk-1 in response to Ag receptor ligation, we ﬁnd that FCRL4 has inhibitory activity in cells coexpressing FGR but an activating function in cells coexpressing HCK p59. We provide evidence that in primary memory B cells, expression of FCRL4 leads to increased expression of IL-10 in the presence of FGR or HCK p59 in response to CpG, but increased levels of IFN- g only in the context of coexpression of FGR. Our study supports the speciﬁc requirement of HCK p59 and FGR src-family kinases for FCRL4-mediated immunomodulatory activity and indicates that palmitoylation serves as an additional level of regulatory control of FCRL4. The Journal of Immunology , 2015, 194: 000–000.
Desempenho empreendedor nas indústrias criativas: propondo um modelo teórico

In recent decades, governments and research institutes have shown growing interest in the development of creative industries. These are industries that produce goods, products and services of a symbolic nature endowed with both economic and cultural value. In Brazil, they already answer for approximately 10 percent of the Gross Domestic Product GDP and are important employers. Professionals active in these industries play an strategic role as they innovate, transform creativity into valuable works and services, and lead the industries’ development. This paper aims to provide the fundamentals of a theoretical model of the entrepreneurial performance of creative professionals. The model is based on hypotheticaldeductive logic and uses the following constructs as antecedent, mediating or moderating variables: individualism and collectivism, centrality of work, self-regulation, entrepreneurial skills, activity sector, and demographic variables. In addition to theoretically and operationally discussing these constructs, the paper offers recommendations for researchers interested in empirically testing the proposed model.
On Compression Functions over Small Groups with Applications to Cryptography

In the area of cryptography, fully homomorphic encryption (FHE) enables any entity to perform arbitrary computation on encrypted data without decrypting the ciphertexts. An ongoing group-theoretic approach to construct FHE schemes uses a certain “compression” function F ( x ) implemented by group operators on a given ﬁnite group G (i.e., it is given by a sequence of elements of G and variable x ), which satisﬁes that F (1) = 1 and F ( σ ) = F ( σ 2 ) = σ where σ ∈ G is some element of order three. The previous work gave an example of such F over G = S 5 by just a heuristic approach. In this paper, we systematically study the possibilities of such F . We construct a shortest possible F over smaller group G = A 5 , and prove that no such F exists over other groups G of order up to 60 = | A 5 | . 2010 Mathematics Subject Classiﬁcation: 20D60, 94A60
The relation between quasars’ optical spectra and variability

Brightness variation is an essential feature of quasars, but its mechanism and relationship to other physical quantities are not understood well. We aimed to find the relationship between the optical variability and spectral features to reveal the regularity behind the random variation. It is known that a quasar’s Fe ii/Hβ flux ratio and equivalent width of [O iii]5007 are negatively correlated; this is called Eigenvector 1. In this work, we visualized the relationship between the position on this Eigenvector 1 (EV1) plane and how the brightness of the quasars had changed after ∼10 yr. We conducted three analyses, using a different quasar sample in each. The first analysis showed the relation between the quasars’ distributions on the EV1 plane and how much they had changed brightness, using 13438 Sloan Digital Sky Survey quasars. This result shows how brightness changes later are clearly related to the position on the EV1 plane. In the second analysis, we plotted the sources reported as “changing-look quasars” (or “changing-state quasars”) on the EV1 plane. This result shows that the position on the EV1 plane corresponds to the activity level of each source, and the bright or dim states of them are distributed on the opposite sides divided by the typical quasar distribution. In the third analysis, we examined the transition vectors on the EV1 plane using sources with multiple-epoch spectra. This result shows that the brightening and dimming sources move on a similar path and they reach a position corresponding to the opposite activity level. We also found this trend is opposite to the empirical rule that $R_{\rm {Fe\, \small {II}}}$ positively correlated with the Eddington ratio, which has been proposed based on the trends of a large number of quasars. From all these analyses, it is indicated that quasars tend to oscillate between both sides of the distribution ridge on the EV1 plane; each of them corresponds to a dim state and a bright state. This trend in optical variation suggests that significant brightness changes, such as changing-look quasars, are expected to repeat.
The impact of film thickness and melt-quenched phase on the phase transition characteristics of Ge2Sb2Te5

This study investigates the detailed phase transition behavior of Ge2Sb2Te5 (GST) thin films in the thickness range between 4–30 nm using a static laser tester. It was found that the crystallization time has a minimum for both as-deposited (around 12 nm) and melt-quenched (around 10 nm) amorphous materials. Capping the GST with a thin SiO2 layer changes this behavior and leads to a monotonic increase in crystallization time with film thickness for as-deposited amorphous samples but a decrease in time for melt-quenched, amorphous samples. The shortest crystallization time of about 7 ns was observed for the melt-quenched, amorphous 6 nm thick film. It was also found that the recrystallization time of melt-quenched, amorphous GST was shorter when the melt-quenched area was surrounded by GST in the hexagonal phase compared to GST in the rocksalt phase. The shorter recrystallization times for thinner, capped GST films indicate a promising scaling behavior of these materials for rewritable optical storage and p.
Learning Green's functions associated with time-dependent partial differential equations

Neural operators are a popular technique in scientific machine learning to learn a mathematical model of the behavior of unknown physical systems from data. Neural operators are especially useful to learn solution operators associated with partial differential equations (PDEs) from pairs of forcing functions and solutions when numerical solvers are not available or the underlying physics is poorly understood. In this work, we attempt to provide theoretical foundations to understand the amount of training data needed to learn time-dependent PDEs. Given input-output pairs from a parabolic PDE in any spatial dimension $n\geq 1$, we derive the first theoretically rigorous scheme for learning the associated solution operator, which takes the form of a convolution with a Green's function $G$. Until now, rigorously learning Green's functions associated with time-dependent PDEs has been a major challenge in the field of scientific machine learning because $G$ may not be square-integrable when $n>1$, and time-dependent PDEs have transient dynamics. By combining the hierarchical low-rank structure of $G$ together with randomized numerical linear algebra, we construct an approximant to $G$ that achieves a relative error of $\smash{\mathcal{O}(\Gamma_\epsilon^{-1/2}\epsilon)}$ in the $L^1$-norm with high probability by using at most $\smash{\mathcal{O}(\epsilon^{-\frac{n+2}{2}}\log(1/\epsilon))}$ input-output training pairs, where $\Gamma_\epsilon$ is a measure of the quality of the training dataset for learning $G$, and $\epsilon>0$ is sufficiently small.
RAZE: Region Guided Self-Supervised Gaze Representation Learning

—Automatic eye gaze estimation is an important prob- lem in vision based assistive technology with use cases in different emerging topics such as augmented reality, virtual reality and human-computer interaction. Over the past few years, there has been an increasing interest in unsupervised and self-supervised learning paradigms as it overcomes the requirement of large scale annotated data. In this paper, we propose RAZE, a Region guided self-supervised gAZE representation learning framework which leverage from non-annotated facial image data. RAZE learns gaze representation via auxiliary supervision i.e. pseudo-gaze zone classiﬁcation where the objective is to classify visual ﬁeld into different gaze zones (i.e. left, right and center) by leveraging the relative position of pupil-centers. Thus, we automatically anno- tate pseudo gaze zone labels of 154K web-crawled images and learn feature representations via ‘Ize-Net’ framework. ‘Ize-Net’ is a capsule layer based CNN architecture which can efﬁciently capture rich eye representation. The discriminative behaviour of the feature representation is evaluated on four benchmark datasets: CAVE, TabletGaze, MPII and RT-GENE . Additionally, we evaluate the generalizability of the proposed network on two other downstream task (i.e. driver gaze estimation and visual attention estimation) which demonstrate the effectiveness of the learnt eye gaze representation.
A Representation Modeling Based Language GAN with Completely Random Initialization

Text generative models trained via Maximum Likelihood Estimation (MLE) suffer from the notorious exposure bias problem, and Generative Adversarial Networks (GANs) are shown to have potential to tackle it. Existing language GANs adopt estimators like REINFORCE or continuous relaxations to model word distributions. The inherent limitations of such estimators lead current models to rely on pre-training techniques (MLE pre-training or pre-trained embeddings). Representation modeling methods which are free from those limitations, however, are seldom explored because of its poor performance in previous attempts. Our analyses reveal that invalid sampling method and unhealthy gradients are the main contributors to its unsatisfactory performance. In this work, we present two techniques to tackle these problems: dropout sampling and fully normalized LSTM. Based on these two techniques, we propose InitialGAN whose parameters are randomly initialized completely. Besides, we introduce a new evaluation metric, Least Coverage Rate, to better evaluate the quality of generated samples. The experimental results demonstrate that InitialGAN outperforms both MLE and other compared models. To the best of our knowledge, it is the ﬁrst time a language GAN can outperform MLE without any pre-training techniques.
Spatiotemporal Pattern Recognition in Single Mixed-Signal VLSI Neurons with Heterogeneous Dynamic Synapses

Mixed-signal neuromorphic processors with brain-like organization and device physics offer an ultra-low-power alternative to the unsustainable developments of conventional deep learning and computing. However, realizing the potential of such neuromorphic hardware requires efficient use of its heterogeneous, analog neurosynaptic circuitry with neurocomputational methods for sparse, spike-timing-based encoding and processing. Here, we investigate the use of balanced excitatory–inhibitory disynaptic lateral connections as a resource-efficient mechanism for implementing a thalamocortically inspired Spatiotemporal Correlator (STC) neural network without using dedicated delay mechanisms. We present hardware-in-the-loop experiments with a DYNAP-SE neuromorphic processor, in which receptive fields of heterogeneous coincidence-detection neurons in an STC network with four lateral afferent connections per column were mapped by random input-sampling. Furthermore, we demonstrate how such a neuron was tuned to detect a particular spatiotemporal feature by discrete address-reprogramming of the analog synaptic circuits. The energy dissipation of the disynaptic connections is one order of magnitude lower per lateral connection (0.65 nJ vs 9.6 nJ per spike) than in the former delay-based hardware implementation of the STC.
Vibrotactile Feedback to Make Real Walking in Virtual Reality More Accessible

This research aims to examine the effects of various vibrotactile feedback techniques on gait (i.e., walking patterns) in virtual reality (VR). Prior studies have demonstrated that gait disturbances in VR users are significant usability barriers. However, adequate research has not been performed to address this problem. In our study, 39 participants (with mobility impairments: 18, without mobility impairments: 21) performed timed walking tasks in a real-world environment and identical activities in a VR environment with different forms of vibrotactile feedback (spatial, static, and rhythmic). Within-group results revealed that each form of vibrotactile feedback improved gait performance in VR significantly (p<.001) relative to the no vibrotactile condition in VR for individuals with and without mobility impairments. Moreover, spatial vibrotactile feedback increased gait performance significantly (p<.001) in both participant groups compared to other vibrotactile conditions. The findings of this research will help to make real walking in VR more accessible for those with and without mobility impairments.
Streaming Tensor Train Approximation

Tensor trains are a versatile tool to compress and work with high-dimensional data and functions. In this work we introduce the Streaming Tensor Train Approximation (STTA), a new class of algorithms for approximating a given tensor $\mathcal T$ in the tensor train format. STTA accesses $\mathcal T$ exclusively via two-sided random sketches of the original data, making it streamable and easy to implement in parallel -- unlike existing deterministic and randomized tensor train approximations. This property also allows STTA to conveniently leverage structure in $\mathcal T$, such as sparsity and various low-rank tensor formats, as well as linear combinations thereof. When Gaussian random matrices are used for sketching, STTA is admissible to an analysis that builds and extends upon existing results on the generalized Nystr\"om approximation for matrices. Our results show that STTA can be expected to attain a nearly optimal approximation error if the sizes of the sketches are suitably chosen. A range of numerical experiments illustrates the performance of STTA compared to existing deterministic and randomized approaches.
Geodesic Model of HF QPOs Tested for Black Holes in Spacetimes Reflecting the Effect of Surrounding Dark Matter

Using the simple but robust model of a shell of dark matter (DM) around a Schwarzschild black hole (BH), represented by the mass ratio of the shell and BH ΔM/M, the shell extension Δr s and its inner radius r s, we study the influence of DM on the spacetime structure and geodesic motion, and provide a classification of the BH+DM shell spacetimes according to the properties of the stable circular geodesics governing Keplerian disks. We focus our attention on the epicyclic motion around circular geodesics that can be related to observational phenomena in X-ray radiation from Keplerian accretion disks, assumed to be influenced by the DM shell only gravitationally. We give the frequencies of the orbital and epicyclic motions and discuss their properties in terms of the parameters governing the DM shell. Using the frequencies in relevant variants of the standard geodesic model of high-frequency quasiperiodic oscillations (HF QPOs), we test the role of DM by fitting the HF QPO data from some microquasars and active galactic nuclei with supermassive BHs where no variant of the geodesic model applied in the standard vacuum BH background is able to explain the data. We thus provide a robust review of the applicability of the geodesic model of HF QPOs, and also provide limits on the amount of DM around a BH. We demonstrate that the geodesic model could be well applied to most observations of active galactic nuclei, with strong restrictions on the amount of invisible matter around BHs.
Incorporating white matter microstructure in the estimation of magnetic susceptibility in ex-vivo mouse brain

Purpose: To extend Quantitative susceptibility mapping (QSM) to account for local white matter (WM) microstructure and demonstrate its effect on ex-vivo mouse brain at ultrahigh field. Methods: Previous studies have shown that the MRI measured Larmor frequency also depends on local magnetic microstructure at the mesoscopic scale. Here we include effects from WM microstructure, by using our previous results for the mesoscopic Larmor frequency of solid cylinders with arbitrary orientations to describe WM axons in terms of concentric cylinders. We demonstrate how to combine multi-gradient echo (MGE) and diffusion MRI (dMRI) images of ex-vivo mouse brains acquired at ultrahigh field (16.4T) to estimate an apparent scalar susceptibility without impractical sample rotations. Results: We find the induced mesoscopic Larmor frequency due to WM microstructure to be substantial, with a magnitude up to 70% of the total frequency shift in highly anisotropic WM. This in turn changed the estimated susceptibility values by up to 56% in WM. Conclusion: Microstructural field effects impact susceptibility estimates, and should not be neglected when imaging anisotropic tissue such as brain WM.
Static Hardware Partitioning on RISC-V: Shortcomings, Limitations, and Prospects

On embedded processors that are increasingly equipped with multiple CPU cores, static hardware partitioning is an established means of consolidating and isolating workloads onto single chips. This architectural pattern is suitable for mixed-criticality workloads that need to satisfy both, real-time and safety requirements, given suitable hardware properties. In this work, we focus on exploiting contemporary virtualisation mechanisms to achieve freedom from interference respectively isolation between workloads. Possibilities to achieve temporal and spatial isolation-while maintaining real-time capabilities-include statically partitioning resources, avoiding the sharing of devices, and ascertaining zero interventions of superordinate control structures. This eliminates overhead due to hardware partitioning, but implies certain hardware capabilities that are not yet fully implemented in contemporary standard systems. To address such hardware limitations, the customisable and configurable RISC-V instruction set architecture offers the possibility of swift, unrestricted modifications. We present findings on the current RISC-V specification and its implementations that necessitate interventions of superordinate control structures. We identify numerous issues adverse to implementing our goal of achieving zero interventions respectively zero overhead: On the design level, and especially with regards to handling interrupts. Based on micro-benchmark measurements, we discuss the implications of our findings, and argue how they can provide a basis for future extensions and improvements of the RISC-V architecture.
Field-induced spin reorientation transitions in antiferromagnetic ring-shaped spin chains

Easy axis antiferromagnets are robust against external magnetic fields of moderate strength. Spin reorientations in strong fields can provide an insight into more subtle properties of antiferromagnetic materials, which are often hidden by their high ground state symmetry. Here, we investigate theoretically effects of curvature in ring-shaped antiferromagnetic achiral anisotropic spin chains in strong magnetic fields. We identify the geometry-governed helimagnetic phase transition above the spin-flop field between vortex and onion states. The curvature-induced Dzyaloshinskii-Moriya interaction results in the spin-flop transition being of first- or second-order depending on the ring curvature. Spatial inhomogeneity of the N\'eel vector in the spin-flop phase generates weak ferromagnetic response in the plane perpendicular to the applied magnetic field. Our work contributes to the understanding of the physics of curvilinear antiferromagnets in magnetic fields and guides prospective experimental studies of geometrical effects relying on spin-chain-based nanomagnets.
Agnostic Learning of General ReLU Activation Using Gradient Descent

We provide a convergence analysis of gradient descent for the problem of agnostically learning a single ReLU function under Gaussian distributions. Unlike prior work that studies the setting of zero bias, we consider the more challenging scenario when the bias of the ReLU function is non-zero. Our main result establishes that starting from random initialization, in a polynomial number of iterations gradient descent outputs, with high probability, a ReLU function that achieves a competitive error guarantee when compared to the error of the best ReLU function. We also provide finite sample guarantees, and these techniques generalize to a broader class of marginal distributions beyond Gaussians.