Miracle-dz/UniFAR-FTUs huggingface dataset:
The Quality Characteristics of Sausage with Added Black Garlic Extracts

In this study, the effects of black garlic extract on the quality characteristics of sausages that were stored at for 4 weeks were evaluated. Sausages were produced that contained 15 brix (S1), 22 brix (S2) and 30 brix (S3) of black garlic extract. The pH values of the black garlic extract groups were lower than the control group and there was a tendency for these to decrease during the storage period. The VBN and TBARS content of all sausages increased during the storage period, but less so in the black garlic extract group than the control group. As the percentage of black garlic extract increased, the L value (lightness) and a value (redness) decreased while the b value (yellowness) increased. The expressible moisture content of the S2 group after 4 weeks was the highest of all groups (17.63%), whereas the cooking loss was lower in S3 than other groups. Hardness in the S2 group after 4 weeks was higher than in other groups, at . The juiciness, texture and acceptability scores for S2 were also higher than the control, S1 and S3 groups. Therefore, the ideal addition of black garlic extract is suggested to be 15~22% in the preparation of sausage.
Tropical grass fertilized with wood ash in Cerrado Oxisol: Concentrations of calcium, magnesium and sulphur

While nutrients such as calcium, magnesium and sulphur are required by plants in lower amounts than the amounts required of nitrogen, phosphorus, and potassium, these nutrients are as important as those required for the growth and development of plants. Varying concentrations of calcium and magnesium can be found in wood ash as residue, which may have a high percentage of these nutrients. This study aims to evaluate the concentration of calcium, magnesium and sulphur in Brachiaria brizantha, a genus of tropical grasses, as a function of the levels of wood ash. The experiment was conducted in a greenhouse. The experimental design was completely randomized in a factorial of 6 × 2, corresponding to 6 doses of wood ash (0, 3, 6, 9, 12 and 15 g dm) and 2 cultivars of B. brizantha (Marandu and Xaraes), and was completed with 6 replicates. Experimental characteristics included the concentrations of calcium, magnesium and sulphur in the dry mass of the shoots and roots of Marandu and Xaraes. The variance of the results was analyzed using an F test at a 5% probability. For wood ash, we performed a regression analysis, and for tropical grasses, we performed a Tukey test at a 5% probability. In the three sections, the highest concentrations of calcium, magnesium and sulphur in the shoots and roots of Marandu and Xaraes are between the wood ash doses of 7.72 and 11.79 g dm, respectively. The wood ash influences the nutritional characteristics of the grasses by increasing the concentrations of calcium and magnesium in the shoots of Marandu and Xaraes, respectively, in excess of 42 to 29% and greater than 77 and 39% increments for the roots. The average concentrations of magnesium in shoots of Marandu and Xaraes were 0.87 and 0.94 g kg, respectively, while the roots had concentrations of 0.94 and 0.95 g kg, respectively. Wood ash as a fertilizer promotes significant changes in the nutritional characteristics of the grasses Marandu and Xaraes when planted in Cerrado Oxisol.
Temporal variations in the diet of the squid Moroteuthis ingens at Macquarie Island: stomach contents and fatty acid analyses

The onychoteuthid squid Moroteuthis ingens was collected as by-catch from a commercial trawl fishery in the vicinity of Macquarie Island, within the Pacific sector of the Southern Ocean. Squid were collected during 3 austral summers and 1 austral winter between January 1995 and June 2000. Stomach contents and fatty acid profiles of both mantle and digestive gland tissues were used to determine if the diet of M. ingens was subject to temporal variations in this region. Discriminant analysis (DA) of stomach contents data and digestive gland fatty acid data indicated that the diet varied significantly on an interannual basis, most likely due to an increase in the consumption of the myctophid Gymnoscopelus nicholsi during the summer of 1999 relative to the summers of 1995 and 2000. Comparisons with oceanographic data reveal that the summer of 1999 was a period of warmer sea-surface temperatures and reduced primary production compared to the summers of 1995 and 2000. Fluctuations in oceanographic conditions may have underpinned variations in the availability of prey during the study period. DA of digestive gland fatty acid data also indicated that the diet varied significantly between the summer and winter of 2000. Stomach contents data indicate that the myctophid Electrona carlsbergi replaced Krefftichthys anderssoni as a key prey species for M. ingens during the winter period, as has been observed for other marine predators in the sub-Antarctic Southern Ocean. A comparison of methods reveals that, while fatty acid analyses greatly aid the interpretation of stomach contents data, both techniques are subject to limitations and are best used in combination.
Limit on the Fierz Interference Term b from a Measurement of the Beta Asymmetry in Neutron Decay.

In the standard model of particle physics, the weak interaction is described by vector and axial-vector couplings only. Nonzero scalar or tensor interactions would imply an additional contribution to the differential decay rate of the neutron, the Fierz interference term. We derive a limit on this hypothetical term from a measurement using spin-polarized neutrons. This method is statistically less sensitive than the determination from the spectral shape but features much cleaner systematics. We obtain a limit of b=0.017(21) at 68.27% C.L. , improving the previous best limit from neutron decay by a factor of four.
Anderson transition in three-dimensional systems with non-Hermitian disorder

We study the Anderson transition for three-dimensional (3D) $N\ifmmode\times\else\texttimes\fi{}N\ifmmode\times\else\texttimes\fi{}N$ tightly bound cubic lattices where both real and imaginary parts of on-site energies are independent random variables distributed uniformly between $\ensuremath{-}W/2$ and $W/2$. Such a non-Hermitian analog of the Anderson model is used to describe random-laser medium with local loss and amplification. We employ eigenvalue statistics to search for the Anderson transition. For 25% smallest-modulus complex eigenvalues we find the average ratio $r$ of distances to the first and the second nearest neighbor as a function of $W$. For a given $N$ the function $r(W)$ crosses from 0.72 to 2/3 with a growing $W$ demonstrating a transition from delocalized to localized states. When plotted at different $N$ all $r(W)$ cross at ${W}_{c}=6.0\ifmmode\pm\else\textpm\fi{}0.1$ (in units of nearest-neighbor overlap integral) clearly demonstrating the 3D Anderson transition. We find that in the non-Hermitian 2D Anderson model, the transition is replaced by a crossover.
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration

We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning (RL). When the state space is large or continuous, traditional tabular approaches are unfeasible and some form of function approximation is mandatory. In this paper, we introduce an optimistically-initialized variant of the popular randomized least-squares value iteration (RLSVI), a model-free algorithm where exploration is induced by perturbing the least-squares approximation of the action-value function. Under the assumption that the Markov decision process has low-rank transition dynamics, we prove that the frequentist regret of RLSVI is upper-bounded by $\widetilde O(d^2 H^2 \sqrt{T})$ where $ d $ are the feature dimension, $ H $ is the horizon, and $ T $ is the total number of steps. To the best of our knowledge, this is the first frequentist regret analysis for randomized exploration with function approximation.
Classification of some vertex operator algebras of rank 3

We discuss the classification of strongly regular vertex operator algebras (VOAs) with exactly three simple modules whose character vector satisfies a monic modular linear differential equation with irreducible monodromy. Our Main Theorem provides a classification of all such VOAs in the form of one infinite family of affine VOAs, one individual affine algebra and two Virasoro algebras, together with a family of eleven exceptional character vectors and associated data that we call the $U$-series. We prove that there are at least $15$ VOAs in the $U$-series occurring as commutants in a Schellekens list holomorphic VOA. These include the affine algebra $E_{8,2}$ and Hohn's Baby Monster VOA $\mathbf{VB}^\natural_{(0)}$ but the other $13$ seem to be new. The idea in the proof of our Main Theorem is to exploit properties of a family of vector-valued modular forms with rational functions as Fourier coefficients, which solves a family of modular linear differential equations in terms of generalized hypergeometric series.
Variable Grouping Based Bayesian Additive Regression Tree

Using ensemble methods for regression has been a large success in obtaining high-accuracy prediction. Examples are Bagging, Random forest, Boosting, BART (Bayesian additive regression tree), and their variants. In this paper, we propose a new perspective named variable grouping to enhance the predictive performance. The main idea is to seek for potential grouping of variables in such way that there is no nonlinear interaction term between variables of different groups. Given a sum-of-learner model, each learner will only be responsible for one group of variables, which would be more efficient in modeling nonlinear interactions. We propose a two-stage method named variable grouping based Bayesian additive regression tree (GBART) with a well-developed python package gbart available. The first stage is to search for potential interactions and an appropriate grouping of variables. The second stage is to build a final model based on the discovered groups. Experiments on synthetic and real data show that the proposed method can perform significantly better than classical approaches.
Two-dimensional Inflow–Outflow Solution of Supercritical Accretion Flow

We present the two-dimensional inflow–outflow solutions of radiation hydrodynamic equations of supercritical accretion flows. Compared with prior studies, we include all components of the viscous stress tensor. We assume steady-state flow and use self-similar solutions in the radial direction to solve the equations in the r–θ domain of spherical coordinates. The set of differential equations have been integrated from the rotation axis to the equatorial plane. We find that the self-similarity assumption requires that the radial profile of density is described by ρ(r) ∝ r−0.5. Correspondingly, the radial profile of the mass inflow rate decreases with decreasing radius as . An inflow–outflow structure has been found in our solution. Inflow exists in the region θ > 65°, while above that the flow moves outward and outflow could launch. The driving forces of the outflow are analyzed and it is found that the radiation force is dominant and pushes the gas particles outward with poloidal velocity ∼0.25c. The properties of the outflow are also studied. The results show that the mass flux-weighted angular momentum of the inflow is lower than that of the outflow, thus the angular momentum of the flow can be transported by the outflow. We also analyze the convective stability of the supercritical disk and find that in the absence of a magnetic field, the flow is convectively unstable. Our analytical results are fully consistent with the previous numerical simulations of supercritical accretion flow.
CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

Diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of permanent blindness in the working-age population. Automatic grading of DR and DME helps ophthalmologists design tailored treatments to patients, thus is of vital importance in the clinical practice. However, prior works either grade DR or DME, and ignore the correlation between DR and its complication, i.e., DME. Moreover, the location information, e.g., macula and soft hard exhaust annotations, are widely used as a prior for grading. Such annotations are costly to obtain, hence it is desirable to develop automatic grading methods with only image-level supervision. In this article, we present a novel cross-disease attention network (CANet) to jointly grade DR and DME by exploring the internal relationship between the diseases with only image-level supervision. Our key contributions include the disease-specific attention module to selectively learn useful features for individual diseases, and the disease-dependent attention module to further capture the internal relationship between the two diseases. We integrate these two attention modules in a deep network to produce disease-specific and disease-dependent features, and to maximize the overall performance jointly for grading DR and DME. We evaluate our network on two public benchmark datasets, i.e., ISBI 2018 IDRiD challenge dataset and Messidor dataset. Our method achieves the best result on the ISBI 2018 IDRiD challenge dataset and outperforms other methods on the Messidor dataset. Our code is publicly available at https://github.com/xmengli999/CANet.
1 Block Coordinate Descent for Sparse NMF

Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural measure of sparsity is the La norm, however its optimization is NP-hard. Mixed norms, such as Ll/L2 measure, have been shown to model sparsity robustly, based on intuitive attributes that such measures need to satisfy. This is in contrast to computationally cheaper alternatives such as the plain L1 norm. However, present algorithms designed for optimizing the mixed norm L1 /L2 are slow and other formulations for sparse NMF have been proposed such as those based on L1 and La norms. Our proposed algorithm allows us to solve the mixed norm sparsity constraints while not sacrificing computation time. We present experimental evidence on real-world datasets that shows our new algorithm performs an order of magnitude faster compared to the current state-of-the-art solvers optimizing the mixed norm and is suitable for large-scale datasets.
Rate-Power Region of SWIPT Systems Employing Nonlinear Energy Harvester Circuits with Memory

In this paper, we study the rate-power region of a simultaneous wireless information and power transfer (SWIPT) system where a transmitter (TX) broadcasts a common signal to an information receiver (IR) and an energy harvester (EH). Since practical EH circuits include a reactive element as part of their signal rectifier and the voltage on this element cannot drop or rise instantaneously, the EH circuit has memory. We model the memory effect of the EH by a Markov reward chain. Furthermore, since an analytical model that includes all nonlinear and memory effects of the EH circuit is not available, we employ a deep neural network (DNN) to model the Markov chain. We formulate an optimization problem to determine the rate-power region of the considered SWIPT system and propose an iterative algorithm based on sequential quadratic programming (SQP) to solve it. Our numerical results show that the optimal input distribution and the rate-power region depend on both the input power level at the EH and the symbol duration.
The ASVspoof 2019 database

Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is vulnerable to spoofing, also referred to as ''presentation attacks. '' These vulnerabilities are generally unacceptable and call for spoofing countermeasures or "presentation attack detection" systems. In addition to impersonation, ASV systems are vulnerable to replay, speech synthesis, and voice conversion attacks. The ASVspoof 2019 edition is the first to consider all three spoofing attack types within a single challenge. While they originate from the same source database and same underlying protocol, they are explored in two specific use case scenarios. Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques. Replay spoofing attacks within a physical access (PA) scenario are generated through carefully controlled simulations that support much more revealing analysis than possible previously. Also new to the 2019 edition is the use of the tandem detection cost function metric, which reflects the impact of spoofing and countermeasures on the reliability of a fixed ASV system. This paper describes the database design, protocol, spoofing attack implementations, and baseline ASV and countermeasure results. It also describes a human assessment on spoofed data in logical access. It was demonstrated that the spoofing data in the ASVspoof 2019 database have varied degrees of perceived quality and similarity to the target speakers, including spoofed data that cannot be differentiated from bona-fide utterances even by human subjects.
SHARP – IV. An apparent flux-ratio anomaly resolved by the edge-on disc in B0712+472

Flux-ratio anomalies in quasar lenses can be attributed to dark matter substructure surrounding the lensing galaxy and thus used to constrain the substructure mass fraction. Previous applications of this approach infer a substructure abundance that is potentially in tension with the predictions of Λ cold dark matter cosmology. However, the assumption that all flux-ratio anomalies are due to substructure is a strong one and alternative explanations have not been fully investigated. Here, we use new high-resolution near-IR Keck II adaptive optics imaging for the lens system CLASS B0712+472 to perform pixel-based lens modelling for this system and, in combination with the new Very Long Baseline Array radio observations, show that the inclusion of the disc in the lens model can explain the flux-ratio anomalies without the need for dark matter substructures. The projected disc mass comprises 16 per cent of the total lensing mass within the Einstein radius and the total disc mass is 1.79 × 1010 M⊙. The case of B0712+472 adds to the evidence that not all flux-ratio anomalies are due to dark subhaloes and highlights the importance of taking the effects of baryonic structures more fully into account in order to obtain an accurate measure of the substructure mass fraction.
Spatial scan statistics using elliptic windows

The spatial scan statistic is widely used to search for clusters. This article shows that the usually applied elimination of secondary clusters as implemented in Sat Scan is sensitive to smooth changes in the shape of the clusters. We present an algorithm for generation of a set of confocal elliptic windows and propose a new way to present the information when a spatial point process is considered. This method gives smooth changes for smooth expansions of the set of clusters. A simulation study is used to show how the elliptic windows outperforms the usual circular windows. The proposed method for graphical representation of the information in a set of clusters contain more information than just presenting nonoverlapping clusters. We suggest that more than one graphical representation of a set of clusters should be used to easily extract more information and to avoid pitfalls of the selected method.
Learning-based Real-time Detection of Intrinsic Reflectional Symmetry

Reflectional symmetry is ubiquitous in nature. While extrinsic reflectional symmetry can be easily parametrized and detected, intrinsic symmetry is much harder due to the high solution space. Previous works usually solve this problem by voting or sampling, which suffer from high computational cost and randomness. In this paper, we propose \YL{a} learning-based approach to intrinsic reflectional symmetry detection. Instead of directly finding symmetric point pairs, we parametrize this self-isometry using a functional map matrix, which can be easily computed given the signs of Laplacian eigenfunctions under the symmetric mapping. Therefore, we train a novel deep neural network to predict the sign of each eigenfunction under symmetry, which in addition takes the first few eigenfunctions as intrinsic features to characterize the mesh while avoiding coping with the connectivity explicitly. Our network aims at learning the global property of functions, and consequently converts the problem defined on the manifold to the functional domain. By disentangling the prediction of the matrix into separated basis, our method generalizes well to new shapes and is invariant under perturbation of eigenfunctions. Through extensive experiments, we demonstrate the robustness of our method in challenging cases, including different topology and incomplete shapes with holes. By avoiding random sampling, our learning-based algorithm is over 100 times faster than state-of-the-art methods, and meanwhile, is more robust, achieving higher correspondence accuracy in commonly used metrics.
Recent Probes of Standard and Non-standard Neutrino Physics With Nuclei

We review current standard and non-standard neutrino physics probes that are based on nuclear measurements. We pay special attention on the discussion of prospects to extract new physics at prominent rare event measurements looking for neutrino-nucleus scattering, as the coherent elastic neutrino-nucleus scattering that may involve lepton flavour violation (LFV) in neutral currents (NC). For the latter processes several appreciably sensitive experiments, like the COHERENT, MINER, CONUS, TEXONO, RED100 and others, using promising target nuclei are in operation or have been planed to operate in the near future. We also discuss comprehensively phenomenological and theoretical studies, in particular those referred to the nuclear physics aspects and provide useful predictions for the relevant experiments. Furthermore, motivated by the recent discovery of CE$\nu$NS at the COHERENT experiment, we summarize the impact of non-standard interactions and novel vector $Z^\prime$ or scalar $\phi$ mediators to the CE$\nu$NS event rates and provides estimates for the corresponding current and future sensitivities.
The survival of start-ups in time of crisis. A machine learning approach to measure innovation

This paper shows how data science can contribute to improving empirical research in economics by leveraging on large datasets and extracting information otherwise unsuitable for a traditional econometric approach. As a test-bed for our framework, machine learning algorithms allow us to create a new holistic measure of innovation built on a 2012 Italian Law aimed at boosting new high-tech firms. We adopt this measure to analyse the impact of innovativeness on a large population of Italian firms which entered the market at the beginning of the 2008 global crisis. The methodological contribution is organised in different steps. First, we train seven supervised learning algorithms to recognise innovative firms on 2013 firmographics data and select a combination of those with best predicting power. Second, we apply the former on the 2008 dataset and predict which firms would have been labelled as innovative according to the definition of the law. Finally, we adopt this new indicator as regressor in a survival model to explain firms' ability to remain in the market after 2008. Results suggest that the group of innovative firms are more likely to survive than the rest of the sample, but the survival premium is likely to depend on location.