Miracle-dz/UniFAR-FTUs huggingface dataset:
Milk Thistle Oilseed Cake Flour Fractions: A Source of Silymarin and Macronutrients for Gluten-Free Bread

The utilization of plant by-products as functional food ingredients has received increasing attention in the last decade. One such by-product generated during milk thistle oil pressing is oilseed cakes, which could be used as a novel food ingredient. Therefore, the study aimed at investigating the effects of the addition of milk thistle oilseed cake (MTOC) flour fractions obtained via dry sieving, differing in particle size (unsieved; coarse: >710 µm; medium: 315–710 µm; and fine: <315 µm), on the quality of gluten-free bread and stability of silymarin during breadmaking. The 10% addition of the fractions into gluten-free bread increased the protein, fibre, fat, ash and silymarin content. The breads with the coarse fraction had the highest content of fibre, whereas the breads with the fine fraction excelled in protein, fat and ash content. The medium fraction was characterized as the richest source of silymarin, whilst the fine fraction was the poorest. Silymarin constituents were slightly released during dough rising but also partially decomposed during baking; moreover, silydianin was the most susceptible and degraded the most. The enriched breads had better sensory and textural properties compared to the control bread. The results suggest that MTOC flour fractions can improve the potential health benefits and nutritional profile of gluten-free bread.
SARS-CoV-2 Vaccines and Adverse Effects in Gynecology and Obstetrics: The First Italian Retrospective Study

The most common effects reported by the Italian Medicine Agency following administration of SARS-CoV-2 vaccine are myalgia, soreness to the arm of inoculation, fever, and asthenia. To date, there are no specific and official reports registered by the Italian Medicine Agency on possible alterations of the menstrual cycle, or of the female reproductive system, following the vaccine. Actually, clinical experience showed a spread of transient adverse drug reactions of the menstrual cycle, following the administration of all COVID-19 vaccine types, both mRNA and Adenovirus vectored ones. In this work, we conducted the first retrospective study on Italian patients vaccinated for SARS-CoV-2 in the period between April 2021 and April 2022, to report the onset of menstrual changes after the vaccine in order to understand: etiology, duration of possible adverse effects, and the extent of the phenomenon. We recruited 100 women aged 18–45, vaccinated for SARS-CoV-2, who were asked to complete a questionnaire consisting of 12 multiple choice questions about the effects of the vaccine on the reproductive system. Thirty-seven of them received three doses of the vaccine, while the remaining 63 received two doses. Symptoms such as delayed menstruation and abnormal uterine bleeding (metrorrhagia, menometrorrhagia, and menorrhagia) were generally reported within the first three weeks of vaccination, especially after the second dose, with a percentage of 23% and 77%, respectively. These preliminary data suggest that this problem may be broader and deserving of further investigation in the future.
Impact of the two Fe unit cell on the electronic structure measured by ARPES in iron pnictides

In all iron pnictides, the positions of the ligand alternatively above and below the Fe plane create 2 inequivalent Fe sites. This results in 10 Fe 3d bands in the electronic structure. However, they do not all have the same status for an ARPES experiment. There are interference effects between the 2 Fe that modulate strongly the intensity of the bands and that can even switch their parity. We give a simple description of these effects, notably showing that ARPES polarization selection rules in these systems cannot be applied by reference to a single Fe ion. We show that ARPES data for the electron pockets in Ba(Fe0.92Co0.08)2As2 are in excellent agreement with this model. We observe both the total suppression of some bands and the parity switching of some other bands. Once these effects are properly taken into account, the structure of the electron pockets, as measured by ARPES, becomes very clear and simple. By combining ARPES measurements in different experimental configurations, we clearly isolate each band forming one of the electron pockets. We identify a deep electron band along one ellipse axis with the dxy orbital and a shallow electron band along the perpendicular axis with the dxz/dyz orbitals, in good agreement with band structure calculations. We show that the electron pockets are warped as a function of kz as expected theoretically, but that they are much smaller than predicted by the calculation.
General parametrization of axisymmetric black holes in metric theories of gravity

Following previous work of ours in spherical symmetry, we here propose a new parametric framework to describe the spacetime of axisymmetric black holes in generic metric theories of gravity. In this case, the metric components are functions of both the radial and the polar angular coordinates, forcing a double expansion to obtain a generic axisymmetric metric expression. In particular, we use a continued-fraction expansion in terms of a compactified radial coordinate to express the radial dependence, while we exploit a Taylor expansion in terms of the cosine of the polar angle for the polar dependence. These choices lead to a superior convergence in the radial direction and to an exact limit on the equatorial plane. As a validation of our approach, we build parametrized representations of Kerr, rotating dilaton, and Einstein-dilaton-Gauss-Bonnet black holes. The match is already very good at lowest order in the expansion and improves as new orders are added. We expect a similar behavior for any stationary and axisymmetric black-hole metric.
Image registration method based on improved Harris corner detector

Harris corner detector is a classic tool to extract feature. It is stable to illumination change and rotation but unstable to more complicated transform. In order to register images with different viewpoints, we extend Harris corner detector to scale-space to gain invariance to scale change, then we apply affine shape adaptation to the scale invariant point until convergence is reached, giving it invariance to affine transform. With these local features, we use general feature descriptor and matching algorithm to generate matches and then use the matches to calculate the geometric transform matrix, which enables the final registration. Result shows that our algorithm can get more accurate matches than scale invariant feature transform SIFT, and less difference exists between registered images.
Natural tolerance to transposition is associated with double-strand break repair and germ-cell differentiation

Transposable elements (TE) are mobile genetic parasites whose unregulated activity in the germline causes DNA damage and sterility. While the regulation of TE mobilization by hosts is studied extensively, little is known about mechanisms that could allow germline cells to persist in the face of genotoxic stress imposed by active transposition. Such tolerance mechanisms are predicted to be beneficial when new TEs invade and host repression has not yet evolved. Here we use hybrid dysgenesis—a sterility syndrome of Drosophila caused by transposition of invading DNA transposons—to uncover genetic variants that confer tolerance to transposition. Using a panel of highly recombinant inbred lines of Drosophila melanogaster, we identified two linked quantitative trait loci (QTL), that determine tolerance in young and old females, respectively. Through transcriptomic and phenotypic comparisons, we provide evidence that young tolerant females exhibit enhanced repair of double-stranded breaks, explaining their ability to withstand high germline transposition rates. We furthermore identify the germline differentiation factor brat as an independent tolerance factor, whose activity may promote germline maintenance in aging dysgenic females. Together, our work reveals the diversity of potential tolerance mechanisms across development, as well as tolerant variants that may be beneficial in the context of P-element transposition.
A Proteome-Wide Map of Chaperone-Assisted Protein Refolding in the Cytosol

The journey by which proteins navigate their energy landscapes to their native structures is complex, involving (and sometimes requiring) many cellular factors and processes operating in partnership with a given polypeptide chain’s intrinsic energy landscape. The cytosolic environment and its complement of chaperones play critical roles in granting proteins safe passage to their native states; however, the complexity of this medium has generally precluded biophysical techniques from interrogating protein folding under cellular-like conditions for single proteins, let alone entire proteomes. Here, we develop a limited-proteolysis mass spectrometry approach paired with an isotope-labeling strategy to globally monitor the structures of refolding E. coli proteins in the cytosolic medium and with the chaperones, GroEL/ES (Hsp60) and DnaK/DnaJ/GrpE (Hsp70/40). GroEL can refold the majority (85%) of the E. coli proteins for which we have data, and is particularly important for restoring acidic proteins and proteins with three to five domains, trends that come to light because our assay measures the structural outcome of the refolding process itself, rather than indirect measures like binding or aggregation. For the most part, DnaK and GroEL refold a similar set of proteins, supporting the view that despite their vastly different structures, these two chaperones both unfold misfolded states, as one mechanism in common. Finally, we identify a cohort of proteins that are intransigent to being refolded with either chaperone. The data support a model in which chaperone-nonrefolders have evolved to fold efficiently once and only once, co-translationally, and remain kinetically trapped in their native conformations.
On the definition of a moist‐air potential vorticity

A new potential vorticity is derived by using a specific entropy formulation expressed in terms of a moist‐air entropy potential temperature. The new formulation is compared with Ertel's version and with others based on virtual and equivalent potential temperatures. The new potential vorticity is subject to conservative properties ensured by the Second Law applied to the moist‐air material derivatives. It is shown that the upper tropospheric and stratospheric (dry) structures are nearly the same as those obtained with Ertel's component. Moreover, new structures are observed in the low troposphere, with negative values associated with moist frontal regions. The negative values are observed in the frontal regions where slantwise convection instabilities may take place, but they are smaller than those observed with the equivalent potential vorticity. The main purpose of the article is to diagnose the behaviour of the new potential vorticity from numerical output generated by the ARPEGE NWP model, with the help of isobaric charts and vertical cross‐sections. Two inversion methods are suggested. The first method could be based on the invertibility principle verified by the virtual potential vorticity, with a possibility to control and modify separately potential vorticity components in the (dry) upper and (moist) lower atmospheric levels. The other method may consist of an inversion process directly applied to the new moist‐air entropy potential vorticity, because the negative values and the solenoidal term are smaller than those observed with equivalent potential vorticity, as shown by numerical evaluations.
Optimal estimation of local time and occupation time measure for an {\alpha}-stable Levy process

We present a novel theoretical result on estimation of local time and occupation time measure of an {\alpha}-stable L\'evy process with {\alpha} in (1, 2). Our approach is based upon computing the conditional expectation of the desired quantities given high frequency data, which is an L^2-optimal statistic by construction. We prove the corresponding stable central limit theorems and discuss a statistical application. In particular, this work extends the results of [Ivanovs and i Podolskij (2021)], which investigated the case of the Brownian motion.
Look twice: A generalist computational model predicts return fixations across tasks and species

Primates constantly explore their surroundings via saccadic eye movements that bring different parts of an image into high resolution. In addition to exploring new regions in the visual field, primates also make frequent return fixations, revisiting previously foveated locations. We systematically studied a total of 44,328 return fixations out of 217,440 fixations. Return fixations were ubiquitous across different behavioral tasks, in monkeys and humans, both when subjects viewed static images and when subjects performed natural behaviors. Return fixations locations were consistent across subjects, tended to occur within short temporal offsets, and typically followed a 180-degree turn in saccadic direction. To understand the origin of return fixations, we propose a proof-of-principle, biologically-inspired and image-computable neural network model. The model combines five key modules: an image feature extractor, bottom-up saliency cues, task-relevant visual features, finite inhibition-of-return, and saccade size constraints. Even though there are no free parameters that are fine-tuned for each specific task, species, or condition, the model produces fixation sequences resembling the universal properties of return fixations. These results provide initial steps towards a mechanistic understanding of the trade-off between rapid foveal recognition and the need to scrutinize previous fixation locations.
Sound approximate and asymptotic probabilistic bisimulations for PCTL

We tackle the problem of establishing the soundness of approximate bisimilarity with respect to PCTL and its relaxed semantics. To this purpose, we consider a notion of bisimilarity inspired by the one introduced by Desharnais, Laviolette, and Tracol, and parametric with respect to an approximation error $\delta$, and to the depth $n$ of the observation along traces. Essentially, our soundness theorem establishes that, when a state $q$ satisfies a given formula up-to error $\delta$ and steps $n$, and $q$ is bisimilar to $q'$ up-to error $\delta'$ and enough steps, we prove that $q'$ also satisfies the formula up-to a suitable error $\delta"$ and steps $n$. The new error $\delta"$ is computed from $\delta$, $\delta'$ and the formula, and only depends linearly on $n$. We provide a detailed overview of our soundness proof. We extend our bisimilarity notion to families of states, thus obtaining an asymptotic equivalence on such families. We then consider an asymptotic satisfaction relation for PCTL formulae, and prove that asymptotically equivalent families of states asymptotically satisfy the same formulae.
A new combinatorial approach for edge universality of Wigner matrices

In this paper we introduce a new combinatorial approach to analyze the trace of large powers of Wigner matrices. Our approach is motivated from the paper by Soshnikov [36]. However the counting approach is different. We start with classical word sentence approach similar to Anderson and Zeitouni [1] and take the motivation from Sinai and Soshnikov [35], Soshnikov [36] and Péché [32] to encode the words to objects similar to Dyck paths. To be precise the map takes a word to a Dyck path with some edges removed from it. Using this new counting we prove edge universality for large Wigner matrices with sub-Gaussian entries. One novelty of this approach is unlike Sinai and Soshnikov [35], Soshnikov [36] and Péché [32] we do not need to assume the entries of the matrices are symmetrically distributed around 0. The main technical contribution of this paper is two folded. Firstly we produce an encoding of the “contributing words” (for definition one might look at Section 7) of the Wigner matrix which retrieves the edge universality. Hence this is the best one can do. We hope this method will be applicable to many other scenarios in random matrices. Secondly in course of the paper we give a combinatorial description of the GOE Tracy Widom law. The explanation for GUE is very similar. This explanation might be important for the models where exact calculations are not available but some combinatorial structures are present.
A Survey of Parameters Associated with the Quality of Benchmarks in NLP

Several benchmarks have been built with heavy investment in resources to track our progress in NLP. Thousands of papers published in response to those benchmarks have competed to top leaderboards, with models often surpassing human performance. However, recent studies have shown that models triumph over several popular benchmarks just by overfitting on spurious biases, without truly learning the desired task. Despite this finding, benchmarking, while trying to tackle bias, still relies on workarounds, which do not fully utilize the resources invested in benchmark creation, due to the discarding of low quality data, and cover limited sets of bias. A potential solution to these issues -- a metric quantifying quality -- remains underexplored. Inspired by successful quality indices in several domains such as power, food, and water, we take the first step towards a metric by identifying certain language properties that can represent various possible interactions leading to biases in a benchmark. We look for bias related parameters which can potentially help pave our way towards the metric. We survey existing works and identify parameters capturing various properties of bias, their origins, types and impact on performance, generalization, and robustness. Our analysis spans over datasets and a hierarchy of tasks ranging from NLI to Summarization, ensuring that our parameters are generic and are not overfitted towards a specific task or dataset. We also develop certain parameters in this process.
Is synthetic data from generative models ready for image recognition?

Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. Though the results are astonishing to human eyes, how applicable these generated images are for recognition tasks remains under-explored. In this work, we extensively study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks, and focus on two perspectives: synthetic data for improving classification models in data-scarce settings (i.e. zero-shot and few-shot), and synthetic data for large-scale model pre-training for transfer learning. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks. Code: https://github.com/CVMI-Lab/SyntheticData.
MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images

For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction. However, they suffer degraded CD performance on small changed areas due to the simple single-scale integration of deep CNNs and transformer modules. To address this issue, we propose a hybrid network based on multi-scale CNN-transformer structure, termed MCTNet, where the multi-scale global and local information is exploited to enhance the robustness of the CD performance on changed areas with different sizes. Especially, we design the ConvTrans block to adaptively aggregate global features from transformer modules and local features from CNN layers, which provides abundant global-local features with different scales. Experimental results demonstrate that our MCTNet achieves better detection performance than existing state-of-the-art CD methods.
Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning

Graph attention networks (GATs) are powerful tools for analyzing graph data from various real-world scenarios. To learn representations for downstream tasks, GATs generally attend to all neighbors of the central node when aggregating the features. In this paper, we show that a large portion of the neighbors are irrelevant to the central nodes in many real-world graphs, and can be excluded from neighbor aggregation. Taking the cue, we present Selective Attention (SA) and a series of novel attention mechanisms for graph neural networks (GNNs). SA leverages diverse forms of learnable node-node dissimilarity to acquire the scope of attention for each node, from which irrelevant neighbors are excluded. We further propose Graph selective attention networks (SATs) to learn representations from the highly correlated node features identified and investigated by different SA mechanisms. Lastly, theoretical analysis on the expressive power of the proposed SATs and a comprehensive empirical study of the SATs on challenging real-world datasets against state-of-the-art GNNs are presented to demonstrate the effectiveness of SATs.
Multiparticle and charge-dependent azimuthal correlations in heavy-ion collisions at the Relativistic Heavy-Ion Collider

We study multi-particle azimuthal correlations in relativistic heavy-ion collisions at a center of mass energy of 200 GeV. We use the IP-Glasma model to initialize the viscous hydrodynamic simulation MUSIC and employ the UrQMD transport model for the low temperature region of the collisions. In addition, we study effects of local charge and global momentum conservation among the sampled particles. With the exception of the lowest order three particle correlator $C_{112}$, our framework provides a good description of the existing charge inclusive azimuthal correlation data for Au+Au and U+U collisions at RHIC. We also present results for charge dependent two and three particle correlators in Au+Au and U+U collisions, and make predictions for isobar (Ru+Ru & Zr+Zr) collisions to provide a much needed baseline for the search for the Chiral Magnetic Effect at RHIC.
Radio Frequency Induced and Neoclassical Asymmetries and their Effects on Turbulent Impurity Transport in a Tokamak

Poloidal asymmetries in the impurity density can be generated by radio frequency heating in the core and by neoclassical effects in the edge of tokamak plasmas. In a pedestal case study, using global neoclassical simulations we find that finite orbit width effects can generate significant poloidal variation in the electrostatic potential, which varies on a small radial scale. Gyrokinetic modeling shows that these poloidal asymmetries can be strong enough to significantly modify turbulent impurity peaking. In the pedestal the E × B drift in the radial electric field can give a larger contribution to the poloidal motion of impurities than that of their parallel streaming. Under such circumstances we find that up‐down asymmetries can also affect impurity peaking. (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)