Miracle-dz/UniFAR-FTUs huggingface dataset:
Solar structure and evolution

The Sun provides a critical benchmark for the general study of stellar structure and evolution. Also, knowledge about the internal properties of the Sun is important for the understanding of solar atmospheric phenomena, including the solar magnetic cycle. Here I provide a brief overview of the theory of stellar structure and evolution, including the physical processes and parameters that are involved. This is followed by a discussion of solar evolution, extending from the birth to the latest stages. As a background for the interpretation of observations related to the solar interior I provide a rather extensive analysis of the sensitivity of solar models to the assumptions underlying their calculation. I then discuss the detailed information about the solar interior that has become available through helioseismic investigations and the detection of solar neutrinos, with further constraints provided by the observed abundances of the lightest elements. Revisions in the determination of the solar surface abundances have led to increased discrepancies, discussed in some detail, between the observational inferences and solar models. I finally briefly address the relation of the Sun to other similar stars and the prospects for asteroseismic investigations of stellar structure and evolution.
The saprophytic growth of Keratinophilic fungi on keratin

The growth of 9 species of keratinophilic fungi belonging to the genera Keratinomyces, Microsporum, Trichophyton and Epidermophyton was studied in vitro on hair, hedgehog spine, nail and callus. On hair digestion is carried out primarily by means of flat fronds of eroding mycelium which first life the cuticular scales and then erode the cortex. The well-known perforating organs arise from these fronds and in their turn may prouce further eroding mycelium which grows longitudinally through the cortex of the hair. The mechanism of penetration by perforating organs is discussed and a special study of T. rubrum showed that its method of attack on hair does not differ fundamentally from that of other keratinophilic fungi.It is also by means of fronds of eroding mycelium growing between the layers of keratinised cells that these fungi digest nail and hedgehog spine. Perforating organs are produced on both substrates by certain species. Eroding mycelium is uncommon in the softer keratin of callus.As frond-like m.
Local Population Dynamics in Metapopulation Models: Implications for Conservation

Abstract: Habitat fragmentation and the division of populations into spatially separated units have led to the increasing use of metapopulation models to characterize these populations. One prominent model that has served as a heuristic tool was introduced by Levins and is based on a collection of simplifying assumptions that exclude information on the dynamics and spatial distribution of local populations. Levins's and similar models predict the proportion of occupied habitat patches at equilibrium and the conditions needed to avoid total extinction. There are many obvious concerns about using such models, including how realistic alterations might change the predictions and whether occupancy has any relationship to population‐level processes. Although many of the assumptions of these simple models are known to be unrealistic, we do not know how the assumptions affect model predictions. We simulated a metapopulation, and our results show that assumptions such as homogeneity of habitat patches, random migration among patches, equivalent extinction probabilities in all patches, and a large number of patches can lead to large overestimations of habitat occupancy. But when we explicitly modeled the underlying population dynamics within each patch, we found (1) that there was a strong correlation between proportion of occupied patches and total metapopulation size and (2) that the distribution of individuals among patches was relatively insensitive to model assumptions. Thus, our results show that although realistic modifications will change model predictions for occupancy, occupancy and population trends will be correlated. These correlations between occupancy and population size suggest that occupancy models may have some utility in conservation applications.
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients’ data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.
A comparative study on polyp classification using convolutional neural networks

Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Most colorectal cancers start as a growth on the inner lining of the colon or rectum, called ‘polyp’. Not all polyps are cancerous, but some can develop into cancer. Early detection and recognition of the type of polyps is critical to prevent cancer and change outcomes. However, visual classification of polyps is challenging due to varying illumination conditions of endoscopy, variant texture, appearance, and overlapping morphology between polyps. More importantly, evaluation of polyp patterns by gastroenterologists is subjective leading to a poor agreement among observers. Deep convolutional neural networks have proven very successful in object classification across various object categories. In this work, we compare the performance of the state-of-the-art general object classification models for polyp classification. We trained a total of six CNN models end-to-end using a dataset of 157 video sequences composed of two types of polyps: hyperplastic and adenomatous. Our results demonstrate that the state-of-the-art CNN models can successfully classify polyps with an accuracy comparable or better than reported among gastroenterologists. The results of this study can guide future research in polyp classification.
Seeing Eye-to-Eye? A Comparison of Object Recognition Performance in Humans and Deep Convolutional Neural Networks under Image Manipulation

For a considerable time, deep convolutional neural networks (DCNNs) have reached human benchmark performance in object recognition. On that account, computational neuroscience and the field of machine learning have started to attribute numerous similarities and differences to artificial and biological vision. This study aims towards a behavioral comparison of visual core object recognition between humans and feedforward neural networks in a classification learning paradigm on an ImageNet data set. For this purpose, human participants (n = 65) competed in an online experiment against different feedforward DCNNs. The designed approach based on a typical learning process of seven different monkey categories included a training and validation phase with natural examples, as well as a testing phase with novel shape and color manipulations. Analyses of accuracy revealed that humans not only outperform DCNNs on all conditions, but also display significantly greater robustness towards shape and most notably color alterations. Furthermore, a precise examination of behavioral patterns highlights these findings by revealing independent classification errors between the groups. The obtained results endorse an implementation of recurrent circuits similar to the primate ventral stream in artificial vision models as a way to achieve adequate object generalization abilities across unexperienced manipulations.
Probabilistic Jacobian-based Saliency Maps Attacks

Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or L0 attacks. Effective and fast L0 attacks, such as the widely used Jacobian-based Saliency Map Attack (JSMA) are practical to fool NNCs but also to improve their robustness. In this paper, we show that penalising saliency maps of JSMA by the output probabilities and the input features of the NNC leads to more powerful attack algorithms that better take into account each input’s characteristics. This leads us to introduce improved versions of JSMA, named Weighted JSMA (WJSMA) and Taylor JSMA (TJSMA), and demonstrate through a variety of white-box and black-box experiments on three different datasets (MNIST, CIFAR-10 and GTSRB), that they are both significantly faster and more efficient than the original targeted and non-targeted versions of JSMA. Experiments also demonstrate, in some cases, very competitive results of our attacks in comparison with the Carlini-Wagner (CW) L0 attack, while remaining, like JSMA, significantly faster (WJSMA and TJSMA are more than 50 times faster than CW L0 on CIFAR-10). Therefore, our new attacks provide good trade-offs between JSMA and CW for L0 real-time adversarial testing on datasets such as the ones previously cited.
Hidden symmetry and tunneling dynamics in asymmetric quantum Rabi models

The asymmetric quantum Rabi model (AQRM) has a broken $\mathbb{Z}_2$ symmetry, with generally a non-degenerate eigenvalue spectrum. In some special cases where the asymmetric parameter is a multiple of the cavity frequency, stable level crossings typical of the $\mathbb{Z}_2$-symmetric quantum Rabi model are recovered, however, without any obvious parity-like symmetry. This unknown "symmetry" has thus been referred to as hidden symmetry in the literature. Here we show that this hidden symmetry is not limited to the AQRM, but exists in various related light-matter interaction models with an asymmetric qubit bias term. Conditions under which the hidden symmetry exists in these models are determined and discussed. By investigating tunnelling dynamics in the displaced oscillator basis, a strong connection is found between the hidden symmetry and selective tunnelling.
Studies of the protective effect and antioxidant mechanism of blueberry anthocyanins in a CC14-induced liver injury model in mice

Abstract This study investigated the protective effects and antioxidant mechanisms of blueberry anthocyanin extract (BBAE) in vivo and in vitro using the model of acute CC14-induced liver injury in mice. It was found that BBAE significantly inhibited CC14-induced liver injury and inhibited acute ethanol-induced increases in alanine aminotransferase, aspartate aminotransferase and malondialdehyde in a dose-dependent manner (P<0.05). The activities of superoxide dismutase, catalase and glycogen and glutathione reductase were markedly increased in the anthocyanin group (P<0.01). BBAE also significantly suppressed cellular injury and apoptotic cell death. In summary, BBAE possesses marked protective effects against CC14-induced liver injury, related to its ability to reduce human embryonic-liver cell (L-02) apoptosis and damage induced by oxidative stress. These findings indicate that BBAE may be of value in protecting against acute liver injury, providing pharmacological evidence supporting its clinical application.
Biodiversity and zoogeography of the Isopoda (Crustacea: Malacostraca) from the Victoria Land Coast, Ross Sea, Antarctica

The study is based on benthic material obtained during the 19th Italica expedition along the Victoria-Land coast. This expedition took place in February 2004 and was the first campaign within the framework of the international and multidisciplinary Victoria-Land Latitudinal Gradient Project. It was also the first expedition using a Rauschert dredge, a gear with a small mesh size (500 µm), in this area. The main sampling was performed at four study sites along a latitudinal (north to south) gradient off the Victoria land coast, i.e. at Cape Adare, Cape Hallett, Coulman Island and Cape Russell. Based on these macrobenthic data the taxa Isopoda was used as a model group to investigate and analyse diversity and zoogeographic patterns on different aspects and applying multiple methods.
Chandra Survey of Nearby Galaxies: An Extended Catalog

She et al. (Paper I) assembled a catalog of nearby galaxies observed with the Chandra X-ray observatory, by cross-matching galaxies in the NASA Extragalactic Database within 50 Mpc and the Chandra archive. That sample has enabled searches of low-mass black holes associated with late-type, bulgeless galaxies and studies of the accretion physics related to low-luminosity active galactic nuclei (AGNs). Using a similar approach, here we construct an extended catalog up to 150 Mpc and make a cross-correlation with a catalog of nearby galaxy groups. The new catalog consists of 1964 galaxies, out of which 1692 have a redshift-independent distance, 1557 are listed in the galaxy group catalog with group properties available, and 782 are identified to be X-ray AGN candidates. Compared with the AGN sample in Paper I, the new sample is 2.5 times larger in size (782 versus 314), with ∼80% of the new members having an Eddington ratio less than 10−4. We confirm that the conclusions based on the previous sample remain. With the new sample, we compare AGN fractions between early-type and late-type galaxies, and between central and satellite galaxies in groups, and find no significant difference. This suggests that the secular process is not the dominant mechanism feeding AGNs in the local universe.
The effect of copper and zinc at neutral and acidic pH on the general haematology and osmoregulation of Oreochromis mossambicus

The objective of this study was to determine the effect of sublethal copper and zinc concentrations at a neutral and an acidic pH, on selected haematological parameters as well as on the total osmolality and electrolyte concentrations of Oreochromis mossambicus. In general, at neutral pH copper and zinc caused blood acidosis, increases in circulating white blood cell numbers, causing stimulation of the immune system, and a rapid release of red blood cells from haemopoietic tissue, as reflected in the decreases in mean corpuscular volume. Increases in red blood cells are attributed to an increase in the oxygen-carrying haemoglobin as an adaptation to altered respiratory homeostasis caused by copper and zinc. These increases are therefore a secondary reaction to the metals and not the result of direct stimulation of the haemopoietic tissue. In contrast, at an acidic pH copper and zinc concentrations usually caused blood alkalosis and decreases in white blood cell numbers, due to the bioconcentration of metals which blocks and suppresses the leucopoietic tissue. Decreases were also recorded in red blood cell counts, haemoglobin, haematocrit and mean corpuscular volume, which can be ascribed to anaemic and hypoxic conditions, gill damage and impaired osmoregulation.
Quantum superposition of two gravitational cat states

We extend our earlier work [] on probing a gravitational cat state (gravcat) to the quantum superposition of two gravcats in an exemplary model and in Bose–Einstein condensates (BEC). In addition to its basic theoretical values in gravitational quantum physics and macroscopic quantum phenomena, this investigation can provide some theoretical support to experimental proposals for measuring gravity-induced entanglement and the quantum nature of perturbative gravity. In the first part we consider cat states generated by double-well potentials. A pair of gravcats, each approximated as a two-level system, is characterized by gravity-induced Rabi oscillations, and by gravity-induced entanglement of its energy eigenstates. In the second part we turn to a (non-relativistic) quantum field theory description and derive a gravitational Gross–Pitaevsky equation for gravcats formed in BEC. Using a mathematical analogy to quantum rotors, we explore the properties of the two-gravcat system for BECs, its physical consequences and observational possibilities. Finally we discuss our results in comparison to predictions of alternative quantum theories, and we explain their implications.
Disentanglement of Color and Shape Representations for Continual Learning

We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjusting the network architecture. We tested classification accuracy and forgetting in a task-incremental setting with Oxford-102 Flowers dataset. We combine our method with Elastic Weight Consolidation, Learning without Forgetting, Synaptic Intelligence and Memory Aware Synapses, and show that feature disentanglement positively impacts continual learning performance.
Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison

Nonparametric and machine learning methods are flexible methods for obtaining accurate predictions. Nowadays, data sets with a large number of predictors and complex structures are fairly common. In the presence of item nonresponse, nonparametric and machine learning procedures may thus provide a useful alternative to traditional imputation procedures for deriving a set of imputed values. In this paper, we conduct an extensive empirical investigation that compares a number of imputation procedures in terms of bias and efficiency in a wide variety of settings, including high-dimensional data sets. The results suggest that a number of machine learning procedures perform very well in terms of bias and efficiency.
Preferences Single-Peaked on a Tree: Multiwinner Elections and Structural Results

A preference profile is single-peaked on a tree if the candidate set can be equipped with a tree structure so that the preferences of each voter are decreasing from their top candidate along all paths in the tree. This notion was introduced by Demange (1982), and subsequently Trick (1989b) described an efficient algorithm for deciding if a given profile is single-peaked on a tree. We study the complexity of multiwinner elections under several variants of the Chamberlin–Courant rule for preferences single-peaked on trees. We show that in this setting the egalitarian version of this rule admits a polynomial-time winner determination algorithm. For the utilitarian version, we prove that winner determination remains NP-hard for the Borda scoring function; indeed, this hardness results extends to a large family of scoring functions. However, a winning committee can be found in polynomial time if either the number of leaves or the number of internal vertices of the underlying tree is bounded by a constant. To benefit from these positive results, we need a procedure that can determine whether a given profile is single-peaked on a tree that has additional desirable properties (such as, e.g., a small number of leaves). To address this challenge, we develop a structural approach that enables us to compactly represent all trees with respect to which a given profile is single-peaked. We show how to use this representation to efficiently find the best tree for a given profile for use with our winner determination algorithms: Given a profile, we can efficiently find a tree with the minimum number of leaves, or a tree with the minimum number of internal vertices among trees on which the profile is single-peaked. We then explore the power and limitations of this framework: we develop polynomial-time algorithms to find trees with the smallest maximum degree, diameter, or pathwidth, but show that it is NP-hard to check whether a given profile is single-peaked on a tree that is isomorphic to a given tree, or on a regular tree.
Food habits of two species of Pseudotolithus (Sciaenidae) off Benin (West Africa) nearshore waters and implications for management

Food habits of Pseudotolithus senegalensis and Pseudotolithus typus in Benin nearshore waters were investigated for 18 months because of the importance of croakers in artisanal catches. Frequency of occurrence, numerical abundance and gravimetric composition were utilized in computing the percent index of relative importance of each food item. The major dietary components of the two species were shrimps. The food composition of P. senegalensis and P. typus did not show any variation with season. The diet composition of the two species of Pseudotolithus portray them as specialized feeders depending on similar food sources with low number of dietary prey items. Shrimps and to some extent juvenile fishes which constitute the important preys of Pseudotolithus spp. , remain an essential link of the trophic system off Benin nearshore waters. Therefore, the dynamics of the abundance of these shrimps and juvenile fishes must be assessed for sustainable production of their predators.
THEMIS: Decentralized and Trustless Ad Platform with Reporting Integrity

Online advertising fuels the (seemingly) free internet. However, although users can access most websites free of charge, they need to pay a heavy cost on their privacy and blindly trust third parties and intermediaries that absorb great amounts of adrevenues and user data. This is one of the reasons users opt out from advertising by resorting ad blockers thatin turn cost publishers millions of dollars in lost adrevenues. Existing privacy-preserving advertising approaches(e.g., Adnostic, Privad, Brave Ads) from both industry and academia cannot guarantee the integrity of the performance analytics they provide to advertisers, while they also rely on centralized management that users have to trust without being able to audit. In this paper, we propose THEMIS, a novel privacy-by-design ad platform that is decentralized and requires zero trust from users. THEMIS (i) provides auditability to all participants, (ii) rewards users for viewing ads, and (iii) allows advertisers to verify the performance and billing reports of their ad campaigns. To demonstrate the feasibility and practicability of our approach, we implemented a prototype of THEMIS using a combination of smart contracts and zero-knowledge schemes. Performance evaluation results show that during adreward payouts, THEMIS can support more than 51M users on a single-sidechain setup or 153M users ona multi-sidechain setup, thus proving that THEMIS scales linearly.