Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximating fixed points of enriched nonexpansive mappings by Krasnoselskij iteration in Hilbert spaces

Using the technique of enrichment of contractive type mappings by Krasnoselskij averaging, presented here for the first time, we introduce and study the class of enriched nonexpansive mappings in Hilbert spaces. In order to approximate the fixed points of enriched nonexpansive mappings we use the Krasnoselskij iteration for which we prove strong and weak convergence theorems. Examples to illustrate the richness of the new class of contractive mappings are also given. Our results in this paper extend some classical convergence theorems established by Browder and Petryshyn in [Browder, F. E. , Petryshyn, W. V. , Construction of fixed points of nonlinear mappings in Hilbert space, J. Math. Anal. Appl. , 20 (1967), 197–228] from the case of nonexpansive mappings to that of enriched nonexpansive mappings,thus including many other important related results from literature as particular cases.
Parabolic equations with dynamic boundary conditions and drift terms

The aim of this paper is to study the wellposedness and L2‐regularity, firstly for a linear heat equation with dynamic boundary conditions by using the approach of sesquilinear forms, and secondly for its backward adjoint equation using the Galerkin approximation and the extension semigroup to a negative Sobolev space.
Deciphering Residual Emissions: Time-dependent Models for the Nonthermal Interstellar Radiation from the Milky Way

Cosmic rays (CRs) in the Galaxy are an important dynamical component of the interstellar medium (ISM) that interact with the other major components (interstellar gas and magnetic and radiation fields) to produce broadband interstellar emissions that span the electromagnetic spectrum. The standard modeling of CR propagation and production of the associated emissions is based on a steady-state assumption, where the CR source spatial density is described using a smoothly varying function of position that does not evolve with time. While this is a convenient approximation, reality is otherwise, where primary CRs are produced in and about highly localized regions, e.g., supernova remnants, which have finite lifetimes. In this paper, we use the latest version of the GALPROP CR propagation code to model time-dependent CR injection and propagation through the ISM from a realistic 3D discretized CR source density distribution, together with full 3D models for the other major ISM components, and make predictions of the associated broadband nonthermal emissions. We compare the predictions for the discretized and equivalent steady-state model, finding that the former predicts novel features in the broadband nonthermal emissions that are absent for the steady-state case. Some of the features predicted by the discretized model may be observable in all-sky observations made by WMAP and Planck, the recently launched eROSITA, the Fermi-LAT, and ground-based observations by HESS, HAWC, and the forthcoming CTA. The nonthermal emissions predicted by the discretized model may also provide explanations of puzzling anomalies in high-energy γ-ray data, such as the Fermi-LAT north/south asymmetry and residuals like the so-called “Fermi bubbles. ”
Non-Gaussian normal diffusion in a fluctuating corrugated channel

A Brownian particle floating in a narrow corrugated (sinusoidal) channel with fluctuating cross section exhibits non-Gaussian normal diffusion. Its displacements are distributed according to a Gaussian law for very short and asymptotically large observation times, whereas a robust exponential distribution emerges for intermediate observation times of the order of the channel fluctuation correlation time. For intermediate to large observation times the particle undergoes normal diffusion with one and the same effective diffusion constant. These results are analytically interpreted without having recourse to heuristic assumptions. Such a simple model thus reproduces recent experimental and numerical observations obtained by investigating complex biophysical systems.
Touché: Towards Ideal and Efficient Cache Compression By Mitigating Tag Area Overheads

Compression is seen as a simple technique to increase the effective cache capacity. Unfortunately, compression techniques either incur tag area overheads or restrict cache block placement to only include neighboring addresses. Ideally, we should be able to place compressed cache blocks without any restrictions or overheads. This paper proposes Touché, a framework for storing multiple compressed blocks from arbitrary addresses within a cacheline without tag area overheads. The Touché framework consists of three components. The first component, called the "Signature" (SIGN) engine, creates shortened signatures from the tag addresses of compressed blocks. Due to this, the SIGN engine can store multiple signatures in each tag entry. On a cache access, the physical cacheline is accessed only if there is a signature match (which has a negligible probability of false positive). The second component, called the "Tag Appended Data" (TADA) mechanism, stores the full tag addresses with data. TADA enables Touché to detect false positive signature matches by providing the full tag address. The third component, called the "Superblock Marker" (SMARK) mechanism, uses a unique marker in the tag entry to indicate compressed cache blocks from neighboring physical addresses in the same cacheline. Touché is hardware-based and achieves an average speedup of 12% (ideal 13%) when compared to an uncompressed baseline.
Bethe Ansatz for XXX chain with negative spin

[Formula: see text] spin chain with spin [Formula: see text] appears as an effective theory of Quantum Chromodynamics. It is equivalent to lattice nonlinear Schroediger’s equation: interacting chain of harmonic oscillators [bosonic]. In thermodynamic limit each energy level is a scattering state of several elementary excitations [lipatons]. Lipaton is a fermion: it can be represented as a topological excitation [soliton] of original [bosonic] degrees of freedom, described by the group [Formula: see text]. We also provide the CFT description (including local quenches) and Yang–Yang thermodynamics of the model.
Parabolic regularity in geometric variational analysis

The paper is mainly devoted to systematic developments and applications of geometric aspects of second-order variational analysis that are revolved around the concept of parabolic regularity of sets. This concept has been known in variational analysis for more than two decades while being largely underinvestigated. We discover here that parabolic regularity is the key to derive new calculus rules and computation formulas for major second-order generalized differential constructions of variational analysis in connection with some properties of sets that go back to classical differential geometry and geometric measure theory. The established results of second-order variational analysis and generalized differentiation, being married to the developed calculus of parabolic regularity, allow us to obtain novel applications to both qualitative and quantitative/numerical aspects of constrained optimization including second-order optimality conditions, augmented Lagrangians, etc. under weak constraint qualifications.
Anti-MANOVA on Compact Manifolds with Applications to 3D Projective Shape Analysis

Methods of hypotheses testing for equality of extrinsic antimeans on compact manifolds are unveiled in this paper. The two and multiple sample problem for antimeans on compact manifolds is addressed for large samples via asymptotic distributions, as well as for small samples using nonparametric bootstrap. An example of face differentiation using 3D VW antimean projective shape analysis for data extracted from digital camera images is also given.
A multi-fidelity neural network surrogate sampling method for uncertainty quantification

We propose a multi-fidelity neural network surrogate sampling method for the uncertainty quantification of physical/biological systems described by ordinary or partial differential equations. We first generate a set of low/high-fidelity data by low/high-fidelity computational models, e.g. using coarser/finer discretizations of the governing differential equations. We then construct a two-level neural network, where a large set of low-fidelity data are utilized in order to accelerate the construction of a high-fidelity surrogate model with a small set of high-fidelity data. We then embed the constructed high-fidelity surrogate model in the framework of Monte Carlo sampling. The proposed algorithm combines the approximation power of neural networks with the advantages of Monte Carlo sampling within a multi-fidelity framework. We present two numerical examples to demonstrate the accuracy and efficiency of the proposed method. We show that dramatic savings in computational cost may be achieved when the output predictions are desired to be accurate within small tolerances.
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images

Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tissue micro-environment by assessing the cell-level information along with the morphology of the gland. However, current automated methods for CRC grading typically utilise small image patches and therefore fail to incorporate the entire tissue micro-architecture for grading purposes. To overcome the challenges of CRC grading, we present a novel cell-graph convolutional neural network (CGC-Net) that converts each large histology image into a graph, where each node is represented by a nucleus within the original image and cellular interactions are denoted as edges between these nodes according to node similarity. The CGC-Net utilises nuclear appearance features in addition to the spatial location of nodes to further boost the performance of the algorithm. To enable nodes to fuse multi-scale information, we introduce Adaptive GraphSage, which is a graph convolution technique that combines multi-level features in a data-driven way. Furthermore, to deal with redundancy in the graph, we propose a sampling technique that removes nodes in areas of dense nuclear activity. We show that modeling the image as a graph enables us to effectively consider a much larger image (around 16× larger) than traditional patch-based approaches and model the complex structure of the tissue micro-environment. We construct cell graphs with an average of over 3,000 nodes on a large CRC histology image dataset and report state-of-the-art results as compared to recent patch-based as well as contextual patch-based techniques, demonstrating the effectiveness of our method.
About Fibonacci trees III: multiple Fibonacci trees

In this third paper, we revisit the question to which extent the properties of the trees associated to the tilings $\{p,4\}$ of the hyperbolic plane are still true if we consider a finitely generated tree by the same rules but rooted at a black node? What happens if, considering the same distinction between black and white nodes but changing the place of the black son in the rules. What happens if we change the representation of the numbers by another set of digits? We tackle all of these questions in the paper. The present paper is an extension of the previous papers arXiv:1904.12135 and arXiv:1907.04677.
Emptiness formation probability and Painlevé V equation in the XY spin chain

We reconsider the problem of finding L consecutive down spins in the ground state of the XY chain, a quantity known as the Emptiness Formation Probability. Motivated by new developments in the asymptotics of Toeplitz determinants, we show how the crossover between the critical and off-critical behaviour of the emptiness formation probability is exactly described by a function of a Painlevé V equation. Following a recent proposal, we also provide a power series expansion for the function in terms of irregular conformal blocks of a conformal field theory with central charge c = 1. Our results are tested against lattice numerical calculations, showing excellent agreement. We finally discuss the free fermion case where the emptiness formation probability is characterized by a Gaussian decay for large L.
Mixture Content Selection for Diverse Sequence Generation

Generating diverse sequences is important in many NLP applications such as question generation or summarization that exhibit semantically one-to-many relationships between source and the target sequences. We present a method to explicitly separate diversification from generation using a general plug-and-play module (called SELECTOR) that wraps around and guides an existing encoder-decoder model. The diversification stage uses a mixture of experts to sample different binary masks on the source sequence for diverse content selection. The generation stage uses a standard encoder-decoder model given each selected content from the source sequence. Due to the non-differentiable nature of discrete sampling and the lack of ground truth labels for binary mask, we leverage a proxy for ground truth mask and adopt stochastic hard-EM for training. In question generation (SQuAD) and abstractive summarization (CNN-DM), our method demonstrates significant improvements in accuracy, diversity and training efficiency, including state-of-the-art top-1 accuracy in both datasets, 6% gain in top-5 accuracy, and 3.7 times faster training over a state-of-the-art model. Our code is publicly available at https://github.com/clovaai/FocusSeq2Seq.
The experience of enrolled nurses caring for multidrug-resistant tuberculosis patients in KwaZulu-Natal

The purpose of this study was to explore and describe the personal experiences of enrolled nurses while caring for patients infected with multidrug-resistant tuberculosis (MDR-TB) in an urban tuberculosis hospital in KwaZulu-Natal province, South Africa. Generic qualitative research was conducted with a sample of purposively selected enrolled nurses who cared for MDR-TB patients. Data was collected through in-depth individual interviews and analysed using Colaizzi’s (1978) method of data analysis. The research findings revealed six major themes: the working context, fear of contracting the disease, problems that have an impact on the quality of nursing care, nurses' perceptions of the patients, support structures and nurses' expressed needs. The findings of this study indicate that the nurses work in a challenging environment and need to be supported, as they experience more negative than positive feelings while caring for these patients. KEY CONCEPTS: Enrolled nurse; experiences: multidrug-resistant tuberculosis; caring; patients
Disc galaxies formed from major mergers in Illustris

We show how wet major mergers can create disc galaxies in a cosmological context, using the Illustris simulation. We select a sample of 38 disc galaxies having experienced a major merger in their history with no subsequent significant minor merger, and study how they transform into discs after the merger. In agreement with what was previously found in controlled simulations of such mergers, we find that their disc is built gradually from young stars formed after the merger in the disc region, while the old stars born before the merger form an ellipsoidal component. Focusing on one fiducial case from our sample, we show how the gas was initially dispersed in the halo region right after the merger, but is then accreted on to a disc to form stars, and builds the disc component. We then select a sample of major mergers creating elliptical galaxies, to show that those cases correspond mainly to dry mergers, where the lack of star formation prevents the formation of a disc in the remnant galaxy. The amount of gas in the remnant galaxy after the merger is therefore essential to determine the final outcome of a major merger.
Two separate cases of extrauterine pregnancy in queens

This report describes the reasons for extrauterine pregnancy development, and on the example of two cases of the secondary extrauterine pregnancy in the two different queens possible consequences and factors affecting final outcome of this events are discussed. In the first case, in the four year old, multiparous domestic shorthair queen, two foetuses in different stages of development were found and removed, during a routine spay. According to the history, three months ago she delivered four healthy kittens. There were no complications observed during the delivery. The queen was healthy and did not show any pathological signs since parturition and all kittens were successfully weaned. In the second case also two dead foetuses (one outside and one inside the uterine horn) were found. In this case a few days after an accident significant deterioration of the health status was observed and improvement allows for surgery was achieved after antibiotic therapy.
Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Robust Principal Component Analysis

This work is concerned with the non-negative rank-1 robust principal component analysis (RPCA), where the goal is to recover the dominant non-negative principal components of a data matrix precisely, where a number of measurements could be grossly corrupted with sparse and arbitrary large noise. Most of the known techniques for solving the RPCA rely on convex relaxation methods by lifting the problem to a higher dimension, which significantly increase the number of variables. As an alternative, the well-known Burer-Monteiro approach can be used to cast the RPCA as a non-convex and non-smooth $\ell_1$ optimization problem with a significantly smaller number of variables. In this work, we show that the low-dimensional formulation of the symmetric and asymmetric positive rank-1 RPCA based on the Burer-Monteiro approach has benign landscape, i.e., 1) it does not have any spurious local solution, 2) has a unique global solution, and 3) its unique global solution coincides with the true components. An implication of this result is that simple local search algorithms are guaranteed to achieve a zero global optimality gap when directly applied to the low-dimensional formulation. Furthermore, we provide strong deterministic and probabilistic guarantees for the exact recovery of the true principal components. In particular, it is shown that a constant fraction of the measurements could be grossly corrupted and yet they would not create any spurious local solution.
THE NEW TRAINING PRACTICES IN CHILDHOOD AND PRIMARY EDUCATION DEGREE OF UNIVERSITY OF SEVILLE: CHALLENGES AND POTENTIALS

This In teacher training, external or educational practice teaching occupies an important place. It is a key period that comes to consolidate the theoretical training received. Simultaneously, one can validate the competences attributed to the degree these students have studied for. Given their nature, the period of practice teaching becomes an experience of “dipping" into diverse educational and professional spaces. In essence, the student can apply the knowledge learnt in the early years of training besides learning about the practical and professional reality of teaching, and develop an approach to various tasks that they will later face in their workplace. With the implementation of Bachelor's Degrees in Education, the University of Seville has launched a new model of practice in training teachers of kindergarten and primary school. This model is backed by law (Order of June 22, 1998 and Royal Decree 1707/2011 of 18 November regulating external academic practices of college students). This new model is consistent with the principles underlying the EHEA (European Higher Education Area). It tries to promote a practical experience for student teachers in which there is intended a greater connection between academic and professional realities, and therefore greater reflection in and on practice (Schon, 1983 [1], 1986 [2]). Numerous studies have highlighted the tenuous connection between undergraduate classes and the experience of professional practice in schools in the context of initial teacher training (Russell, 2012 [3]). According to Schon (1983 [1]), this disconnection is due to the dominance of a technical rationality perspective in university education. From our experience as supervisors of practice teaching for prospective Infant and Primary teachers for the 2010-2013 courses, we find that the reform of university education is faced with a professional culture that makes it difficult to achieve the necessary coordination between departments so as to implement a more integrated curriculum and to agree on actions that will realize these principles. Despite these difficulties, a group of Education Faculty teachers from different areas set out to work in a coordinated and integrated way to teach the subject given prior to the students doing their practice teaching. This has created a new working philosophy in which the perspective of the students and their needs as future teachers assume the fundamental role in the teaching-learning process. Therefore, practice supervisors have the responsibility of presenting the content of the different areas in an integrated and holistic way.
Deep Learning for Spectrum Sensing

In cognitive radio systems, the ability to accurately detect primary user’s signal is essential to secondary user in order to utilize idle licensed spectrum. Conventional energy detector is a good choice for blind signal detection, while it suffers from the well-known SNR-wall due to noise uncertainty. In this letter, we firstly propose a deep learning based signal detector which exploits the underlying structural information of the modulated signals, and is shown to achieve the state of the art detection performance, requiring no prior knowledge about channel state information or background noise. In addition, the impacts of modulation scheme and sample length on performance are investigated. Finally, a deep learning based cooperative detection system is proposed, which is shown to provide substantial performance gain over conventional cooperative sensing methods.
Change point analysis of historical battle deaths

It has been claimed and disputed that World War II has been followed by a ‘long peace’: an unprecedented decline of war. We conduct a full change point analysis of well‐documented, publicly available battle deaths data sets, using new techniques that enable the robust detection of changes in the statistical properties of such heavy‐tailed data. We first test and calibrate these techniques. We then demonstrate the existence of changes, independent of data presentation, in the early to mid‐19th century, as the Congress of Vienna system moved towards its collapse, in the early to mid‐20th century, bracketing the World Wars, and in the late 20th century, as the world reconfigured around the end of the Cold War. Our analysis provides a methodology for future investigations and an empirical basis for political and historical discussions.
Plasma Catecholamines and the Effects of Adrenergic Beta Receptor Blockade on Cardiovascular and Mental Reactions during Emotional Stress

Abstract : Cardiovascular reactions during emotional stress were studied in ten subjects (five labile hypertensives, four alcoholics, one healthy subject). The reactions observed included a very marked fall in forearm vascular resistance marked tachycardia, and moderate rises in systolic and diastolic pressures. Subsequent to adrenergic beta-receptor blockade with I.V. propranolol, the same stressors produced similar blood pressure responses, but the changes in heart rate and vascular resistance were markedly reduced. The data from the very limited number of experiments to date indicate that labile hypertensive subjects react in a more pronounced manner to stress-inducing agents than do chronic alcoholics with respect to rise in arterial pressure, elevated heart rate and forearm vasodilation. The autonomic findings and their relations to plasma levels of adrenaline and noradrenaline as well as reported subjective reactions are discussed. (Author)