Miracle-dz/UniFAR-FTUs huggingface dataset:
Introduction to Various Aspects of Degree Theory in Banach Spaces

Function-analytic preliminaries The Leray-Schauder degree for differentiable maps The Leray-Schauder degree for not necessarily differentiable maps The Poincare--Bohl theorem and some of its applications The product theorem and some of its consequences The finite-dimensional case On spheres Some extension and homotopy theorems The Borsuk theorem and some of its consequences The linear homotopy theorem Proof of the Sard-Smale theorem
Gerstmann–Sträussler–Scheinker Disease with F198S Mutation Induces Independent Tau and Prion Protein Pathologies in Bank Voles

Gerstmann–Sträussler–Scheinker disease (GSS) is a rare genetic prion disease. A large GSS kindred linked to the serine-for-phenylalanine substitution at codon 198 of the prion protein gene (GSS-F198S) is characterized by conspicuous accumulation of prion protein (PrP)-amyloid deposits and neurofibrillary tangles. Recently, we demonstrated the transmissibility of GSS-F198S prions to bank vole carrying isoleucine at 109 PrP codon (BvI). Here we investigated: (i) the transmissibility of GSS-F198S prions to voles carrying methionine at codon 109 (BvM); (ii) the induction of hyperphosphorylated Tau (pTau) in two vole lines, and (iii) compared the phenotype of GSS-F198S-induced pTau with pTau induced in BvM following intracerebral inoculation of a familial Alzheimer’s disease case carrying Presenilin 1 mutation (fAD-PS1). We did not detect prion transmission to BvM, despite the high susceptibility of BvI previously observed. Immunohistochemistry established the presence of induced pTau depositions in vole brains that were not affected by prions. Furthermore, the phenotype of pTau deposits in vole brains was similar in GSS-F198S and fAD-PS1. Overall, results suggest that, regardless of the cause of pTau deposition and its relationship with PrPSc in GSS-F198S human-affected brains, the two components possess their own seeding properties, and that pTau deposition is similarly induced by GSS-F198S and fAD-PS1.
Master Computer Science Image Segmentation on the Skin Diseases

Composed of dermis, epidermis and subcutaneous tissues, skin is the largest organ of a human body. Human skin contains seven layers and protects other organs from external in-vasions such as chemical damage, artificial skin damage and skin diseases. Despite of all this protection skin is not resistant to diseases. Viral, fungal and allergic are the most common types of skin diseases appear on human skin. This thesis focuses on psoriasis which is caused by an autoimmune disorder and is not contagious. People infected with psoriasis develop thick regions of red and white patches. These are also known as lesions. Commonly these lesions appear on elbows, knees, back and scalp. Objective here is to focus on utilizing deep learning models on skin diseases. Intention is to apply image segmentation techniques to identify and extract damaged skin area from the image. Subsequently, extracted skin lesions will represent morphological and texture features of the psoriasis. In
Professional Burnout: Models Explaining the Phenomena in Nursing

Introduction : Burnout is a syndrome defined by emotional exhaustion leading to depersonalization and reductions in personal accomplishments at work. The negative consequences can be negative for the efficiency of the organization, reducing productivity and quality of care. Aim : The purpose of this critical review is it to elaborate on the main theoretical models that describe and interpret professional burnout in contemporary nursing. Methods : For this paper’s needs a systematic review following the Preferred Reporting Items for Systematic Reviews was conducted. The search engines used were PubMed, CINAHL, PsychInfo, Scopus, and Embase. The inclusion criteria were any primary studies examining models of burnout in nurses in peer-reviewed journals, and published in English dating back to mid-1970s until 2022. Results : Many theoretical models have been developed by researchers who attempted to interpret burnout syndrome and how it interacts with the individual and the environment. Hereafter are listed the most common models for describing and explaining burnout syndrome. Thus, our search revealed nine main models which are: Job-Demand-Control-Support; Stages of Disillusionment; Staff Burnout Model; Model of 3 Dimensions; Unfairness and Fairness Model; 12-Phase Burnout Check; Effort-Reward Imbalance Model and Job Demands Resources Model. Discussion : Many studies emphasize the relationships between burnout, job dis-satisfaction and declining mental health coupled often with cynicism. Within the theoretical analysis though, the same factors arise which are viewed either as predictors of burnout or may be also viewed as outcomes of it. This observation highlights a further characteristic of the burnout literature in nursing, namely the similarity of the findings and the cross-sectional nature of the evidence. Yet, in order to promote theoretical understanding of burnout, research needs to set priorities on the use of more routine but concrete empirical data on employee behaviors such as attrition levels, absenteeism and turnover. Conclusions : Nurses around the world need to recognize that burnout is a solid occupational hazard per se that affects not only themselves but patients, organizations, health care systems and even society in general. In this context, burnout in nursing is also associated with worsening safety and quality of care.
AN ARBITRARY-ORDER ULTRA-WEAK DISCONTINUOUS GALERKIN METHOD FOR TWO-DIMENSIONAL SEMILINEAR SECOND-ORDER ELLIPTIC PROBLEMS ON CARTESIAN GRIDS

In this paper, we present and analyze a new ultra-weak discontinuous Galerkin (UWDG) (cid:12)nite element method for two-dimensional semilinear second-order elliptic problems on Cartesian grids. Unlike the traditional local discontinuous Galerkin (LDG) method, the pro- posed UWDG method can be applied without introducing any auxiliary variables or rewriting the original equation into a system of equations. The UWDG scheme is presented in details, including the de(cid:12)nition of the numerical (cid:13)uxes, which are necessary to obtain optimal error estimates. The proposed scheme can be made arbitrarily high-order accurate in two-dimensional space. The error estimates of the presented scheme are analyzed. The order of convergence is proved to be p + 1 in the L 2 -norm, when tensor product polynomials of degree at most p and grid size h are used. Several numerical examples are provided to con(cid:12)rm the theoretical results.
Discrete time symmetry breaking in quantum circuits: exact solutions and tunneling

We discuss general properties of discrete time quantum symmetry breaking in degenerate parametric oscillators. Recent experiments in superconducting quantum circuit with Josephson junction nonlinearities give rise to new properties of strong parametric coupling and nonlinearities. Exact analytic solutions are obtained for the steady-state of this single-mode case of subharmonic generation. We also obtain analytic solutions for the tunneling time over which the time symmetry-breaking is lost above threshold. We find that additional anharmonic terms found in the superconducting case increase the tunneling rate, and can also lead to new regimes of tristability as well as bistability. Our analytic results are confirmed by number state calculations.
Unsupervised Learning to Solve Inverse Problems: Application to Single-Pixel Imaging

R´esum´e – Ces derni`eres ann´ees, les approches bas´ees sur l’apprentissage profond ont obtenu des performances de pointe pour solution-ner de nombreux probl`emes d’imagerie inverse allant de l’imagerie m´edicale `a la photographie computationnelle. Ces m´ethodes n´ecessitent g´en´eralement des paires de signaux et mesures pour l’apprentissage. Cependant, pour divers probl`emes d’imagerie, nous avons g´en´eralement acc ` es qu’ ` a des mesures comprim ´ ees des signaux sous-jacents, rendant l’approche bas ´ ee sur l’apprentissage compliqu ´ ee, voire impossible car les observations compress´ees ne contiennent pas d’information dans l’espace nul de l’op´erateur de d´etection directe. Le r´ecent cadre d’imagerie ´equivariante surmonte cette limitation en exploitant l’invariance aux transformations (translations, rotations, etc.) pr´esentes dans les signaux na-turels. Dans cet article, nous exploitons ce nouveau cadre d’apprentissage non-supervis´e pour reconstruire des donn´ees d’une camera mono-pixel `a partir de mesures compress´ees uniquement. Une s´erie d’exp´eriences montre que la m´ethode propos´ee a des performances comparables `a celles de l’approche supervis´ee standard. Abstract – In recent years, learning-based approaches have obtained state-of-the-art performance in multiple imaging inverse problems rang-ing from medical imaging to computational photography. These methods generally require pairs of signals and associated measurements for training. However, in various imaging problems, we usually only have access to compressed measurements of the underlying signals, hindering this learning-based approach. Learning from measurement data only is impossible in general, as the compressed observations do not contain information in the nullspace of the forward sensing operator. The recent equivariant imaging framework overcomes this limitation by exploiting the invariance to transformations (translations, rotations, etc.) present in natural signals. In this paper, we leverage this novel unsupervised learning framework for reconstructing single-pixel imaging data from compressed measurements alone. A series of experiments show that the proposed method performs comparably to the standard supervised approach.
Fast Radial Search for Progressive Photon Mapping

Global illumination is critical to realistic rendering and as such many different algorithms were developed to solve it, one of which – photon mapping – was designed to efficiently render caustics and indirect lighting. It achieves this by saving photons in a data structure during the first step and then using the result by collecting photons near specific search origins. This step is very time intensive due to the sheer amount of searches performed each iteration. In this paper, we will compare the performance of two spatial data structures and parallelized search algorithms written in CUDA for the GPU by execution time and memory usage for the photon-gathering use case. The algorithms were implemented in an open-source progressive photon mapping project [11] and are using parts of S. Reinwald’s fast-KNN as a basis [9].
A GNN-Based Method for Group Detection from Spatio-Temporal Data

Group detection from spatio-temporal data is helpful in many applications, such as automatic driving and social sciences. Most previous works in this domain are based on conventional machine learning methods with feature engineering; only a few works are based on deep learning. We proposed a graph neural network (GNN) based method for group detection. Our method is an extension of neural relational inference (NRI) [1]. We made the following changes to the original NRI: (1) We applied symmetric edge features with symmetric edge updating processes to output symmetric edge representations corresponding to the symmetric binary group relationships. (2) Inspired by Wavenet [2], we applied a gated dilated residual causal convolutional block to capture both short and long dependency of the sequences of edge features. We name our method “WavenetNRI”. Our experiments compare our method with several baselines, including the original NRI on two types of data sets: (1) six spring simulation data sets; (2) five pedestrian data sets. Experimental results show that on the spring simulation data sets, NRI and WavenetNRI with supervised training outperform all other baselines, and NRI performs slightly better than WavenetNRI. On the pedestrian data sets, our method WavenetNRI with supervised training outperforms other pairwise classification-based baselines. However, it cannot compete against the clustering-based methods. In the ablation study, we study the effects of our changes to NRI on group detection. We find that on the spring simulation data sets, the gated dilated residual causal convolutional block can slightly improve the performance of NRI. On the pedestrian data sets, the symmetric edge features with the symmetric edge updating processes can significantly improve the performance of NRI.
Organic Food and Farming Policy in Denmark Promoting a Transition to Green Growth

Citation for published version (APA): Daugbjerg, C. , & Schvartzman, Y. (2022). Organic Food and Farming Policy in Denmark: Promoting a Transition to Green Growth. In C. de la Porte, G. B. Eydal, J. Kauko, D. Nohrstedt, P. 't Hart, & B. S. Tranøy (Eds.), Successful Public Policy in the Nordic Countries: Cases, Lessons, Challenges (pp. 25-44). Oxford University Press. https://doi.org/10.1093/oso/9780192856296.003. 0002
The Limits of Learning and Planning: Minimal Sufficient Information Transition Systems

In this paper, we view a policy or plan as a transition system over a space of information states that reflect a robot's or other observer's perspective based on limited sensing, memory, computation, and actuation. Regardless of whether policies are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. Toward the quest to find the best policies, we establish in a general setting that minimal information transition systems (ITSs) exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for feasible policies.
Cluster-based Sampling Algorithm for Lightweight IoT Intrusion Detection System

As IoT is becoming pervasive, the need for securing its weakest nodes, thin nodes operating in untrusted environments, is becoming critical. The evolution of lightweight IDS has been achieved to provide an acceptable level of security for such types of nodes, but a key component is still missing. IDS is step two of a two-step process starting with sampling the traffic into a well-representing sample before being utilized by the IDS for attack detection. The literature is still missing lightweight sampling algorithms to work hand-in-hand with lightweight IDS for achieving the security level intended for thin IoT nodes with minimal stress on their already limited resources. This work is the first to develop a lightweight sampling algorithm catered to perform at low sampling ratios and designed to require limited memory, limited computational resources and enable privacy-preserving collective learning. This work is the first conceptualization of lightweight sampling algorithms and has the potential of significantly reducing resource stress while only resulting in slight distortion to the sample. This compromise would be very beneficial for thin IoT nodes.
Saturated-configuration-interaction calculations for five-valent Ta and Db

Accurate atomic structure calculations of complicated atoms with 4 or more valence electrons begin to push the memory and time limits of supercomputers. This paper presents a robust method of decreasing the size of \textit{ab initio} configuration interaction and many-body perturbation theory calculations without undermining the accuracy of the resulting atomic spectra. Our method makes it possible to saturate the CI matrix in atoms with many valence electrons. We test our method on the five-valence-electron atom tantalum and verify the convergence of the calculated energies. We then apply the method to calculate spectra and isotope shifts of tantalum's superheavy analogue dubnium. Isotope-shift calculations can be used to predict the spectra of superheavy isotopes which may be produced in astrophysical phenomena.
Bridging the skill gap between the acquired university curriculum and the requirements of the job market: A data-driven analysis of scienti ﬁ c literature Journal of Innovation & Knowledge

This research provides a comprehensive, ﬁ rst-of-its-kind, in-depth, data-driven analysis of the discussions on "curriculum alignment" in the light of "learned skills" and "acquired skills", as illustrated by cross-disciplin-ary records in Scopus. It was undertaken from 2010 to 2021 on 10,214 data points obtained to fully grasp the issues, names and themes that have contributed to the ﬁ eld over the past decade, and it presents the case for the increased value and new application of bibliometric analyses. When faced with scholarly research not included in Scopus, on the one hand, and links between previously divided research groups, on the other, ensuring the compatibility of the various scienti ﬁ c information archives is essential. Only in this manner can the research artefacts be made evident, the concerns and problems that have been either overlooked or under-researched be identi ﬁ ed, and practical debate between academia and policymakers be facilitated. © 2022 The Authors. Published by Elsevier España, S.L. U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY license
Nested Quantum Error Correction Codes

The theory of quantum error correction was established more than a decade ago as the primary tool for fighting decoherence in quantum information processing. Although great progress has already been made in this field, limited methods are available in constructing new quantum error correction codes from old codes. Here we exhibit a simple and general method to construct new quantum error correction codes by nesting certain quantum codes together. The problem of finding long quantum error correction codes is reduced to that of searching several short length quantum codes with certain properties. Our method works for all length and all distance codes, and is quite efficient to construct optimal or near optimal codes. Two main known methods in constructing new codes from old codes in quantum error-correction theory, the concatenating and pasting, can be understood in the framework of nested quantum error correction codes.
Power spectrum and intermittency of the transmitted flux of QSOs Ly-alpha absorption spectra

Using a set of 28 high resolution, high signal to noise ratio (S/N) QSO Ly-alpha absorption spectra, we investigate the non-Gaussian features of the transmitted flux fluctuations, and their effect upon the power spectrum of this field. We find that the spatial distribution of the local power of the transmitted flux on scales k >= 0.05 s/km is highly spiky or intermittent. The probability distribution functions (PDFs) of the local power are long-tailed. The power on small scales is dominated by small probability events, and consequently, the uncertainty in the power spectrum of the transmitted flux field is generally large. This uncertainty arises due to the slow convergence of an intermittent field to a Gaussian limit required by the central limit theorem (CLT). To reduce this uncertainty, it is common to estimate the error of the power spectrum by selecting subsamples with an "optimal" size. We show that this conventional method actually does not calculate the variance of the original intermittent field but of a Gaussian field. Based on the analysis of intermittency, we propose an algorithm to calculate the error. It is based on a bootstrap re-sampling among all independent local power modes. This estimation doesn't require any extra parameter like the size of the subsamples, and is sensitive to the intermittency of the fields. This method effectively reduces the uncertainty in the power spectrum when the number of independent modes matches the condition of the CLT convergence.
Puppeteer: Exploring Intuitive Hand Gestures and Upper-Body Postures for Manipulating Human Avatar Actions

Body-controlled avatars provide a more intuitive method to real-time control virtual avatars but require larger environment space and more user effort. In contrast, hand-controlled avatars give more dexterous and fewer fatigue manipulations within a close-range space for avatar control but provide fewer sensory cues than the body-based method. This paper investigates the differences between the two manipulations and explores the possibility of a combination. We first performed a formative study to understand when and how users prefer manipulating hands and bodies to represent avatars’ actions in current popular video games. Based on the top video games survey, we decided to represent human avatars’ motions. Besides, we found that players used their bodies to represent avatar actions but changed to using hands when they were too unrealistic and exaggerated to mimic by bodies (e.g., flying in the sky, rolling over quickly). Hand gestures also provide an alternative to lower-body motions when players want to sit during gaming and do not want extensive effort to move their avatars. Hence, we focused on the design of hand gestures and upper-body postures. We present Puppeteer, an input prototype system that allows players directly control their avatars through intuitive hand gestures and upper-body postures. We selected 17 avatar actions discovered in the formative study and conducted a gesture elicitation study to invite 12 participants to design best representing hand gestures and upper-body postures for each action. Then we implemented a prototype system using the MediaPipe framework to detect keypoints and a self-trained model to recognize 17 hand gestures and 17 upper-body postures. Finally, three applications demonstrate the interactions enabled by Puppeteer.
Master Computer Science [Integrating transcriptomic sequencing data at single-cell/nucleus level reveals cellular heterogeneity in FSHD]

Background: Multiple publications have suggested that DUX4 is a key gene in the cure of FSHD. However, the expression of DUX4 is sporadic and heterogeneous. Conventional bulk RNA-seq is challenging to identify essential cells as they are masked by most unaffected cells in the sample. However, with the development of single-cell sequencing technology, some FSHD samples have been applied for this purpose. There is very little literature to explore the heterogeneous expression of DUX4 through integrating data on FSHD samples. In this study, we aimed to select the superior integration method to detect DUX4 expression by combining multiple data sets to explore the physiological significance of FSHD. Methods: This study provides multiple integrations of five datasets related to FSHD through a macro perspective, an induction dataset focused on the sc level, and a patient dataset focused on the sn level in three parts. The integration is carried out using the harmony approach and creating a complete pipeline. Finally, a simple interactive shiny app is created using the R language. Results: The analysis showed that the FSHD atlas was first successfully established by integrating all the datasets. Then, to gain more insight into the expression of DUX4 in FSHD patients and healthy controls, the conjecture that the introduction of DUX4i single-cell RNA-seq might capture cells affected by FSHD without detecting the DUX4 target gene was established. The results showed that although there is a link between the two associations, it is unclear whether this partial overlap is due to this conjecture or to myoblast growth, which requires us to design further experiments. Next, pseudo-temporal trajectory analysis allowed us to dissect the transcriptome dynamics of FSHD1 and FSHD2 and define the transcriptome characteristics of the nuclei in different states. Finally create a simple interactive shiny app web page.