Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Affecting Clinical Decision-Making Practice among Nurses Working in Jimma University Medical Center; Jimma Southwest Ethiopia

Background: Clinical decision-making is a process of making judgments regarding patient care and management. Nurses should be able to use decisionmaking skills to provide safe and effective nursing care. The dynamic nature of health care environment requires nurses to be competent decision-makers in order to respond to clients’ health care need. Objective: The main aim of this study was to assess factors affecting clinical decision making practice among nurses working in Jimma university medical center. Method: Facility based descriptive cross-sectional study was conducted by using quantitative method supplemented by qualitative data among 251 nurses. Structured self-administered questionnaire and in-depth interview guide were used to collect data. Descriptive statistics and multivariable logistic regression analysis were used to analyse quantitative data. Statistical significance was declared at a p-value less than 0.05. Qualitative data was analysed based on thematic frameworks to support the quantitative results. Results: Nurses who reported greater self-confidence were 3.482 times more likely to perform clinical decisions than less confident nurses (AOR3.482, 95% CI 1.655, 7.327). Those nurses who get support from immediate supervisor/s make clinical decision practice 2.878 times more likely than those not supported (AOR 2.87, 95% CI 8 1.388, 5.967) and nurses who doesn’t get chance of continuous educational development make clinical decision practice 79% less likely compared to nurses who already get continuous educational development (AOR 0.214, CI 0.116, 0.395). Commitment, ongoing supervision and feedback, authority and autonomy, good communication facilitates clinical decision-making practice whereas poor resource management, patient-nurse ratio, structure and culture of the health care system, absence professional development, low level of self -confidence and level of knowledge inhibits clinical decision-making practice among the study participants. Conclusion: Commitment, supervision and feedback, autonomy, good communication were identified as facilitating factors of clinical decision-making practice. On the other hand; poor resource management, patient-nurse ratio, structure and culture of the health care system, absence of continuous professional development, low level confidence and low level of knowledge on basics of nursing profession are identified as factors inhibiting clinical decision-making practice among nurses.
Traveling waves of the quintic focusing NLS-Szeg{\"o} equation

We study the influence of Szeg{o} projector $\Pi$ on the L 2 --critical one-dimensional non linear focusing Schr{o}dinger equation, leading to the quintic focusing NLS-Szeg{o} equation i$\partial$ t u + $\partial$ 2 x u + $\Pi$(|u| 4 u) = 0, (t, x) $\in$ R x R, u(0, $\times$) = u 0. This equation is globally well-posed in H 1 + = $\Pi$(H 1 (R)), for every initial datum u 0. The solution L 2-scatters both forward and backward in time if u 0 has sufficiently small mass. We prove the orbital stability with scaling of the traveling wave : u $\omega$,c (t, x) = e i$\omega$t Q(x + ct), for some $\omega$, c > 0, where Q is a ground state associated to Gagliardo-Nirenberg type functional I ($\gamma$) (f) = $\partial$ x f 2 L 2 f 4 L 2 + $\gamma$ --i$\partial$ x f, f 2 L 2 f 2 L 2 f 6 L 6 , $\forall$f $\in$ H 1 + \{0}, for some $\gamma$ $\ge$ 0. The ground states are completely classified in the case $\gamma$ = 2, leading to the actual orbital stability without scaling for appropriate traveling waves. As a consequence, the scattering mass threshold of the focusing quintic NLS-Szeg{o} equation is strictly below the mass of ground state associated to the functional I (0) , unlike the recent result by Dodson [6] on the usual quintic focusing non linear Schr{o}dinger equation.
Cosmological signatures of dark sector physics: the evolution of haloes and spin alignment

The standard cosmological paradigm currently lacks a detailed account of physics in the dark sector, the dark matter and energy that dominate cosmic evolution. In this paper, we consider the distinguishing factors between three alternative models - warm dark matter, quintessence and coupled dark matter-energy - and \lcdm{} through numerical simulations of cosmological structure formation. Key halo statistics - halo spin/velocity alignment between large-scale structure and neighboring haloes, halo formation time and migration - were compared across cosmologies within the redshift range 0$\leq$$z$$\leq$2.98. We found the alignment of halo motion and spin to large-scale structures and neighbouring haloes to be similar in all cosmologies for a range of redshifts. The search was extended to low density regions, avoiding non-linear disturbances of halo spins, yet very similar alignment trends were found between cosmologies which are difficult to characterize and use as a probe of cosmology. We found haloes in quintessence cosmologies form earlier than their \lcdm{} counterparts. Relating this to the fact that such haloes originate in high density regions, such findings could hold clues to distinguishing factors for the quintessence cosmology from the standard model. Although in general, halo statistics are not an accurate probe of the dark sector physics.
Investigation of chemical differentiation among the NGC 2264 cluster-forming clumps

We have carried out mapping observations of molecular emission lines of HC$_{3}$N and CH$_{3}$OH toward two massive cluster-forming clumps, NGC2264-C and NGC2264-D, using the Nobeyama 45-m radio telescope. We derive an $I$(HC$_{3}$N)/$I$(CH$_{3}$OH) integrated intensity ratio map, showing a higher value at clumps including 2MASS point sources at the northern part of NGC2264-D. Possible interpretations of the $I$(HC$_{3}$N)/$I$(CH$_{3}$OH) ratio are discussed. We have also observed molecular emission lines from CCS and N$_{2}$H$^{+}$ toward five positions in each clump. We investigate the $N$(N$_{2}$H$^{+}$)/$N$(CCS) and $N$(N$_{2}$H$^{+}$)/$N$(HC$_{3}$N) column density ratios among the ten positions in order to test whether they can be used as chemical evolutionary indicators in these clumps. The $N$(N$_{2}$H$^{+}$)/$N$(CCS) ratio shows a very high value toward a bright embedded IR source (IRS1), whereas the $N$(N$_{2}$H$^{+}$)/$N$(HC$_{3}$N) ratio at IRS1 is comparable with those at the other positions. These results suggest that UV radiation affects the chemistry around IRS1. We find that there are positive correlations between these column density ratios and the excitation temperatures of N$_{2}$H$^{+}$, which implies the chemical evolution of clumps. These chemical evolutionary indicators likely reflect the combination of evolution along the filamentary structure and evolution of each clump.
RPGAN: GANs Interpretability via Random Routing

In this paper, we introduce Random Path Generative Adversarial Network (RPGAN) -- an alternative design of GANs that can serve as a tool for generative model analysis. While the latent space of a typical GAN consists of input vectors, randomly sampled from the standard Gaussian distribution, the latent space of RPGAN consists of random paths in a generator network. As we show, this design allows to understand factors of variation, captured by different generator layers, providing their natural interpretability. With experiments on standard benchmarks, we demonstrate that RPGAN reveals several interesting insights about the roles that different layers play in the image generation process. Aside from interpretability, the RPGAN model also provides competitive generation quality and allows efficient incremental learning on new data.
Extended and Symmetric Loss of Stability for Canards in Planar Fast-Slow Maps

We study fast-slow maps obtained by discretisation of planar fast-slow systems in continuous time. We focus on describing the so-called delayed loss of stability induced by the slow passage through a singularity in fast-slow systems. This delayed loss of stability can be related to the presence of canard solutions. Here we consider three types of singularities: transcritical, pitchfork, and fold. First, we show that under an explicit Runge-Kutta discretisation the delayed loss of stability, due to slow passage through a transcritical or a pitchfork singularity, can be arbitrarily long. In contrast, we also show that under a Kahan-Hirota-Kimura discretisation scheme, the delayed loss of stability related to all three singularities is completely symmetric in the linearised approximation, in perfect accordance with the continuous time setting.
Synchronization and coalescence in a dissipative two-qubit system

The possibility of detuned spins displaying synchronous oscillations in local observables is analysed in the presence of coupling, collective dissipation and incoherent pumping. We show that there exist two distinct scenarios in which synchronization can emerge, related respectively to the presence of a non-degenerate long-lived eigenmode and to the presence of a single-frequency regime. Both scenarios can arise by tuning parameters in this system, owing to the presence of coalascence. The former, known as transient synchronization, is here generalized in the presence of incoherent pumping, and is due to long-lasting coherences leading to a progressive frequency selection. On the other hand, in spite of the spins detuning, the dynamics can be governed by a single frequency. Still, we show that synchronization can be established only after a transient, when phase-locking arises. Spectral features of synchronization in these two scenarios are analysed for two-time correlations.
Orbital Stability of Circumstellar Planets in Binary Systems

Planets that orbit only one of the stars in stellar binary systems (i.e., circumstellar) are dynamically constrained to a limited range of orbital parameters, and understanding conditions on their stability is thus of great importance in exoplanet searches. We perform ∼700 million N-body simulations to identify how stability regions depend on properties of the binary, as well as the starting planetary inclination and mean longitude relative to the binary orbit. Moreover, we provide grid interpolation maps and lookup tables for the community to use our results. Through Monte Carlo methods, we determine that planets with a semimajor axis ap ≲ 8% of the binary semimajor axis abin will likely be stable, given the known distribution of binary star parameters. This estimate varies in the Lidov–Kozai regime or for retrograde orbits to 4% or 10% of abin, respectively. Our method to quickly determine the circumstellar stability limit is important for interpreting observations of binaries using direct imaging with the James Webb Space Telescope, photometry with the Transiting Exoplanet Survey Satellite, or even astrometry with Gaia.
Differentiable Reasoning on Large Knowledge Bases and Natural Language

Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering. General neural architectures that jointly learn representations and transformations of text are very data-inefficient, and it is hard to analyse their reasoning process. These issues are addressed by end-to-end differentiable reasoning systems such as Neural Theorem Provers (NTPs), although they can only be used with small-scale symbolic KBs. In this paper we first propose Greedy NTPs (GNTPs), an extension to NTPs addressing their complexity and scalability limitations, thus making them applicable to real-world datasets. This result is achieved by dynamically constructing the computation graph of NTPs and including only the most promising proof paths during inference, thus obtaining orders of magnitude more efficient models. Then, we propose a novel approach for jointly reasoning over KBs and textual mentions, by embedding logic facts and natural language sentences in a shared embedding space. We show that GNTPs perform on par with NTPs at a fraction of their cost while achieving competitive link prediction results on large datasets, providing explanations for predictions, and inducing interpretable models. Source code, datasets, and supplementary material are available online at this https URL.
Quantitative study of electronic whispering gallery modes in electrostatic-potential induced circular graphene junctions

Electronic whispering gallery modes (EWGMs) have been recently observed in several circular graphene junctions, pn and pp′, created in scanning tunneling microscopy experiments. By computing the local density of states within the Dirac–Weyl formalism for massless fermions we demonstrate that the EWGMs may really be emerged in any type of the electrostatic-potential induced circular graphene junctions, including uni-junctions (e.g. np- or pp′-junctions) as well as bipolar-junctions (e.g. pnp-heterojunctions). Surprisingly, quantitative analyses show that for all the EWGMs identified (regardless of junction types) the quality (Q) factors seem to be ≤102, very small compared to those in ordinary optical whispering gallery modes microresonators, while the corresponding mode radii may tunably be in nanometer-scale. Our theoretical results are in good agreement with existent experimental data, putting a question to the application potential of the EWGMs identified.
Low-Complexity Random Rotation-Based Schemes for Intelligent Reflecting Surfaces

The employment of intelligent reflecting surfaces (IRSs) is a potential and promising solution to increase the spectral and energy efficiency of wireless communication networks. Despite their many advantages, IRS-aided communications have limitations as they are subject to high propagation losses. To overcome this, the phase rotation (shift) at each element needs to be designed in such a way as to increase the channel gain at the destination. However, this increases the system’s complexity as well as its power consumption. In this article, we present an analytical framework for the performance of random rotation-based IRS-aided communications. Under this framework, we propose four low-complexity and energy efficient schemes, based on a coding or a selection approach. Both of these approaches employ random phase rotations and require limited knowledge of channel state information. Specifically, the coding-based schemes use time-varying random phase rotations to produce an equivalent time-varying channel. On the other hand, the selection-based schemes select a partition of the IRS elements based on the received signal power at the destination. Analytical expressions for the achieved outage probability and energy efficiency of each scheme are derived. It is demonstrated that all schemes can provide significant performance gains as well as full diversity order.
The disordered lattice free field pinning model approaching criticality

We continue the study, initiated in [Giacomin and Lacoin, JEMS 2018], of the localization transition of a lattice free field $\phi=(\phi(x))_{x \in Z^d}$, $d\ge 3$, in presence of a quenched disordered substrate. The presence of the substrate affects the interface at the spatial sites in which the interface height is close to zero. This corresponds to the Hamiltonian $$ \sum_{x\in Z^d }(\beta \omega_x+h)\delta_x,$$ where $\delta_x=1_{[-1,1]}(\phi(x))$, and $(\omega_x)_{x\in Z^d}$ is an IID centered field. A transition takes place when the average pinning potential $h$ goes past a threshold $h_c(\beta)$: from a delocalized phase $h h_c(\beta)$ where the field sticks to the substrate. In [Giacomin and Lacoin, JEMS 2018] the critical value of $h$ is identified and it coincides, up to the sign, with the $\log$-Laplace transform of $\omega=\omega_x$, that is $-h_c(\beta)=\lambda(\beta):=\log E[e^{\beta\omega}]$. Here we obtain the sharp critical behavior of the free energy approaching criticality: $$\lim_{u\searrow 0} \frac{ F(\beta,h_c(\beta)+u)}{u^2}= \frac{1}{2\, \textrm{Var}\left(e^{\beta \omega-\lambda(\beta)}\right)}. $$ Moreover, we give a precise description of the trajectories of the field in the same regime: the absolute value of the field is $\sqrt{2\sigma_d^2\vert\log(h-h_c(\beta))\vert}$ to leading order when $h\searrow h_c(\beta)$ except on a vanishing fraction of sites ($\sigma_d^2$ is the single site variance of the free field).
Revisiting the identification of wintertime atmospheric circulation regimes in the Euro‐Atlantic sector

Atmospheric circulation is often clustered in so‐called circulation regimes, which are persistent and recurrent patterns. For the Euro‐Atlantic sector in winter, most studies identify four regimes: the Atlantic Ridge, Scandinavian Blocking and the two phases of the North Atlantic Oscillation. These results are obtained by applying k‐means clustering to the first several empirical orthogonal functions (EOFs) of geopotential height data. Studying the observed circulation in reanalysis data, it is found that when the full field data are used for the k‐means cluster analysis instead of the EOFs, the optimal number of clusters is no longer four but six. The two extra regimes that are found are the opposites of the Atlantic Ridge and Scandinavian Blocking, meaning they have a low‐pressure area roughly where the original regimes have a high‐pressure area. This introduces an appealing symmetry in the clustering result. Incorporating a weak persistence constraint in the clustering procedure is found to lead to a longer duration of regimes, extending beyond the synoptic time‐scale, without changing their occurrence rates. This is in contrast to the commonly used application of a time‐filter to the data before the clustering is executed, which, while increasing the persistence, changes the occurrence rates of the regimes. We conclude that applying a persistence constraint within the clustering procedure is a better way of stabilizing the clustering results than low‐pass filtering the data.
Persistent Homology of Graph Embeddings.

Popular network models such as the mixed membership and standard stochastic block model are known to exhibit distinct geometric structure when embedded into $\mathbb{R}^{d}$ using spectral methods. The resulting point cloud concentrates around a simplex in the first model, whereas it separates into clusters in the second. By adopting the formalism of generalised random dot-product graphs, we demonstrate that both of these models, and different mixing regimes in the case of mixed membership, may be distinguished by the persistent homology of the underlying point distribution in the case of adjacency spectral embedding. Moreover, despite non-identifiability issues, we show that the persistent homology of the support of the distribution and its super-level sets can be consistently estimated. As an application of our consistency results, we provide a topological hypothesis test for distinguishing the standard and mixed membership stochastic block models.
FasterSeg: Searching for Faster Real-time Semantic Segmentation

We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Utilizing neural architecture search (NAS), FasterSeg is discovered from a novel and broader search space integrating multi-resolution branches, that has been recently found to be vital in manually designed segmentation models. To better calibrate the balance between the goals of high accuracy and low latency, we propose a decoupled and fine-grained latency regularization, that effectively overcomes our observed phenomenons that the searched networks are prone to "collapsing" to low-latency yet poor-accuracy models. Moreover, we seamlessly extend FasterSeg to a new collaborative search (co-searching) framework, simultaneously searching for a teacher and a student network in the same single run. The teacher-student distillation further boosts the student model’s accuracy. Experiments on popular segmentation benchmarks demonstrate the competency of FasterSeg. For example, FasterSeg can run over 30% faster than the closest manually designed competitor on Cityscapes, while maintaining comparable accuracy.
The Five Elements of Flow

In this work we propose five concrete steps to improve the performance of optical flow algorithms. We carefully reviewed recently introduced innovations and well-established techniques in deep learning-based flow methods including i) pyramidal feature representations, ii) flow-based consistency checks, iii) cost volume construction practices or iv) distillation, and present extensions or alternatives to inhibiting factors we identified therein. We also show how changing the way gradients propagate in modern flow networks can lead to surprising boosts in performance. Finally, we contribute a novel feature that adaptively guides the learning process towards improving on under-performing flow predictions. Our findings are conceptually simple and easy to implement, yet result in compelling improvements on relevant error measures that we demonstrate via exhaustive ablations on datasets like Flying Chairs2, Flying Things, Sintel and KITTI. We establish new state-of-the-art results on the challenging Sintel and Kitti 2015 test datasets, and even show the portability of our findings to different optical flow and depth from stereo approaches.
An analytic construction of singular solutions related to a critical Yamabe problem

Abstract We answer affirmatively a question posed by Aviles in 1983, concerning the construction of singular solutions of semilinear equations without using phase-plane analysis. Fully exploiting the semilinearity and the stability of the linearized operator in any dimension, our techniques involve a careful gluing in weighted spaces that handles multiple occurrences of criticality, without the need of derivative estimates. The above solution constitutes an Ansatz for the Yamabe problem with a prescribed singular set of maximal dimension for which, using the same machinery, we provide an alternative construction to the one given by Pacard. His linear theory uses Lp-theory on manifolds, while our strategy relies solely on asymptotic analysis and is suitable for generalization to non-local problems. Indeed, in a forthcoming paper, we will prove analogous results in the fractional setting.
Enhancing neutrino event reconstruction with pixel-based 3D readout for liquid argon time projection chambers

In this paper we explore the potential improvements in neutrino event reconstruction that a 3D pixelated readout could offer over a 2D projective wire readout for liquid argon time projection chambers. We simulate and study events in two generic, idealized detector configurations for these two designs, classifying events in each sample with deep convolutional neural networks to compare the best 2D results to the best 3D results. In almost all cases we find that the 3D readout provides better reconstruction efficiency and purity than the 2D projective wire readout, with the advantages of 3D being particularly evident in more complex topologies, such as electron neutrino charged current events. We conclude that the use of a 3D pixelated detector could significantly enhance the reach and impact of future liquid argon TPC experiments physics program, such as DUNE.