Miracle-dz/UniFAR-FTUs huggingface dataset:
Type Ia supernovae have two physical width–luminosity relations and they favour sub-Chandrasekhar and direct collision models - II. Colour evolution

While the width-luminosity relation among type Ia supernovae (slower is brighter) is one of the best studied properties of this type of events, its physical basis has not been identified convincingly. The ’luminosity’ is known to be related to a clear physical quantity the amount of 56Ni synthesised, but the ’width’ has not been quantitatively linked yet to a physical time scale. In Paper I it was shown that the gamma-ray escape time t0, which determines the long term evolution of the bolometric light curve, can be robustly inferred from observations, and its observed values span a narrow range of 30-45 days for the full range of observed 56Ni masses. In this paper we show that the recombination time of 56Fe and 56Co from doubly to singly ionized states causes the typical observed break in the color curve B-V due to a cliff in the mean opacities, and is a robust width measure of the light curve, which is insensitive to radiation transfer uncertainties. We show that a simple photospheric model can be used to calculate the recombination time to an accuracy of ∼ 5 days, allowing a quantitative understanding of the color WLR. The two physical times scales of the width luminosity relation are shown to be set by two (direction averaged and 56Ni weighted) column densitiesthe total column density which sets t0 and the 56Ni column density which sets the recombination time. Central detonations of sub-Mch WDs and direct WD collision models have gamma-ray escape times and recombination times which are consistent with observations across the luminosity range of type Ia’s. Delayed detonation Chandrasekhar mass models have recombination times that are broadly consistent with observations, with tension at the bright end of the luminosity range and inconsistent gamma-ray escape times at the faint end.
Leaf phenology and freeze tolerance of the invasive tree Pyrus calleryana (Roseaceae) and potential native competitors

Abstract. Pyrus calleryana is one of the most problematic invasive species in the eastern United States. The mechanisms that enable Py. calleryana to establish and outcompete native plants are not fully understood but likely include a profile of advantageous traits. Extended leaf phenology is a characteristic noted in many woody invasive plants. Leaf phenology of Py. calleryana and two native woody species, Populus deltoides and Platanus occidentalis, was observed in natural areas near Dayton, OH from December 2019 to November 2020. A frost event in May also gave us the serendipitous opportunity to assess frost tolerance of these species. We found that Py. calleryana began leafing out almost a month before its native competitors in the spring and kept its leaves on significantly longer than both Po. deltoides and Pl. occidentalis throughout the fall. After the frost event, almost every leaf on Pl. occidentalis died and almost 70% of the leaves on Po. deltoides were damaged; however, Py. calleryana exhibited damage on only 6% of its leaves. Our study suggests that Py. calleryana has a nearly 1-mo advantage in leaf phenology in both spring and fall, and much greater frost tolerance, as compared with native species. These attributes likely contribute to its capacity to outcompete native trees in early successional habitats.
Josephson effect between trapped Bose-Einstein condensates

We study the Josephson effect between atomic Bose-Einstein condensates. By drawing on an electrostatic analogy, we derive a semiclassical functional expression for the three-dimensional Josephson coupling energy in terms of the condensate density. Estimates of the capacitive energy and of the Josephson plasma frequency are also given. The effect of dissipation due to the incoherent exchange of normal atoms is analysed. We conclude that coherent Josephson dynamics may already be observable in current experimental systems.
On the mean value of the index of composition of an integer

For each integer n ≥ 2, let be the index of composition of n, where γ(n) ≔ ∏p∣np. The index of composition of an integer measures the multiplicity of its prime factors. In this paper, we obtain a new asymptotic formula of the sum ∑n≤xλ-k(n). Furthermore, we improve the error term under the Riemann Hypothesis.
Fitting Analysis using Differential evolution Optimization (FADO)

The goal of population spectral synthesis (PSS) is to decipher from the spectrum of a galaxy the mass, age and metallicity of its constituent stellar populations. This technique has been established as a fundamental tool in extragalactic research. It has been extensively applied to large spectroscopic data sets, notably the SDSS, leading to important insights into the galaxy assembly history. However, despite significant improvements over the past decade, all current PSS codes suffer from two major deficiencies that inhibit us from gaining sharp insights into the star-formation history (SFH) of galaxies and potentially introduce substantial biases in studies of their physical properties (e.g., stellar mass, mass-weighted stellar age and specific star formation rate). These are i) the neglect of nebular emission in spectral fits, consequently, ii) the lack of a mechanism that ensures consistency between the best-fitting SFH and the observed nebular emission characteristics of a star-forming (SF) galaxy. In this article, we present FADO (Fitting Analysis using Differential evolution Optimization): a conceptually novel, publicly available PSS tool with the distinctive capability of permitting identification of the SFH that reproduces the observed nebular characteristics of a SF galaxy. This so-far unique self-consistency concept allows us to significantly alleviate degeneracies in current spectral synthesis. The innovative character of FADO is further augmented by its mathematical foundation: FADO is the first PSS code employing genetic differential evolution optimization. This, in conjunction with other unique elements in its mathematical concept (e.g., optimization of the spectral library using artificial intelligence, convergence test, quasi-parallelization) results in key improvements with respect to computational efficiency and uniqueness of the best-fitting SFHs.
Spatial search by quantum walk

Grover's quantum search algorithm provides a way to speed up combinatorial search, but is not directly applicable to searching a physical database. Nevertheless, Aaronson and Ambainis showed that a database of N items laid out in d spatial dimensions can be searched in time of order {radical}(N) for d>2, and in time of order {radical}(N) poly(log N) for d=2. We consider an alternative search algorithm based on a continuous-time quantum walk on a graph. The case of the complete graph gives the continuous-time search algorithm of Farhi and Gutmann, and other previously known results can be used to show that {radical}(N) speedup can also be achieved on the hypercube. We show that full {radical}(N) speedup can be achieved on a d-dimensional periodic lattice for d>4. In d=4, the quantum walk search algorithm takes time of order {radical}(N) poly(log N), and in d<4, the algorithm does not provide substantial speedup.
Efficient cosmological parameter estimation with Hamiltonian Monte Carlo technique

Traditional Markov Chain Monte Carlo methods suffer from low acceptance rate, slow mixing and low efficiency in high dimensions. Hamiltonian Monte Carlo resolves this issue by avoiding the random walk. Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) technique built upon the basic principle of Hamiltonian mechanics. Hamiltonian dynamics allows the chain to move along trajectories of constant energy, taking large jumps in the parameter space with relatively inexpensive computations. This new technique improves the acceptance rate by a factor of 4 and boosts up the efficiency by at least a factor of D in a D-dimensional parameter space. Therefor shorter chains will be needed for a reliable parameter estimation comparing to a traditional MCMC chain yielding the same performance. Besides that, the HMC is well suited for sampling from non-Gaussian and curved distributions which are very hard to sample from using the traditional MCMC methods. The method is very simple to code and can be easily plugged into standard parameter estimation codes such as CosmoMC. In this paper we demonstrate how the HMC can be efficiently used in cosmological parameter estimation.
Efficient Self-Adjusting Search Trees via Lazy Updates

Self-adjusting data structures are a classic approach to adapting the complexity of operations to the data access distribution. While several self-adjusting variants are known for both binary search trees and B-Trees, existing constructions come with limitations. For instance, existing works on self-adjusting B-Trees do not provide static-optimality and tend to be complex and inefficient to implement in practice. In this paper, we provide a new approach to build efficient self-adjusting search trees based on state-of-the-art non-adaptive structures. We illustrate our approach to obtain a new efficient self-adjusting Interpolation Search Tree (IST) and B-Tree, as well as a new self-adjusting tree called the Log Tree. Of note, our self-adjusting IST has expected complexity in $O(\log \frac{\log m}{\log ac(x)})$, where $m$ is the total number of requests and $ac(x)$ is the number of requests to key $x$. Our technique leads to simple constructions with a reduced number of pointer manipulations: this improves cache efficiency and even allows an efficient concurrent implementation.
Engaging Citizen Scientists to Keep Transit Times Fresh and Ensure the Efficient Use of Transiting Exoplanet Characterization Missions

This white paper advocates for the creation of a community-wide program to maintain precise mid-transit times of exoplanets that would likely be targeted by future platforms. Given the sheer number of targets that will require careful monitoring between now and the launch of the next generation of exoplanet characterization missions, this network will initially be devised as a citizen science project -- focused on the numerous amateur astronomers, small universities and community colleges and high schools that have access to modest sized telescopes and off-the-shelf CCDs.
The Language of Adolescents in Depicting Migrants

This study (N = 161 Italian adolescents attending 11th and 12th grade of secondary school) investigated how adolescents linguistically portray migrants. Over a year and a half, the study considered whether positive factors known to reduce social discrimination – i.e., multiple categorization of migrants and/or respondent's identification with the human group – are associated with relatively unbiased linguistic descriptions of migrants. The coding system included three categories of terms referring to the outgroup: generalized/categorical definitions, individuating piecemeal information or membership in the human group. We found that adolescents who used multiple categorizations to describe migrants and self-identified with the human group (at T1) linguistically described migrants in human and individuating terms (at T2) to a higher extent. The findings are discussed underlying the implications of defining the self and outgroups in multiple complex ways through language, as an ecological means used by adolescents to communicate their views of others.
Exploration of the Feasibility of Remote Assessment of Functioning in Children and Adolescents with Developmental Disabilities: Parents’ Perspectives and Related Contextual Factors

The COVID-19 pandemic interrupted face-to-face health services, leveraging telehealth strategies. The aim of this cross-sectional study was to investigate, from a parent’s perspective, the feasibility of a remote assessment of functioning in children with developmental disabilities during the pandemic and related contextual factors, based on how parents carry out the assessment. Parents of children with developmental disabilities (mean age = 7.56 ± 3.68) responded to a remote assessment via electronic forms and telephone interview. We analyzed parents’ perspectives about the feasibility of the assessment. We also tested the association between feasibility score and sociodemographics/pandemic experience. Regression analysis tested if children’s functioning characteristics predicted feasibility. A total of 57 mothers completed the remote assessment, and more than 95% did not report difficulties in accessing/responding to electronic forms. They scored remote assessment as easy and feasible, and reported no difficulties with telephone interview. Greater feasibility rates were related to lower maternal age (rho Spearman = −0.290; p = 0.029). The model shows that children’s characteristics predicted 20.4% of feasibility (p < 0.005). Remote assessment showed to be feasible. Younger mothers might consider easier-to-use technologies, beyond considering remote assessment more viable. These results can guide the next steps in research and remote clinical practice.
Microlensing maps for the galactic bulge

Microlensing maps -- that is, contours of equal numbers of events per $10^6$ source stars -- are provided for the inner Galaxy under two alternative hypotheses : (1) the bulge is an oblate axisymmetric spheroid or (2) the bulge is a prolate bar. Oblate spheroids yield a total of $\sim 12$ events per year per $10^6$ stars at Baade's Window ($\sim 15$ events if the disk is maximal). The event rate is slightly lower for prolate bars viewed at $\sim 45^\circ$ and the maps have a characteristic asymmetry between positive and negative longitudes. Prolate bars can yield mild amplifications of the event rate if viewed almost down the long axis. The disk provides the dominant lensing population on the bulge major axis for $|\ell | \gta 6^\circ$. Measurements of the rate at major axis windows can test for disk dark matter or maximal disk models.
Computation Offloading Outage Probability Analysis and Min-Max Fairness Optimization in RIS-Assisted MEC System

Reconfigurable intelligent surface (RIS) is able to enhance the wireless channels, and further improve the performance in terms of outage in the mobile edge computing (MEC) system. Taking the computation offloading outage probability (COOP) as the metric, we investigate the coverage performance of the RIS-assisted MEC system. Specifically, we first obtain the closed-form expression of the COOP. Then we derive an asymptotic COOP expression at the high transmit power region and minimize the COOP in the single-smart device (SD) scenario. Finally, aiming at ensuring fairness among SDs in the multi-SD scenario, we propose an alternating optimization-based COOP min-max fairness (AOCMMF) algorithm to obtain the suboptimal solution of the nonconvex min-max problem. The simulation results verify that our closed-form and asymptotic expressions depict the COOP correctly. The proposed AOCMMF scheme can reduce the maximum COOP by 42.23% compared with the equal edge computing resource scheme, and the min-max fairness among SDs is realized.
Two-component instantaneous wall shear rate measurements in reversing flows

Validation experiments of the two-dimensional inverse algorithm are performed in a pulsed Poiseuille flow exposing shear reversal phases. The method is applied to the three-segment electrodiffusion (ED) probe for which a specific nondimensionalization process is suggested, allowing to better link measurements from a real ED probe to the modeled one in the inverse problem. This approach provided a two-component wall shear rate in good agreement with the one obtained from laser Doppler anemometry (LDA) measurements, thus validating the ability of ED probes to deal with high-amplitude unsteady flows. The classic linear velocity approximation ($u=sy$) in the probe vicinity is also investigated in such a flow.
The Origin of the Large-Scale Structure in the Universe: Theoretical and Statistical Aspects

We review some theoretical and statistical aspects of the origin of the large-scale structure in the Universe, in view of the two most widely known and accepted scenarios: the inflaton and the curvaton scenarios. Among the theoretical aspects, we point out the impossibility of having a low inflationary energy scale in the simplest curvaton model. A couple of modifications to the simplest setup are explored, corresponding to the implementation of a second (thermal) inflationary period whose end makes the curvaton field `heavy', triggering either its oscillations or immediate decay. Low scale inflation is then possible to attain with H_\ast being as low as 1 TeV. Among the statistical aspects, we study the bispectrum B_\zeta(k_1,k_2,k_3) of the primordial curvature perturbation \zeta whose normalisation \fnl gives information about the level of non-gaussianity in \zeta. In connection with \fnl, several conserved and/or gauge invariant quantities described as the second-order curvature perturbation have been given in the literature. We review each of these quantities showing how to interpret one in terms of the others, and analyze the respective expected \fnl in both the inflaton and the curvaton scenarios as well as in other less known models for the generation of primordial perturbations and/or non-gaussianities. The \delta N formalism turns out to be a powerful technique to compute \fnl in multi-component slow-roll inflation, as the knowledge of the evolution of some family of unperturbed universes is the only requirement. We present for the first time this formalism and apply it to selected examples.
Applying Deep Learning for Breast Cancer Detection in Radiology

Recent advances in deep learning have enhanced medical imaging research. Breast cancer is the most prevalent cancer among women, and many applications have been developed to improve its early detection. The purpose of this review is to examine how various deep learning methods can be applied to breast cancer screening workflows. We summarize deep learning methods, data availability and different screening methods for breast cancer including mammography, thermography, ultrasound and magnetic resonance imaging. In this review, we will explore deep learning in diagnostic breast imaging and describe the literature review. As a conclusion, we discuss some of the limitations and opportunities of integrating artificial intelligence into breast cancer clinical practice.
Traditional Ethnobotanical Knowledge of the Central Lika Region (Continental Croatia)—First Record of Edible Use of Fungus Taphrina pruni

This study analyzed the use of plants and fungi, some wild and some cultivated, in three municipalities of Lika-Senj County (Perušić, Gospić and Lovinac). The range of the study area was about 60 km. Forty in-depth semi-structured interviews were performed. The use of 111 plant taxa from 50 plant families and five taxa of mushrooms and fungi belonging to five families was recorded (on average 27 taxa per interview). The results showed quite large differences between the three studied areas in terms of ethnobotanical and ecological knowledge. In the Perušić area, (101 taxa mentioned), some people still use wild plants on a daily basis for various purposes. The most commonly noted plants are Prunus spinosa, Taraxacum spp. , Rosa canina, Urtica dioica, Juglans regia and Fragaria vesca. In the Lovinac region, people used fewer species of plants (76 species mentioned). The most common species used there are: Rosa canina, Achillea millefolium, Cornus mas, Crataegus monogyna, Sambucus nigra and Prunus domestica. In the town of Gospić, the collection and use of plants was not so widespread, with only 61 species mentioned, the most common being: Achillea millefolium, Cornus mas, Sambucus nigra, Viola sp. , Prunus domestica and Rosa canina. The medicinal use of herbal tea Rubus caesius and Cydonia oblonga against diarrhea was well known in the study area and is used medicinally, mainly in the rural parts of the Gospić area. The consumption of the Sorbus species (S. aria, S. domestica and S. torminalis) is an interesting local tradition in Perušić and Lovinac. Species that are difficult to find in nature today and are no longer used include: Veratrum sp. , Rhamnus alpinum ssp. fallax, Gentiana lutea and Ribes uva-crispa. The use of Chenopodium album has also died out. We can assume that the differences in ethnobotanical knowledge between the three studied areas are partly due to minor differences in climate and topography, while other causes lie in the higher degree of rurality and stronger ties to nature in the Lovinac and Perušić areas. The most important finding of the study is the use of the parasitic fungus Taphrina pruni (Fuckel) Tul. as a snack. The use of Helleborus dumetorum for ethnoveterinary practices is also worth noting. The traditional use of plants in the study area shows many signs of abandonment, and therefore efforts must be made to maintain the knowledge recorded in our study.
Impact of Classical Control Electronics on Qubit Fidelity

Quantum processors rely on classical electronic controllers to manipulate and read out the state of quantum bits (qubits). As the performance of the quantum processor improves, nonidealities in the classical controller can become the performance bottleneck for the whole quantum computer. To prevent such limitation, this paper presents a systematic study of the impact of the classical electrical signals on the qubit fidelity. All operations, i.e., single-qubit rotations, two-qubit gates, and readout, are considered, in the presence of errors in the control electronics, such as static, dynamic, systematic, and random errors. Although the presented study could be extended to any qubit technology, it currently focuses on single-electron spin qubits, because of several advantages, such as purely electrical control and long coherence times, and for their potential for large-scale integration. As a result of this study, detailed electrical specifications for the classical control electronics for a given qubit fidelity can be derived. We also discuss how qubit fidelity is affected by the limited performance of the general-purpose room-temperature equipment typically employed to control the few qubits available today. Ultimately, we show that tailor-made electronic controllers can achieve significantly lower power, cost, and size, as required to support the scaling up of quantum computers.
Low-Delay Analog Joint Source-Channel Coding With Deep Learning

We consider the design of low-delay joint source-channel coding (JSCC) schemes for the transmission of discrete-time analog sources over noisy channels based on deep neural networks. The design problem is addressed as optimization of an autoencoder model, and several scenarios are discussed. For point-to-point communication of independent and identically distributed (i.i.d) Gaussian sources and Gauss-Markov sources over additive-white Gaussian noise (AWGN) channels, the encoder and decoder are constructed using recurrent neural networks (RNNs). With minimum prior knowledge used for design, the performance of these RNNs-based models is optimized using fine tuning techniques during training. Sinusoidal representation networks (SIRENs)-based models are proposed and optimized for three JSCC problems namely, transmitting multivariate Gaussian sources over AWGN channels, transmitting i.i. d Gaussian sources with side information at the decoder, and for communicating correlated sources over orthogonal Gaussian channels. We show that these deep learning-based JSCC schemes perform comparably or better than state-of-the-art (SOTA) traditional schemes. The proposed scheme can extend flexibly to different pairs of source and channel dimensions. Moreover, the spontaneously learned encoder mappings exhibit structured patterns that are interpretable.