Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical description of optical and x-ray absorption spectra of MgO including many-body effects

Here we report the optical and x-ray absorption (XAS) spectra of the wide-band-gap oxide MgO using density functional theory (DFT) and many-body perturbation theory (MBPT). Our comprehensive study of the electronic structure shows that while the band gap is underestimated with the exchange-correlation functional PBEsol (4.58 eV) and the hybrid functional HSE06 (6.58 eV) compared to the experimental value (7.7 eV), it is significantly improved (7.52 eV) and even overcompensated (8.53 eV) when quasiparticle corrections are considered. Inclusion of excitonic effects by solving the Bethe-Salpeter equation (BSE) yields the optical spectrum in excellent agreement with experiment. Excellent agreement is observed also for the O and Mg K-edge absorption spectra, demonstrating the importance of the electron-hole interaction within MBPT. Projection of the electron-hole coupling coefficients from the BSE eigenvectors on the band structure allows us to determine the origin of prominent peaks and identify the orbital character of the relevant contributions. The real space projection of the lowest energy exciton wavefunction of the optical spectrum indicates a Wannier-Mott type, whereas the first exciton in the O K-edge is more localized.
On The Verification of Neural ODEs with Stochastic Guarantees

We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by solving a set of global-optimization problems. For this purpose, we introduce Stochastic Lagrangian Reachability (SLR), an abstraction-based technique for constructing a tight Reachtube (an over-approximation of the set of reachable states over a given time-horizon), and provide stochastic guarantees in the form of confidence intervals for the Reachtube bounds. SLR inherently avoids the infamous wrapping effect (accumulation of over-approximation errors) by performing local optimization steps to expand safe regions instead of repeatedly forward-propagating them as is done by deterministic reachability methods. To enable fast local optimizations, we introduce a novel forward-mode adjoint sensitivity method to compute gradients without the need for backpropagation. Finally, we establish asymptotic and non-asymptotic convergence rates for SLR.
Binary interactions enhance the Diffuse Supernova Neutrino Background

Binary interactions, especially mass transfer and mergers, can strongly influence the evolution of massive stars and change their final properties and the occurrence of supernovae. Here, we investigate how binary interactions affect predictions of the diffuse flux of neutrinos. By performing stellar population syntheses including prescriptions for binary interactions, we show that the resulting detection rates of the diffuse supernova neutrino background is enhanced by 15-20% compared to estimates without binary considerations, but this may be as large as 75%. The large range of possible enhancement is driven by the presently sparse knowledge of the evolution of rapidly rotating carbon-oxygen cores, especially those created as a result of mergers near the white dwarf to core collapse boundary.
Estimating Real World Probabilities: A Forward-Looking Behavioral Framework

We show that disentangling sentiment-induced biases from fundamental expectations significantly improves the accuracy and consistency of probabilistic forecasts. Using data from 1994 to 2017, we analyze 15 stochastic models and risk-preference combinations and in all possible cases a simple behavioral transformation delivers substantial forecast gains. Our results are robust across different evaluation methods, risk-preference hypotheses and sentiment calibrations, demonstrating that behavioral effects can be effectively used to forecast asset prices. Further analyses confirm that our real-world densities outperform densities recalibrated to avoid past mistakes and improve predictive models where risk aversion is dynamically estimated from option prices.
Reliability of dynamic network reconfiguration: Impact of code implementation, parameter selection, scan duration, and task condition

Abstract Multilayer network models have been proposed as an effective means to capture the dynamic configuration of distributed neural circuits and quantitatively describe how communities vary over time. However, test-retest reliabilities for multilayer network measures are yet to be fully quantified. Here, we systematically evaluated the impact of code implementation, network parameter selections, scan duration, and task condition on test-retest reliability of key multilayer network measures (i.e., flexibility, integration, recruitment). We found that each of these factors impacted reliability, although to differing degrees. The choice of parameters is a longstanding difficulty of multilayer modularity-maximization algorithms. As suggested by prior work, we found that optimal parameter selection was a key determinant of reliability. However, due to changes in implementation of the multilayer community detection algorithm, our findings revealed a more complex story than previously appreciated, as the parameter landscape of reliability was found to be dependent on the implementation of the software. Consistent with findings from the static functional connectivity literature, scan duration was found to be a much stronger determinant of reliability than scan condition. We found that both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks can be highly reliable when the parameters are optimized and scan duration is sufficient, although reliability in the movie watching condition was significantly higher than in the other three tasks. Accordingly, the minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, which is less than the 30 min needed for the other three tasks. Collectively, our results quantified test-retest reliability for multilayer network measures and support the utility of movie fMRI as a reliable context in which to investigate time-invariant network dynamics. Our practice of using test-retest reliability to optimize free parameters of multilayer modularity-maximization algorithms has the potential to enhance our ability to use these measures for the study of individual differences in cognitive traits. Highlights Caution is needed when implementing the generalized Louvain community detection code. We recommend optimizing multilayer network parameters using test-retest reliability. Scan duration was a much stronger determinant of reliability than scan condition. Minimal data need for movie condition is 20 min and for other conditions 30 min. Combining different conditions improved test-retest reliability of dynamic estimates.
A Survey on Methods for the Safety Assurance of Machine Learning Based Systems

Methods for safety assurance suggested by the ISO 26262 automotive functional safety standard are not sufficient for applications based on machine learning (ML). We provide a structured, certification oriented overview on available methods supporting the safety argumen-tation of a ML based system. It is sorted into life-cycle phases, and maturity of the approach as well as applicability to different ML types are collected. From this we deduce current open challenges: powerful solvers, inclusion of expert knowledge, validation of data representativity and model diversity, and model introspection with provable guarantees.
Multianalytical Study of Laser Pulse Duration Effects in the IR Laser Cleaning of Wall Paintings from the Monumental Cemetery of Pisa

The feasibility of laser cleaning for the removal of a variety of surface deposits from fragments of real wall paintings from the monumental cemetery of Pisa using Nd:YAG at 1064 nm at (μs), (ns), and (ps) regimes is presented. Multianalytical investigations of the samples from irradiated surfaces of fragments were carried out in order to characterize the original and added materials and to detect any laser-induced alterations; analysis included scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), laser-induced breakdown spectroscopy (LIBS), pyrolysis-gas chromatography-mass spectrometry (PY-GC-MS), and gas chromatography-mass spectrometry (GC-MS). The presence of nitrocellulose and pure lead contaminations on the surface of the samples has been identified. Assessment of the laser cleaning has highlighted the importance of the optimization of laser parameters, specifically pulse duration and fluence at the specified wavelength.
Ab initio
 ultrafast spin dynamics in solids

Spin relaxation and decoherence is at the heart of spintronics and spin-based quantum information science. Currently, no theoretical approaches can accurately predict spin relaxation of solids including necessary scattering pathways for required ns to ms simulation time. We present a first-principles real-time density-matrix approach based on Lindblad dynamics to simulate ultrafast spin dynamics including various scattering processes for general solid-state systems. Through the complete theoretical descriptions of pump, probe and scattering processes including electron-phonon, electron-impurity and electron-electron scatterings, our method can directly simulate the ultrafast pump-probe measurements for coupled spin and electron dynamics at any temperatures and doping levels. We apply this method to a prototypical system GaAs and obtain excellent agreement with experiments. We found that the relative contributions of different scattering mechanisms and phonon modes vary considerably between spin and carrier relaxation processes. Importantly, in sharp contrast to previous work based on model Hamiltonians, we point out that at low temperatures the electron-electron scattering becomes very important for spin relaxation. Most importantly, we examine the applicable conditions of the commonly-used phenomenological D'yakonov-Perel' relation, which may break down for individual scattering processes. Our work provides a predictive computational platform for spin relaxation in solids, which has unprecedented potentials for designing new materials ideal for spintronics and quantum information technology.
Qualitative Research Approach for The Conceptual Understanding of Spirituality for Healthcare Service Users: A Methodological Guide

This is a methodological paper which addresses the need to conceptualize the characteristics of spirituality from the perspective of healthcare service users by using qualitative approach. The need to provide a conception of spirituality as derived from empirical data lead to the justification of choice for the constructivist grounded theory over other qualitative methodologies. This paper highlights the subjective nature of spirituality that suits with the symbolic interactionist and interpretivist as philosophical paradigm underpin such approach in qualitative inquiry. The implication of applying qualitative approach and constructivist grounded theory is that the empirical work may portray the contextual nature of spirituality for the population of interest, especially when it is arguable non-applicable to global context.
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation

Reinforcement learning (RL) algorithms usually require a substantial amount of interaction data and perform well only for specific tasks in a fixed environment. In some scenarios such as healthcare, however, usually only few records are available for each patient, and patients may show different responses to the same treatment, impeding the application of current RL algorithms to learn optimal policies. To address the issues of mechanism heterogeneity and related data scarcity, we propose a data-efficient RL algorithm that exploits structural causal models (SCMs) to model the state dynamics, which are estimated by leveraging both commonalities and differences across subjects. The learned SCM enables us to counterfactually reason what would have happened had another treatment been taken. It helps avoid real (possibly risky) exploration and mitigates the issue that limited experiences lead to biased policies. We propose counterfactual RL algorithms to learn both population-level and individual-level policies. We show that counterfactual outcomes are identifiable under mild conditions and that Q- learning on the counterfactual-based augmented data set converges to the optimal value function. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed approach.
Experimental investigation of the two‐phase theory in a fluidized‐bed combustor

Though the two-phase theory of fluidization is well-accepted, no direct experimental measurements of the different gas concentrations predicted to occur in bubble and particulate phases could be found in the literature. For the first time, theoretical predictions of these different gas concentrations have been validated experimentally, using a combined oxygen/bubble probe. Based on the two-phase theory, a mathematical model was developed for the combustion of a batch of char particles in a fluidized-bed combustor. The experimental oxygen concentration in the particulate phase as a function of time was well predicted by the model. Slight discrepancies for the bubble phase values were eliminated when low-oxygen-concentration bubbles were excluded from the data, attributed to some char combustion occurring in bubbles being contrary to the model assumption. The temperature difference between char and bed particles (ΔT) was the only adjustable parameter in the model. A value of 20° C fitted the burnoff times measured by visual observation of the top of the bed, for both 5 and 10 g char batch masses. Model predictions of the oxygen concentrations were not sensitive to ΔT during the first half of burnoff, when mass transfer controlled the combustion rate, so the mass transfer processes were predicted correctly by the model effectively with no adjustable parameter. The ΔT value of 20° C was significantly lower than experimental measurements of maximum burning char particle temperatures, reported to be 70° C for the small-diameter bed particles used in this work. The discrepancy was attributed to two factors : (i) the decrease in char particle temperature towards the end of the burnoff, when kinetics significantly affected the combustion rate ; and (ii) a lower char particle temperature in the particulate phase than in the bubble phase, with experimental char particle temperature measurements biased towards the higher bubble phase values. It was inferred : (i) that the maximum values of ΔT measured experimentally are too high for calculation of the char particle combustion rate during the kinetic-controlled latter stage of burnoff ; and (ii) that reported values of the heat transfer coefficient from burning char particles to the particulate phase deduced from these particle temperature measurements may have been underestimated.
Testing and reconstruction via decision trees

We study sublinear and local computation algorithms for decision trees, focusing on testing and reconstruction. Our first result is a tester that runs in $\mathrm{poly}(\log s, 1/\varepsilon)\cdot n\log n$ time, makes $\mathrm{poly}(\log s,1/\varepsilon)\cdot \log n$ queries to an unknown function $f$, and: $\circ$ Accepts if $f$ is $\varepsilon$-close to a size-$s$ decision tree; $\circ$ Rejects if $f$ is $\Omega(\varepsilon)$-far from decision trees of size $s^{\tilde{O}((\log s)^2/\varepsilon^2)}$. Existing testers distinguish size-$s$ decision trees from those that are $\varepsilon$-far from from size-$s$ decision trees in $\mathrm{poly}(s^s,1/\varepsilon)\cdot n$ time with $\tilde{O}(s/\varepsilon)$ queries. We therefore solve an incomparable problem, but achieve doubly-exponential-in-$s$ and exponential-in-$s$ improvements in time and query complexities respectively. We obtain our tester by designing a reconstruction algorithm for decision trees: given query access to a function $f$ that is close to a small decision tree, this algorithm provides fast query access to a small decision tree that is close to $f$. By known relationships, our results yield reconstruction algorithms for numerous other boolean function properties -- Fourier degree, randomized and quantum query complexities, certificate complexity, sensitivity, etc. -- which in turn yield new testers for these properties. Finally, we give a hardness result for testing whether an unknown function is $\varepsilon$-close-to or $\Omega(\varepsilon)$-far-from size-$s$ decision trees. We show that an efficient algorithm for this task would yield an efficient algorithm for properly learning decision trees, a central open problem of learning theory. It has long been known that proper learning algorithms for any class $\mathcal{H}$ yield property testers for $\mathcal{H}$; this provides an example of a converse.
Planning From Pixels in Atari With Learned Symbolic Representations

Width-based planning methods have been shown to yield state-of-the-art performance in the Atari 2600 domain using pixel input. One successful approach, RolloutIW, represents states with the B-PROST boolean feature set. An augmented version of RolloutIW, pi-IW, shows that learned features can be competitive with handcrafted ones for width-based search. In this paper, we leverage variational autoencoders (VAEs) to learn features directly from pixels in a principled manner, and without supervision. The inference model of the trained VAEs extracts boolean features from pixels, and RolloutIW plans with these features. The resulting combination outperforms the original RolloutIW and human professional play on Atari 2600 and drastically reduces the size of the feature set.
Clinical Update on Home Testing for Male Fertility

Male factor infertility accounts for about 50% of the incidence of infertility in couples. In current practice, the men must attend a clinic or hospital facility to provide a semen analysis, which is key to the diagnosis of the male reproductive potential. However, many men are often embarrassed with the process and conventional semen analysis requires complex, labor intensive inspection with a microscope. To mitigate these problems, one of the solutions can be at-home semen analysis. In this review we examine the literature of currently available at home semen analysis test kits, describe their limitations, and compare them to the conventional lab-based methods.
A two-step chemistry for highlighting heteroatom species in petroleum materials using carbon-13 NMR spectroscopy

A variety of characterization techniques have been reported for probing the type and distribution of heteroatom species in fossil fuel materials, usually coal and coal extracts. Often, a chemical transformation of the fossil fuel material has been used in combination with a suitable analysis technique in order to enhance the detection and identification of classes of heteroatom groups and to relate this information to aspects of the material's physical and chemical properties. These chemical reactions range from mild treatments including phase-transfer alkylation to rather more severe treatments such as metal reductions and oxidative degradation. Although these latter chemistries can provide information on heteroatom species which are normally unreactive to mild chemical treatments, the severity of these techniques reduces the selectivity for specific molecular types and greatly increases the probability for side reactions. In order to avoid some of the shortcomings inherent in one-step chemistries, we wish to report a two-step approach which combines an alkali metal reduction reaction with a phase transfer (PT)) methylation reaction. A petroleum vacuum residuum (975 /sup 0/F/sup +/) has been chosen to demonstrate the application of this technique for highlighting heteroatom species, particularly sulfur species. Isotopically enriched methyl groups--93.1 mol% /sup 13/CH/sub 3/I in allmore » samples discussed here--are used as chemical tags in the second step of this process to facilitate the detection of reaction products in the residuum by high resolution /sup 13/C NMR spectroscopy. The corresponding one-step products from these two chemical reactions are compared to the sequential two-step product. « less
Input State Stability of Gated Graph Neural Networks

In this paper, we aim to find the conditions for input-state stability (ISS) and incremental input-state stability ($\delta$ISS) of Gated Graph Neural Networks (GGNNs). We show that this recurrent version of Graph Neural Networks (GNNs) can be expressed as a dynamical distributed system and, as a consequence, can be analysed using model-based techniques to assess its stability and robustness properties. Then, the stability criteria found can be exploited as constraints during the training process to enforce the internal stability of the neural network. Two distributed control examples, flocking and multi-robot motion control, show that using these conditions increases the performance and robustness of the gated GNNs.
atTRACTive: Semi-automatic white matter tract segmentation using active learning

Accurately identifying white matter tracts in medical images is essential for various applications, including surgery planning and tract-specific analysis. Supervised machine learning models have reached state-of-the-art solving this task automatically. However, these models are primarily trained on healthy subjects and struggle with strong anatomical aberrations, e.g. caused by brain tumors. This limitation makes them unsuitable for tasks such as preoperative planning, wherefore time-consuming and challenging manual delineation of the target tract is typically employed. We propose semi-automatic entropy-based active learning for quick and intuitive segmentation of white matter tracts from whole-brain tractography consisting of millions of streamlines. The method is evaluated on 21 openly available healthy subjects from the Human Connectome Project and an internal dataset of ten neurosurgical cases. With only a few annotations, the proposed approach enables segmenting tracts on tumor cases comparable to healthy subjects (dice=0.71), while the performance of automatic methods, like TractSeg dropped substantially (dice=0.34) in comparison to healthy subjects. The method is implemented as a prototype named atTRACTive in the freely available software MITK Diffusion. Manual experiments on tumor data showed higher efficiency due to lower segmentation times compared to traditional ROI-based segmentation.
Intelligent Routing Based on Reinforcement Learning for Software-Defined Networking

Traditional routing protocols employ limited information to make routing decisions, which can lead to a slow adaptation to traffic variability, as well as restricted support to the Quality of Service (QoS) requirements of applications. This article introduces a novel approach for routing in Software-defined networking (SDN), called Reinforcement Learning and Software-Defined Networking Intelligent Routing (RSIR). RSIR adds a Knowledge Plane to SDN and defines a routing algorithm based on Reinforcement Learning (RL) that takes into account link-state information to make routing decisions. This algorithm capitalizes on the interaction with the environment, the intelligence provided by RL and the global view and control of the network furnished by SDN, to compute and install, in advance, optimal routes in the forwarding devices. RSIR was extensively evaluated by emulation using real traffic matrices. Results show RSIR outperforms the Dijkstra’s algorithm in relation to the stretch, link throughput, packet loss, and delay when available bandwidth, delay, and loss are considered individually or jointly for the computation of optimal paths. The results demonstrate that RSIR is an attractive solution for intelligent routing in SDN.