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In breast surgical planning, accurate registration of MR images across patient positions has the potential to improve the localisation of tumours during breast cancer treatment. While learning-based registration methods have recently become the state-of-the-art approach for most medical image registration tasks, these methods have yet to make inroads into breast image registration due to certain difficulties-the lack of rich texture information in breast MR images and the need for the deformations to be diffeomophic. In this work, we propose learning strategies for breast MR image registration that are amenable to diffeomorphic constraints, together with early experimental results from in-silico and in-vivo experiments. One key contribution of this work is a registration network which produces superior registration outcomes for breast images in addition to providing diffeomorphic guarantees.

We solve the Schr\"{o}dinger equation for an arbitrary one-dimensional potential energy to calculate the transmission coefficient in the fission channel of compound nucleus reactions. We incorporate the calculated transmission coefficients into the statistical Hauser-Feshbach model calculation for neutron-induced reactions on $^{235,238}$U and $^{239}$Pu. The one-dimensional model reproduces the evaluated fission cross section data reasonably well considering the limited number of model parameters involved. A resonance-like structure appears in the transmission coefficient for a double-humped fission barrier shape that includes an intermediate well, which is understood to be a quantum mechanical effect in the fission channel. The calculated fission cross sections for the neutron-induced reactions on $^{235,238}$U and $^{239}$Pu all exhibit a similar structure.

The integration of emotional intelligence in machines is an important step in advancing human-computer interaction. This demands the development of reliable end-to-end emotion recognition systems. However, the scarcity of public affective datasets presents a challenge. In this literature review, we emphasize the use of generative models to address this issue in neurophysiological signals, particularly Electroencephalogram (EEG) and Functional Near-Infrared Spectroscopy (fNIRS). We provide a comprehensive analysis of different generative models used in the field, examining their input formulation, deployment strategies, and methodologies for evaluating the quality of synthesized data. This review serves as a comprehensive overview, offering insights into the advantages, challenges, and promising future directions in the application of generative models in emotion recognition systems. Through this review, we aim to facilitate the progression of neurophysiological data augmentation, thereby supporting the development of more efficient and reliable emotion recognition systems.

A major revision of the evaluation of $^{239}$Pu neutron-induced reaction cross sections is reported in the fast energy range. The evaluation starts at 2.5 keV incident neutron energy and has been extended up to 30 MeV. Several other notable changes are included in this evaluation since the release of ENDF/B-VIII.0 including the adoption of the Standards fission cross section, inclusion of new radiative capture data of Mosby et al., inclusion of the (n,2n) data of Meot et al., in addition to advances in the treatment of reaction modeling. In contrast to previous evaluation efforts, this evaluation is reproducible with detailed information stored chronologically utilizing a Git repository. The final evaluation results have been compiled into an ENDF-formatted file, which has been processed successfully through NJOY, checked for internal consistency, benchmarked versus older evaluations and validated against a suite of critical assemblies and pulsed-spheres., Comment: 31 pages, 31 figures, internal Los Alamos report

Prompt and $\beta$-delayed fission observables, such as the average number of prompt and delayed neutrons, the independent and cumulative fission product yields, and the prompt $\gamma$-ray energy spectra for the photonuclear reactions on $^{235,238}$U and $^{239}$Pu are calculated with the Hauser-Feshbach Fission Fragment Decay (HF$^3$D) model and compared with available experimental data. In the analysis of neutron-induced fission reactions to the case of photo-induced fission, an excellent reproduction of the delayed neutron yields supports a traditional assumption that the photo-fission might be similar to the neutron-induced fission at the same excitation energies regardless of the spin and parity of the fissioning systems.

Nuclear data is critical for many modern applications from stockpile stewardship to cutting edge scientific research. Central to these pursuits is a robust pipeline for nuclear modeling as well as data assimilation and dissemination. We summarize a small portion of the ongoing nuclear data efforts at Los Alamos for medium mass to heavy nuclei. We begin with an overview of the NEXUS framework and show how one of its modules can be used for model parameter optimization using Bayesian techniques. The mathematical framework affords the combination of different measured data in determining model parameters and their associated correlations. It also has the advantage of being able to quantify outliers in data. We exemplify the power of this procedure by highlighting the recently evaluated 239-Pu cross section. We further showcase the success of our tools and pipeline by covering the insight gained from incorporating the latest nuclear modeling and data in astrophysical simulations as part of the Fission In R-process Elements (FIRE) collaboration., Comment: 6 pages, 5 figures, Nuclear Data (2022) conference proceedings. Comments welcome!

We study $\gamma$-ray emission following $^{239}\mathrm{Pu}(n,\mathrm{f})$ over an incident neutron energy range of $2 < E_i < 40$ MeV. We present the first experimental evidence for positive correlations between the total angular momentum generated in fission and the excitation energy of the compound nucleus prior to fission. The $\gamma$-ray multiplicity increases linearly with incident energy below the 2\textsuperscript{nd}-chance fission threshold with a slope of $0.085 \pm 0.010$ MeV$^{-1}$. This linear trend appears to hold for the average excitation energy of the compound nucleus between $9 < \langle E_x \rangle < 19$ MeV. Most of the multiplicity increase comes from an enhancement around a $\gamma$-ray energy of 0.7 MeV, which we interpret as stretched quadrupole $\gamma$ rays that indicate an increase in total fission-fragment angular momentum with excitation energy., Comment: 9 pages, 5 figures

Denoising is of utmost importance for the visualization and processing of images featuring low signal-to-noise ratio. Total variation methods are among the most popular techniques to perform this task improving the signal-to-noise ratio while preserving coherent intensity discontinuities. In this work, a novel method, termed maximum likelihood data, is proposed, endowing the total variation formulation with the capability to deal with noise-specific models and pre-processing stages for a certain image of interest. To do this, the data fidelity term is modified by means of a maximum likelihood estimator between the original and the denoised image. To assess the improvements of the proposed method with respect to the total variation formulation, we study the denoising of high-frequency ultrasonic images on in-silico and in-vivo setups. The proposed method delivered a better contrast, preservation and localization of the structures while diminishing the intensity bias of the total variation formulation for the multiplicative noise. The enhancement of medical images through denoising helps to improve the outcome of subsequently applied image processing such as registration and segmentation procedures.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at: https://github.com/RagMeh11/QU-BraTS., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA): https://www.melba-journal.org/papers/2022:026.html

We investigate the angular momentum removal from fission fragments (FFs) through neutron and $\gamma$-ray emission, where we find that about half the neutrons are emitted with angular momenta $\ge 1.5\hbar$ and that the change in angular momentum after the emission of neutrons and statistical $\gamma$ rays is significant, contradicting usual assumptions. Per fission event, in our simulations, the neutron and statistical $\gamma$-ray emissions change the spin of the fragment by 3.5 -- 5~$\hbar$, with a large standard deviation comparable to the average value. Such wide angular momentum removal distributions can hide any underlying correlations in the fission fragment initial spin values. Within our model, we reproduce data on spin measurements from discrete transitions after neutron emissions, especially in the case of light FFs. The agreement further improves for the heavy fragments if one removes from the analysis the events that would produce isomeric states. Finally, we show that while in our model the initial FF spins do not follow a saw-tooth like behavior observed in recent measurements, the average FF spin computed after neutron and statistical $\gamma$ emissions exhibits a shape that resembles a saw tooth. This suggests that the average FF spin measured after statistical emissions is not necessarily connected with the scission mechanism as previously implied., Comment: 9 pages, 6 figures; version accepted for publication

The emission of neutrons and gamma rays by fission fragments reveal important information about the properties of fragments immediately following scission. The initial fragment properties, correlations between fragments, and emission competition give rise to correlations in neutron-gamma emission. Neutron-gamma correlations are important in nonproliferation applications because the characterization of fissionable samples relies on the identification of signatures in the measured radiation. Furthermore, recent theoretical and experimental advances have proposed to explain the mechanism of angular momentum generation in fission. In this paper, we present a novel analysis method of neutrons and gamma rays emitted by fission fragments that allows us to discern structure in the observed correlations. We have analyzed data collected on \ce{^{252}Cf}(sf) at the Chi-Nu array at the Los Alamos Neutron Science Center. Through our analysis of the energy-differential neutron-gamma multiplicity covariance, we have observed enhanced neutron-gamma correlations, corresponding to rotational band gamma-ray transitions, at gamma-ray energies of $0.7$ and $1.2$ MeV. To shed light on the origin of this structure, we compare the experimental data with the predictions of three model calculations. The origin of the observed correlation structure is understood in terms of a positive spin-energy correlation in the generation of angular momentum in fission., Comment: 21 pages, 4 figures, 1 table

The Hauser-Feshbach Fission Fragment Decay (HF$^3$D) model is extended to calculate the prompt fission neutron spectrum (PFNS) for the thermal neutron induced fission on $^{235}$U, where the evaporated neutrons from all possible fission fragment pairs are aggregated. By studying model parameter sensitivities on the calculated PFNS, as well as non-statistical behavior of low-lying discrete level spin distribution, we conclude that discrepancies between the aggregation calculation and the experimental PFNS seen at higher neutron emission energies can be attributed to both the primary fission fragment yield distribution and the possible high spin states that are not predicted by the statistical theory of nuclear structure.

The CGMF code implements the Hauser-Feshbach statistical nuclear reaction model to follow the de-excitation of fission fragments by successive emissions of prompt neutrons and $\gamma$ rays. The Monte Carlo technique is used to facilitate the analysis of complex distributions and correlations among the prompt fission observables. Starting from initial configurations for the fission fragments in mass, charge, kinetic energy, excitation energy, spin, and parity, $Y(A,Z,KE,U,J,\pi)$, CGMF samples neutron and $\gamma$-ray probability distributions at each stage of the decay process, conserving energy, spin and parity. Nuclear structure and reaction input data from the RIPL library are used to describe fission fragment properties and decay probabilities. Characteristics of prompt fission neutrons, prompt fission gamma rays, and independent fission yields can be studied consistently. Correlations in energy, angle and multiplicity among the emitted neutrons and $\gamma$ rays can be easily analyzed as a function of the emitting fragments.

The Hauser-Feshbach fission fragment decay model, $\mathtt{HF^3D}$, which calculates the statistical decay of fission fragments, has been expanded to include multi-chance fission, up to neutron incident energies of 20 MeV. The deterministic decay takes as input pre-scission quantities - fission probabilities and the average energy causing fission - and post-scission quantities - yields in mass, charge, total kinetic energy, spin, and parity. From these fission fragment initial conditions, the full decay is followed through both prompt and delayed particle emissions, allowing for the calculation of prompt neutron and $\gamma$ properties, such as multiplicity and energy distributions, both independent and cumulative fission yields, and delayed neutron observables. In this work, we describe the implementation of multi-chance fission into the $\mathtt{HF^3D}$ model, and show an example of prompt and delayed quantities beyond first-chance fission, using the example of neutron-induced fission on $^{235}$U. This expansion represents significant progress in consistently modeling the emission of prompt and delayed particles from fissile systems., Comment: 15 pages, 11 figures, submitted to PRC

Probabilistic machine learning techniques can learn both complex relations between input features and output quantities of interest as well as take into account stochasticity or uncertainty within a data set. In this initial work, we explore the use of one such probabilistic network, the Mixture Density Network (MDN), to reproduce fission yields and their uncertainties. We study mass yields for the spontaneous fission of $^{252}$Cf, exploring the number of training samples needed for converged predictions, how different levels of uncertainty propagate from the training set to the MDN predictions, and how well physical constraints of the yields - such as normalization and symmetry - are upheld by the algorithm. Finally, we test the ability of the MDN to interpolate between and extrapolate beyond samples in the training set using energy-dependent mass yields for the neutron-induced fission on $^{235}$U. The MDN provides a reliable way to include and predict uncertainties and is a promising path forward for supplementing sparse sets of nuclear data., Comment: 20 pages, 8 figures, submitted to J. Phys. G

Several sources of angular anisotropy for fission fragments and prompt neutrons have been studied in neutron-induced fission reactions. These include kinematic recoils of the target from the incident neutron beam and the fragments from the emission of the prompt neutrons, preferential directions of the emission of the fission fragments with respect to the beam axis due to the population of particular transition states at the fission barrier, and forward-peaked angular distributions of pre-equilibrium neutrons which are emitted before the formation of a compound nucleus. In addition, there are several potential sources of angular anisotropies that are more difficult to disentangle: the angular distributions of prompt neutrons from fully accelerated fragments or from scission neutrons, and the emission of neutrons from fission fragments that are not fully accelerated. In this work, we study the effects of the first group of anisotropy sources, particularly exploring the correlations between the fission fragment anisotropy and the resulting neutron anisotropy. While kinematic effects were already accounted for in our Hauser-Feshbach Monte Carlo code, $\mathtt{CGMF}$, anisotropic angular distributions for the fission fragments and pre-equilibrium neutrons resulting from neutron-induced fission on $^{233,234,235,238}$U, $^{239,241}$Pu, and $^{237}$Np have been introduced for the first time. The effects of these sources of anisotropy are examined over a range of incident neutron energies, from thermal to 20 MeV, and compared to experimental data from the Chi-Nu liquid scintillator array. The anisotropy of the fission fragments is reflected in the anisotropy of the prompt neutrons, especially as the outgoing energy of the prompt neutrons increases, allowing for an extraction of the fission fragment anisotropy to be made from a measurement of the neutrons., Comment: 10 pages, 7 figures, submitted to Phys. Rev. C

Excited nuclear fragments are emitted during nuclear fission. The de-excitation of these fission fragments takes place as sequential emission of neutrons followed by photons. A correlation between neutron and photon multiplicities accompanying fission is thus expected. Fission event generators based on established statistical nuclear physics models predict a negative event-by-event correlation in neutron-photon multiplicity. A survey of published experimental results of an event-by-event covariance between the neutron and photon multiplicities emitted following the spontaneous fission of $^{252}$Cf is presented. Analytic unfolding expressions are developed in this work to determine the bias introduced by background sources, particle misclassification, pulse pileup, and inelastic photon production. The published experimental data are re-analyzed using these unfolding techniques and are found to be in qualitative agreement with the predictions of model-based calculations. In particular, we have concluded that there exists a significant event-by-event neutron-photon emission competition following the spontaneous fission of $^{252}$Cf., Comment: 8pages, 3 figures, 2 table

We propose a novel method to extract the prompt neutron multiplicity distribution, $P(\nu)$, in fission reactions based on correlations between prompt neutrons, $\gamma$ rays, and fragment kinetic energy arising from energy conservation. In this approach, only event-by-event measurements of the total $\gamma$-ray energy released as a function of the total kinetic energy (TKE) of the fission fragments are performed, and no neutron detection is required. Using the $\texttt{CGMF}$ fission event generator, we illustrate the method and explore the accuracy of extracting the neutron multiplicity distribution when taking into account the energy resolution and calibration of the energy measurements. We find that a TKE resolution of under 2 MeV produces reasonably accurate results, independent of typical $\gamma$-ray energy measurement resolution., Comment: 5 pages, 4 figures, published in PRC

Background: Spontaneous fission events emit prompt neutrons correlated with one another in emission angle and energy. Purpose: We explore the relationship in energy and angle between correlated prompt neutrons emitted from 252Cf spontaneous fission. Methods: Measurements with the Chi-Nu array provide experimental data for coincident neutrons tagged with a fission chamber signal with 10 degree angular resolution and 1 ns timing resolution for time-of-flight energy calculations. The experimental results are compared to simulations produced by the fission event generators CGMF, FREYA, and MCNPX-POLIMI IPOL(1)=1. Results: We find that the measurements and the simulations all exhibit anisotropic neutron emission, though differences exist between fission event generators. Conclusions: This work shows that the dependence of detected neutron energy on the energy of a neutron detected in coincidence, although weak, is non-negligible, indicating that there may be correlations in energy between two neutrons emitted in the same fission event.

In this work we propose to validate the predictive capabilities of one-dimensional (1D) blood flow models with full three-dimensional (3D) models in the context of patient-specific coronary hemodynamics in hyperemic conditions. Such conditions mimic the state of coronary circulation during the acquisition of the Fractional Flow Reserve (FFR) index. Demonstrating that 1D models accurately reproduce FFR estimates obtained with 3D models has implications in the approach to computationally estimate FFR. To this end, a sample of 20 patients was employed from which 29 3D geometries of arterial trees were constructed, 9 obtained from coronary computed tomography angiography (CCTA) and 20 from intra-vascular ultrasound (IVUS). For each 3D arterial model, a 1D counterpart was generated. The same outflow and inlet pressure boundary conditions were applied to both (3D and 1D) models. In the 1D setting, pressure losses at stenoses and bifurcations were accounted for through specific lumped models. Comparisons between 1D models ($\text{FFR}_{\text{1D}}$) and 3D models ($\text{FFR}_{\text{3D}}$) were performed in terms of predicted $\text{FFR}$ value. Compared to $\text{FFR}_{\text{3D}}$, $\text{FFR}_{\text{1D}}$ resulted with a difference of 0.00$\pm$0.03 and overall predictive capability AUC, Acc, Spe, Sen, PPV and NPV of 0.97, 0.98, 0.90, 0.99, 0.82, and 0.99, with an FFR threshold of 0.8. We conclude that inexpensive $\text{FFR}_{\text{1D}}$ simulations can be reliably used as a surrogate of demanding $\text{FFR}_{\text{3D}}$ computations., Comment: 11 pages, 2 figures, 2 tables, submited to Scientific Reports

We have developed a Hauser-Feshbach fission fragment decay model, HF$^3$D, which can be applied to the statistical decay of more than 500 primary fission fragment pairs (1,000 nuclides) produced by the neutron induced fission of $^{235}$U. The fission fragment yield $Y(A)$ and the total kinetic energy TKE are model inputs, and we estimate them from available experimental data for the $^{235}$U(n$\rm_{th}$,f) system. The model parameters in the statistical decay calculation are adjusted to reproduce some fission observables, such as the neutron emission multiplicity $\overline{\nu}$, its distribution $P(\nu)$, and the mass dependence $\overline\nu(A)$. The calculated fission product yield and isomeric ratio are compared with experimental data. We show that the calculated independent fission product yield $Y_I(A)$ at the thermal energy reproduces the experimental data well, while the calculated isomeric ratios tend to be lower than the Madland-England model prediction. The model is extended to higher incident neutron energies up to the second chance fission threshold. We demonstrate for the first time that most of the isomeric ratios stay constant, although the production of isomeric state itself changes as the incident energy increases.

The $(n,\gamma f)$ process is reviewed in light of modern nuclear reaction calculations in both slow and fast neutron-induced fission reactions on $^{235}$U and $^{239}$Pu. Observed fluctuations of the average prompt fission neutron multiplicity and average total $\gamma$-ray energy below 100 eV incident neutron energy are interpreted in this framework. The surprisingly large contribution of the M1 transitions to the pre-fission $\gamma$-ray spectrum of $^{239}$Pu is explained by the dominant fission probabilities of 0$^+$ and $2^+$ transition states, which can only be accessed from compound nucleus states formed by the interaction of $s$-wave neutrons with the target nucleus in its ground state, and decaying through M1 transitions. The impact of an additional low-lying M1 scissors mode in the photon strength function is analyzed. We review experimental evidence for fission fragment mass and kinetic energy fluctuations in the resonance region and their importance in the interpretation of experimental data on prompt neutron data in this region. Finally, calculations are extended to the fast energy range where $(n,\gamma f)$ corrections can account for up to 3\% of the total fission cross section and about 20\% of the capture cross section., Comment: 14 pages, 19 figures

The Hauser-Feshbach statistical model is applied to the de-excitation of primary fission fragments using input mass yields calculated with macroscopic-microscopic models of the potential energy surface. We test the sensitivity of the prompt fission observables to the input mass yields for two important reactions, $^{235}$U$(n_\mathrm{th},f)$ and $^{239}$Pu$(n_\mathrm{th},f)$, for which good experimental data exist. General traits of the mass yields, such as the location of the peaks and their widths, can impact both the prompt neutron and $\gamma$-ray multiplicities, as well as their spectra. Specifically, we use several mass yields to determine a linear correlation between the calculated prompt neutron multiplicity $\bar{\nu}$ and the average heavy-fragment mass $\langle A_h\rangle$ of the input mass yields $\partial\bar{\nu}/\partial\langle A_h\rangle = \pm 0.1\,n/f/\mathrm{u}$. The mass peak width influences the correlation between the total kinetic energy of the fission fragments and the total number of prompt neutrons emitted $\bar{\nu}_T(\mathrm{TKE})$. Typical biases on prompt particle observables from using calculated mass yields instead of experimental ones are: $\delta \bar{\nu} = 4\%$ for the average prompt neutron multiplicity, $\delta \bar{M}_\gamma = 1\%$ for the average prompt $\gamma$-ray multiplicity, $\delta \bar{\epsilon}_n^\mathrm{LAB} = 1\%$ for the average outgoing neutron energy, $\delta \bar{\epsilon}_\gamma = 1\%$ for the average $\gamma$-ray energy, and $\delta \langle\mathrm{TKE}\rangle = 0.4\%$ for the average total kinetic energy of the fission fragments., Comment: 12 pages, 8 figures, 2 tables

Detailed information on the fission process can be inferred from the observation, modeling and theoretical understanding of prompt fission neutron and $\gamma$-ray~observables. Beyond simple average quantities, the study of distributions and correlations in prompt data, e.g., multiplicity-dependent neutron and \gray~spectra, angular distributions of the emitted particles, $n$-$n$, $n$-$\gamma$, and $\gamma$-$\gamma$~correlations, can place stringent constraints on fission models and parameters that would otherwise be free to be tuned separately to represent individual fission observables. The FREYA~and CGMF~codes have been developed to follow the sequential emissions of prompt neutrons and $\gamma$-rays~from the initial excited fission fragments produced right after scission. Both codes implement Monte Carlo techniques to sample initial fission fragment configurations in mass, charge and kinetic energy and sample probabilities of neutron and $\gamma$~emission at each stage of the decay. This approach naturally leads to using simple but powerful statistical techniques to infer distributions and correlations among many observables and model parameters. The comparison of model calculations with experimental data provides a rich arena for testing various nuclear physics models such as those related to the nuclear structure and level densities of neutron-rich nuclei, the $\gamma$-ray~strength functions of dipole and quadrupole transitions, the mechanism for dividing the excitation energy between the two nascent fragments near scission, and the mechanisms behind the production of angular momentum in the fragments, etc. Beyond the obvious interest from a fundamental physics point of view, such studies are also important for addressing data needs in various nuclear applications. (See text for full abstract.), Comment: 39 pages, 57 figure files, published in Eur. Phys. J. A, reference added this version