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This white paper is the result of a collaboration by those that attended a workshop at the Facility for Rare Isotope Beams (FRIB), organized by the FRIB Theory Alliance (FRIB-TA), on Theoretical Justifications and Motivations for Early High-Profile FRIB Experiments. It covers a wide range of topics related to the science that will be explored at FRIB. After a brief introduction, the sections address: (II) Overview of theoretical methods, (III) Experimental capabilities, (IV) Structure, (V) Near-threshold Physics, (VI) Reaction mechanisms, (VII) Nuclear equations of state, (VIII) Nuclear astrophysics, (IX) Fundamental symmetries, and (X) Experimental design and uncertainty quantification., Comment: 227 pages, 24 figures

The $^3$He$(\alpha,\gamma)^7$Be radiative capture reaction plays a key role in the creation of elements in stars as well as in the production of solar neutrinos, the observation of which is one of the main tools to study the properties of our sun. Since accurate experimental measurements of this fusion cross section at solar energies are difficult due to the strong Coulomb repulsion between the reactants, the onus falls on theory to provide a robust means for extrapolating from the region where experimental data is available down to the desired astrophysical regime. We present the first microscopic calculations of $^3$He$(\alpha,\gamma)^7$Be with explicit inclusion of three-nucleon forces. Our prediction of the astrophysical $S$ factor qualitatively agrees with experimental data. We further incorporate experimental bound-state and scattering information in our calculation to arrive at a more quantitative description. This process reveals that our current model lacks sufficient repulsion in the $1/2^+$ channel of our model space to simultaneously reproduce elastic-scattering data. This deficit suggests that $^3$He$(\alpha,\gamma)^7$Be probes aspects of the nuclear force that are not currently well-constrained., Comment: 7 pages, 4 figures, 2 tables

We report on frequency tripling of high energy, high repetition rate ns pulses from a cryogenically gas cooled multi-slab Yb:YAG laser system Bivoj/DiPOLE, using a type-I phase matched LBO crystal for second harmonic generation and type-II phase matched LBO crystal for sum frequency generation. We achieved conversion to 343 nm with stable energy of 50 J at repetition rate of 10 Hz and conversion efficiency of 53%.

We study barrier discharges in CO$_2$ using optical emission spectroscopy and electrical measurements. We record the spectra of weak CO$_2$ barrier discharge emission in UV-VIS-NIR range. The CO spectral bands of 3rd positive, Angstrom and Asundi systems are identified in the spectra, together with the CO+2 bands of the UV-doublet and the Fox-Duffendack-Baker systems. The effect of CO$_2$ flow rate variations on the relative intensities of the CO and CO$_2$ optical emissions is also investigated. We analyze the optical emission of selected vibrational spectral bands and attempt to determine the E/N using intensity ratio method with kinetic data available in the literature. The method, using intensities from different spectral bands, is tested on sinusoidal driven atmospheric pressure Townsend (APTD) and single-filament barrier discharges at atmospheric pressure. For APTD at different flows and known CO conversion factors, an attempt is made to use the ratio of selected CO to CO$_2$ band intensities. The technique of time-correlated single photon counting is used to obtain necessary sub-nanosecond temporal resolution and, importantly, high signal sensitivity for the single-filament experiment and its utilization gives a promising result, E/N of 330 Td in barrier discharge streamer. Nevertheless, the uncertainties coming from different kinetic data and the low sensitivity of some rate coefficient ratios complicate an easy utilisation of the method. Future steps are therefore proposed, including uncertainty quantification and sensitivity analysis of the simplified model and necessary CO+2 and CO synthetic spectra fitting., Comment: 19 pages, 15 figures

Current LLM alignment techniques use pairwise human preferences at a sample level, and as such, they do not imply an alignment on the distributional level. We propose in this paper Alignment via Optimal Transport (AOT), a novel method for distributional preference alignment of LLMs. AOT aligns LLMs on unpaired preference data by making the reward distribution of the positive samples stochastically dominant in the first order on the distribution of negative samples. We introduce a convex relaxation of this first-order stochastic dominance and cast it as an optimal transport problem with a smooth and convex cost. Thanks to the one-dimensional nature of the resulting optimal transport problem and the convexity of the cost, it has a closed-form solution via sorting on empirical measures. We fine-tune LLMs with this AOT objective, which enables alignment by penalizing the violation of the stochastic dominance of the reward distribution of the positive samples on the reward distribution of the negative samples. We analyze the sample complexity of AOT by considering the dual of the OT problem and show that it converges at the parametric rate. Empirically, we show on a diverse set of alignment datasets and LLMs that AOT leads to state-of-the-art models in the 7B family of models when evaluated with Open LLM Benchmarks and AlpacaEval.

We report the first \textit{ab initio} calculation of the nuclear-structure-dependent radiative correction $ \delta_{ \mathrm{NS} } $ to the $ {}^{10}\mathrm{C} \rightarrow {}^{10}\mathrm{B} $ superallowed transition, computed with the no-core shell model and chiral effective field theory. We obtain $\delta_{ \mathrm{NS} } = - 0.422 (29)_{ \mathrm{nuc} } (12)_{ n,\mathrm{el} } $ with a $1.6$-times reduction in the total uncertainty when compared to the current literature estimate based on the shell model and Fermi gas picture. This work paves the way for a precise determination of $V_{ud}$ from superallowed beta decays within a systematically improvable framework., Comment: 10 pages, 6 figures

Efficient and accurate updating of knowledge stored in Large Language Models (LLMs) is one of the most pressing research challenges today. This paper presents Larimar - a novel, brain-inspired architecture for enhancing LLMs with a distributed episodic memory. Larimar's memory allows for dynamic, one-shot updates of knowledge without the need for computationally expensive re-training or fine-tuning. Experimental results on multiple fact editing benchmarks demonstrate that Larimar attains accuracy comparable to most competitive baselines, even in the challenging sequential editing setup, but also excels in speed - yielding speed-ups of 8-10x depending on the base LLM - as well as flexibility due to the proposed architecture being simple, LLM-agnostic, and hence general. We further provide mechanisms for selective fact forgetting, information leakage prevention, and input context length generalization with Larimar and show their effectiveness. Our code is available at https://github.com/IBM/larimar, Comment: ICML 2024

We developed a common algorithmic solution addressing the problem of resource-constrained outreach encountered by social change organizations with different missions and operations: Breaking Ground -- an organization that helps individuals experiencing homelessness in New York transition to permanent housing and Leket -- the national food bank of Israel that rescues food from farms and elsewhere to feed the hungry. Specifically, we developed an estimation and optimization approach for partially-observed episodic restless bandits under $k$-step transitions. The results show that our Thompson sampling with Markov chain recovery (via Stein variational gradient descent) algorithm significantly outperforms baselines for the problems of both organizations. We carried out this work in a prospective manner with the express goal of devising a flexible-enough but also useful-enough solution that can help overcome a lack of sustainable impact in data science for social good.

Muon capture on nuclei is one of the most promising probes of the nuclear electroweak current driving the yet-hypothetical neutrinoless double-beta ($0\nu\beta\beta$) decay. Both processes involve vector and axial-vector currents at finite momentum transfer, $q\sim 100$ MeV, as well as the induced pseudoscalar and weak-magnetism currents. Comparing measured muon-capture rates with reliable $ab~initio$ nuclear-theory predictions could help us validate these currents. To this end, we compute partial muon-capture rates for $^{6}$Li, $^{12}$C and $^{16}$O, feeding the ground and excited states in $^{6}$He, $^{12}$B and $^{16}$N, using $ab~initio$ no-core shell model with two- and three-nucleon chiral interactions. We remove the spurious center-of-mass motion by introducing translationally invariant operators and approximate the effect of hadronic two-body currents by Fermi-gas model. We solve the bound-muon wave function from the Dirac wave equations in the Coulomb field created by a finite nucleus. We find that the computed rates to the low-lying states in the final nuclei are in good agreement with the measured counterparts. We highlight sensitivity of some of the transitions to the sub-leading three-nucleon interaction terms. We also compare summed rates to several tens of final states with the measured total capture rates and note that we slightly underestimate the total rate with this simple approach due to limited range of excitation energies., Comment: 15 pages, 11 figures, 5 tables; corresponds to the published version

We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level causal structures in an unsupervised manner. Instance-level causality identifies causal relationships among individual events, providing more fine-grained information for decision-making. Existing work in the literature either requires strong assumptions, such as linearity in the intensity function, or heuristically defined model parameters that do not necessarily meet the requirements of Granger causality. We propose Instance-wise Self-Attentive Hawkes Processes (ISAHP), a novel deep learning framework that can directly infer the Granger causality at the event instance level. ISAHP is the first neural point process model that meets the requirements of Granger causality. It leverages the self-attention mechanism of the transformer to align with the principles of Granger causality. We empirically demonstrate that ISAHP is capable of discovering complex instance-level causal structures that cannot be handled by classical models. We also show that ISAHP achieves state-of-the-art performance in proxy tasks involving type-level causal discovery and instance-level event type prediction.

The no-core shell model (NCSM) is an \textit{ab initio} method that solves the nuclear many-body problem by expanding the many-particle wave function into a (typically) harmonic oscillator basis and minimizing the energy to obtain the expansion coefficients. Extensions of the NCSM, such as its coupling with microscopic-cluster basis states, further allow for an \textit{ab initio} treatment of light-ion nuclear reactions of interest for both astrophysics and nuclear technology applications. A downside of the method is the exponential scaling of the basis size with increasing number of nucleons and excitation quanta, which limits its applicability to mass $A\lesssim 16$ nuclei, except for variants where the basis is further down-selected via some truncation scheme. We consider a basis selection method for the NCSM that captures the essential degrees of freedom of the nuclear wave function leading to a favorable complexity scaling for calculations and enabling \textit{ab initio} reaction calculations in $sd$-shell nuclei. The particle configurations within the NCSM basis are ordered based on their contribution to the first moment of the Hamiltonian matrix that results from the projection onto the many-body basis. The truncation scheme then consists in retaining only the lowest-first-moment configurations, which typically contain only few many-body basis states (Slater determinants). We present calculations for $^7$Li and $n+^{12}$C scattering using nucleon-nucleon interactions derived from chiral effective field theory and softened using the similarity renormalization group method. The obtained energy levels invariably demonstrate exponential convergence with the size of the basis, and we find improved convergence in scattering calculations. To demonstrate the possibilities enabled by the approach, we also present a first calculation for the scattering of neutrons from $^{24}$Mg., Comment: 13 pages, 8 figures

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. In this study, we process fast camera data, at rates exceeding 100kfps, on $\textit{in situ}$ Field Programmable Gate Array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real-time. Our system utilizes a convolutional neural network (CNN) model which predicts the $n$=1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6$\mu$s and a throughput of up to 120kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains., Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Rev. Sci. Instrum. 95, 073509 (2024) and may be found at https://doi.org/10.1063/5.0190354

The magnetic dipole transition strength $B(M1)$ of $^{48}$Ca is dominated by a single resonant state at an excitation energy of 10.23 MeV. Experiments disagree about $B(M1)$ and this impacts our understanding of spin flips in nuclei. We performed ab initio computations based on chiral effective field theory and found that $B(M1:0^+\rightarrow1^+)$ lies in the range from $7.0$ to $10.2~\mu_N^2$. This is consistent with a $(\gamma,n)$ experiment but larger than results from $(e,e^\prime)$ and $(p,p')$ scattering. Two-body currents yield no quenching of the $B(M1)$ strength and continuum effects reduce it by about 10%. For a validation of our approach, we computed magnetic moments in $^{47,49}$Ca and performed benchmark calculations in light nuclei., Comment: Published version

We propose a distributional framework for benchmarking socio-technical risks of foundation models with quantified statistical significance. Our approach hinges on a new statistical relative testing based on first and second order stochastic dominance of real random variables. We show that the second order statistics in this test are linked to mean-risk models commonly used in econometrics and mathematical finance to balance risk and utility when choosing between alternatives. Using this framework, we formally develop a risk-aware approach for foundation model selection given guardrails quantified by specified metrics. Inspired by portfolio optimization and selection theory in mathematical finance, we define a metrics portfolio for each model as a means to aggregate a collection of metrics, and perform model selection based on the stochastic dominance of these portfolios. The statistical significance of our tests is backed theoretically by an asymptotic analysis via central limit theorems instantiated in practice via a bootstrap variance estimate. We use our framework to compare various large language models regarding risks related to drifting from instructions and outputting toxic content., Comment: ICML 2024

We propose an experiment to measure the nuclear charge radii of light elements with up to 20~times higher accuracy. These are essential both for understanding nuclear physics at low energies, and for experimental and theoretical applications in simple atomic systems. Such comparisons advance the understanding of bound-state quantum electrodynamics and are useful for searching for new physics beyond the Standard Model. The energy levels of muonic atoms are highly susceptible to nuclear structure, especially to the mean square charge radius. The radii of the lightest nuclei (with the atomic number, $Z=1,2$) have been determined with high accuracy using laser spectroscopy in muonic atoms, while those of medium mass and above were determined using X-ray spectroscopy with semiconductor detectors. In this communication, we present a new experiment, aiming to obtain precision measurements of the radii of light nuclei $3 \leq Z \leq 10$ using single-photon energy measurements with cryogenic microcalorimeters; a quantum-sensing technology capable of high efficiency with outstanding resolution for low-energy X-rays.

Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using prediction intervals calibrated to an ad-hoc operating point, making evaluation and comparison across different studies relatively difficult. Our work leverages: (1) the concept of operating characteristics curves and (2) the notion of a gain over a null reference, to derive a novel operating point agnostic assessment methodology for prediction intervals. The paper defines the Uncertainty Characteristics Curve and demonstrates its utility in selected scenarios. We argue that the proposed method addresses the current need for comprehensive assessment of prediction intervals and thus represents a valuable addition to the uncertainty quantification toolbox., Comment: Published at Workshop on Distribution-Free Uncertainty Quantification, International Conference on Machine Learning (ICML), July 2022. arXiv admin note: substantial text overlap with arXiv:2106.00858

This article describes a multi-modal method using simulated Lidar data via ray tracing and image pixel loss with differentiable rendering to optimize an object's position with respect to an observer or some referential objects in a computer graphics scene. Object position optimization is completed using gradient descent with the loss function being influenced by both modalities. Typical object placement optimization is done using image pixel loss with differentiable rendering only, this work shows the use of a second modality (Lidar) leads to faster convergence. This method of fusing sensor input presents a potential usefulness for autonomous vehicles, as these methods can be used to establish the locations of multiple actors in a scene. This article also presents a method for the simulation of multiple types of data to be used in the training of autonomous vehicles., Comment: 7 pages, 8 figures

Observations of anomalies in the electron-positron angular correlations in high-energy decays in $^4$He, $^8$Be, and $^{12}$C have been reported recently by the ATOMKI collaboration. These could be explained by the creation and subsequent decay of a new boson with a mass of ${\sim}17$ MeV. Theoretical understanding of pair creation in the proton capture reactions used in these experiments is important for the interpretation of the anomalies. We apply the ab initio No-Core Shell Model with Continuum (NCSMC) to the proton capture on $^7$Li. The NCSMC describes both bound and unbound states in light nuclei in a unified way with chiral two- and three-nucleon interactions as the only input. We investigate the structure of $^8$Be, the $p+^7$Li elastic scattering, the $^7$Li($p,\gamma$)$^8$Be cross section and the internal pair creation $^7$Li($p,e^+ e^-$)$^8$Be. We discuss the impact of a proper treatment of the initial scattering state on the electron-positron angular correlation spectrum and compare our results to available ATOMKI data sets. Finally, we calculate $^7$Li($p,X$)$^8$Be cross sections for several proposed models of the hypothetical X17 particle., Comment: 32 pages, 13 figures. Matches more closely the published version

Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Specifically, our close to real-time tracking and predictions culminates in a digital twin to protect public health and inform the public of fire and air quality risk. This tool tracks fire incidents in real-time, utilizes the 3D building footprints of Austin to simulate smoke outputs, and predicts fire incident smoke falloffs within the complex city environment. Results from this study include a complete fire and smoke digital twin model for Austin. We work in cooperation with the City of Austin Fire Department to ensure the accuracy of our forecast and also show that air quality sensor density within our cities cannot validate urban fire presence. We additionally release code and methodology to replicate these results for any city in the world. This work paves the path for similar digital twin models to be developed and deployed to better protect the health and safety of citizens., Comment: 8 pages, 8 figures, conference

Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have emerged to address these issues. This paradigm relies on generative AI models to generate unbiased, privacy-preserving data while maintaining fidelity to the original data. However, assessing the trustworthiness of synthetic datasets and models is a critical challenge. We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation. We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases like education, healthcare, banking, and human resources, spanning different data modalities such as tabular, time-series, vision, and natural language. This holistic assessment is essential for compliance with regulatory safeguards. We introduce a trustworthiness index to rank synthetic datasets based on their safeguards trade-offs. Furthermore, we present a trustworthiness-driven model selection and cross-validation process during training, exemplified with "TrustFormers" across various data types. This approach allows for controllable trustworthiness trade-offs in synthetic data creation. Our auditing framework fosters collaboration among stakeholders, including data scientists, governance experts, internal reviewers, external certifiers, and regulators. This transparent reporting should become a standard practice to prevent bias, discrimination, and privacy violations, ensuring compliance with policies and providing accountability, safety, and performance guarantees., Comment: submitted

Molecules containing short-lived, radioactive nuclei are uniquely positioned to enable a wide range of scientific discoveries in the areas of fundamental symmetries, astrophysics, nuclear structure, and chemistry. Recent advances in the ability to create, cool, and control complex molecules down to the quantum level, along with recent and upcoming advances in radioactive species production at several facilities around the world, create a compelling opportunity to coordinate and combine these efforts to bring precision measurement and control to molecules containing extreme nuclei. In this manuscript, we review the scientific case for studying radioactive molecules, discuss recent atomic, molecular, nuclear, astrophysical, and chemical advances which provide the foundation for their study, describe the facilities where these species are and will be produced, and provide an outlook for the future of this nascent field.

This white paper was submitted to the 2022 Fundamental Symmetries, Neutrons, and Neutrinos (FSNN) Town Hall Meeting in preparation for the next NSAC Long Range Plan. We advocate to support current and future theoretical and experimental searches for physics beyond the Standard Model using nuclear $\beta$ decay., Comment: 12 pages, 2 figures, White paper submitted to the 2022 Fundamental Symmetries, Neutrons, and Neutrinos (FSNN) Town Hall Meeting at the University of North Carolina Chapel Hill

We apply the No-Core Shell Model with Continuum (NCSMC) that is capable of describing both bound and unbound states in light nuclei in a unified way with chiral two- and three-nucleon interactions as the only input. The NCSMC can predict structure and dynamics of light nuclei and, by comparing to available experimental data, test the quality of chiral nuclear forces. We discuss applications of NCSMC to the $\alpha{-}\alpha$ scattering and the structure of $^8$Be, the p+$^7$Be and p+$^7$Li radiative capture and the production of the hypothetical X17 boson claimed in ATOMKI experiments. The $^7$Be(p,$\gamma$)$^8$B reaction plays a role in Solar nucleosynthesis and Solar neutrino physics and has been subject of numerous experimental investigations. We also highlight our investigation of the neutron rich exotic $^8$He that has been recently studied experimentally at TRIUMF with an unexpected deformation reported., Comment: 6 pages, 4 figures, contribution to the proceedings of the International Nuclear Physics Conference 2022 (INPC 2022)

We present a systematic study of the $^{10-14}\text{C}$ isotopes within the \textit{ab initio} no-core shell model theory. We apply four different realistic nucleon-nucleon (NN) interactions: (i) the charge-dependent Bonn 2000 (CDB2K) potential (ii) the inside non-local outside Yukawa (INOY) potential (iii) the next-to-next-to-next-to-leading order (N$^3$LO) potential, and (iv) the optimized next-to-next-to-leading order (N$^2$LO$_{opt}$) potential. We report the low-lying energy spectra of both positive and negative parity states for the $^{10-14}\text{C}$ isotopes and investigate the level structures. We also calculate electromagnetic properties such as transition strengths, quadrupole and magnetic moments. The dependence of point-proton radii on the harmonic oscillator frequency and basis space is shown. We present calculations of the translation invariant one-body density matrix in the no-core shell model and discuss isotopic trends in the density distribution. The maximum basis space reached is $10 \hbar \Omega$ for $^{10}\text{C}$ and $8 \hbar \Omega$ for $^{11-14}\text{C}$, with a maximum M-scheme dimension of $1.3 \times 10^{9}$ for $^{10}\text{C}$. We found that while the INOY interaction gives the best description of the ground state energies, the N$^3$LO interaction best reproduces the point-proton radii., Comment: 21 pages, 12 figures

I present an overview of experiments at TRIUMF ARIEL and ISAC facilities covering both the current and the future envisioned programs. I also briefly review theory program at TRIUMF that relates to the ARIEL experimental program. I highlight several recent experimental results from the nuclear astrophysics, nuclear structure, fundamental symmetries, and the sterile neutrino search. Finally, I mention ongoing theoretical ab initio calculations of the proton capture on 7Li related to the X17 boson observation., Comment: Contribution to proceedings of the workshop New Scientific Opportunities with the TRIUMF ARIEL e-linac, 10 pages, 5 figures

This paper addresses the task of explaining anomalous predictions of a black-box regression model. When using a black-box model, such as one to predict building energy consumption from many sensor measurements, we often have a situation where some observed samples may significantly deviate from their prediction. It may be due to a sub-optimal black-box model, or simply because those samples are outliers. In either case, one would ideally want to compute a ``responsibility score'' indicative of the extent to which an input variable is responsible for the anomalous output. In this work, we formalize this task as a statistical inverse problem: Given model deviation from the expected value, infer the responsibility score of each of the input variables. We propose a new method called likelihood compensation (LC), which is founded on the likelihood principle and computes a correction to each input variable. To the best of our knowledge, this is the first principled framework that computes a responsibility score for real valued anomalous model deviations. We apply our approach to a real-world building energy prediction task and confirm its utility based on expert feedback., Comment: 8 pages, 7 figures

In situ visualization techniques are hampered by a lack of foresight: crucial simulation phenomena can be missed due to a poor sampling rate or insufficient detail at critical timesteps. Keeping a human in the loop is impractical, and defining statistical triggers can be difficult. This paper demonstrates the potential for using a machine-learning-based simulation surrogate as an oracle to identify expected critical regions of a large-scale simulation. These critical regions are used to drive the in situ analysis, providing greater data fidelity and analysis resolution with an equivalent I/O budget to a traditional in situ framework. We develop a distributed asynchronous in situ visualization by integrating TACC Galaxy with CB-Geo MPM for material point simulation of granular flows. We employ a PyTorch-based 3D Graph Network Simulator (GNS) trained on granular flow problems as an oracle to predict the dynamics of granular flows. Critical regions of interests are manually tagged in GNS for in situ rendering in MPM., Comment: 6 pages

Rendering on conventional computers is capable of generating realistic imagery, but the computational complexity of these light transport algorithms is a limiting factor of image synthesis. Quantum computers have the potential to significantly improve rendering performance through reducing the underlying complexity of the algorithms behind light transport. This paper investigates hybrid quantum-classical algorithms for ray tracing, a core component of most rendering techniques. Through a practical implementation of quantum ray tracing in a 3D environment, we show quantum approaches provide a quadratic improvement in query complexity compared to the equivalent classical approach. Based on domain specific knowledge, we then propose algorithms to significantly reduce the computation required for quantum ray tracing through exploiting image space coherence and a principled termination criteria for quantum searching. We show results for both Whitted style ray tracing, and for accelerating ray tracing operations when performing classical Monte Carlo integration for area lights and indirect illumination., Comment: 27 pages, 15 figures

We present an introduction to ab initio nuclear theory with a focus on nuclear reactions. After a high-level overview of ab initio approaches in nuclear physics, we give a more detailed description of the no-core shell model technique equivalent to a large extent to configuration-interaction methods applied in quantum chemistry. We then introduce the no-core shell model with continuum approach that provides a quantum many-body description of nuclear reactions. After a brief review of nuclear reactions important for astrophysics, we present examples of results of ab initio calculations of radiative capture and transfer reactions in light nuclei., Comment: Contribution to the "Handbook of Nuclear Physics", Springer, 2022, edited by I. Tanihata, H. Toki, and T. Kajino