Miracle-dz/UniFAR-FTUs huggingface dataset:
Nuclear Dynamics and Reactions in the Ab Initio Symmetry-Adapted Framework

We review the ab initio symmetry-adapted (SA) framework for determining the structure of stable and unstable nuclei, along with related electroweak, decay and reaction processes. This framework utilizes the dominant symmetry of nuclear dynamics, the shape-related symplectic Sp(3,R) symmetry, which has been shown to emerge from first principles and to expose dominant degrees of freedom that are collective in nature, even in the lightest species or seemingly spherical states. This feature is illustrated for a broad scope of nuclei ranging from helium to titanium isotopes, enabled by recent developments of the ab initio symmetry-adapted no-core shell model expanded to the continuum through the use of the SA basis and that of the resonating group method. The review focuses on energies, electromagnetic transitions, quadrupole and magnetic moments, radii, form factors, and response function moments, for ground-state rotational bands and giant resonances. The method also determines the structure of reaction fragments that is used to calculate decay widths and alpha-capture reactions for simulated x-ray burst abundance patterns, as well as nucleon-nucleus interactions for cross sections and other reaction observables. 1 ar X iv :2 10 8. 04 89 4v 1 [ nu cl -t h] 1 0 A ug 2 02 1
Interactions of slow multiply charged ions with large, free radiosensitizing metallic nanoparticles

Cette these est consacree a l'etude de l'interaction d'ions multicharges avec des particules metalliques de taille nanometrique. Ce travail a eu pour but d'etudier les processus fondamentaux ainsi que d'eclairer leur role comme radio-sensibilisants dans le traitement de cancer par hadrontherapie. Le nouveau dispositif developpe dans ce cadre consiste en une source d'agregats de type magnetron, d'une chambre de depot afin de permettre la caracterisation de la taille des nanoparticules neutres par analyse microscopique, et d'un spectrometre de masse par temps de vol capable de detecter des systemes positivement charges jusqu'a une masse de 50 000 ua. Les etudes de collisions ont ete realisees avec des agregats de Bi (2 nm ; 200 atomes) et de Ag (6 nm ; 5000 atomes). Dans le deux cas, le processus de capture multielectronique cree un systeme multicharge. Dans le cas du Bi, une grande partie fragmente par la fission asymetrique emettant des petits fragments. Dans le cas des particules plus grandes (Ag), les systemes multicharges ne fragmentent pas, par contre des petits fragments sont aussi observes mais ils sont le produit de la pulverisation de la nano-surface lors de collisions penetrantes. En perspective, des experiences seront realisees avec des nanoparticules metalliques fonctionnalisees ainsi que le comptage des electrons emis lors de la collision.
Truncated Emphatic Temporal Difference Methods for Prediction and Control

Emphatic Temporal Difference (TD) methods are a class of off-policy Reinforcement Learning (RL) methods involving the use of followon traces. Despite the theoretical success of emphatic TD methods in addressing the notorious deadly triad (Sutton and Barto, 2018) of off-policy RL, there are still three open problems. First, the motivation for emphatic TD methods proposed by Sutton et al. (2016) does not align with the convergence analysis of Yu (2015). Namely, a quantity used by Sutton et al. (2016) that is expected to be essential for the convergence of emphatic TD methods is not used in the actual convergence analysis of Yu (2015). Second, followon traces typically suffer from large variance, making them hard to use in practice. Third, despite the seminal work of Yu (2015) confirming the asymptotic convergence of some emphatic TD methods for prediction problems, there is still no finite sample analysis for any emphatic TD method for prediction, much less control. In this paper, we address those three open problems simultaneously via using truncated followon traces in emphatic TD methods. Unlike the original followon traces, which depend on all previous history, truncated followon traces depend on only finite history, reducing variance and enabling the finite sample analysis of our proposed emphatic TD methods for both prediction and control.
Snakes AI Competition 2020 and 2021 Report

The Snakes AI Competition was held by the Innopolis University and was part of the IEEE Conference on Games2020 and 2021 editions. It aimed to create a sandbox for learning and implementing artificial intelligence algorithms in agents in a ludic manner. Competitors of several countries participated in both editions of the competition, which was streamed to create asynergy between organizers and the community. The high-quality submissions and the enthusiasm around the developed framework create an exciting scenario for future extensions.
Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval with Partial Query

Text-based image retrieval has seen considerable progress in recent years. However, the performance of existing methods suffers in real life since the user is likely to provide an incomplete description of an image, which often leads to results filled with false positives that fit the incomplete description. In this work, we introduce the partial-query problem and extensively analyze its influence on text-based image retrieval. Previous interactive methods tackle the problem by passively receiving users’ feedback to supplement the incomplete query iteratively, which is time-consuming and requires heavy user effort. Instead, we propose a novel retrieval framework that conducts the interactive process in an Ask-and-Confirm fashion, where AI actively searches for discriminative details missing in the current query, and users only need to confirm AI’s proposal. Specifically, we propose an object-based interaction to make the interactive retrieval more user-friendly and present a reinforcement-learning-based policy to search for discriminative objects. Furthermore, since fully-supervised training is often infeasible due to the difficulty of obtaining human-machine dialog data, we present a weakly-supervised training strategy that needs no human-annotated dialogs other than a text-image dataset. Experiments show that our framework significantly improves the performance of text-based image retrieval. Code is available at https://github.com/CuthbertCai/Ask-Confirm.
A chemo-mechano-thermodynamical contact theory for adhesion, friction, and (de)bonding reactions

This work presents a self-contained continuum formulation for coupled chemical, mechanical, and thermal contact interactions. The formulation is very general and, hence, admits arbitrary geometry, deformation, and material behavior. All model equations are derived rigorously from the balance laws of mass, momentum, energy, and entropy in the framework of irreversible thermodynamics, thus exposing all the coupling present in the field equations and constitutive relations. In the process, the conjugated kinematic and kinetic variables for mechanical, thermal, and chemical contact are identified, and the analogies between mechanical, thermal, and chemical contact are highlighted. Particular focus is placed on the thermodynamics of chemical bonding distinguishing between exothermic and endothermic contact reactions. Distinction is also made between long-range, non-touching surface interactions and short-range, touching contact. For all constitutive relations, examples are proposed and discussed comprehensively with particular focus on their coupling. Finally, three analytical test cases are presented that illustrate the thermo-chemo-mechanical contact coupling and are useful for verifying computational models. Although the main novelty is the extension of existing contact formulations to chemical contact, the presented formulation also sheds new light on thermo-mechanical contact, because it is consistently derived from basic principles using only a few assumptions.
DEMix Layers: Disentangling Domains for Modular Language Modeling

We introduce a new domain expert mixture (DEMix) layer that enables conditioning a language model (LM) on the domain of the input text. A DEMix layer includes a collection of expert feedforward networks, each specialized to a domain, that makes the LM modular: experts can be mixed, added, or removed after initial training. Extensive experiments with autoregressive transformer LMs (up to 1.3B parameters) show that DEMix layers reduce test-time perplexity (especially for out-of-domain data), increase training efficiency, and enable rapid adaptation. Mixing experts during inference, using a parameter-free weighted ensemble, enables better generalization to heterogeneous or unseen domains. We also show it is possible to add experts to adapt to new domains without forgetting older ones, and remove experts to restrict access to unwanted domains. Overall, these results demonstrate benefits of domain modularity in language models.
Goldilocks mixing in oceanic shear-induced turbulent overturns

Abstract We present a new, simple and physically motivated parameterization, based on the ratio of Thorpe and Ozmidov scales, for the irreversible turbulent flux coefficient $\varGamma _{\mathcal {M}}= {\mathcal {M}}/\epsilon$, i.e. the ratio of the irreversible rate ${\mathcal {M}}$ at which the background potential energy increases in a stratified flow due to macroscopic motions to the dissipation rate of turbulent kinetic energy $\epsilon$. Our parameterization covers all three key phases (crucially, in time) of a shear-induced stratified turbulence life cycle: the initial, ‘hot’ growing phase, the intermediate energetically forced phase and the final ‘cold’ fossilization decaying phase. Covering all three phases allows us to highlight the importance of the intermediate one, to which we refer as the ‘Goldilocks’ phase due to its apparently optimal (and so neither too hot nor too cold, but just right) balance, in which energy transfer from background shear to the turbulent mixing is most efficient. The value of $\varGamma _{\mathcal {M}}$ is close to 1/3 during this phase, which we demonstrate appears to be related to an adjustment towards a critical or marginal Richardson number for sustained turbulence ${\sim }0.2\text {--}0.25$. Importantly, although buoyancy effects are still significant at leading order for the turbulent dynamics during this intermediate phase, the marginal balance in the flow ensures that the turbulent mixing of the (density) scalar is nevertheless effectively ‘locked’ to the turbulent mixing of momentum. We present supporting evidence for our parameterization through comparison with six oceanographic datasets that span various turbulence generation regimes and a wide range of geographical location and depth. Using these observations, we highlight the significance of parameterizing an inherently variable flux coefficient for capturing the turbulent flux associated with rare energetic, yet fundamentally shear-driven (and so not strongly stratified) overturns that make a disproportionate contribution to the total mixing. We also highlight the importance of representation of young turbulent patches in the parameterization for connecting the small scale physics to larger scale applications of mixing such as ocean circulation and tracer budgets. Shear-induced turbulence is therefore central to irreversible mixing in the world's oceans, apparently even close to the seafloor, and it is critically important to appreciate the inherent time dependence and evolution of mixing events: history matters to mixing.
Heywood Cases in Unidimensional Factor Models and Item Response Models for Binary Data

Heywood cases are known from linear factor analysis literature as variables with communalities larger than 1.00, and in present day factor models, the problem also shows in negative residual variances. For binary data, factor models for ordinal data can be applied with either delta parameterization or theta parametrization. The former is more common than the latter and can yield Heywood cases when limited information estimation is used. The same problem shows up as non convergence cases in theta parameterized factor models and as extremely large discriminations in item response theory (IRT) models. In this study, we explain why the same problem appears in different forms depending on the method of analysis. We first discuss this issue using equations and then illustrate our conclusions using a small simulation study, where all three methods, delta and theta parameterized ordinal factor models (with estimation based on polychoric correlations and thresholds) and an IRT model (with full information estimation), are used to analyze the same datasets. The results generalize across WLS, WLSMV, and ULS estimators for the factor models for ordinal data. Finally, we analyze real data with the same three approaches. The results of the simulation study and the analysis of real data confirm the theoretical conclusions.
Prototype Completion for Few-Shot Learning

Few-shot learning (FSL) aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes marginal improvements. In this paper, 1) we figure out the reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning feature extractor is less meaningful; 2) instead of fine-tuning feature extractor, we focus on estimating more representative prototypes. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative features for seen attributes as priors. Second, a part/attribute transfer network is designed to learn to infer the representative features for unseen attributes as supplementary priors. Finally, a prototype completion network is devised to learn to complete prototypes with these priors. Moreover, to avoid the prototype completion error, we further develop a Gaussian based prototype fusion strategy that fuses the mean-based and completed prototypes by exploiting the unlabeled samples. At last, we also develop an economic prototype completion version for FSL, which does not need to collect primitive knowledge, for a fair comparison with existing FSL methods without external knowledge. Extensive experiments show that our method: i) obtains more accurate prototypes; ii) achieves superior performance on both inductive and transductive FSL settings.
Explicitly Solvable Systems of First-Order Difference Equations With Homogeneous Polynomial Right-Hand Sides

Physics Department, University of Rome ”La Sapienza”, Rome, Italy b INFN, Sezione di Roma 1 Department of Physics, Payame Noor University, PO BOX 19395-3697 Tehran, Iran In this short paper we identify special systems of (an arbitrary number) N of first-order Difference Equations with nonlinear homogeneous polynomials of arbitrary degree M in their right-hand sides, which feature very simple explicit solutions. A novelty of these findings is to consider special systems characterized by constraints involving both their parameters and their initial data.
Gravity versus Magnetic Fields in Forming Molecular Clouds

Magnetic fields are dynamically important in the diffuse interstellar medium. Understanding how gravitationally bound, star-forming clouds form requires modeling of the fields in a self-consistent, supernova-driven, turbulent, magnetized, stratified disk. We employ the FLASH magnetohydrodynamics code to follow the formation and early evolution of clouds with final masses of 3–8 × 103 M ⊙ within such a simulation. We use the code’s adaptive mesh refinement capabilities to concentrate numerical resolution in zoom-in regions covering single clouds, allowing us to investigate the detailed dynamics and field structure of individual self-gravitating clouds in a consistent background medium. Our goal is to test the hypothesis that dense clouds are dynamically evolving objects far from magnetohydrostatic equilibrium. We find that the cloud envelopes are magnetically supported with field lines parallel to density gradients and flow velocity, as indicated by the histogram of relative orientations and other statistical measures. In contrast, the dense cores of the clouds are gravitationally dominated, with gravitational energy exceeding internal, kinetic, or magnetic energy and accelerations due to gravity exceeding those due to magnetic or thermal pressure gradients. In these regions, field directions vary strongly, with a slight preference toward being perpendicular to density gradients, as shown by three-dimensional histograms of relative orientation.
RAIKOU (来光): A General Relativistic, Multiwavelength Radiative Transfer Code

We present a general relativistic radiative transfer code RAIKOU (来光) for multiwavlength studies of spectra and images including the black hole shadows around Kerr black holes. Important radiative processes in hot plasmas around black holes, i.e., (cyclo-)synchrotron, bremsstrahlung emission/absorption, and Compton/inverse-Compton scattering, are incorporated. The Maxwell–Jüttner and single/broken power-law electron distribution functions are implemented to calculate the radiative transfer via both thermal and nonthermal electrons. Two calculation algorithms are implemented for studies of the images and broadband spectra. An observer-to-emitter ray-tracing algorithm, which inversely solves the radiative transfer equation from the observer screen to emitting plasmas, is suitable for an efficient calculations of the images, e.g., the black hole shadows observed by the Event Horizon Telescope, and spectra without Compton effects. On the other hand, an emitter-to-observer Monte Carlo algorithm, by which photons are transported with a Monte Carlo method including the effects of Compton/inverse-Compton scatterings, enables us to compute multiwavelength spectra, with their energy bands broadly ranging from radio to very high energy gamma-ray. The X-ray black hole shadows, which are formed via synchrotron emission and inverse-Compton scattering processes and will be observed in the future X-ray interferometry missions, can be also computed with this algorithm. The code is generally applicable to accretion flows around Kerr black holes with relativistic jets and winds/coronae with various mass accretion rates (i.e., radiatively inefficient accretion flows, super-Eddington accretion flows, and others). We demonstrate an application of the code to a radiatively inefficient accretion flow onto a supermassive black hole.
Evolution of C iv Absorbers. II. Where Does C iv Live?

We use the observed cumulative statistics of C iv absorbers and dark matter halos to infer the distribution of C iv-absorbing gas relative to galaxies at redshifts 0 ≤ z ≤ 5. We compare the cosmic incidence dN/dX of C iv absorber populations and galaxy halos, finding that massive L ≥ L ⋆ halos alone cannot account for all the observed W r ≥ 0.05 Å absorbers. However, the dN/dX of lower-mass halos exceeds that of W r ≥ 0.05 Å absorbers. We also estimate the characteristic gas radius of absorbing structures required for the observed C iv dN/dX, assuming each absorber is associated with a single galaxy halo. The W r ≥ 0.3 Å and W r ≥ 0.6 Å C iv gas radii are ∼30%–70% (∼20%–40%) of the virial radius of L ⋆ (0.1L ⋆) galaxies, and the W r ≥ 0.05 Å gas radius is ∼100%–150% (∼60%–100%) of the virial radius of L ⋆ (0.1L ⋆) galaxies. For stronger absorbers, the gas radius relative to the virial radius rises across Cosmic Noon and falls afterwards, while for weaker absorbers, the relative gas radius declines across Cosmic Noon and then dramatically rises at z < 1. A strong luminosity-dependence of the gas radius implies highly extended C iv envelopes around massive galaxies before Cosmic Noon, while a luminosity-independent gas radius implies highly extended envelopes around dwarf galaxies after Cosmic Noon. From available absorber-galaxy and C iv evolution data, we favor a scenario in which low-mass galaxies enrich the volume around massive galaxies at early epochs and propose that the outer halo gas (>0.5 R v ) was produced primarily in ancient satellite dwarf galaxy outflows, while the inner halo gas (<0.5 R v ) originated from the central galaxy and persists as recycled accreting gas.
Higher-order topological Anderson insulators in quasicrystals

The disorder effects on higher-order topological phases in periodic systems have attracted much attention. However, in aperiodic systems, such as quasicrystalline systems, the interplay between disorder and higher-order topology is still unclear. In this paper, we investigate the effects of disorder on two types of second-order topological insulators, including a quasicrystalline quadrupole insulator and a modified quantum spin Hall insulator, in a two-dimensional Amman-Beenker tiling quasicrystalline lattice. We demonstrate that the higher-order topological insulators are robust against weak disorder in both models. More striking, the disorder-induced higher-order topological insulators called higher-order topological Anderson insulators are found at a certain region of disorder strength in both models. Our paper extends the study of the interplay between disorder and higher-order topology to quasicrystalline systems.
Parameters Fixing Strategy for Quantum Approximate Optimization Algorithm

The quantum approximate optimization algorithm (QAOA) has numerous promising applications in solving the combinatorial optimization problems on near-term Noisy Intermediate Scalable Quantum (NISQ) devices. QAOA has a quantum-classical hybrid structure. Its quantum part consists of a parameterized alternating operator ansatz, and its classical part comprises an optimization algorithm, which optimizes the parameters to maximize the expectation value of the problem Hamiltonian. This expectation value depends highly on the parameters, this implies that a set of good parameters leads to an accurate solution. However, at large circuit depth of QAOA, it is difficult to achieve global optimization due to the multiple occurrences of local minima or maxima. In this paper, we propose a parameters fixing strategy which gives high approximation ratio on average, even at large circuit depths, by initializing QAOA with the optimal parameters obtained from the previous depths. We test our strategy on the Max-cut problem of certain classes of graphs such as the 3-regular graphs and the Erdös-Rényi graphs.
Post-hoc Interpretability for Neural NLP: A Survey

Neural networks for NLP are becoming increasingly complex and widespread, and there is a growing concern if these models are responsible to use. Explaining models helps to address the safety and ethical concerns and is essential for accountability. Interpretability serves to provide these explanations in terms that are understandable to humans. Additionally, post-hoc methods provide explanations after a model is learned and are generally model-agnostic. This survey provides a categorization of how recent post-hoc interpretability methods communicate explanations to humans, it discusses each method in-depth, and how they are validated, as the latter is often a common concern.