Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-electron atoms in momentum space.

Philip Eric Regier: Two-Electron Atoms in Momentum Space, Master's thesis, University of Waterloo, November, 1983. A Gaussian "geminal basis set is employed for calculating . * approximate eigenfunctions of the Schrodinger equation for two-electron atoms. The basis geminals are non-factorable two-electron functions with ‘explicit correlation terms. Sixteen term variational wavefunctions are obtained for H” , He and Li+ and are the best of their type and size. Previous wavefunctions were found to be improperly optimized. A variation-iteration method for finding solutions to the momentum space Schrodinger equation is also considered. This technique successively iterates an initial waveifcqnction, producing a sequence of wavefunctions and energies which converge to * the exact wavefunction and energy. Using the Fourier transforms of the sixteen term wavefunctions as the initial wavefunctions, first-iterated wavefunctions. and half-iterated energies were produced. Previous work using this method with Gaussian orbital basis function was found to be in error. (iv) R eproduced with perm ission o f the copyright ow ner. Further reproduction prohibited w ithout perm ission. Gaussian geminal wavefunctions of fifty terms were also constructed, using* the generator-coordinate methocl. This technique uses the motivation of Monte Carlo quadrature to map several variational parameters onto all of the non­ linear parameters • of the wavefunction. The technique of importance sampling and the control variate method were found to be very useful in this regard. Compton profiles and one-electron momentum densities were produced from the -Gaussian geminal wavefunctions. The most accurate Compton profile yet obtained for helium is presented. The first-iterated'wavefunction was not used as the resulting integrals could not be evaluated.
SOLVING DELAY DIFFERENTIAL EQUATIONS BY ELZAKI TRANSFORM METHOD

In this paper, we implement the Elzaki transform method for the solution of delay differential equations (DDEs). The method executes the DDEs by implementing its properties on the given DDE. Also, the method treats the nonlinear terms with a well posed formula. The method is easy to implement with high level of accuracy. Also, restricted transformations, perturbation, linearization or discretization are not recognized. The resulting numerical evidences show that the method converges favourably to the analytic solution. All computational frameworks are performed with maple 18 software.
Minimal locating-paired-dominating sets in triangular and king grids

Let G = (V,E) be a finite or infinite graph. A set S ? V is paired-dominating if S induces a matching in G and S dominates all vertices of G. A set S ? V is locating if for any two distinct vertices u, v in V \ S, N(u) ? S 6= N(v) ? S, where N(u) and N(v) are open neighborhoods of vertices u and v. We find the minimal density of locating-paired-dominating sets in the infinite triangular grid, which is equal to 4/15. We also present bounds for the minimal density D of locating-paired-dominating sets in the infinite king grid, which is 3/14 ? D ? 2/9
Using piezoelectric sensor and actuator for ultrasonic assisted system

ABSTRACT An ultrasonic assisted system mainly consists of the transducer and the match-fitting cutter is designed to provide miniature vibration amplitude. In the transducer, a set of piezoelectric ceramics is used as actuator in charge of generating large enough vibration amplitude, which will be immediately measured by the neighboring sensor also made by piezoelectric ceramics. Since the driving frequency is easily affected by the changing working environment, variation of the vibration output is detected in time by the PZT sensor to support the feedback autoresonance control purpose. However, measurement on the transducer is not equivalent to that on the cutter. Further investigation on the measured output of the cutter in comparison with that of the transducer is carried out. It is found that there exists a reasonable and predictable relation between them. ANSYS is used for modal and harmonic analysis. The estimated resonance frequency and vibration amplitude are approximate to the experimental results, which verifies the accuracy of the proposed measurement method.
Using a network of 2D video disdrometers for external radar calibration of NASA's S-band polarimetric radar

A fundamental requirement of weather radars is accurate calibration of the absolute reflectivity, the oft quoted figure being within ± 1dB, but for quantitative application such as hourly rain accumulation (especially for extreme events), a smaller uncertainty of ±0.5dB can be regarded as a goal. To a large extent, maintenance of an initial calibration state can be realized using either on-line or off-line procedures, e.g., continuous monitoring of transmit power, receiver calibration using test signal generators, standard noise source injection, etc (e.g., Cavalli 1998; Free et al 2005). When combined with modern high quality test equipment together with advances in the performance of analog front ends, digital IF receivers and programmable signal processors, the overall relative stability of the radar system can be maintained at a very high level (e.g., Cavalli 1998; Darlington et al 200x), the level of uncertainty so realized being much less than that due to the natural spatio-temporal variability of the precipitation. Attaining the objective of absolute accuracy is more difficult and relies on standard external targets/sources such as, for example, metal spheres, solar flux, passive or active calibrators, etc. However, it is generally accepted that the standard methods cannot achieve uncertainty levels of ±0.5 dB (through error budget considerations). Other methods such as selfconsistency (Goddard et al., 1994) use polarimetric data from rain [Z h, Zdr, Kdp] to calibrate the system offsets in Z h but uncertainties in the assumed mean drop axis ratio versus D relation as well as dependence on filtering/thresholding of the polarimetric data precludes achieving the elusive figure of ±0.5 dB.
Robustness of person-fit decisions in computerized adaptive testing

Person-fit statistics test whether or not the likelihood of a respondent’s complete vector of item scores on a test is low given the hypothesized item response theory (IRT) model. This binary information may be insufficient for diagnosing the cause of a misfitting item-score vector. This paper applies different types of person-fit analysis in a computer adaptive testing context and investigates the robustness of several methods to multidimensional test data. Both global person-fit statistics to make the binary decision about fit or misfit of a person’s item-score vector and local checks are applied. Results showed that there are differences between the methods with respect to the robustness in a multidimensional context and that some methods are more useful than othermethods.
Leveling the Playing Field: Knowledge Production in the Digital Age*

80% of all journals are not freely available–even though access to existing knowledge is crucial for pushing the research frontier. In this paper, we examine the impact of Sci-Hub, an online platform providing free access to scientific articles, on knowledge creation. Using data on 300 million geo-coded download requests, and the near-universe of scientific articles we employ an instrumented difference-in-differences design. We find that Sci-Hub has significantly changed consumption patterns of scientific works, with a substitution of references from opento closed-access publications. In turn, greater access to frontier knowledge resulted in higher-quality research output as measured by citations, but not more publications.
Journal of Developmental Biology

Collagen XI alpha 1 (Col11a1) is an extracellular matrix molecule required for embryonic development with a role in both nucleating the formation of fibrils and regulating the diameter of heterotypic fibrils during collagen fibrillar assembly. Although found in many different tissues throughout the vertebrate body, Col11a1plays an essential role in endochondral ossification. To further understand the function of Col11a1 in the process of bone formation, we compared skeletal mineralization in wildtype (WT) mice and Col11a1-deficient mice using X-ray microtomography (micro-CT) and histology. Changes in trabecular bone microstructure were observed and are presented here. Additionally, changes to the periosteal bone collar of developing long bones were observed and resulted in an increase in thickness in the case of Col11a1-deficient mice compared to WT littermates. Vertebral bodies were incompletely formed in the absence of Col11a1. The data demonstrate that Col11a1 depletion results in alteration to newly formed bone and is consistent with a role for Col11a1 in mineralization. These findings indicate that expression of Col11a1 in the growth plate and perichondrium is essential for trabecular OPEN ACCESS J. Dev. Biol. 2015, 3 2 bone and bone collar formation during endochondral ossification. The observed changes to mineralized tissues further define the function of Col11a1.
TOF technique for laser-driven proton beam diagnostics for the ELIMED beamline

The Time of Flight (TOF) method for laser-driven ion beam diagnostics has been extensively investigated so far for low energy ion diagnostics and several works, reported in literature [1,2], have shown its efficiency in the measurement of particle beam characteristics such as ion species, energy spectrum and current. Moreover, such technique allows obtaining a shot-to-shot on-line monitoring of optically accelerated particles, necessary to control the reproducibility of the accelerated beam and to deliver a beam suitable for any kind of applications. For this reason, the ELIMED beamline [3,4], which will be entirely developed at INFN-LNS and installed in 2017 within the ion beamline ELIMAIA (ELI Multidisciplinary Applications of laser-Ion Acceleration) experimental hall at ELI-Beamlines in Prague, will be equipped with an on-line diagnostics system composed by silicon carbide and diamond detectors, using the TOF technique. In this contribution, the procedure developed for TOF signal analysis will be briefly reported.
Modeling latent curves for genotype by environment interaction

Abstract: In plant research data for a population of plants is often collected through repeated field and greenhouse trials under different environmental conditions. An appropriate model for this type of data should also deal with genotype by environment interaction. While the environments in field trials can frequently be characterized by geographic or meteorological conditions, it is equally likely that they are ordered according to a latent property. Therefore the order of the environments can often be unknown or unclear. We propose to use smooth latent curves to estimate this underlying order. The method is illustrated with simulated and empirical data.
MELON: NeRF with Unposed Images in SO(3)

Neural radiance fields enable novel-view synthesis and scene reconstruction with photorealistic quality from a few images, but require known and accurate camera poses. Conventional pose estimation algorithms fail on smooth or self-similar scenes, while methods performing inverse rendering from unposed views require a rough initialization of the camera orientations. The main difficulty of pose estimation lies in real-life objects being almost invariant under certain transformations, making the photometric distance between rendered views non-convex with respect to the camera parameters. Using an equivalence relation that matches the distribution of local minima in camera space, we reduce this space to its quotient set, in which pose estimation becomes a more convex problem. Using a neural-network to regularize pose estimation, we demonstrate that our method - MELON - can reconstruct a neural radiance field from unposed images with state-of-the-art accuracy while requiring ten times fewer views than adversarial approaches.
HAT-CL: A Hard-Attention-to-the-Task PyTorch Library for Continual Learning

Catastrophic forgetting, the phenomenon in which a neural network loses previously obtained knowledge during the learning of new tasks, poses a significant challenge in continual learning. The Hard-Attention-to-the-Task (HAT) mechanism has shown potential in mitigating this problem, but its practical implementation has been complicated by issues of usability and compatibility, and a lack of support for existing network reuse. In this paper, we introduce HAT-CL, a user-friendly, PyTorch-compatible redesign of the HAT mechanism. HAT-CL not only automates gradient manipulation but also streamlines the transformation of PyTorch modules into HAT modules. It achieves this by providing a comprehensive suite of modules that can be seamlessly integrated into existing architectures. Additionally, HAT-CL offers ready-to-use HAT networks that are smoothly integrated with the TIMM library. Beyond the redesign and reimplementation of HAT, we also introduce novel mask manipulation techniques for HAT, which have consistently shown improvements across various experiments. Our work paves the way for a broader application of the HAT mechanism, opening up new possibilities in continual learning across diverse models and applications.
Reinforcement Learning for Credit Index Option Hedging

In this paper, we focus on finding the optimal hedging strategy of a credit index option using reinforcement learning. We take a practical approach, where the focus is on realism i.e. discrete time, transaction costs; even testing our policy on real market data. We apply a state of the art algorithm, the Trust Region Volatility Optimization (TRVO) algorithm and show that the derived hedging strategy outperforms the practitioner's Black&Scholes delta hedge.
DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Rendering

Realistic human-centric rendering plays a key role in both computer vision and computer graphics. Rapid progress has been made in the algorithm aspect over the years, yet existing human-centric rendering datasets and benchmarks are rather impoverished in terms of diversity (e.g., outfit's fabric/material, body's interaction with objects, and motion sequences), which are crucial for rendering effect. Researchers are usually constrained to explore and evaluate a small set of rendering problems on current datasets, while real-world applications require methods to be robust across different scenarios. In this work, we present DNA-Rendering, a large-scale, high-fidelity repository of human performance data for neural actor rendering. DNA-Rendering presents several appealing attributes. First, our dataset contains over 1500 human subjects, 5000 motion sequences, and 67.5M frames' data volume. Upon the massive collections, we provide human subjects with grand categories of pose actions, body shapes, clothing, accessories, hairdos, and object intersection, which ranges the geometry and appearance variances from everyday life to professional occasions. Second, we provide rich assets for each subject – 2D/3D human body keypoints, foreground masks, SMPLX models, cloth/accessory materials, multi-view images, and videos. These assets boost the current method's accuracy on downstream rendering tasks. Third, we construct a professional multi-view system to capture data, which contains 60 synchronous cameras with max 4096 × 3000 resolution, 15 fps speed, and stern camera calibration steps, ensuring high-quality resources for task training and evaluation.Along with the dataset, we provide a large-scale and quantitative benchmark in full-scale, with multiple tasks to evaluate the existing progress of novel view synthesis, novel pose animation synthesis, and novel identity rendering methods. In this manuscript, we describe our DNA-Rendering effort as a revealing of new observations, challenges, and future directions to human-centric rendering. The dataset, code, and benchmarks will be publicly available at https://dna-rendering.github. io/.
What's meant by explainable model: A Scoping Review

We often see the term explainable in the titles of papers that describe applications based on artificial intelligence (AI). However, the literature in explainable artificial intelligence (XAI) indicates that explanations in XAI are application- and domain-specific, hence requiring evaluation whenever they are employed to explain a model that makes decisions for a specific application problem. Additionally, the literature reveals that the performance of post-hoc methods, particularly feature attribution methods, varies substantially hinting that they do not represent a solution to AI explainability. Therefore, when using XAI methods, the quality and suitability of their information outputs should be evaluated within the specific application. For these reasons, we used a scoping review methodology to investigate papers that apply AI models and adopt methods to generate post-hoc explanations while referring to said models as explainable. This paper investigates whether the term explainable model is adopted by authors under the assumption that incorporating a post-hoc XAI method suffices to characterize a model as explainable. To inspect this problem, our review analyzes whether these papers conducted evaluations. We found that 81% of the application papers that refer to their approaches as an explainable model do not conduct any form of evaluation on the XAI method they used.
Curvature-based Clustering on Graphs

Unsupervised node clustering (or community detection) is a classical graph learning task. In this paper, we study algorithms, which exploit the geometry of the graph to identify densely connected substructures, which form clusters or communities. Our method implements discrete Ricci curvatures and their associated geometric flows, under which the edge weights of the graph evolve to reveal its community structure. We consider several discrete curvature notions and analyze the utility of the resulting algorithms. In contrast to prior literature, we study not only single-membership community detection, where each node belongs to exactly one community, but also mixed-membership community detection, where communities may overlap. For the latter, we argue that it is beneficial to perform community detection on the line graph, i.e., the graph's dual. We provide both theoretical and empirical evidence for the utility of our curvature-based clustering algorithms. In addition, we give several results on the relationship between the curvature of a graph and that of its dual, which enable the efficient implementation of our proposed mixed-membership community detection approach and which may be of independent interest for curvature-based network analysis.
Rydberg polaritons and Rydberg superatoms - novel tools for quantum nonlinear optics

Mapping the strong interaction between Rydberg excitations in ultra-cold atomic ensembles onto single photons enables the realization of optical nonlinearities which can modify light on the level of individual photons. This novel approach forms the basis of a growing Rydberg quantum optics toolbox, which already contains photonic logic building-blocks such as single-photon sources, switches, transistors, and conditional π-phase shifts. This thesis reports on two experiments investigating strong photon-photon interactions mediated by Rydberg interactions. First, applying a ladder-type EIT scheme to Rydberg atoms in D-states with high principal quantum number results in the Rydberg interaction mediated nonlinearity accompanied by a time-dependent decay of transmission of probe light through the medium. In a joint experimental and theoretical analysis, this effect is attributed to the dephasing of propagating polaritons into stationary Rydberg excitations caused by the state mixing interaction occurring with RydbergD-state atoms. Second, via a two-photon Raman excitation scheme Rydberg atoms are efficiently excited in a small atomic cloud. As the size of the Rydberg blockade exceeds the dimension of the medium, only a single Rydberg excitation can be present at a time. By fast engineered dephasing this Rydberg excitation is decoupled from the light field. Measurement of the transmitted light and the amount of excited Rydberg atoms gives evidence for the realization of a deterministic single-photon absorber.
Gradient strikes back: How filtering out high frequencies improves explanations

Recent years have witnessed an explosion in the development of novel prediction-based attribution methods, which have slowly been supplanting older gradient-based methods to explain the decisions of deep neural networks. However, it is still not clear why prediction-based methods outperform gradient-based ones. Here, we start with an empirical observation: these two approaches yield attribution maps with very different power spectra, with gradient-based methods revealing more high-frequency content than prediction-based methods. This observation raises multiple questions: What is the source of this high-frequency information, and does it truly reﬂect decisions made by the system? Lastly, why would the absence of high-frequency information in prediction-based methods yield better explainability scores along multiple metrics? We analyze the gradient of three representative visual classiﬁcation models and observe that it contains noisy information emanating from high-frequencies. Furthermore, our analysis reveals that the operations used in Convolutional Neural Networks (CNNs) for downsampling appear to be a signiﬁcant source of this high-frequency content – suggesting aliasing as a possible underlying basis. We then apply an optimal low-pass ﬁlter for attribution maps and demonstrate that it improves gradient-based attribution methods. We show that (i) removing high-frequency noise yields signiﬁcant improvements in the explainabil-ity scores obtained with gradient-based methods across multiple models – leading to (ii) a novel ranking of state-of-the-art methods with gradient-based methods at the top. We believe that our results will spur renewed interest in simpler and computationally more efﬁcient gradient-based methods for explainability.
Addressing the checkerboard problem in an Eulerian meshless method for incompressible flows

In this paper, we look at the pressure checkerboard problem that arises in an Eulerian meshless method that solves the incompressible Navier-Stokes equations using the generalized finite difference method (GFDM). Although, the checkerboard problem has been dealt with extensively in mesh-based methods, the literature in connection with meshless methods is comparatively scarce. In this paper, we explore the occurrence of the checkerboard problem in a meshless method. A few unsuccessful attempts to resolve the checkerboard problem are reported. The successful fix for the problem entails an algorithm that adapts the point cloud by adding points in the regions of pressure oscillations. The algorithm uses an error indicator that detects the presence of the checkerboard oscillations in the solution. The algorithm minimizes the computational effort since it ensures the use of additional points only in regions of concern, as directed by the error indicator, in contrast to an approach of using a highly refined set of points throughout the domain. It also requires no a priori estimates of the regions where the oscillations occur and integrates conveniently in the framework of the meshless method since no re-meshing strategies are involved. The results are compared with literature and a good match is observed.