Miracle-dz/UniFAR-FTUs huggingface dataset:
EVO/EVO+ Visian Implantable Collamer Lenses for the correction of myopia and myopia with astigmatism

ABSTRACT Introduction Intraocular lens implantation in phakic eyes for the correction of refractive error is currently a widespread procedure. The EVO and EVO+ Visian Implantable Collamer Lenses (ICL) are two of the most prevalent lenses implanted. They incorporate a central orifice to avoid the need for iridotomy. The main difference between both ICL is the higher optical diameter zone provided by the EVO+, allowing a better quality of vision at night. This review aims to provide an overview of the current ICL models available for correcting myopia and myopic astigmatism. Areas Covered During the last decade, more than 100 scientific papers analyzing the performance of EVO and EVO+ lenses have been published. This review describes the objective visual performance achieved with the implantation of central hole ICL lenses and the subjective perception of the patients implanted with these lenses. In addition, the safety and the potential complications associated with undergoing an EVO and EVO+ ICL implantation have been addressed. Expert Opinion Refractive surgeons and candidates to undergo ICL implantation should be aware of the excellent safety and visual outcomes provided by the implantation of central hole ICL lenses. However, future research could address minor issues currently not resolved.
An Analytical Model for Spherical Galaxies and Bulges

Un modele de masse pour les galaxies elliptiques, qui approche la loi R 1/4 de Vaucouleur, est developpe. Il est demontre que les proprietes intrinseques et les distributions de densite de ce modele peuvent etre evaluees analytiquement. La brillance de surface et les dispersions de vitesse projetees, la fonction de distribution et la densite des etats sont exprimees en terme de fonctions elementaires.
Multiple trial vectors in differential evolution for engineering design

This article presents a modified version of the differential evolution algorithm to solve engineering design problems. The aim is to allow each parent vector in the population to generate more than one trial (child) vector at each generation and therefore to increase its probability of generating a better one. To deal with constraints, some criteria based on feasibility and a diversity mechanism to maintain infeasible solutions in the population are used. The approach is tested on a set of well-known benchmark problems. After that, it is used to solve engineering design problems and its performance is compared with those provided by typical penalty function approaches and also against state-of-the-art techniques.
Self-consistent solution for a collisionless plasma slab in motion across a magnetic field

The problem of the dynamics of a plasma slab moving across a magnetic field is treated in the framework of the kinetic theory. A velocity distribution function (VDF) is found for each plasma species, electrons and protons, in terms of the constants of motion defined by the geometry of the problem. The zero- and first-order moments of the VDF are introduced into the right-hand side term of Maxwell's equations to compute the electric and magnetic vector potentials and corresponding fields. The solutions are found numerically. We obtain a region of plasma convection-the slab proper-where the plasma moves with a uniform velocity, V-x=V-0=(ExB/B-2)(x). At the core margins two plasma "wings" are formed, each being the result of a pair of interpenetrated boundary layers with different transition lengths. Inside these wings, the plasma velocity is not uniform, V(x)not equal(ExB/B-2)(x). It decreases from the maximum value obtained in the core to a minimum value in the central region of the wings where a flow reversal is found with the plasma convecting in the opposite direction to the core motion. There is also an asymmetry of the velocity gradient at the borders of the core, which results in a corresponding asymmetry in the thickness of the wings. Furthermore, it is found that the reversed plasma flow in the thinner wing is larger than that in the broader wing. For a fixed direction of the magnetic field the two plasma wings interchange position with respect to the center of the slab when the plasma bulk velocity reverses sign. (C) 2005 American Institute of Physics.
Density functional modeling, and molecular docking with SARS-CoV-2 spike protein (Wuhan) and omicron S protein (variant) studies of new heterocyclic compounds including a pyrazoline nucleus

Abstract Nowadays, different vaccines and antiviral drugs have been developed and their effectiveness has been proven against SARS-CoV-2. Pyrazoline derivatives are biologically active molecules and exhibit broad-spectrum biological activity properties. In this scope, four new molecules (4a–d) including a pyrazoline core were synthesized in order to predict their antiviral properties theoretically. Compounds 4a–d were purified by the crystallization method. The structures of 4a–d were completely characterized by NMR, IR, and elemental analysis. The molecular structures of the compounds in the ground state have been optimized using density functional theory with the B3LYP/6-31++G(d,p) level. The quantum chemical parameters were predicted by density functional theory calculations. Moreover, the molecular docking studies of 4a–d with SARS-CoV-2 Spike protein (Wuhan) and omicron S protein (variant) were presented to investigate and predict potential interactions. The binding sites, binding types and energies, bond distances of the non-covalent interactions and calculated inhibition constants (calc. Ki) as a consequence of molecular docking for 4a–d were presented in this study. Furthermore, the stability of the protein-4a complex obtained from the docking was investigated through molecular dynamics simulation. Communicated by Ramaswamy H. Sarma
Deep Visual Anomaly Detection in Industrial Manufacturing: A Survey

The recent rapid development of deep learning has laid a milestone in visual anomaly detection (VAD). In this paper, we provide a comprehensive review of deep learning-based visual anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current VAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for visual anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research ﬁndings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.
Eigenvalue estimates for Fourier concentration operators on two domains

We study concentration operators associated with either the discrete or the continuous Fourier transform, that is, operators that incorporate a spatial cut-off and a subsequent frequency cut-off to the Fourier inversion formula. Their spectral profiles describe the number of prominent degrees of freedom in problems where functions are assumed to be supported on a certain domain and their Fourier transforms are known or measured on a second domain. We derive eigenvalue estimates that quantify the extent to which Fourier concentration operators deviate from orthogonal projectors, by bounding the number of eigenvalues that are away from 0 and 1 in terms of the geometry of the spatial and frequency domains, and a factor that grows at most poly-logarithmically on the inverse of the spectral margin. The estimates are non-asymptotic in the sense that they are applicable to concrete domains and spectral thresholds, and almost match asymptotic benchmarks. Our work covers for the first time non-convex and non-symmetric spatial and frequency concentration domains, as demanded by numerous applications that exploit the expected approximate low dimensionality of the modeled phenomena. The proofs build on Israel's work on one dimensional intervals [arXiv: 1502.04404v1]. The new ingredients are the use of redundant wave-packet expansions and a dyadic decomposition argument to obtain Schatten norm estimates for Hankel operators.
The Shell of the Recurrent Nova T Pyxidis: A Model Based on Shocks

A model for the shell of the recurrent nova T Pyxidis is presented, consistent with the bulk of observational data on this object. Evolutionary calculations of thermonuclear runaways on the surface of an accreting white dwarf that simulate the observed outburst characteristics of this nova—recurrence time, rise time, time of decline, luminosity—provide the mass of the ejected shell, its composition, and the effective temperature and luminosity of the nova in quiescence (for a white dwarf mass of 1.25 M☉, a core temperature of 107 K, and an accretion rate of 10-7 M☉ yr-1). These results are used as input parameters for the SUMA code, which simulates the physical conditions of an emitting gaseous cloud under the combined effects of photoionization and shocks. The shell model involves two emitting regions besides the central star (which is the main UV radiation source), both resulting from compressed gas behind a shock front. The shocks are assumed to be produced by the collision between two extended shells ejected at consecutive outbursts. As a result of the collision, a secondary blast wave propagates outward into the older ejecta, while a reflected (reverse) shock wave propagates back through the new ejecta. Applying the SUMA code to each zone, we find that different lines are emitted in the two zones, and combined, they account for the entire observed line spectrum. The absolute fluxes in different spectral ranges agree with those derived from observations, assuming a distance of ~2 kpc. The derived abundances imply that mixing has taken place between the nova ejecta and interstellar material. Nevertheless, helium is overabundant and N/O is high. The model accounts for the observed structure of the nebula surrounding T Pyx, including the inner shell, the stagnant outer shell, and the extended faint region beyond it.
Noise and thermal depinning of Wigner crystals

We examine changes in the depinning threshold and conduction noise fluctuations for driven Wigner crystals in the presence of quenched disorder. At low temperatures there is a well defined depinning threshold and a strong peak in the noise power with 1/f noise characteristics. At higher temperatures, the depinning threshold shifts to lower drives and the noise, which is also reduced in power, becomes more white in character. At lower temperatures, a washboard frequency appears when the system depins elastically or forms a moving smectic state; however, this washboard signal is strongly reduced for higher temperatures and completely disappears above the melting temperature of a system without quenched disorder. Our results are in good agreement with recent transport and noise studies for systems where electron crystal depinning is believed to arise, and also show how noise can be used to distinguish between crystal, glass, and liquid phases.
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification

Pre-trained seq2seq models excel at graph semantic parsing with rich annotated data, but generalize worse to out-of-distribution (OOD) and long-tail examples. In comparison, symbolic parsers under-perform on population-level metrics, but exhibit unique strength in OOD and tail generalization. In this work, we study compositionality-aware approach to neural-symbolic inference informed by model confidence, performing fine-grained neural-symbolic reasoning at subgraph level (i.e., nodes and edges) and precisely targeting subgraph components with high uncertainty in the neural parser. As a result, the method combines the distinct strength of the neural and symbolic approaches in capturing different aspects of the graph prediction, leading to well-rounded generalization performance both across domains and in the tail. We empirically investigate the approach in the English Resource Grammar (ERG) parsing problem on a diverse suite of standard in-domain and seven OOD corpora. Our approach leads to 35.26% and 35.60% error reduction in aggregated SMATCH score over neural and symbolic approaches respectively, and 14% absolute accuracy gain in key tail linguistic categories over the neural model, outperforming prior state-of-art methods that do not account for compositionality or uncertainty.
ExplainableFold: Understanding AlphaFold Prediction with Explainable AI

This paper presents ExplainableFold (xFold), which is an Explainable AI framework for protein structure prediction. Despite the success of AI-based methods such as AlphaFold (αFold) in this field, the underlying reasons for their predictions remain unclear due to the black-box nature of deep learning models. To address this, we propose a counterfactual learning framework inspired by biological principles to generate counterfactual explanations for protein structure prediction, enabling a dry-lab experimentation approach. Our experimental results demonstrate the ability of ExplainableFold to generate high-quality explanations for AlphaFold's predictions, providing near-experimental understanding of the effects of amino acids on 3D protein structure. This framework has the potential to facilitate a deeper understanding of protein structures. Source code and data of the ExplainableFold project are available at https://github.com/rutgerswiselab/ExplainableFold.
Atomic Transition Probabilities for Transitions of Si i and Si ii and the Silicon Abundances of Several Very Metal-poor Stars

We report new measurements of branching fractions for 20 UV and blue lines in the spectrum of neutral silicon (Si i) originating in the 3s 23p4s 3Po 1,2,1Po 1, and 3s3p 3 1Do 1,2 upper levels. Transitions studied include both strong, nearly pure LS multiplets as well as very weak spin-forbidden transitions connected to these upper levels. We also report a new branching fraction measurement of the 4P1/2–2Po 1/2,3/2 intercombination lines in the spectrum of singly ionized silicon (Si ii). The weak spin-forbidden lines of Si i and Si ii provide a stringent test on recent theoretical calculations, to which we make comparison. The branching fractions from this study are combined with previously reported radiative lifetimes to yield transition probabilities and log(gf) values for these lines. We apply these new measurements to abundance determinations in five metal-poor stars.
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models

We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface models or point clouds, and represents them as neural fields. The concept of neural fields has previously been combined with a global latent vector, a regular grid of latent vectors, or an irregular grid of latent vectors. Our new representation encodes neural fields on top of a set of vectors. We draw from multiple concepts, such as the radial basis function representation, and the cross attention and self-attention function, to design a learnable representation that is especially suitable for processing with transformers. Our results show improved performance in 3D shape encoding and 3D shape generative modeling tasks. We demonstrate a wide variety of generative applications: unconditioned generation, category-conditioned generation, text-conditioned generation, point-cloud completion, and image-conditioned generation. Code: https://1zb.github. io/3DShape2VecSet/.
Correction [of numerical results in “ELF terrestrial radio wave field calculations with the zonal‐harmonics series”]

The presence of a numerical error in a previous publication is noted and the error source identified. However, the theoretical formulation is correct. Corrected numerical results are presented which favorably compare with results computed by other researchers. Also, theoretical results are produced using a predicted ionospheric profile of an upper atmosphere nuclear detonation. Finally, field attenuation for hypothetical two-layer ionospheric profiles is plotted as a function of lower-layer conductivity. It is seen that some hypothetical cases can exhibit high attenuation values in comparison to the normal-day's attenuation value. Some new theoretical expressions for the zonal-harmonics analytical formulation are exhibited, and the improvement expected in computational efficiency and in numerical precision by breakoff of the ground wave from the rest of the summation is discussed.
Input Perturbation Reduces Exposure Bias in Diffusion Models

Denoising Diffusion Probabilistic Models have shown an impressive generation quality, although their long sampling chain leads to high computational costs. In this paper, we observe that a long sampling chain also leads to an error accumulation phenomenon, which is similar to the exposure bias problem in autoregressive text generation. Specifically, we note that there is a discrepancy between training and testing, since the former is conditioned on the ground truth samples, while the latter is conditioned on the previously generated results. To alleviate this problem, we propose a very simple but effective training regularization, consisting in perturbing the ground truth samples to simulate the inference time prediction errors. We empirically show that, without affecting the recall and precision, the proposed input perturbation leads to a significant improvement in the sample quality while reducing both the training and the inference times. For instance, on CelebA 64$\times$64, we achieve a new state-of-the-art FID score of 1.27, while saving 37.5% of the training time. The code is publicly available at https://github.com/forever208/DDPM-IP
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean

Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this success, the inner workings of DNNs are often not transparent, making them difficult to interpret or understand. This lack of interpretability has led to increased research on inherently interpretable neural networks in recent years. Models such as Neural Additive Models (NAMs) achieve visual interpretability through the combination of classical statistical methods with DNNs. However, these approaches only concentrate on mean response predictions, leaving out other properties of the response distribution of the underlying data. We propose Neural Additive Models for Location Scale and Shape (NAMLSS), a modelling framework that combines the predictive power of classical deep learning models with the inherent advantages of distributional regression while maintaining the interpretability of additive models. The code is available at the following link: https://github.com/AnFreTh/NAMpy
More about process identification

More results are given about a new method of Process Identification. This method, called "Modulating Functions Method" had been described in a previous paper by the same authors [5]. It essentially consists of extracting unknown coefficients by using definite integrals involving only recorded functions, and none of their derivatives. New formulas concern time-varying coefficients and pure delays.
Aleatoric and Epistemic Discrimination in Classification

Machine learning (ML) models can underperform on certain population groups due to choices made during model development and bias inherent in the data. We categorize sources of discrimination in the ML pipeline into two classes: aleatoric discrimination , which is inherent in the data distribution, and epistemic discrimination , which is due to decisions during model development. We quantify aleatoric discrimination by determining the performance limits of a model under fairness constraints, assuming perfect knowledge of the data distribution. We demonstrate how to characterize aleatoric discrimination by applying Blackwell’s results on comparing statistical experiments. We then quantify epistemic discrimination as the gap between a model’s accuracy given fairness constraints and the limit posed by aleatoric discrimination. We apply this approach to benchmark existing interventions and investigate fairness risks in data with missing values. Our results indicate that state-of-the-art fairness interventions are effective at removing epistemic discrimination. However, when data has missing values, there is still signiﬁcant room for improvement in handling aleatoric discrimination.
Five ways to recover the symbol of a non-binary localization operator

Five constructive methods for recovering the symbol of a time-frequency localization operator with non-binary symbol are presented, two based on earlier work and three novel methods. For the two derivative methods which have previously been applied to binary symbols, we propose a changed symbol estimator and provide additional estimates that show how we can recover non-binary symbols. The three novel methods each have their own advantages and are all applicable to non-binary symbols. Two of them rely on prescribing the input of the localization operator and examining the output, allowing for targeting of the part of the symbol one wishes to recover while the last one relies on spectral information about the operator. All five methods are also implemented numerically and evaluated with the code available.
Distributed Optimization Methods for Multi-Robot Systems: Part II - A Survey

Although the field of distributed optimization is well-developed, relevant literature focused on the application of distributed optimization to multi-robot problems is limited. This survey constitutes the second part of a two-part series on distributed optimization applied to multi-robot problems. In this paper, we survey three main classes of distributed optimization algorithms -- distributed first-order methods, distributed sequential convex programming methods, and alternating direction method of multipliers (ADMM) methods -- focusing on fully-distributed methods that do not require coordination or computation by a central computer. We describe the fundamental structure of each category and note important variations around this structure, designed to address its associated drawbacks. Further, we provide practical implications of noteworthy assumptions made by distributed optimization algorithms, noting the classes of robotics problems suitable for these algorithms. Moreover, we identify important open research challenges in distributed optimization, specifically for robotics problem.