Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory of electron correlation in atoms and molecules

Corrections to the Hartree-Fock (H. F. ) wave function, ϕ0, and energy of a many-electron system are given. By the use of operator techniques in perturbation theory, the first-order w. f. , X'1, is obtained in terms of pair functions. These satisfy equations just like those of an actual two-electron system, except that now each electron moves in the H. F. field of the entire N-electron 'medium’ added to the field of nuclei. Every pair function must be orthogonalized to each of the two H. F. orbitals associated with it to get the complete X'1. This X'1 determines both E2 and E3. The second-order energy, E2, comes out as the sum of pair interactions and three- and four-body correlations due to the exclusion principle. The latter may be incorporated into the pair energies if each pair function is orthogonalized also to the remaining H. F. orbitals of ϕ0. The approach allows the valence and inner shells, etc., to be discussed separately and extends some of the concepts of quantum chemistry based on orbital approximations so as to include correlation.
Comparing predictive accuracy I: an asymptotic test

We propose and evaluate an explicit test of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss function need not be quadratic, and need not even be symmetric), and forecast errors can be non-Gaussian, nonzero mean, serially correlated and contemporaneously correlated.
Assessing the contribution of different factors in regional climate model projections using the factor separation method

This study applies the factor separation (FS) method to investigate the contributions of different factors, along with their synergy, on a set of regional climate model (RCM) projections for the Mediterranean region. The FS method is applied to six projections for the period 1970–2100 performed with the regional model RegCM4 over the Med‐CORDEX domain. Two different sets of factors are intercompared, namely the driving global climate model (GCM) boundary conditions against two model physics settings (convection scheme and irrigation). We demonstrate the usefulness of the FS method to assess different sources of uncertainty in RCM‐based regional climate projections.
Design of Efficient Methods for the Detection of Tomato Leaf Disease Utilizing Proposed Ensemble CNN Model

Early diagnosis of plant diseases is of vital importance since they cause social, ecological, and economic losses. Therefore, it is highly complex and causes excessive workload and time loss. Within the scope of this article, nine tomato plant leaf diseases as well as healthy ones were classified using deep learning with new ensemble architectures. A total of 18.160 images were used for this process. In this study, in addition to the proposed two new convolutional neural networks (CNN) models, four other well-known CNN models (MobileNetV3Small, EfficientNetV2L, InceptionV3 and MobileNetV2) are used. A fine-tuning method is applied to the newly proposed CNNs models and then hyperparameter optimization is performed with the particle swarm optimization algorithm (PSO). Then, the weights of these architectures are optimized by the grid search method and triple and quintuple ensemble models are created and the datasets are classified with the help of the five-fold cross-validation. The experimental results demonstrate that the proposed ensemble models stand out with their fast training and testing time and superior classification performances with an accuracy of 99.60%. This research will help experts enable the early detection of plant diseases in a simple and quick manner and prevent the formation of new infections.
Field statistics in nonlinear composites. I. Theory

This work presents a means for extracting the statistics of the local fields in nonlinear composites from the effective potential of suitably perturbed composites. The idea is to introduce a parameter in the local potentials, generally a tensor, such that differentiation of the corresponding effective potential with respect to the parameter yields the volume average of the desired quantity. In particular, this provides a generalization to the nonlinear case of well-known formulas in the context of linear composites, which express phase averages and second moments of the local fields in terms of derivatives of the effective potential. Such expressions are useful since they allow the generation of estimates for the field statistics in nonlinear composites, directly from homogenization estimates for appropriately defined effective potentials. Here, use is made of these expressions in the context of the ‘variational’, ‘tangent second-order’ and ‘second-order’ homogenization methods, to obtain rigorous estimates for the first and second moments of the fields in nonlinear composites. While the variational estimates for these quantities are found to be identical to those proposed in previous works, the tangent second-order and second-order estimates are found be different. In particular, the new estimates for the first moments given in this work are found to be entirely consistent with the corresponding estimates for the macroscopic behaviour. Sample results for two-phase, power-law composites are provided in part II of this work.
An adaptive optics approach for laser beam correction in turbulence utilizing a modified plenoptic camera

Adaptive optics has been widely used in the field of astronomy to correct for atmospheric turbulence while viewing images of celestial bodies. The slightly distorted incoming wavefronts are typically sensed with a Shack-Hartmann sensor and then corrected with a deformable mirror. Although this approach has proven to be effective for astronomical purposes, a new approach must be developed when correcting for the deep turbulence experienced in ground to ground based optical systems. We propose the use of a modified plenoptic camera as a wavefront sensor capable of accurately representing an incoming wavefront that has been significantly distorted by strong turbulence conditions (C2n <10-13 m- 2/3). An intelligent correction algorithm can then be developed to reconstruct the perturbed wavefront and use this information to drive a deformable mirror capable of correcting the major distortions. After the large distortions have been corrected, a secondary mode utilizing more traditional adaptive optics algorithms can take over to fine tune the wavefront correction. This two-stage algorithm can find use in free space optical communication systems, in directed energy applications, as well as for image correction purposes.
An Implementation of State-of-the-Art Graph Bisection Algorithms ∗

In recent decades, various graph partitioning heuristic algorithms have been developed by researchers, which are widely used in “realworld” applications. As a starting point of graph partition problem, graph bisection algorithms provide basic approaches and solutions for other graph partition instances. This work implemented part of contemporary graph bisection algorithms which produce high-quality partitions for graphs in wide-range domains. These algorithms include Kernighan-Lin heuristic[3], Linear Kernighan-Lin heuristic[4], Greedy Growing Partitioning[5], Min-Max Greedy heuristic[6], Randomized Tabu Search heuristic[6], Reactive Randomized Tabu Search heuristic[6]. Multilevel graph partition technique[5] has not been included in current implementation to achieve better performance. Instead, these heuristics are extended in grid environment so as to significantly reduce run-time by distributing mutually exclusive iterations over computing nodes with support from GXP cluster shell[9]. Further, this parallel bisection needs no communication among computing nodes, thus bearing a high fault-tolerance. Finally, experiments show that although with more computation effort comparing to mulitilevel partition, this graph bisection library is still practically useful with its state-of-the-art result.
Fourier reconstruction in spotlight mode SAR imaging

The spotlight mode SAR imaging procedure can be modeled in two ways. One is from the projectional information introduced from computer-aided tomography (CAT) founded on the projection-slice theorem. By quoting the concept "projection" from CAT to microwave imaging, the spotlight mode SAR can be interpreted as a narrow band tomography reconstruction problem in the far field and small rotation angle situation. Another kind of modeling in the spotlight mode SAR comes from the conventional stripmap SAR directly, in which the processed echo returns are verified to have the same Fourier information as that in the first way. From these two points of view, the spotlight mode SAR can be unified to the problem of Fourier reconstruction (FR). A common problem motion through resolution cells (MTRC) caused by relative rotation in spotlight SAR is characterized as a main factor in imaging smearing or defocusing. Data polar formatting is adopted as an effective solution either of data reconstruction or phase focusing.
Fault-Level Grading of Photovoltaic Cells Employing Lightweight Deep Learning Models

The deployment of photovoltaic (PV) cells as a renewable energy resource has been boosted recently, which enhanced the need to develop an automatic and swift fault detection system for PV cells. Prior to isolation for repair or replacement, it is critical to judge the level of the fault that occurred in the PV cell. The aim of this research study is the fault-level grading of PV cells employing deep neural network models. The experiment is carried out using a publically available dataset of 2,624 electroluminescence images of PV cells, which are labeled with four distinct defect probabilities defined as the defect levels. The deep architectures of the classical artificial neural networks are developed while employing hand-crafted texture features extracted from the EL image data. Moreover, optimized architectures of the convolutional neural network are developed with a specific emphasis on lightweight models for real-time processing. The experiments are performed for two-way binary classification and multiclass classification. For the first binary categorization, the proposed CNN model outperformed the state-of-the-art solution with a margin of 1.3% in accuracy with a significant 50% less computational complexity. In the second binary classification task, the CPU-based proposed model outperformed the GPU-based solution with a margin of 0.9% accuracy with an 8× lighter architecture. Finally, the multiclass categorization of PV cells is performed and the state-of-the-art results with 83.5% accuracy are achieved. The proposed models offer a lightweight, efficient, and computationally cheaper CPU-based solution for the real-time fault-level categorization of PV cells.
Surfaces and branched surfaces transverse to pseudo-Anosov flows on 3-manifolds

Given a circular pseudo-Anosov flow φ on an irreducible, atoroidal 3- manifold M , we classify all closed surfaces in M which are transverse and "almost transverse" to φ , generalizing the Schwartzmann-Fried clas- sification of cross-sections to φ . In particular, there exists an "almost transverse" surface representing any class in H 2 (M Z) which is nonneg- ative on all homology directions of φ . As an application, if σ is a fibered face of the unit ball of Thurston's polyhedral norm on H 2 (M '; R), we give conditions under which OerteΓs conjecture can be verified, that there exists a single taut branched surface in M carrying norm-minimizing representatives of every class in Cone(σ), and in particular carrying fiber representatives of every class in int(Cone(σ)).
Every cell needs a beautiful image: on-the-fly contacting measurements for high-throughput production

The future of the energy transition will lead to a terrawatt-scale photovoltaic market, which can be served cost-effectively primarily by means of high-throughput production of solar cells. In addition to high-throughput production, characterization must be adapted to highest cycle times. Therefore, we present an innovative approach to detect image defects in solar cells using on-the-fly electroluminescence measurements. When a solar cell passes a standard current–voltage (I–V) unit, the cell is stopped, contacted, measured, released, and afterwards again accelerated. In contrast to this, contacting and measuring the sample on-the-fly saves a lot of time. Yet, the resulting images are blurred due to high-speed motion. For the development of such an on-the-fly contact measurement tool, a deblurring method is developed in this work. Our deep-learning-based deblurring model enables to present a clean EL image of the solar cell to the human operator and allows for a proper defect detection, reaching a correlation coefficient of 0.84.
Data-Driven Edge Offloading for Wireless Control Systems

As industrial plants embrace modern technologies, such as edge computing and wireless networks, industrial control systems have evolved into multitier cyber–physical systems. While traditional local controllers enjoy reliable connectivity to sensors/actuators, they suffer from the limited computation capacity of embedded devices. In contrast, edge servers introduce more computation resources connected to sensors/actuators through wireless networks. Offloading control functions to edge servers presents new opportunities to enhance control performance but also poses critical challenges. As wireless networks have limited bandwidth and varying reliability, it is important to optimize control performance by dynamically offloading a subset of the control functions to edge servers under the bandwidth constraint. Furthermore, the selection of offloaded control functions depends on both the cyber (wireless) and physical states of the wireless control systems. In this article, we tackle the problem of optimizing the control performance of multiple control loops through dynamic edge offloading. We establish a data-driven model to predict the control performance of each feedback control loop based on its cyber–physical states. We then develop a dynamic edge offloading approach to optimize the overall control performance of a system with multiple feedback control loops while guaranteeing their stability under fluctuating cyber–physical conditions. Finally, we demonstrate the efficacy of the data-driven model and offloading approach in case studies comprising simulations of up to 20 industrial robots.
The next SDSS white dwarf catalog

We have begin work on the next Sloan Digital Sky Survey (SDSS) spectroscopically-identified white dwarf catalog. It will feature white dwarf stars from Data Release 7, the final data release from the SDSS-I and SDSS-II surveys. Besides incorporating the latest spectrophotometry improvements from the SDSS, we have improved our spectroscopic models and expanded our grid. We expect on the order of 5000–6000 additional white dwarf stars not included in the earlier Data Release 4 catalog.
Pattern formation with a conservation law

The evolution of many pattern-forming systems is strongly influenced by the presence of a conserved quantity. Diverse physical phenomena such as magnetoconvection, rotating fluid convection, binary fluid convection, vibrated granular and fluid layers, filament dynamics and sandbank formation, all possess a conservation law which plays a central role in their nonlinear dynamics. In this thesis, this influence of a conserved quantity is examined through analyses of three distinct pattern-formation problems. Firstly, the consequences of conservation of mass are investigated in a phenomenological model of a vibrated granular layer. A new weakly nonlinear analysis is performed that reveals the existence of modulational instabilities for patterns composed of either stripes and squares. The nonlinear evolution of these instabilities is numerically studied and a plethora of patterns and localised arrangements are exhibited. The second component of this work concerns an oscillatory bifurcation in the presence of a conserved quantity. Building upon existing theory for the corresponding stationary bifurcation, universal amplitude equations are constructed through symmetry and asymptotic considerations. Subsequently, the stability properties of travelling and standing waves are found to be significantly altered and new modulational instabilities are uncovered. Numerical simulations reveal that, in the presence of a conserved quantity, travelling and standing waves lose stability to spatially localised patterns, either coherent, time-periodic or chaotic. Finally, wave-like behaviour of large-scale modes is examined through an analysis of a model for Faraday waves, that has been modified to account for flnite fluid depth. Several approaches to the weakly nonlinear analysis are considered and two sets of amplitude equations are derived that account for the unusual wave-like behaviour of large-scale modes. Numerical simulations reveal amplitude-modulated and localised patterns away from the small-amplitude, weak-viscosity limit.
Towards the Ultimate Conservative Difference Scheme

A method of second-order accuracy is described for integrating the equations of ideal compressible flow. The method is based on the integral conservation laws and is dissipative, so that it can be used across shocks. The heart of the method is a one-dimensional Lagrangean scheme that may be regarded as a second-order sequel to Godunov's method. The second-order accuracy is achieved by taking the distributions of the state quantities inside a gas slab to be linear, rather than uniform as in Godunov's method. The Lagrangean results are remapped with least-squares accuracy onto the desired Euler grid in a separate step. Several monotonicity algorithms are applied to ensure positivity, monotonicity, and nonlinear stability. Higher dimensions are covered through time splitting. Numerical results for one-dimensional and two-dimensional flows are presented, demonstrating the efficiency of the method. The paper concludes with a summary of the results of the whole series “Towards the Ultimate Conservative Difference Scheme. ”
Tree‐based iterative input variable selection for hydrological modeling

Input variable selection is an important issue associated with the development of several hydrological applications. Determining the optimal input vector from a large set of candidates to characterize a preselected output might result in a more accurate, parsimonious, and, possibly, physically interpretable model of the natural process. In the hydrological context, the modeled system often exhibits nonlinear dynamics and multiple interrelated variables. Moreover, the number of candidate inputs can be very large and redundant, especially when the model reproduces the spatial variability of the physical process. The ideal input selection algorithm should therefore provide modeling flexibility, computational efficiency in dealing with high dimension data set, scalability with respect to input dimensionality and minimum redundancy. In this paper, we propose the tree‐based iterative input variable selection algorithm, a novel hybrid model‐based/model‐free approach specifically designed to fulfill these four requirements. The algorithm structure provides robustness against redundancy, while the tree‐based nature of the underlying model ensures the other key properties. The approach is first tested on a well‐known benchmark case study to validate its accuracy and subsequently applied to a real‐world streamflow prediction problem in the upper Ticino River Basin (Switzerland). Results indicate that the algorithm is capable of selecting the most significant and nonredundant inputs in different testing conditions, including the real‐world large data set characterized by the presence of several redundant variables. This permits one to identify a compact representation of the observational data set, which is key to improving the model performance and assisting with the interpretation of the underlying physical processes.
Digital Platform for Automatic Qualitative and Quantitative Reading of a Cryptococcal Antigen Point-of-Care Assay Leveraging Smartphones and Artificial Intelligence

Cryptococcosis is a fungal infection that causes serious illness, particularly in immunocompromised individuals such as people living with HIV. Point of care tests (POCT) can help identify and diagnose patients with several advantages including rapid results and ease of use. The cryptococcal antigen (CrAg) lateral flow assay (LFA) has demonstrated excellent performance in diagnosing cryptococcosis, and it is particularly useful in resource-limited settings where laboratory-based tests may not be readily available. The use of artificial intelligence (AI) for the interpretation of rapid diagnostic tests can improve the accuracy and speed of test results, as well as reduce the cost and workload of healthcare professionals, reducing subjectivity associated with its interpretation. In this work, we analyze a smartphone-based digital system assisted by AI to automatically interpret CrAg LFA as well as to estimate the antigen concentration in the strip. The system showed excellent performance for predicting LFA qualitative interpretation with an area under the receiver operating characteristic curve of 0.997. On the other hand, its potential to predict antigen concentration based solely on a photograph of the LFA has also been demonstrated, finding a strong correlation between band intensity and antigen concentration, with a Pearson correlation coefficient of 0.953. The system, which is connected to a cloud web platform, allows for case identification, quality control, and real-time monitoring.
The Major Facilitator Superfamily and Antimicrobial Resistance Efflux Pumps of the ESKAPEE Pathogen Staphylococcus aureus

The ESKAPEE bacterial pathogen Staphylococcus aureus has posed a serious public health concern for centuries. Throughout its evolutionary course, S. aureus has developed strains with resistance to antimicrobial agents. The bacterial pathogen has acquired multidrug resistance, causing, in many cases, untreatable infectious diseases and raising serious public safety and healthcare concerns. Amongst the various mechanisms for antimicrobial resistance, integral membrane proteins that serve as secondary active transporters from the major facilitator superfamily constitute a chief system of multidrug resistance. These MFS transporters actively export structurally different antimicrobial agents from the cells of S. aureus. This review article discusses the S. aureus-specific MFS multidrug efflux pump systems from a molecular mechanistic perspective, paying particular attention to structure–function relationships, modulation of antimicrobial resistance mediated by MFS drug efflux pumps, and direction for future investigation.
An MCTS-Based Solution Approach to Solve Large-Scale Airline Crew Pairing Problems

The airline crew pairing (ACP) problem is one of the most challenging problems in airline operations. It aims to decide the optimal connections among pairs of flights assigned to flight crews. However, the number of connections grows exponentially with the increasing number of flights. Conventional approaches usually follow a two-stage method, i.e., divide-and-conquer. The flight sequences (pairings) spanning multiple days are firstly generated for each crew. Then, the best pairing set is chosen based on minimum operational costs. When the number of flights is large, it becomes too difficult to generate all feasible pairings and find the optimum. In order to solve large-scale ACP problems efficiently, we propose a novel iterative optimization framework based on monte carlo tree search (MCTS). Thus, the speed of pairing generation and solution accuracy can be improved. To evaluate the performance of the proposed method, we conduct experiments on different scales of real-world instances provided by airline companies. The empirical results show that the proposed approach is capable of producing high-quality solutions, especially in large-scale instances.