Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Annealing Boosts Prediction of Multimolecular Adsorption on Solid Surfaces Avoiding Combinatorial Explosion

Quantum annealing has been used to predict molecular adsorption on solid surfaces. Evaluation of adsorption, which takes place in all solid surface reactions, is a crucially important subject for study in various fields. However, predicting the most stable coordination by theoretical calculations is challenging for multimolecular adsorption because there are numerous candidates. This report presents a novel method for quick adsorption coordination searches using the quantum annealing principle without combinatorial explosion. This method exhibited much faster search and more stable molecular arrangement findings than conventional methods did, particularly in a high coverage region. We were able to complete a configurational prediction of the adsorption of 16 molecules in 2286 s (including 2154 s for preparation, only required once), whereas previously it has taken 38 601 s. This approach accelerates the tuning of adsorption behavior, especially in composite materials and large-scale modeling, which possess more combinations of molecular configurations.
Sustainability, Resilience, and Returns During COVID-19: Empirical Evidence from US and Indian Stock Markets

The growing number of consulting reports published globally show mixed evidence of higher returns for environmental, social, and governance (ESG) indices as compared to equity indices. The present study analyzes whether or not sustainability provided resilience, during turbulent times, to the US and India, who were worst hit by the COVID-19 pandemic. The study tests whether higher ESG scores led to higher stock returns during and after the COVID-19 pandemic. The findings revealed little and negative associations of sustainability with stock returns for sample firms during the COVID-19 crisis. There is no empirical evidence indicating that sustainability guarantees resilience during crisis times. Investors have their own preference channels and taste for sustainability that are beyond their financial motives. JEL Codes: Q01, G120
Electrodeposition of Iron Triad Metal Coatings: Miles to Go

The possibilities and future perspectives of electrochemical deposition of bimetallic compositions and alloys containing Fe, Co, Ni, Cr, W, and Mo are reviewed. The synthesis of two- and three-component materials, as well as compositionally more complex alloys, is considered. The method of synthesizing of materials via electrodeposition from solutions containing metal ions and metalloids is one of the most promising approaches because it is fast, cheap, and it is possible to control the composition of the final product with good precision. Corrosion, catalytic and magnetic properties should be distinguished. Due to these properties, the range of applications for these alloys is very wide. The idea of a correlation between the magnetic and catalytic properties of the iron-triad metal alloys is considered. This should lead to a deeper understanding of the interplay of the properties of electrodeposited alloys. In addition to deposition from aqueous (classical) solvents, the advantages and perspectives of electrochemical deposition from ionic liquids (ILs) and deep eutectic solvents (DES) are briefly discussed. The successful use and development of this method of electrodeposition of alloys, which are quite difficult or impossible to synthesize in classical solvents, has been demonstrated and confirmed.
EVENODD Code Implementation and Generalizations

With the explosive growth of data generation, the demand for storage capacity and scale of cloud storage systems increases, and at the same time data failure becomes more and more common, people also pay more attention to the effectiveness and reliability of data storage. Increasing data redundancy is an effective way to improve the capacity for data fault tolerance, the initially used replication redundancy strategy is simple and easy to implement, with low computational complexity, but has a large data overhead. While the Erasure Code is more cumbersome to encode, it can obtain higher reliability with less redundancy. Therefore, the redundancy strategy based on erasure codes is widely used, EVENODD code is one of the very popular application methods. In this paper, the principle of EVENODD code is introduced and an algorithm is designed to encode and decode the EVENODD code, besides, the algorithm's performance is analyzed, and the results show that the program running time is an incremental process when m increases. In addition, this article calculates how many XOR operations are required to encode and recover from single or double disk failures to analyze the time complexity, it is also found that the time complexity of the algorithm is O(n2) in all encoding and decoding scenarios. Finally, a method to reduce the read cost of single-disk failure recovery is discussed.
Dynamic stabilization of regular linear systems

We consider a general class of infinite-dimensional linear systems, called regular linear systems, for which convenient representations are known to exist both in time and in frequency domain. For this class of systems, we investigate the concepts of stabilizability and detectability, in particular, their invariance under feedback and their relationship to exponential stability. We introduce two concepts of dynamic stabilization, the first formulated as usual, with the plant and the controller connected in feedback, and the second with two feedback loops. Even for finite-dimensional systems, the second concept, stabilization with an internal loop in the controller, is more general. We argue that the more general concept is the natural one, and we derive sufficient conditions under which an observer-based stabilizing controller with an internal loop can be constructed.
Evaluation of the mass‐flux approach to parametrizing deep convection

Cloud‐resolving model (CRM) simulations of deep convection in a variety of environmental conditions are used to analyse assumptions in bulk‐updraught convection parametrization schemes, which are commonly employed in climate and weather‐prediction models. It is shown that the mass‐flux approach can faithfully represent the effect of convection on the large‐scale fields, provided that the mass flux and other bulk properties of the updraughts (and downdraughts) are accurately predicted. Entrainment and detrainment (which govern the shape of the mass‐flux profile and the dilution of the bulk updraught with environment air) are calculated from CRM simulations. Limitations in the basic premiss of the classical entraining/detraining plume model are revealed and an alternative method of predicting bulk‐updraught temperature is suggested.
Ecophysiological responses of Nothofagus obliqua forests to recent climate drying across the Mediterranean-Temperate biome transition in south-central Chile

The forests of south‐central Chile are facing a drying climate and a megadrought that started in 2010. This study addressed the physiological responses of five Nothofagus obliqua stands across the Mediterranean‐Temperate gradient (35.9°−40.3°S) using carbon isotope discrimination (Δ13 C) and intrinsic water use efficiency (iWUE) in tree rings during 1967–2017. Moreover, tree ring δ18O was evaluated in the northernmost site to better understand the effects of the megadrought in this drier location. These forests have become more efficient in their use of water. However, trees from the densest stand are discriminating more against 13C, probably due to reduced photosynthetic rates associated with increasing light competition. The strongest associations between climate and Δ13C were found in the northernmost stand, suggesting that warmer and drier conditions could have reduced 13C discrimination. Tree growth in this site has not decreased, and δ18O was negatively related to annual rainfall. However, a shift in this relationship was found since 2007, when both precipitation and δ18O decreased, while correlations between δ18O and growth increased. This implies that tree growth and δ18O are coupled in recent years, but precipitation is not the cause, suggesting that trees probably changed their water source to deeper and more depleted pools. Our research demonstrates that forests are not reducing their growth in central Chile, mainly due to a shift toward the use of deeper water sources. Despite a common climate trend across the gradient, there is a non‐uniform response of N. obliqua forests to climate drying, being their response site‐specific.
Quantum-state transfer from light to macroscopic oscillators

We propose a scheme for transferring quantum states from the propagating light fields to macroscopic, collective vibrational degree of freedom of a massive mirror by exploiting radiation pressure effects. This scheme may prepare an Einstein-Podolsky-Rosen state in position and momentum of a pair of distantly separated movable mirrors by utilizing the entangled light fields produced from a nondegenerate optical parametric amplifier.
SEM modeling with singular moment matrices Part III: GLS estimation

Abstract We discuss generalized least squares (GLS) and maximum likelihood (ML) estimation for structural equations models (SEM), when the sample moment matrices are possibly singular. This occurs in several instances, for example, for panel data when there are more panel waves than independent replications or for time series data where the number of time points is large, but only one unit is observed. In previous articles, it was shown that ML estimation of the SEM is possible by using a correct Gaussian likelihood function. In this article, the usual GLS fit function is modified so that it is also defined for singular sample moment matrices S. In large samples, GLS and ML estimation perform similarly, and the modified GLS approach is a good alternative when S becomes nearly singular. Both GLS approaches do not work for N = 1, since here S = 0 and the modified GLS approach yields biased estimates. In conclusion, ML estimation (and pseudo ML under misspecification) is recommended for all sample sizes including N = 1.
Numerical convergence of the MPFA O‐method and U‐method for general quadrilateral grids

Control‐volume discretization methods are applicable for problems that require good numerical approximations of fluxes. Here, a class of flux‐continuous discretization methods, denoted multipoint flux approximation methods (MPFA), is discussed, and two different approaches are derived. The resulting discrete equations use pressures as the only unknowns, and the fluxes will be given explicitly as weighted sums of the discrete pressures. The two methods are denoted the O‐method and the U‐method, respectively, and differ in the way that continuity requirements are embedded in the discrete equations. Numerical tests are provided for smooth problems and problems with discontinuous coefficients for both the O‐method and the U‐method. Convergence rates of the methods are indicated through numerical experiments on smooth and rough grids. Copyright © 2005 John Wiley & Sons, Ltd.
Upper Bounds on Matching Families in

Matching families are one of the major ingredients in the construction of locally decodable codes (LDCs) and the best known constructions of LDCs with a constant number of queries are based on matching families. The determination of the largest size of any matching family in , where is the ring of integers modulo , is an interesting problem. In this paper, we show an upper bound of for the size of anymatching family in , where and are two distinct primes. Our bound is valid when is a constant, , and . Our result improves an upper bound of Dvir and coworkers.
Coronavirus Diagnosis Based on Chest X-Ray Images and Pre-Trained DenseNet-121

A serious global problem called COVID-19 has killed a great number of people and rendered many projects useless. The obtained individual's identification at the appropriate time is one of the crucial methods to reduce losses. By detecting and recognizing contaminated individuals in the early stages, artificial intelligence can help many associations in these situations. In this study, we offer a fully automated method to identify COVID-19 from a patient's chest X-ray images without the need for a clinical expert's assistance. A new dataset was released, which consists of 300 chest X-ray images from 100 healthy individuals, 100 individuals who were infected with Covid 19, and 100 images of viral pneumonitis. 100 more for testing, too. In order to attain an F1 score of 0.98, a Recall of 0.98, and also an Accuracy of 0.98 with this dataset, a classification method deep learning-based learning algorithm DenseNet-121, transfer learning, as well as data augmentation techniques were implemented. Therefore, even though there are not enough training photos, these findings are far better than other state-of-the-art.
On the Study of Cognitive Bidirectional Relaying with Asymmetric Traffic Demands

Abstract In this paper, we consider a cognitive radio network scenario, where two primary users want to exchange information with each other and meanwhile, one secondary node wishes to send messages to a cognitive base station. To meet the target quality of service (QoS) of the primary users and raise the communication opportunity of the secondary nodes, a cognitive bidirectional relaying (BDR) scheme is examined. First, system outage probabilities of conventional direct transmission and BDR schemes are presented. Next, a new system parameter called operating region is defined and calculated, which indicates in which position a secondary node can be a cognitive relay to assist the primary users. Then, a cognitive BDR scheme is proposed, giving a transmission protocol along with a time-slot splitting algorithm between the primary and secondary transmissions. Information-theoretic metric of ergodic capacity is studied for the cognitive BDR scheme to evaluate its performance. Simulation results show that with the proposed scheme, the target QoS of the primary users can be guaranteed, while increasing the communication opportunity for the secondary nodes.
Prediction of Biceps Muscle Electromyogram Signal Using a NARX Neural Network

Background: This study was conducted to compare the response between the results of experimental data and the results achieved by the NARX neural network model to predict the electromyogram (EMG) signal on the biceps muscle in nonlinear stimulation conditions as a new stimulation model. Methods: This model is applied to design the controllers based on functional electrical stimulation (FES). To this end, the study was conducted in five stages, including skin preparation, placement of recording and stimulation electrodes, along with the position of the person to apply the stimulation signal and recording EMG, stimulation and recording of single-channel EMG signal, signal preprocessing, and training and validation of the NARX neural network. The electrical stimulation applied in this study is based on a chaotic equation derived from the Rossler equation and on the musculocutaneous nerve, and the response to this stimulation, i.e., the EMG signal, is from the biceps muscle as a single channel. The NARX neural network was trained, along with the stimulation signal and the response of each stimulation for 100 recorded signals from 10 individuals, and then validated and retested for trained data and new data after processing and synchronizing both signals. Results: The results indicate that the Rossler equation can create nonlinear and unpredictable conditions for the muscle, and we also can predict the EMG signal with the NARX neural network as a predictive model. Conclusion: The proposed model appears to be a good method to predict control models based on FES and to diagnose some diseases.
Distributed Exact Structural Clustering on Large Graph

Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applied over the years. However, the processing time cost of sequential SCAN and its variants cannot be tolerable on large graphs. The existing parallel variants of SCAN are focusing on fully utilizing the computing capacity of multi-core computer architectures and inventing sophisticated optimization techniques on single computing node. As the objects and their relationships in cyberspace are varying over time, the scale of graph data is increasing with high rate. The graph clustering algorithms on single node are facing challenges from limited computing resources, such as computing performance, memory size and storage volume. The distributed processing algorithm is called for processing large graphs. This work presents a distributed structural graph clustering algorithm using Spark. Furthermore, the edge pruning technique and adaptive checking are optimized to improve clustering efficiency. And the label propagation clustering is simplified to reduce the communication cost in the distributed clustering iterations. It also conduct extensive experiments on real-world datasets to testify the efficiency and scalability of the distributed algorithm. Experimental results show that efficient clustering performance can be achieved and it scales well under different settings.
5G-Kube: Complex Telco Core Infrastructure Deployment Made Low-Cost

Network Function Virtualization (NFV) along with Software Defined Networking (SDN) have brought an evolution in telecommunications laying out the bases for 5G networks and its softwarization. Accordingly, new implementations of telecom standards, such as the 3GPP 5G Core, are defined as fully-virtualized infrastructures consisting of different components and leveraging a cloud-native approach. At the same time, standard-oriented solutions, such as ETSI Management and Orchestration (MANO), have emerged to master the complexity of Virtualized Network Functions (VNFs) orchestration, including 5G Core VNFs. While MANO operates at the NFV level, it also leverages existing cloud infrastructures for the deployment of VNFs by interoperating with resource orchestrators at the cloud level. From the business perspective, that requires telco operators to interact with different technology providers, from NFV/MANO software producers to cloud computing providers, and to hire technicians proficient in the technologies of both telco and computing worlds, that are a rather difficult human resourcing to find. The main claim of the article is that the Development and Operations (DevOps) tools in the IT world are mature enough to leverage them directly in the telco world, without superimposing other interlaced standard/software. That allows to significantly reduce OPEX cost of complex telco infrastructures by supporting all needed automation and by avoiding the combined use of (too) complex layered standards/software stacks, such as in the case of MANO. Accordingly, in this article, we leverage container-based technologies and Kubernetes to design and evaluate a novel deployment approach, called 5G-Kube, for softwarized 5G core networks. 5G-Kube, which is openly to the community, has been also evaluated in two different use cases of the 5G Core and Kubernetes deployment fitting, namely, Industry 4.0 and Smart Cities.
Multimodal Approaches based on Fake News Detection

People or organizations acting in their own or other people's best interests may produce fake news. Misinformation is typically produced with personal, political, or economic objectives in mind. Fake news is mainly known as one of the most severe dangers to worldwide commerce, and journalism with significant dependent team. To detect the fake news, many approaches are used. The proposed model used machine learning (ML) for exploiting the multi-domain visual data information in identification of fake news. In this work, an application considered to predict whether an image or video has been manipulated or not. And also, OCR is used for identifying the text on image and model will forecast outcome. The present system, used machine learning techniques like Naïve Bayes and support vector machine to predict the fake news. The present system achieved 97% accuracy to forecast model outcome.
RW Ursae Minoris (1956): An Evolving Postnova System

From 1999–2002 photometric observations of the old nova RW UMi (1956), we systematically detect a dominant modulation with a period P0 peaking in the range 1.7–2.4 hr. This period, on average, is about 45% longer than the previously suggested orbital period of ∼1.42 hr. This could be due to the occasional presence of low‐frequency quasi‐periodic oscillations (QPOs) that can eventually dominate the light curve. Rather irregular QPOs, on the order of the orbital period, can be produced by vertical oscillations of the inner‐disk region excited by the rotating magnetosphere of the white dwarf. Thus, RW UMi could be an intermediate polar system. Since we find that the luminosity of the postnova is decaying at a rate of ∼0.02 mag yr -1, we should observe some important changes in its photometric behavior in the future.
Si Isotopic Ratios in Supernovae for Presolar Grains

Primitive meteorites contain presolar grains that are considered to have traces of the nucleosynthesis at their birth as large isotopic anomalies. The isotopic signatures are important to identify the origin of the grains. Supernova-originating presolar grains, such as SiC type X and low-density graphite, have also been identified by the excesses of 28Si with respect to the solar Si isotopic ratios. At the same time, most of them are depleted in 30Si rather than in 29Si compared to the solar Si ratios. A small number of them show 29Si deficits. We investigate the Si isotopic ratios of supernova ejecta with different-mass progenitors considering inhomogeneous mixing. The mixture components consist of four layers of the supernova ejecta: the Ni, Si/S, He/C, and He/N layers. Detailed nucleosynthesis during stellar evolution and supernova explosion is calculated using 3.3, 4, 6, and 8 M☉ He star models. Supernova explosions for all of the stellar models and a hypernova explosion for the 8 M☉ He star model are considered. The 30Si deficits with respect to solar ratios found in most grains are explained by the mixtures of the 3.3 and 4 M☉ supernova models and the 8 M☉ hypernova model. The 29Si deficits are explained by the mixtures of the 6 and 8 M☉ supernova models. Although the Si abundance in the Ni layer is small, the Ni layer is important to reproduce the Si isotopic ratios of the grains. The Si isotopic ratios in the Ni layer largely depend on the stellar mass, because a shorter explosion timescale is more favorable to the production of 29Si than 30Si. Most presolar grains from supernovae with deficits of 30Si rather than of 29Si compared to the solar Si isotopic ratios would be from less massive supernovae whose progenitor masses are smaller than ~15 M☉ and hypernovae whose progenitor masses are heavier than ~20 M☉.
Enhancement Method of Low Quality Fingerprint Based on U-Net

Fingerprints are one of the most commonly used biometrics in public safety. The quality of the fingerprint image directly affects the performance of the fingerprint recognition system. This paper proposes a low-quality fingerprint enhancement model based on U-Net, which can effectively remove the background noise of the fingerprint image and repair the damaged ridge structure of the fingerprint. Furthermore, to train the proposed model, we create a dataset containing low-quality fingerprints and their associated ground truth. We compare the proposed method with several state-of-the-art methods on the home-made dataset and the public dataset FVC2002, and the experimental results demonstrate that the proposed method performs better.