Miracle-dz/UniFAR-FTUs huggingface dataset:
New Recommendation System Model Based on Semantic Similarity in Movie Domain

Recommender systems (RS) automatically select the most appropriate items to each user, thus shortening his product searching time and adapting the selection as his particular preferences evolve over time. It has been found that different recommendation methods use different techniques to recommend objects to consumers. There is possibility that the majority of the previous researches have problems. Currently, the most important problem in recommendation system is cold start that during this study, we consider to this issue by proposing RECOMOVIER model. Four techniques have been developed for designing new model including: Collaborative Filtering (CF), Content Base Filtering (CBF), and Hybrid and Cascade method. The main goal of this research is omitting the cold start problem for new user that helps to increase the accuracy of results of recommendation system. Analytical review of existing recommendation system model is utilized as research methodology which guides us to understand more about pros and cons of current recommender methods and systems. In order to implement the proposed model the C# programming language and two sets of data that were downloaded and modified from Movielens website were used. This application is tested by users as every user filled the form and then got feedback from this application. The results of experiments show that, using user profile has positive influence on increasing the accuracy of results of recommendation system by omitting the cold start problem from RS systems. The proposed model in this paper would be valuable and beneficial for future researchers and practitioners interested in developing recommendation systems. KEYWORDS: Recommendation Systems (RS), Collaborative Filtering (CF), Content Base Filtering (CBF), Hybrid
Discussion on Design Method of Prestressed Concrete Frame Joints with Spread-Ended Beams

Based on the previous experimental and numerical analysis studies, the design menthod for prestressed concrete frame joints with spread-ended beams are proposed according to home and abroad related design codes and references, and the designed method is verified by test data. The results show that the method is feasible and can meet the needs of engineering. This method can be used in engineering design in engineering practice. Finally, the design measures for the beam-column joint core and the spread-ended region are recommended.
The implementation of concave micro optical devices using a polymer dispensing technique

This study demonstrates a novel approach to implementing a double-concave (DCV) microlens using a simple polymer dispensing and sucking process. The DCV microlens is implemented at room temperature using a commercially-available pneumatic-controlled polymer dispensing system. The DCV lens profile can be tuned by varying the volume of the dispensed polymer. It is also easy to integrate the present polymer DCV microlens with other suspended micromachined devices such as silicon nitride film and silicon-on-glass (SOG) micromachined structures. This study further employs the process for DCV to implement a concave mirror. The measurement results show a typical DCV lens (made of NOA63 polymer) with negative focal lengths of −1.42 mm (red laser) and −1.17 mm (blue laser), and a concave mirror with a focal length of 3.28 mm. Moreover, this study also demonstrates the integration of a DCV microlens with other optical components, such as plano-convex and double-convex lenses.
Stability of 2D FDTD algorithms with local mesh refinement for Maxwell's equations

We perform stability analysis on the finite-difference time domain method (FDTD) when extended to incorporate local space-time adaptive mesh refinement (AMR). The neutrally stable Yee algorithm becomes extremely sensitive to perturbations introduced by the interpolation schemes employed at grid refinement interfaces. In this paper we investigate the stability of a range of interpolation schemes using Gustafsson-Kreiss-Sundstrom-Trefethen (GKS-T) mode and reflection/transmission coefficients analysis on the infinite domain with a single interface. This analysis allows detection of trapping instabilities, exponentially growing modes, mode resonances with the interface and mode-mode resonances. We also apply matrix stability analysis for more complicated computational domains containing multiple grid refinement interfaces.
Leader-Following Consensus of Fractional Nonlinear Multiagent Systems

The leader-following consensus of fractional nonlinear multiagent systems is investigated over an undirected fixed interaction graph. Mittag-Leffler stability and the fractional Lyapunov direct method are firstly introduced into the fractional multiagent systems. The sufficient conditions are given to guarantee that the leader-following consensus can be achieved in the systems with both single-integrator dynamics and double-integrator dynamics. Finally, the numerical simulations are given to verify the correctness of the presented theory.
A parameter for the selection of an optimum balance calibration model by Monte Carlo simulation

The current trend in balance calibration-matrix generation is to use non-linear regression and statistical methods. Methods typically include Modified-Design-of-Experiment (MDOE), Response-Surface-Models (RSMs) and Analysis of Variance (ANOVA). These methods are used to reduce the number of calibration loads, to identify relevant calibration matrix coefficients, and to avoid or eliminate invalid or detrimental coefficients. The methods are currently focused on the balance loading scheme and the curve-fitting processes. They do not take into account the load dependent uncertainty inherent in the calibration loads, nor do they estimate the uncertainty of the balance when installed in a wind tunnel. This paper proposes a simulation approach which extends the matrix generation process to take into account both the uncertainties of the applied calibration loads as well as the uncertainty of the electrical response of the balance to these loads. Further, calibration uncertainties are propagated by simulation from calibration through to balance installation to produce uncertainties which may be expected in a wind tunnel. A Performance Weighted Efficiency (PWE) parameter is defined and used to select the best calibration matrix with respect to optimum in-tunnel balance performance. It is proposed that the PWE parameter, either in this form, or with modification, may be an effective balance comparison parameter.
The Macroeconomic Policy Mix in a Monetary Union with Flexible Inflation Targeting

Policy mix problems may arise in a monetary union with centralized monetary policy and decentralized fiscal policy. A consequence of this may be an inappropriate stabilization of shocks. This paper addresses how policy coordination problems between fiscal authorities depend on the type of shocks and the objectives of the monetary authority. It is shown that non-coordinated fiscal policies tend to be too counter-cyclical in the case of aggregate shocks, and that this bias can be reduced by lowering the weight to output stability in monetary policy. Oppositely, for country-specific shocks non-coordinated fiscal policies tend to be too pro-cyclical, and this bias can be reduced by increasing the weight to output stability in monetary policy. Considering the assignment of policy tasks - within the set of binding policy rules for fiscal and monetary policies - it is found that flexible inflation targeting dominates strict inflation targeting.
A Coupled Lattice Boltzmann-Volume Penalization for Flows Past Fixed Solid Obstacles with Local Mesh Refinement

A coupled Lattice Boltzmann-Volume Penalization (LBM-VP) with local mesh refinement is presented to simulate flows past obstacles in this article. Based on the finite-difference LBM, the local mesh refinement is incorporated into the LBM to improve computing efficiency. The volume penalization method is introduced into the LBM by an external forcing term. In the LBM-VP method, the processes of interpolating velocities on the boundaries points and distributing the force density to the Eulerian points near the boundaries are unnecessary. Performing the LBM-VP on a certain point, only the variables of this point are needed, which means the whole procedure can be conducted parallelly. As a consequence, the whole computing efficiency can be improved. To verify the presented method, flows past a single circular cylinder, a pair of cylinders in tandem arrangement, and a NACA-0012 are investigated. A good agreement between the present results and the data in the previous literatures is achieved, which demonstrates the accuracy and effectiveness of the present method to solve the flows past obstacle problems.
English in Public Schools Located in Metropolitan Lima, Peru: An Analysis of Eleventh-Grade Students’ Level and Perceptions

English Language Teaching (ELT) public policies are present in most of the countries of the Americas due to the importance of said language in the international context. The objective of this research was to know the English level of eleventh-grade students in public schools located in Metropolitan Lima, Peru, as well as their perceptions of their own English learning process within the framework of the new national plan called Ingles, Puertas al Mundo (English, Doors to the World) . The sample was composed of 72 students from four schools of the city. This study was conducted using a mixed-method (quantitative-qualitative) approach. The instruments used were a standardized English test (New Inside Out Quick Placement Test) and a structured interview guide. The results revealed that the students’ English level is below the level outlined in the national policy. Regarding the perception of their own learning, students have different motivations to learn English, and enjoy the blended model introduced by the national English plan. However, they agree that their English level is very basic and that they would not be able to reach the established communication objectives after graduating from high school.
Tannin‐tolerant bacteria from crossbred Holstein x Zebu cows

The objective of this work was to isolate and characterize tannin-tolerant ruminal bacteria from crossbred Holstein x Zebu cows fed a chopped mixture of elephant grass (Pennisetum purpureum), young stems of "angico-vermelho" (Parapiptadenia rigida), and banana tree (Musa sp.) leaves. A total of 117 bacteria strains were isolated from enrichment cultures of rumen microflora in medium containing tannin extracts. Of these, 11 isolates were able to tolerate up to 3 g L-1 of tannins. Classical characterization procedures indicated that different morphological and physiological groups were represented. Restriction fragments profiles using Alu1 and Taq1 of 1,450 bp PCR products from the 16S rRNA gene grouped the 11 isolates into types I to VI. Sequencing of 16S rRNA PCR products was used for identification. From the 11 strains studied, seven were not identifiable by the methods used in this work, two were strains of Butyrivibrio fibrisolvens, and two of Streptococcus bovis.
An Hybrid Text-Image Based Authentication for Cloud Services

The problem of securing access to the online information is acute today when access to bank accounts, health records, intellectual property and business or politically sensitive information are made by only a few clicks, regardless of geographic location. At the same time, more and more of these accesses are made from handsets. Cloud Computing is eminently suitable for addressing problems related to limited client resources, as it offloads computation from clients and offers dynamic provisioning of compute resources. Authentication of the companys users to the cloud service is mandatory because in this way it is eliminated the attacks risks to enter into the Cloud services. A suitable authentication is required for organizations that want to access the Cloud services. Our solution regards increasing security at the Security Access Point level of Cloud Computing and it is in fact a strong hybrid user authentication solution based on using image combined with text in order to avoid the weakness of simple user and password solution for authentication. A two factor password image based authentication method is proposed in this paper for cloud services. This authentication approach is used without additional hardware involved and presents the advantages of utilization in terms of security and usability. Every time when the user will be asked to provide his/her identity, a form for each image included in the photo will be listed. The user will have to remember the secret code for each image and to carefully introduce them in the forms. The global cloud access solution will be based on our hybrid proposed text-image based solution, and will be completed by the X.509 certificates.
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers

Training disentangled representations with generative adversarial networks (GANs) remains challenging, with leading implementations failing to achieve comparable performance to Variational Autoencoder (VAE)-based methods. After $\beta$-VAE and FactorVAE discovered that regularizing the total correlation of the latent vectors promotes disentanglement, numerous VAE-based methods emerged. Such a discovery has yet to be made for GANs, and reported disentanglement scores of GAN-based methods are significantly inferior to VAE-based methods on benchmark datasets. To this end, we propose a novel regularizer that achieves higher disentanglement scores than state-of-the-art VAE- and GAN-based approaches. The proposed contrastive regularizer is inspired by a natural notion of disentanglement: latent traversal. Latent traversal refers to generating images by varying one latent code while fixing the rest. We turn this intuition into a regularizer by adding a discriminator that detects how the latent codes are coupled together, in paired examples. Numerical experiments show that this approach improves upon competing state-of-the-art approaches on benchmark datasets.
$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches.
Centering Data Improves the Dynamic Mode Decomposition

Dynamic mode decomposition (DMD) is a data-driven method that models high-dimensional time series as a sum of spatiotemporal modes, where the temporal modes are constrained by linear dynamics. For nonlinear dynamical systems exhibiting strongly coherent structures, DMD can be a useful approximation to extract dominant, interpretable modes. In many domains with large spatiotemporal data---including fluid dynamics, video processing, and finance---the dynamics of interest are often perturbations about fixed points or equilibria, which motivates the application of DMD to centered (i.e. mean-subtracted) data. In this work, we show that DMD with centered data is equivalent to incorporating an affine term in the dynamic model and is not equivalent to computing a discrete Fourier transform. Importantly, DMD with centering can always be used to compute eigenvalue spectra of the dynamics. However, in many cases DMD without centering cannot model the corresponding dynamics, most notably if the dynamics have full effective rank. Additionally, we generalize the notion of centering to extracting arbitrary, but known, fixed frequencies from the data. We corroborate these theoretical results numerically on three nonlinear examples: the Lorenz system, a surveillance video, and brain recordings. Since centering the data is simple and computationally efficient, we recommend it as a preprocessing step before DMD; furthermore, we suggest that it can be readily used in conjunction with many other popular implementations of the DMD algorithm.
Efficient N-Dimensional Convolutions via Higher-Order Factorization

With the unprecedented success of deep convolutional neural networks came the quest for training always deeper networks. However, while deeper neural networks give better performance when trained appropriately, that depth also translates in memory and computation heavy models, typically with tens of millions of parameters. Several methods have been proposed to leverage redundancies in the network to alleviate this complexity. Either a pretrained network is compressed, e.g. using a low-rank tensor decomposition, or the architecture of the network is directly modified to be more effective. In this paper, we study both approaches in a unified framework, under the lens of tensor decompositions. We show how tensor decomposition applied to the convolutional kernel relates to efficient architectures such as MobileNet. Moreover, we propose a tensor-based method for efficient higher order convolutions, which can be used as a plugin replacement for N-dimensional convolutions. We demonstrate their advantageous properties both theoretically and empirically for image classification, for both 2D and 3D convolutional networks.
Understanding Behavior of Clinical Models under Domain Shifts

The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare. Deep learning, in particular, has been a game changer in building predictive models, thus leading to community-wide data curation efforts. However, due to inherent variabilities in population characteristics and biological systems, these models are often biased to the training datasets. This can be limiting when models are deployed in new environments, when there are systematic domain shifts not known a priori. In this paper, we propose to emulate a large class of domain shifts, that can occur in clinical settings, with a given dataset, and argue that evaluating the behavior of predictive models in light of those shifts is an effective way to quantify their reliability. More specifically, we develop an approach for building realistic scenarios, based on analysis of \textit{disease landscapes} in multi-label classification. Using the openly available MIMIC-III EHR dataset for phenotyping, for the first time, our work sheds light into data regimes where deep clinical models can fail to generalize. This work emphasizes the need for novel validation mechanisms driven by real-world domain shifts in AI for healthcare.
Importance of Deck Details in Bridge Aerodynamics

This paper focuses on the effects of the deck equipment – such as median dividers, edge safety barriers or parapets – on the aerodynamic response of long-span suspension and cable-stayed bridges. The importance of modelling such members in the analysis of bridge aerodynamics is demonstrated using numerical simulations both with and without barriers. The numerical technique is applied to two famous long-span bridge decks (the Normandy cable-stayed bridge and the Great Belt suspension bridge), presented by different cross section geometries and various aerodynamic characteristics. The obtained results are compared with each other and with wind-tunnel data. In particular, the numerical modelling of the barriers provides a closer insight into the mechanisms responsible for the vortex-induced oscillations of the Great Belt East bridge in 1998.
Testing the gravitational redshift with Galileo satellites

We present the results of the analysis of the GREAT (Galileo gravitational Redshift test with Eccentric sATellites) experiment from SYRTE (Observatoire de Paris), funded by the European Space Agency. An elliptic orbit induces a periodic modulation of the fractional frequency difference between a ground clock and the satellite clock, while the good stability of Galileo clocks allows to test this periodic modulation to a high level of accuracy. Galileo satellites GSAT0201 and GSAT0202, with their large eccentricity and on-board H-maser clocks, are perfect candidates to perform this test. By analyzing 1008 days of eccentric Galileo satellites data we measure the fractional deviation of the gravitational redshift from the prediction by general relativity to be $(+0.19 \pm 2.48)\times10^{-5}$ at 1 sigma, improving the best previous test by Gravity Probe A by a factor~5.6. Moreover, we apply the exact same analysis to two almost circular Galileo satellites, in order to show the robustness of the method. By analyzing 899 days of circular Galileo satellites data we measure the fractional deviation of the gravitational redshift from the prediction by general relativity to be $(+0.29 \pm 2.00)\times10^{-2}$ at 1 sigma.
Shareholder Activism with Weak Corporate Governance: Social Pressure, Private Cost and Organized Crime

Prior research provides mixed results on the ability of American shareholder activists to improve managerial behavior. In Japan, the means of external control (e.g., takeover, litigation) are not as effective as they are in the U.S. Challenging management during annual meetings may be the only option left for disgruntled shareholders. Yet, the situation is complicated by the existence of corporate racketeers who disrupt meetings to blackmail managers. In response, authorities have encouraged companies to hold their annual meetings on the same day to spread the racketeers’ thin over simultaneous events. However, this policy has a similar effect on legitimate activists. Our empirical results indicate that, contrary to governmental expectations, shareholder activism leads to improvement in a company’s corporate governance, informational environment and profitability. It also enables firms to attract foreign shareholders. In addition, firms that collude to have their meetings on the same day have lower profitability and worse governance. This provides evidence of the positive role activists may play and suggests the authorities should reverse their policy of discouraging shareholder activism.
AN EMPIRICAL DETERMINATION OF THE INTERGALACTIC BACKGROUND LIGHT FROM UV TO FIR WAVELENGTHS USING FIR DEEP GALAXY SURVEYS AND THE GAMMA-RAY OPACITY OF THE UNIVERSE

We have previously calculated the intergalactic background light (IBL) as a function of redshift from the Lyman limit in the far-ultraviolet to a wavelength of 5 μm in the near-infrared range, based purely on data from deep galaxy surveys. Here, we use similar methods to determine the mid- and far-infrared IBL from 5 to 850 μm. Our approach enables us to constrain the range of photon densities by determining the uncertainties in observationally determined luminosity densities and spectral gradients. By also including the effect of the 2.7 K cosmic background photons, we determine upper and lower limits on the opacity of the universe to γ-rays up to PeV energies within a 68% confidence band. Our direct results on the IBL are consistent with those from complimentary γ-ray analyses using observations from the Fermi γ-ray space telescope and the H.E. S.S. air Čerenkov telescope. Thus, we find no evidence of previously suggested processes for the modification of γ-ray spectra other than that of absorption by pair production alone.