Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic field amplification and structure formation by 
the Rayleigh-Taylor instability

We report on results of high resolution two fluid non-linear simulations of the Rayleigh Taylor Instability (RTI) at the interface between a solar prominence and the corona. These follow results reported earlier by Popescu Braileanu et al. (2021a,b) on linear and early non-linear RTI dynamics in this environment. The simulations use a two fluid model that includes collisions between neutrals and charges, including ionization/recombination, energy and momentum transfer, and frictional heating. High resolution 2.5D magnetized RTI simulations with the magnetic field dominantly normal to and slightly sheared with respect to the prominence plane demonstrate that in a fully developed state of RTI a large fraction of the gravitational energy of a prominence thread can be converted into quasi-turbulent energy of the magnetic field. RTI magnetic energy generation is further accompanied by magnetic and plasma density structure formation, including dynamic formation, break-up, and merging of current sheets and plasmoid sub-structures. The simulations show the role of flow decoupling and ionization/recombination reactions between the neutrals and charges on the structure formation in magnetized RTI. We provide a careful examination of sources and form of numerical dissipation of the evolving magnetic field structures.
Short‐term traffic forecasting: Overview of objectives and methods

In the last two decades, the growing need for short‐term prediction of traffic parameters embedded in a real‐time intelligent transportation systems environment has led to the development of a vast number of forecasting algorithms. Despite this, there is still not a clear view about the various requirements involved in modelling. This field of research was examined by disaggregating the process of developing short‐term traffic forecasting algorithms into three essential clusters: the determination of the scope, the conceptual process of specifying the output and the process of modelling, which includes several decisions concerning the selection of the proper methodological approach, the type of input and output data used, and the quality of the data. A critical discussion clarifies several interactions between the above and results in a logical flow that can be used as a framework for developing short‐term traffic forecasting models.
Double forms: Regular elliptic bilaplacian operators

Double forms are sections of the vector bundles ΛkT ∗M⊗ΛmT ∗M, where in this work (M, g) is a compact Riemannian manifold with boundary. We study graded second-order differential operators on double forms, which are used in physical applications. A Combination of these operators yields a fourth-order operator, which we call a double bilaplacian. We establish the regular ellipticity of the double bilaplacian for several sets of boundary conditions. Under additional conditions, we obtain a Hodge-like decomposition for double forms, whose components are images of the second-order operators, along with a biharmonic element. This analysis lays foundations for resolving several topics in incompatible elasticity, most prominently the existence of stress potentials and Saint-Venant compatibility.
A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this field to facilitate researchers and practitioners working on recommender systems. Specifically, based on the data usage during recommendation modeling, we divide the work into collaborative filtering and information-rich recommendation: 1) collaborative filtering, which leverages the key source of user-item interaction data; 2) content enriched recommendation, which additionally utilizes the side information associated with users and items, like user profile and item knowledge graph; and 3) temporal/sequential recommendation, which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative work for each type, we finally discuss some promising directions in this field.
Efficient Preconditioners for Interior Point Methods via a New Schur Complement-Based Strategy

We propose a novel preconditioned inexact primal-dual interior point method for constrained convex quadratic programming problems. The algorithm we describe invokes the preconditioned conjugate gradient method on a new reduced Schur complement KKT system, in implicit form. In contrast to standard approaches, the Schur complement formulation we consider enables reuse of the factorization of the KKT matrix with rows and columns corresponding to inequality constraints excluded, across all interior point iterations. Further, two new preconditioners are presented for the resulting reduced system, that alleviate the ill-conditioning associated with slack variables in primal-dual interior point methods. Each of the preconditioners we propose also provably reduces the number of unique eigenvalues for the coefficient matrix, and thus the CG iteration count. One preconditioner is efficient when the number of equality constraints is small, while the other is efficient when the number of remaining degrees of freedom is small. Numerical experiments with synthetic problems and problems from the Maros-M\'esz\'aros QP collection show that our preconditioned inexact interior point solvers are effective at improving conditioning and reducing cost. Across all test problems for which the direct method is not fastest, our preconditioned methods achieve a reduction in cost by a geometric mean of $1.432$ relative to the best alternative preconditioned method for each problem.
BALanCe: Deep Bayesian Active Learning via Equivalence Class Annealing

Active learning has demonstrated data eﬃciency in many ﬁelds. Existing active learning algorithms, especially in the context of deep Bayesian active models, rely heavily on the quality of uncertainty estimations of the model. However, such uncertainty estimates could be heavily biased, especially with limited and imbalanced training data. In this paper, we propose BALanCe , a deep Bayesian active learning framework that mitigates the eﬀect of such biases. Concretely, BALanCe employs a novel acquisition function which leverages the structure captured by equivalence hypothesis classes and facilitates diﬀerentiation among diﬀerent equivalence classes. Intuitively, each equivalence class consists of instantiations of deep models with similar predictions, and BALanCe adaptively adjusts the size of the equivalence classes as learning progresses. Besides the fully sequential setting, we further propose Batch-BALanCe —a generalization of the sequential algorithm to the batched setting—to eﬃciently select batches of training examples that are jointly eﬀective for model improvement. We show that Batch-BALanCe achieves state-of-the-art performance on several benchmark datasets for active learning, and that both algorithms can eﬀectively handle realistic challenges that often involve multi-class and imbalanced data.
Comparing time varying regression quantiles under shift invariance

This article investigates whether time-varying quantile regression curves are the same up to the horizontal shift or not. The errors and the covariates involved in the regression model are allowed to be locally stationary. We formalize this issue in a corresponding non-parametric hypothesis testing problem, and develop an integrated-squared-norm based test (SIT) as well as a simultaneous confidence band (SCB) approach. The asymptotic properties of SIT and SCB under null and local alternatives are derived. Moreover, the asymptotic properties of these tests are also studied when the compared data sets are dependent. We then propose valid wild bootstrap algorithms to implement SIT and SCB. Furthermore, the usefulness of the proposed methodology is illustrated via analysing simulated and real data related to COVID-19 outbreak and climate science. bootstrap, comparison of curves, confidence band, hypothesis testing, locally stationary process, nonparametric quantile regression, COVID-19. 1 ar X iv :2 01 1. 06 33 3v 2 [ st at . M E ] 2 4 D ec 2 02 1
Parasitic losses in modular permanent-magnet generators

A modular arrangement facilitates the construction of multipole, permanent-magnet, direct-coupled generators for large grid-connected wind turbines. However it introduces new forms of loss into the stator and rotor structures due to the periodic spatial variation in the stator back iron permeance. A mathematical model of the modular machine is described in the paper and its predictions are shown to be in agreement with measured losses.
Characterization of Randomly Time-Variant Linear Channels

This paper is concerned with various aspects of the characterization of randomly time-variant linear channels. At the outset it is demonstrated that time-varying linear channels (or filters) may be characterized in an interesting symmetrical manner in time and frequency variables by arranging system functions in (timefrequency) dual pairs. Following this a statistical characterization of randomly time-variant linear channels is carried out in terms of correlation functions for the various system functions. These results are specialized by considering three classes of practically interesting channels. These are the wide-sense stationary (WSS) channel, the uncorrelated scattering (US) channel, and the wide-sense stationary uncorrelated scattering (WSSUS) channel. The WSS and US channels are shown to be (time-frequency) duals. Previous discussions of channel correlation functions and their relationships have dealt exclusively with the WSSUS channel. The point of view presented here of dealing with the dually related system functions and starting with the unrestricted linear channels is considerably more general and places in proper perspective previous results on the WSSUS channel. Some attention is given to the problem of characterizing radio channels. A model called the Quasi-WSSUS channel is presented to model the behavior of such channels. All real-life channels and signals have an essentially finite number of degrees of freedom due to restrictions on time duration and bandwidth. This fact may be used to derive useful canonical channel models with the aid of sampling theorems and power series expansions. Several new canonical channel models are derived in this paper, some of which are dual to those of Kailath.
A vacuum packaged differential resonant accelerometer using gap sensitive electrostatic stiffness changing effect

This paper proposes an INS (Inertial Navigation System) grade, surface micro-machined differential resonant accelerometer (DRXL) by using the epitaxially grown thick polysilicon process. This proposed DRXL device produces a differential digital output upon an applied acceleration, and the principle is a gap-dependent electrical stiffness variation of the electrostatic resonator with torsion beam structures. Using this new operating concept, we designed, fabricated and tested the proposed device. The final device was fabricated by using the wafer level vacuum packaging process. The hermetic sealing cap structure was made of Pyrex 7740 glass with Ti layer as gettering material, and this cap wafer was anodically bonded with the polysilicon wafer at vacuum ambience. The measured Q-factor of the vacuum packaged DRXL was about 1/spl times/10/sup 3/ and the estimated inner pressure was about 200[mTorr]. We also achieved 73[Hz] output frequency change per unit G(9.8 m/s/sup c/) input with 12,716[Hz] nominal resonant frequency.
Integration of passive components for microwave filters in MCM-D

The increasing demand for high density packaging technologies and the evolution to mixed digital and analogue devices has been the on-set of increasing research in microwave thin film multi-layer technologies such as the MCM-D technology. In this technology passive components may be integrated in the substrate and combined to realise more complex microwave functions. In this paper a basic comparison and a process parameter sensitivity analysis is made for the transmission line characteristics of interconnections in single layer MIC technology and in thin film MCM technology. For reasons of better accessibility, enhanced phase velocity and reduced process tolerance susceptibility, coplanar lines on MCM show to be advantageous. However, the multi-layered nature offers alternative possibilities to implement passive components. In this way a coplanar floating patch capacitor is introduced. Other passive components, such as thin film NiCr resistors, high quality spiral and line inductors and other types of capacitors are discussed. These passives have been combined to realise integrated passive filter functions, of which an example is shown.
Towards medical assistant diagnosis using deep learning approach

In the face of massive electronic medical record data, discovering the relationship between symptoms and diagnosis can help doctors improve the efficiency of clinical diagnosis and treatment. An end-to-end method based on deep learning for disease classification is proposed. In semantic level, word embedding and Bert are used. In order to get the dependency relationship in the international classification of diseases (ICD), the tree long short-term memory (LSTM) is adopted. An adaptive weighted loss function is designed to solve the imbalance problem of sample classification. Finally, we propose a classifier based on attention mechanism to get the prediction results. The experimental tests demonstrate the effectiveness of the method.
Feedback and Engagement on an Introductory Programming Module

We ran a study on engagement and achievement for a first year undergraduate programming module which used an online learning environment containing tasks which generate automated feedback. Students could also access human feedback from traditional labs. We gathered quantitative data on engagement and achievement which allowed us to split the cohort into 6 groups. We then ran interviews with students after the end of the module to produce qualitative data on perceptions of what feedback is, how useful it is, the uses made of it, and how it bears on engagement. A general finding was that human and automated feedback are different but complementary. However there are different feedback needs by group. Our findings imply: (1) that a blended human-automated feedback approach improves engagement; and (2) that this approach needs to be differentiated according to type of student. We give implications for the design of feedback for programming modules.
Improving Prediction Accuracy of Covid-19 Data Analysis

Since early 2020 Covid-19 has casted great shadow to everyone worldwide. Due to large number of infected and death cases, accurate predictions can help the government and medical divisions in pandemic preparedness and prevention. In this paper, we used the Prophet machine learning model to analyze the historical data of the past year to predict the future trend of confirmed, death, recovered total cases. To improve the prediction accuracy, we optimized the data by detecting “turning points” of confirmed cases and then redo predictions. From the result comparison of predictions using original data vs. optimized data it is shown that our optimization improves the prediction accuracy significantly.
Mapping the Landscape of Technology Attributes Research: A Bibliometric Analysis of SSCI Metadata

In the era of big data, technology iteration is accelerating and technology complexity is increasing. In this context, firms could not keep up with the pace of technological progress only by internal research and development. Thus, external technology acquisition has become a powerful source to enhance their technology capabilities. As the primary considerations of external technology acquisition, technology attributes are mostly investigated in the existing literature, which is scattered in the related research, lacking systematicness and not forming a complete analysis framework. Therefore this paper aims to map the landscape of technology attributes and present a systematic framework, so as to provide a theoretical reference for newcomers in this field and help them understand it comprehensively and quickly. SSCI metadata was analyzed based on the bibliometrics method. Results show that: (1) technology attributes research is in the stage of explosive growth with an average annual growth rate of 8.21%; (2) USA and China have a deep collaboration in this field; (3) the themes in this field contain determinants, performance, innovation, perceptions, information-technology, user acceptance, etc., which could be incorporated in a cognitive-temporal-spatial dimensional framework.
A Hypergraph-based Method for Pharmaceutical Data Similarity Retrieval

Drug and compound similarity retrieval is very important for new drug research and development. Most pharmaceutical database provide keyword searching service. Because keyword search method cannot identify entity semantic similarity information, so the retrieval results often got poor drug or compound semantic similarity. In this paper, we propose an attribute semantic similarity retrieval method for pharmaceutical data based on hypergraph and natural language processing technology. Firstly, we use natural language processing technology to research and construct the drug attribute semantic similarity network. Then, we continue building a hypergraph based on drug attribute to get better retrieval efficiency. The experimental results show that, our method can provide similarity retrieval service for researchers.
A new model for the description of gate voltage and temperature dependence of gate induced drain leakage (GIDL) in the low electric field region [DRAMs]

Gate induced drain leakage (GIDL) is frequently described by band-to-band tunneling. This mechanism is insensitive to temperature and occurs only under strong electric fields. Under the condition of small electric fields, however, GIDL exhibits a strong dependence on temperature, which is due to trap-assisted generation of electron hole pairs. This generation mechanism is based on the Shockley-Read-Hall (SRH) equation involving field dependent emission probabilities due to Fowler-Nordheim (FN) and Poole-Frenkel effect. The proposed model of an acceptor-like interface trap is able to reproduce the experimental results. Temperature and voltage dependencies for a p-MOSFET are correctly calculated for one single fitting parameter, i.e., the interface trap density corresponding to N/sub t/=1/spl times/10/sup 14/(1/eV m/sup 2/).
Robustness of vibration suppression feedback loop for speed control system

A vibration suppression feedback loop for a speed control system with motor, gear and inertia load is proposed. Robustness of the motor is assured by a motor disturbance rejection control which also dynamically decouples the motor and the load. In this way the design of the feedback loop is much simplified. The parameters introduced into the proposed feedback loop have a clear physical meaning and enable a tuning of the load damping and eigen frequency. In the present paper the authors deal with the system's robustness against the load torque change and against the load parameters variation. Higher degree of robustness than in the case of a PI control without a vibration suppression feedback loop is confirmed. <<ETX>>
Standardization of Quality of Diagnoses, Interventions, and Outcomes (Q-DIO) Measurement Instrument for Use in Slovenia

Abstract Purpose To describe the cross-cultural adaptation of the Quality of Diagnoses, Interventions and Outcomes (Q-DIO) Instrument into the Slovene language. Methods Based on general international guidelines, a six-step process of localization to translate and adjust the instrument from English into the Slovene language was used. Content validity was quantified based on an agreement of eight experts. The instrument was tested using a sample of 140 nursing documentations from two Slovenian tertiary hospitals. Results 26 of 29 items showed an excellent content validity index ranging from 0.857 to 1.000, and a modified kappa index above 0.856. The content validity indexes of the three remaining items adjusted based on experts’ comments were subsequently estimated at 1.000. Construct validity was significantly different between the two groups of documentations. The Cronbach coefficient for the whole questionnaire was 0.860. Cronbach’s alpha if item deleted reamins above 0.80 for all items. The criteria for the difficulty grades of items and discrimination validity were acceptably met for more than 75% of items. Conclusion Based on the results of the study, it may be concluded that Q-DIO is a reliable instrument for measuring the quality of nursing documentation. The deviations in the results of some items are due to poor nursing documentation quality, and indicate that nursing classifications have not yet been fully implemented into practice in the study setting. Additional testing of the instrument is recommended.
Influence of dietary phytase and protease, individually or in combination, on growth performance, intestinal morphology, microbiota composition and nutrient utilisation in broiler chickens fed sesame meal-based diets

Abstract Efficacy of phytase and protease supplements, individually or in combination, on growth performance, carcass, intestinal morphology, microbiota composition and nutrient digestibility was investigated in broilers fed diets containing sesame meal. A total of 375 day-old male broiler chickens of ROSS 308 strain were randomly allocated into five dietary treatments with five replicates per treatment and 15 broilers per each. Dietary treatments were T1: a basal diet (control), T2: sesame meal diet (150 g/kg sesame meal to replace soybean meal), T3: T2 + 500 FTU/kg phytase, T4: T2 + 0.2 g/kg protease and T5: T2 + combination of phytase and protease supplements. At the end of the study, body weight gain of the T5 broilers was greater than those of the T1 birds (p<.05). Feed conversion ratio improved in broiler chickens fed the T3, T4 and T5 diets compared with T1 diet (p<.05). An increase in villus length (VL) was found in broilers which received T3, T4 and T5 diets compared with control (p<.05). The greatest value of crypt depth (CD), and villus surface area observed in the birds fed with T4 diet compared with control (p<.05). Inclusion of phytase in broiler diet increased VL/CD ratio compared with control group (p<.05). Combination of phytase and protease (T5) increased the caecal Lactobacillus and decreased E. coli counts compared with T1, T2 and T4, while T3 group was intermediate (p<.05). Ileal nutrients digestibility was greatest in broilers which received T5 diet (p<.05). In conclusion, addition of protease and phytase to sesame meal diets may be a suitable strategy for improving growth performance, intestinal morphology, microbiota composition and nutrient utilisation in broiler chickens. Highlights Combination of phytase and protease enzymes in sesame meal diets improved growth performance, nutrient utilisation and microbiota activity of broiler chickens. Supplemental phytase or protease improved jejunal morphology of broiler chickens fed sesame meal diets.