Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning for Architecture Search by Network Transformation

Deep neural networks have shown effectiveness in many challenging tasks and proved their strong capability in automatically learning good feature representation from raw input. Nonetheless, designing their architectures still requires much human effort. Techniques for automatically designing neural network architectures such as reinforcement learning based approaches recently show promising results in benchmarks. However, these methods still train each network from scratch during exploring the architecture space, which results in extremely high computational cost. In this paper, we propose a novel reinforcement learning framework for automatic architecture designing, where the action is to grow the network depth or layer width based on the current network architecture with function preserved. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. The experiments on image benchmark datasets have demonstrated the efficiency and effectiveness of our proposed solution compared to existing automatic architecture designing methods.
Characterizing seasonal groundwater storage in alpine catchments using time‐lapse gravimetry, water stable isotopes and water balance methods

Alpine areas play a major role in water supply in downstream valleys by releasing water during warm and dry periods. However, the hydrogeology of alpine catchments, which are particularly exposed to the effects of climate change, is currently not well understood. Increasing our knowledge of alpine hydrogeological processes is thus of considerable importance for any forward‐looking hydrological investigations in alpine areas. The objectives of this study are to quantify seasonal groundwater storage variations in a small Swiss alpine catchment and to evaluate the capabilities of time‐lapse gravimetry in the identification of zones of high groundwater storage fluctuations. Time‐lapse gravimetric measurements enable rapid localisation of zones of dynamic groundwater storage changes and help to highlight aquifers with a higher storage decrease. Temperature sensors enable measurement of the temporal trend in stream and spring drying in the post‐snowmelt period. Stable isotope measurements allow us to identify the origin of surface water exiting the catchment. The results improve our comprehension of a conceptual schema highlighting two different hydrogeological systems: (a) a shallow, rapidly depleted one fed directly by snowmelt and (b) a deeper one, with a slower recession, fed by main recharge during peak snowmelt and emerging at the lower part of the catchment below the talus and moraine of the catchment where bedrock is exposed. These dynamics confirm the high variability of storage in the talus and moraine aquifers and highlight the dominant role of Quaternary deposits and their connectivity to store water over seasonal and multi‐year time‐scales. The mechanisms explaining the importance of Quaternary deposits are the combination of moraine and talus with different permeabilities allowing the storage of sufficient quantities of water permitting continuous release during drier periods of the year.
Quasi-stationary modeling of the DBD plasma flow control around airfoil

This paper presents the results of numerical simulation of a dielectric barrier discharge (DBD) plasma actuator and shows its effectiveness to control air flow around the NACA(National Advisory Committee for Aeronautics)0015 airfoil. The actuator consists of two tape electrodes separated by a dielectric layer, and it is mounted on the suction side of the airfoil at 18% of the chord length. An alternating voltage with 20 kV magnitude and 10 kHz frequency is applied between both electrodes. The physical model of the DBD includes the drift of two ionic species, positive and negative, and the Poisson equation for the electric potential distribution. The spatio-temporal distribution of the electric field, the space charge density in the ambient air, and the surface charge density on the dielectric layer have been determined. The time average electric body force was entered into the air flow model, which was solved using the Spalart–Allmaras turbulence technique. The simulation of the air flow was performed for the free-stream velocities between 5 m/s and 20 m/s (Reynolds numbers 1.65 × 105–6.61 × 105 based on the chord length). The results of computations show the effect of the electrohydrodynamic actuation on the flow pattern, the lift and drag coefficients, the pressure coefficient, and the flow fluctuation near the airfoil. The ability of the DBD actuation to effectively control the aerodynamic airfoil characteristics has been confirmed, and its limitations for the discussed case have been determined.
IM-GSO: A Community Directed Group Search Optimization Approach for Influence Maximization

Abstract Influence maximization is a fundamental problem in the study of complex relationship networks, such as viral marketing in business application areas. It is directed towards extracting a minimal (or k-sized) subset of most influential nodes with largest cascading effect across the network as per seeding budget. The problem is categorized as NP hard and hence greedy/heuristic techniques are extensively studied in the literature for generating reasonably acceptable solutions. This article proposes a novel nature based heuristic optimization algorithm IM-GSO to dynamically evolve near to optimal K-sized influential seed nodes for varied structural real world networks. IM-GSO smartly incorporates hidden structural patterns like communities, node degrees, betweenness and similarities for efficient candidate population generation. This smartly initialized population is then evolved using a discrete adaption of Group Search Optimization (GSO) algorithm. Correctness of IM-GSO is verified by optimizing two prominent spread estimation functions SIMPATH and MAGA, on varied sized (small/medium/large) networks. Detailed experimental evaluations by execution of 10,000 Monte Carlo simulations under Information Cascade (IC) model indicates a significantly high influence spread for IM-GSO seeds in contrast to standard heuristics techniques.
Whole-Body Balance Control of a Humanoid Robot in Real Time Based on ZMP Stability Regions Approach

Abstract One of the most important and complex tasks for a humanoid robot is to avoid overturning during a bipedal gait. This work aims at setting the base for designing a general balance controller to be used with any humanoid robot. In addition, it is based on a strong simplification of humanoid model which attempts to be used in real-time applications. In particular, several “stability zones” are defined as function of the criticalness of balance. The results are presented in simulation and experimentally, using the humanoid platforms HOAP-3 and TEO.
Sentiment Analysis of Short Texts from Social Networks Using Sentiment Dictionaries and Blending of Machine Learning Algorithms

This paper focuses on the field of sentiment analysis of natural language texts. To be precise, short texts extracted from social networks are analyzed. At present two groups of applied methods can be distinguished in this field: machine learning methods and methods based on sentiment lexicons. The paper reviews the principal methods in this field and proposes an approach for short text sentiment analysis, combining sentiment lexicons and blending of machine learning algorithms for the problem of three-class text classification. Various formulas for determining weights of the words in vector representation of texts are considered as well. This approach is applied to a dataset that consists of 10,000 manually marked posts extracted from VKontakte social network. Class distribution of dataset objects for the studied area often tends to be unbalanced. Standard models show moderate quality but only due to the fact that the majority of data points are classified as belonging to the dominant class. In this work an approach that shows good metric scores for all the three classes is described.
Text Normalization and Spelling Correction In Kazakh Language

Text normalization is significant step in preprocessing of informal, social media and short texts in the Natural Language Processing (NLP) tasks. Researches in the field are mostly on English, but not on the agglutinative languages such as Kazakh, Korean, Japanese, which are determined as morphologically rich languages, and complex compared to English. In this paper, we present text normalization and auto correction of words for Kazakh language, we convert informal text into grammatically correct form. To do the auto correction task, firstly we countered keyboard error while typing words, then choose the best match from them. Additionally, we categorized words to several groups and separated text into modules of words. The exact match score of the overall system on the provided datasets are 85.40 per cent.
Random Walk Based Sampling for Load Balancing in Multi-Server Systems

In multi-server systems, a classical job assignment algorithm works as follows: at the arrival of each job, pick d servers independently and uniformly at random and send the job to the least loaded server among the d servers. This model is known as the power-of-d choices algorithm. In this paper, we analyze a variant of this algorithm, where d servers are sampled through d independent non-backtracking random walks on a k-regular graph. The random walkers are periodically reset to independent uniform random positions. Under some assumptions on the underlying graph, we show that the system dynamics under this new algorithm converges to the solution of a deterministic ordinary differential equation (ODE), which is the same ODE as the classical power-of-d choices. We also show that the new algorithm stablizes the system, and the stationary distribution of the system converges to the stationary solution of the ODE. The new scheme can be considered as a derandomized version of power-of-d choices as it reduces the use of randomness while maintaining the performance of power-of-d choices.
Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.
IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude Toward Risk

This paper develops a new Instrumental Variables estimator for spatial, dynamic panels with interactive effects under large N and T asymptotics. For this class of models, most approaches available in the literature are based on quasi-maximum likelihood estimation. The approach put forward here is appealing from both a theoretical and a practical point of view for a number of reasons. Firstly, it is linear in the parameters of interest and computationally inexpensive. Secondly, the IV estimator is free from asymptotic bias. Thirdly, the approach can accommodate endogenous regressors, so long as external instruments are available. The IV estimator is consistent and asymptotically normal as N, T → ∞, such that N/T → c, where 0 < c < ∞. We study the determinants of risk attitude of banking institutions. The results show that the capital regulation introduced by the Dodd-Frank Act has succeeded in influencing banks’ behaviour.
Foreword to the Issue on Remote Sensing of Regional Land Use and Land Cover

This issue showcases some good work from Chinese authors who attended the International Workshop of Earth Observation and Remote Sensing Applications (EORSA), held in Beijing, China, June 30-July 2, 2008. The five articles in this issue focus on remote sensing of regional land use and land cover.
Precise Dissolution Control and Bioavailability Evaluation for Insoluble Drug Berberine via a Polymeric Particle Prepared Using Supercritical CO2

It is still controversial whether poor aqueous solubility is the most primary reason for the low oral bioavailability of insoluble drugs. Therefore, in this study, berberine-loaded solid polymeric particles (BPs) of varied dissolution profiles with β-cyclodextrin (β-CD) as carrier were fabricated using solution-enhanced dispersion by supercritical fluids (SEDS), and the relationship between dissolution and berberine (BBR) bioavailability was evaluated. Dissolution property was controlled via particle morphology manipulation, which was achieved by adjusting several key operating parameters during the SEDS process. Characterization on BP using infrared spectroscopy, differential scanning calorimetry, and X-ray diffraction indicated that BBR was dispersed in amorphous form, while nuclear magnetic resonance spectroscopy showed that methoxy groups of BBR were included into the cavities of β-CD. In vivo pharmacokinetic studies showed that oral bioavailability increased by about 54% and 86% when the dissolution rate of BBR was increased by 51% and 83%, respectively. The entry speed of BBR into the bloodstream was also advanced with the degree of dissolution enhancement. It seemed that dissolution enhancement gave positive effect to the oral bioavailability of berberine, but this might not be the crucial point. Meanwhile, supercritical CO2 technology is a promising method for pharmaceutical research due to its advantages in regulating drug-dosage properties.
Editing fluid simulations with jet particles

Fluid simulation is an important topic in computer graphics in the pursuit of adding realism to films, video games and virtual environments. The results of a fluid simulation are hard to edit in a way that provide a physically plausible solution. Edits need to preserve the incompressibility condition in order to create natural looking water and smoke simulations. In this thesis we present an approach that allows a simple artist-friendly interface for designing and editing complex fluid-like flows that are guaranteed to be incompressible in two and three dimensions. Key to our method is a formulation for the design of flows using jet particles. Jet particles are Lagrangian solutions to a regularised form of Euler’s equations, and their velocity fields are divergence-free which motivates their use in computer graphics. We constrain their dynamics to design divergence-free flows and utilise them effectively in a modern visual effects pipeline. Using just a handful of jet particles we produce visually convincing flows that implicitly satisfy the incompressibility condition. We demonstrate an interactive tool in two dimensions for designing a range of divergence-free deformations. Further we describe methods to couple these flows with existing simulations in order to give the artist creative control beyond the initial outcome. We present examples of local temporal edits to smoke simulations in 2D and 3D. The resulting methods provide promising new ways to design and edit fluid-like deformations and to create general deformations in 3D modelling. We show how to represent existing divergence-free velocity fields using jet particles, and design new vector fields for use in fluid control applications. Finally we provide an efficient implementation for deforming grids, meshes, volumes, level sets, vectors and tensors, given a jet particle flow.
In Situ Growth of a High-Performance All-Solid-State Electrode for Flexible Supercapacitors Based on a PANI/CNT/EVA Composite

For the development of light, flexible, and wearable electronic devices, it is crucial to develop energy storage components combining high capacity and flexibility. Herein, an all-solid-state supercapacitor is prepared through an in situ growth method. The electrode contains polyaniline deposited on a carbon nanotube and a poly (ethylene-co-vinyl acetate) film. The hybrid electrode exhibits excellent mechanical and electrochemical performance. The optimized few-layer polyaniline wrapping layer provides a conductive network that effectively enhances the cycling stability, as 66.4% of the starting capacitance is maintained after 3000 charge/discharge cycles. Furthermore, the polyaniline (PANI)-50 displays the highest areal energy density of 83.6 mWh·cm−2, with an areal power density of 1000 mW·cm−2, and a high areal capacity of 620 mF cm−2. The assembled device delivers a high areal capacity (192.3 mF·cm−2) at the current density of 0.1 mA·cm−2, a high areal energy (26.7 mWh·cm−2) at the power density of 100 mW·cm−2, and shows no significant decrease in the performance with a bending angle of 180°. This unique flexible supercapacitor thus exhibits great potential for wearable electronics.
Surface-Induced Nanostructures and Phase Diagrams of ABC Linear Triblock Copolymers under Spherical Confinement: A Self-Consistent Field Theory Simulation

We investigate the nanostructures and phase diagrams of ABC linear triblock copolymers confined in spherical cavities by using real-space self-consistent field theory. Various 3D morphologies, such as spherical concentric lamellae, dumbbell-like cylinder, and rotational structures, are identified in the phase diagrams, which are constructed on the basis of the diameters of spherical cavities and the interaction between the polymers and preferential surfaces. We designate specific monomer-monomer interactions and block compositions, with which the polymers spontaneously form a cylindrical morphology in bulk, and firstly study morphology transformation with a neutral surface when a confining radius progressively increases. We then focus on phase morphologies under the preferential surfaces and consolidate them into phase diagrams. The spherical radius and the degree of preferential interactions can obviously induce the formation of a cylindrical morphology. Theoretical results correspond to an amount of recent experimental observations to a high degree and contribute to synthesising functional materials.
The High Density Polyethylene Composite with Recycled Radiation Cross-Linked Filler of rHDPEx

This article discusses the possibilities of using radiation cross-linked high density polyethylene (HDPEx) acting as a filler in the original high density polyethylene (HDPE) matrix. The newly created composite is one of the possible answers to questions relating to the processing of radiation cross-linked thermoplastics. Radiation cross-linked networking is—nowadays, a commonly used technology that can significantly modify the properties of many types of thermoplastics. This paper describes the influence of the concentration of filler, in the form of grit or powder obtained by the grinding/milling of products/industrial waste from radiation cross-linked high density polyethylene (rHDPEx) on the mechanical and processing properties and the composite structure. It was determined that, by varying the concentration of the filler, it is possible to influence the mechanical behaviour of the composite. The mechanical properties of the new composite—measured at room temperature, are generally comparable or better than the same properties of the original thermoplastic. This creates very good assumptions for the effective and economically acceptable, processing of high density polyethylene (rHDPEx) waste. Its processability however, is limited; it can be processed by injection moulding up to 60 wt %.
Inclusive Web Empirical Studies in Remote and In-Situ Settings: A User Evaluation of the RemoTest Platform

ABSTRACT Web accessibility evaluation requires tests to be carried out with real users with disabilities performing real tasks or activities. To recruit an appropriate group of users and to observe their performance in the real world is difficult. For this reason we have developed RemoTest, a platform that assists researchers designing experiments, conducting remote and in-situ experimental sessions and analyzing the data gathered while the users are accessing the Web. Although this tool is oriented to experimenters, it is necessary to check whether the evaluation environments created by RemoTest are accessible or not to the users that participate in the tests. To this end, we conducted formal in-situ evaluations with 36 users with diverse characteristics. For this assessment, the participants were asked to install the platform, to fill in some automatically created questionnaires and to carry out several web navigation tasks. From the data gathered we analyzed the ease of the installation process, the accessibility of the automatically generated questionnaires, and user satisfaction. The results revealed the suitability of the platform for conducting inclusive experiments both in remote and in-situ contexts and provided guidelines on how the experiments should be set out.