Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Architecture for Automatic Essay Scoring

Automatic evaluation of essay (AES) and also called automatic essay scoring has become a severe problem due to the rise of online learning and evaluation platforms such as Coursera, Udemy, Khan academy, and so on. Researchers have recently proposed many techniques for automatic evaluation. However, many of these techniques use hand-crafted features and thus are limited from the feature representation point of view. Deep learning has emerged as a new paradigm in machine learning which can exploit the vast data and identify the features useful for essay evaluation. To this end, we propose a novel architecture based on recurrent networks (RNN) and convolution neural network (CNN). In the proposed architecture, the multichannel convolutional layer learns and captures the contextual features of the word n-gram from the word embedding vectors and the essential semantic concepts to form the feature vector at essay level using max-pooling operation. A variant of RNN called Bi-gated recurrent unit (BGRU) is used to access both previous and subsequent contextual representations. The experiment was carried out on eight data sets available on Kaggle for the task of AES. The experimental results show that our proposed system achieves significantly higher grading accuracy than other deep learning-based AES systems and also other state-of-the-art AES systems.
BYOL-Explore: Exploration by Bootstrapped Prediction

We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments. BYOL-Explore learns a world representation, the world dynamics, and an exploration policy all-together by optimizing a single prediction loss in the latent space with no additional auxiliary objective. We show that BYOL-Explore is effective in DM-HARD-8, a challenging partially-observable continuous-action hard-exploration benchmark with visually-rich 3-D environments. On this benchmark, we solve the majority of the tasks purely through augmenting the extrinsic reward with BYOL-Explore s intrinsic reward, whereas prior work could only get off the ground with human demonstrations. As further evidence of the generality of BYOL-Explore, we show that it achieves superhuman performance on the ten hardest exploration games in Atari while having a much simpler design than other competitive agents.
Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature

Many methods for performing multi-objective optimisation of computationally expensive problems have been proposed recently. Typically, a probabilistic surrogate for each objective is constructed from an initial dataset. The surrogates can then be used to produce predictive densities in the objective space for any solution. Using the predictive densities, we can compute the expected hypervolume improvement (EHVI) due to a solution. Maximising the EHVI, we can locate the most promising solution that may be expensively evaluated next. There are closed-form expressions for computing the EHVI, integrating over the multivariate predictive densities. However, they require partitioning the objective space, which can be prohibitively expensive for more than three objectives. Furthermore, there are no closed-form expressions for a problem where the predictive densities are dependent, capturing the correlations between objectives. Monte Carlo approximation is used instead in such cases, which is not cheap. Hence, the need to develop new accurate but cheaper approximation methods remains. Here we investigate an alternative approach toward approximating the EHVI using Gauss-Hermite quadrature. We show that it can be an accurate alternative to Monte Carlo for both independent and correlated predictive densities with statistically significant rank correlations for a range of popular test problems.
Twin-width and types

We study problems connected to first-order logic in graphs of bounded twin-width. Inspired by the approach of Bonnet et al. [FOCS 2020], we introduce a robust methodology of local types and describe their behavior in contraction sequences -- the decomposition notion underlying twin-width. We showcase the applicability of the methodology by proving the following two algorithmic results. In both statements, we fix a first-order formula $\varphi(x_1,\ldots,x_k)$ and a constant $d$, and we assume that on input we are given a graph $G$ together with a contraction sequence of width at most $d$. (A) One can in time $O(n)$ construct a data structure that can answer the following queries in time $O(\log \log n)$: given $w_1,\ldots,w_k$, decide whether $\phi(w_1,\ldots,w_k)$ holds in $G$. (B) After $O(n)$-time preprocessing, one can enumerate all tuples $w_1,\ldots,w_k$ that satisfy $\phi(x_1,\ldots,x_k)$ in $G$ with $O(1)$ delay. In the case of (A), the query time can be reduced to $O(1/\varepsilon)$ at the expense of increasing the construction time to $O(n^{1+\varepsilon})$, for any fixed $\varepsilon>0$. Finally, we also apply our tools to prove the following statement, which shows optimal bounds on the VC density of set systems that are first-order definable in graphs of bounded twin-width. (C) Let $G$ be a graph of twin-width $d$, $A$ be a subset of vertices of $G$, and $\varphi(x_1,\ldots,x_k,y_1,\ldots,y_l)$ be a first-order formula. Then the number of different subsets of $A^k$ definable by $\phi$ using $l$-tuples of vertices from $G$ as parameters, is bounded by $O(|A|^l)$.
Deep Learning-Based Device-Free Localization in Wireless Sensor Networks

Location-based services are witnessing a rise in popularity owing to their key features of delivering personalized digital experience. The recent developments in wireless sensing techniques make the realization of device-free localization (DFL) feasible within wireless sensor network (WSN) architectures. The DFL is an emerging technology that utilizes radio signal information for detecting and positioning a passive movable target without attached devices. However, determining the characteristics of the massive raw signals and extracting meaningful discriminative features relevant to the localization are highly intricate tasks due to the different patterns associated with different locations. To overcome these issues, deep learning (DL) techniques can be utilized here owing to their remarkable performance gains in similar practical problems. In this direction, we propose a DFL framework consists of multiple convolutional neural network (CNN) layers along with deep autoencoders based on the restricted Boltzmann machines (RBM) to construct a convolutional deep belief network (CDBN) for features recognition and extracting. Each CNN layer has stochastic pooling to sample down the feature map and reduced the dimensions of the required data without losing important information. This dimensionality reduction can alleviate the heavy computation while ensuring precise localization. The proposed framework is validated using real experimental dataset. The results show that the proposed model is able to achieve a high accuracy of 98% with reduced data dimensions and low signal-to-noise ratios (SNRs).
Realistic One-shot Mesh-based Head Avatars

We present a system for realistic one-shot mesh-based human head avatars creation, ROME for short. Using a single photograph, our model estimates a person-specific head mesh and the associated neural texture, which encodes both local photometric and geometric details. The resulting avatars are rigged and can be rendered using a neural network, which is trained alongside the mesh and texture estimators on a dataset of in-the-wild videos. In the experiments, we observe that our system performs competitively both in terms of head geometry recovery and the quality of renders, especially for the cross-person reenactment. See results https://samsunglabs.github. io/rome/
Membrane flutter in three-dimensional inviscid flow

Abstract We develop a model and numerical method to study the large-amplitude flutter of rectangular membranes (of zero bending rigidity) that shed a trailing vortex-sheet wake in a three-dimensional (3-D) inviscid fluid flow. We apply small initial perturbations and track their decay or growth to large-amplitude steady-state motions. For 12 combinations of boundary conditions at the membrane edges we compute the stability thresholds and the subsequent large-amplitude dynamics across the three-parameter space of membrane mass density, pretension and stretching rigidity. With free side edges we find good agreement with previous 2-D results that used different discretization methods. We find that the 3-D dynamics in the 12 cases naturally forms four groups based on the conditions at the leading and trailing edges. The deflection amplitudes and oscillation frequencies have scalings similar to those in the 2-D case. The conditions at the side edges, although generally less important, may have small or large qualitative effects on the membrane dynamics – e.g. steady vs unsteady, periodic vs chaotic or the variety of spanwise curvature distributions – depending on the group and the physical parameter values.
Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian Processes

A digital twin is a computer model that represents an individual, for example, a component, a patient or a process. In many situations, we want to gain knowledge about an individual from its data while incorporating imperfect physical knowledge and also learn from data from other individuals. In this paper, we introduce a fully Bayesian methodology for learning between digital twins in a setting where the physical parameters of each individual are of interest. A model discrepancy term is incorporated in the model formulation of each personalized model to account for the missing physics of the low-fidelity model. To allow sharing of information between individuals, we introduce a Bayesian Hierarchical modelling framework where the individual models are connected through a new level in the hierarchy. Our methodology is demonstrated in two case studies, a toy example previously used in the literature extended to more individuals and a cardiovascular model relevant for the treatment of Hypertension. The case studies show that 1) models not accounting for imperfect physical models are biased and over-confident, 2) the models accounting for imperfect physical models are more uncertain but cover the truth, 3) the models learning between digital twins have less uncertainty than the corresponding independent individual models, but are not over-confident.
Stellar ages, masses, extinctions, and orbital parameters based on spectroscopic parameters of Gaia DR3

Gaia DR3 provides radial velocities for 33 million stars and spectroscopically derived atmospheric parameters for more than five million targets. When combined with the astrometric data, these allow us to derive orbital and stellar parameters that are key in order to understand the stellar populations of the Milky Way and perform galactic archaeology. We use the calibrated atmospheric parameters, 2MASS and Gaia-EDR3 photometry, and parallax-based distances to compute, via an isochrone fitting method, the ages, initial stellar masses and reddenings for the stars with spectroscopic parameters. We also derive the orbits (actions, eccentricities, apocentre, pericentre and Zmax) for all of the stars with measured radial velocities and astrometry, adopting two sets of line-of-sight distances from the literature and an axisymmetric potential of the Galaxy. Comparisons with reference catalogues of field and cluster stars suggest that reliable ages are obtained for stars younger than 9-10Gyr when the estimated relative age uncertainty is<50%. For older stars, ages tend to be under-estimated. The most reliable stellar type for age determination are turn-off stars, even when the input atmospheric parameters have large uncertainties. Ages for giants and main-sequence stars are retrieved with uncertainties of ~2Gyr when extinction towards the star's line-of sight is smaller than A_V<2.5mag. The full catalogue is made publicly available to be downloaded. With it, the full chemo-dynamical properties of the extended Solar neighbourhood unfold, and allow us to better identify the properties of the spiral arms, to parameterise the dynamical heating of the disc, or to thoroughly study the chemical enrichment of the Milky Way.
Demonstrating two-qubit gates at the quantum speed limit using superconducting qubits

The speed of elementary quantum gates, particularly two-qubit gates, ultimately sets the limit on the speed at which quantum circuits can operate. In this work, we experimentally demonstrate commonly used two-qubit gates at nearly the fastest possible speed allowed by the physical interaction strength between two superconducting transmon qubits. We achieve this quantum speed limit by implementing experimental gates designed using a machine learning inspired optimal control method. Importantly, our method only requires the single-qubit drive strength to be moderately larger than the interaction strength to achieve an arbitrary two-qubit gate close to its analytical speed limit with high ﬁdelity. Thus, the method is applicable to a variety of platforms including those with comparable single-qubit and two-qubit gate speeds, or those with always-on interactions.
PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based Network

—In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos. Our framework includes attention and adaptation to different quantization parameters (QPs) in a single model. The attention module exploits global receptive ﬁelds that can capture and align long-range correlations between consecutive frames, which can be beneﬁcial for enhancing perceptual quality of videos. The frame to be enhanced is fed into the deep network together with its neighboring frames, and in the ﬁrst stage features at different depths are extracted. Then extracted features are fed into attention blocks to explore global temporal correlations, followed by a series of upsampling and convolution layers. Finally, the resulting features are processed by the QP-conditional adaptation module which leverages the corresponding QP information. In this way, a single model can be used to enhance adaptively to various QPs without requiring multiple models speciﬁc for every QP value, while having similar performance. Experimental results demonstrate the superior performance of the proposed PeQuENet compared with the state- of-the-art compressed video quality enhancement algorithms.
Mapping of cloud cover from satellite data over Thailand

In this study, an empirical model for estimating cloud cover from satellite data was developed. The Skyview instruments were installed at four stations located in Chiang Mai (18.78°N, 98.98°E), Ubon Ratchathani (15.25°N, 104.87°E), Nakhon Pathom (13.82°N, 100.04°E) and Songkhla (7.20°N, 100.60°E) in order to record the images of the sky. A five-year period (2009-2013) of the cloud cover derived from the sky images and cloud index derived from MTSAT–1R satellite data were acquired. Based on these data, a model relating the cloud cover to the cloud index was formulated. To validate its performance, the model was used to calculate cloud cover at the four stations during a two-year period (2014-2015). It was found that the measured and calculated cloud cover data were in reasonable agreement with root mean square differences (RMSD) and mean bias difference (MBD) of 12.9% and 3.5%, respectively. Finally, the model was used to calculate cloud cover over Thailand and the results were shown as maps.
Life support systems

The present invention relates to a life support system, particularly relates to a heart-lung machine (2), which comprises a plurality of medical devices (4.1 to 4.4), the control panel (6) and the position of the specified control (8). Said control panel (6) having a separate control element (10.1 to 10.4) for operating the medical device (4.1 to 4.4), characterized in that said control panel (6) is configured to protrude substantially straight exceeds the plurality of medical devices (4.1 to 4.4) a base region (12). Said control panel (6) is configured in such a manner projecting: assigning each device (4.1 to 4.4) only with a specific instrument (4.1 to 4.4) and the control of a control element alignment (10.1 to 10.4) .
E NERGY C ONSUMPTION , I NCOME , T RADING O PENNESS , AND E NVIRONMENTAL P OLLUTION : T ESTING E NVIRONMENTAL K UZNETS C URVE H YPOTHESIS

The purpose of this study is to examine the effects of energy consumption, economic growth, and trade openness on environmental pollution in a developing country, especially in the case of Vietnam. The study was conducted on the basis of time-series data collected in the period of time between the years 1990 and 2014. By a method of autoregressive distributed lag and testing the hypothesis of the environmental Kuznets curve, our result demonstrated that environmental Kuznets curve could be found in both the long run and short run. There existed an inverted U-shaped relationship between different pollutants and per capita income. Further, energy consumption could positively affect carbon dioxide (CO2) emissions in the short run, but negatively could affect CO2 emissions in the long run because of transformation from non-renewable energy sources to renewable energy sources. In addition, environmental pollution converged on its long-run equilibrium by at least 29.4% with the speed adjustment via the channel of income, energy consumption, and trade openness. In terms of trade openness, the country has a positive and significant effect on CO2 emissions in both the long run and short run.
Parametric Reliability of 6T-SRAM Core Cell Arrays

The increasing integration density of microelectronic circuits results in aging mechanisms, some of them shifting the parameters of MOS transistors during lifetime. This work focuses on the impact of these parametrical degradation mechanisms on Static Random Access Memory (SRAM) arrays. First, the impact of each effect on single SRAM cells was simulated. Because of presently non-sufficient simulation models, these results had to be confirmed and completed by measurements. By using novel measurement techniques which were developed within this work, the impact of the worst effect on large-scale SRAM arrays could be examined for the first time. The most critical scenarios during lifetime could be identified. Finally, a comparison of known countermeasures was performed in order to choose the most promising methods.
#Activism versus Real Activism: Manifestations of Digital Social Influence in Social Networks

Along with the rapid proliferation of Social Networking Sites (SNSs), there is renewed discussion on the influence of substantial and highly interactive digital social circles. There is much debate about an individual's interactions with his/her digital social circles and how s/he forms behaviours as a result of such interactions. This study hypothesizes two salient types of actions that individuals are likely to engage in, as a consequence of social influence from SNSs: symbolic (e.g. #activism) and substantive actions (e.g. real activism). Using a paired sample of survey data gathered from 311 respondents, this research attempts to understand which social influence perspectives are likely to influence symbolic and substantive actions.
Energy Absorbers with

Baluchestan. , Zahedan, Iran. Abstract In this study, the crashworthiness of cylindrical tubes with variable thickness, under axial load, is investigated. The loading is applied quasi static. Wall-Thickness changes are considered to follow the nonlinear power distribution equatiion. Firstly, by considering the elastic-perfectly plastic behavior for material, an analytical relition is extracted based on Alexander's theory. Then, in order to increase the accuracy of the results, the effects of work hardening is applied to material behavior and the formulas are rewritten. Finally, to validate the analytical results, using simulation in ABAQUS FE software, several models have been studied in different modes and the results have been compared. The results show an acceptable agreement between the analytical results and the simulation. The final result of this research is presenting two analutical relition beteen absorbed energy and mean force with mechanical properties and geometic parameters of anergy absorber. Moreover, by simplifying the drived analytical equation, we can obtain the basic equations
Adaptive estimation of continuous gait phase based on capacitive sensors

Abstract Continuous gait phase plays an important role in robotic prosthesis control. In this paper, we have conducted the offline adaptive estimation (at different speeds and on different ramps) of continuous gait phase of robotic transtibial prosthesis based on the adaptive oscillators. We have used the capacitive sensing method to record the deformation of the muscles. Two transtibial amputees joined in this study. Based on the strain signals of the prosthetic foot and the capacitive signals of the residual limb, the maximum and minimum of estimation errors are 0.80 rad and 0.054 rad, respectively, and their corresponding ratios in one gait cycle are 1.27% and 0.86%, respectively. This paper proposes an effective method to estimate the continuous gait phase based on the capacitive signals of the residual muscles, which provides a basis for the continuous control of robotic transtibial prosthesis.
Memetic Algorithms for Spatial Partitioning Problems

Spatial optimization problems (SOPs) are characterized by spatial relationships governing the decision variables, objectives, and/or constraint functions. In this article, we focus on a specific type of SOP called spatial partitioning, which is a combinatorial problem due to the presence of discrete spatial units. Exact optimization methods do not scale with the size of the problem, especially within practicable time limits. This motivated us to develop population-based metaheuristics for solving such SOPs. However, the search operators employed by these population-based methods are mostly designed for real-parameter continuous optimization problems. For adapting these methods to SOPs, we apply domain knowledge in designing spatially aware search operators for efficiently searching through the discrete search space while preserving the spatial constraints. To this end, we put forward a simple yet effective algorithm called swarm-based spatial memetic algorithm (SPATIAL) and test it on the school (re)districting problem. Detailed experimental investigations are performed on real-world datasets to evaluate the performance of SPATIAL. Besides, ablation studies are performed to understand the role of the individual components of SPATIAL. Additionally, we discuss how SPATIAL is helpful in the real-life planning process and its applicability to different scenarios and motivate future research directions.
Evaluation of out‐of‐plane seismic performance of column‐to‐column precast concrete cladding panels in one‐storey industrial buildings

Recent earthquakes in Italy (L'Aquila 2009 and Emilia 2012) highlighted the vulnerability of precast cladding panels, typically associated with a connection system not designed to account for displacement and rotation compatibility between the panels and the supporting structure. Experimental investigations were performed in the past to investigate the in‐plane performance of cladding panels and design recommendations have been made accordingly; however, in the case of out‐of‐plane seismic loads, the load demand is commonly evaluated in the design practice by means of formulations for nonstructural components.