Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of sparse Gaussian graphical models with hidden clustering structure

Estimation of Gaussian graphical models is important in natural science when modeling the statistical relationships between variables in the form of a graph. The sparsity and clustering structure of the concentration matrix is enforced to reduce model complexity and describe inherent regularities. We propose a model to estimate the sparse Gaussian graphical models with hidden clustering structure, which also allows additional linear constraints to be imposed on the concentration matrix. We design an efficient two-phase algorithm for solving the proposed model. We develop a symmetric Gauss-Seidel based alternating direction method of the multipliers (sGS-ADMM) to generate an initial point to warm-start the second phase algorithm, which is a proximal augmented Lagrangian method (pALM), to get a solution with high accuracy. Numerical experiments on both synthetic data and real data demonstrate the good performance of our model, as well as the efficiency and robustness of our proposed algorithm.
Sketch and test: visual crowd research using p5.js

Designing visual crowd experiments requires both control and versatility. While behavioural and computer sciences have produced a fair number of tools facilitating this process, a gap remains when it comes to the combination of accessibility and versatility. The Processing language is widely used by artist and designers of varying levels of expertise and thus fulfils these conditions. Here, we investigated whether the Processing inspired JavaScript library p5.js can be used for visual crowd research. We specifically explored how to use p5.js in combination with MTurk and report a simple way of using p5.js 'sketches' to perform online tests. We report four complementary experimental paradigms to illustrated the accessibility and versatility pf p5.js: Change blindness, BubbleView, Gauge figure (attitude probe) and Composition. Results reveal that previous literature findings can be reproduced and novel insights can easily be achieved. The creative freedom of p5.js together with low threshold crowd access seems a powerful combination for all areas that involve vision: perception, design, art history, communication and beyond.
Low Mach number fluctuating hydrodynamics model for ionic liquids

We present a new mesoscale model for ionic liquids based on a low Mach number fluctuating hydrodynamics formulation for multicomponent charged species. The low Mach number approach eliminates sound waves from the fully compressible equations leading to a computationally efficient incompressible formulation. The model uses a Gibbs free energy functional that includes enthalpy of mixing, interfacial energy, and electrostatic contributions. These lead to a new fourth-order term in the mass equations and a reversible stress in the momentum equations. We calibrate our model using parameters for [DMPI+][F6P-], an extensively-studied room temperature ionic liquid (RTIL), and numerically demonstrate the formation of mesoscopic structuring at equilibrium in two and three dimensions. In simulations with electrode boundaries the measured double layer capacitance decreases with voltage, in agreement with theoretical predictions and experimental measurements for RTILs. Finally, we present a shear electroosmosis example to demonstrate that the methodology can be used to model electrokinetic flows.
Efficient simulation of biperiodic, layered structures based on the T-matrix method

Predicting the optical response of macroscopic arrangements of individual scatterers is a computational challenge, as the problem involves length scales across multiple orders of magnitude. We present a full-wave optical method to highly efficiently compute the scattering of light at objects that are arranged in bi-periodic arrays. Multiple arrays or homogeneous thin-films can be stacked to build up an entire multicomposite material in the third dimension. The scattering properties of the individual objects in each array are described by the T-matrix formalism. Therefore, arbitrarily shaped objects and even molecules can be the basic constituent of the arrays. Taking the T-matrix of the individual scatterer as the point of departure allows to explain the optical properties of the bulk material from the scattering properties of its constituents. Unique to our approach is the solution to Maxwell's equations in a basis set with well defined helicity. Therefore, chiral media are particularly easy to consider as materials for both scatterers or embedding media. We exemplify the efficiency of the algorithm with an exhaustive parametric study of anti-reflective coatings for solar cells made from cylinders with a high degree of helicity preservation. The example shows a speed-up of about 500 with respect to finite-element computations.
Reeb orbits that force topological entropy

Abstract We develop a forcing theory of topological entropy for Reeb flows in dimension three. A transverse link L in a closed contact $3$ -manifold $(Y,\xi )$ is said to force topological entropy if $(Y,\xi )$ admits a Reeb flow with vanishing topological entropy, and every Reeb flow on $(Y,\xi )$ realizing L as a set of periodic Reeb orbits has positive topological entropy. Our main results establish topological conditions on a transverse link L, which imply that L forces topological entropy. These conditions are formulated in terms of two Floer theoretical invariants: the cylindrical contact homology on the complement of transverse links introduced by Momin [A. Momin. J. Mod. Dyn. 5 (2011), 409–472], and the strip Legendrian contact homology on the complement of transverse links, introduced by Alves [M. R. R. Alves. PhD Thesis, Université Libre de Bruxelles, 2014] and further developed here. We then use these results to show that on every closed contact $3$ -manifold that admits a Reeb flow with vanishing topological entropy, there exist transverse knots that force topological entropy.
Band gap Engineering by Sublattice Mixing in Cs$_2$AgInCl$_6$: High-throughput Screening from First-principles

Lead-free double perovskite materials (viz. Cs$_2$AgInCl$_6$) are being explored as stable and non-toxic alternatives of lead halide perovskites. In order to expand the optical response of Cs$_2$AgInCl$_6$ in visible region, we report here the stability, electronic structure and optical properties of Cs$_2$AgInCl$_6$ by sublattice mixing of various elements. Here we have employed high-throughput screening using a hierarchical first-principles based approach starting from density functional theory (DFT) with appropriate exchange and correlation functionals to beyond DFT methods under the framework of many body perturbation theory (viz. G$_0$W$_0$@HSE06). We have started with 32 primary set of combinations of metals M(I), M(II), M(III) and halogen X at Ag/In and Cl site respectively, where concentration of each set is varied to build a database of nearly 140 combinations. The most suitable mixed sublattices are identified to engineer the band gap of Cs$_2$AgInCl$_6$ to have its application in optoelectronic devices under visible light.
High-fidelity multistate STIRAP assisted by shortcut fields

Multistate stimulated Raman adiabatic passage (STIRAP) is a process which allows for adiabatic population transfer between the two ends of a chainwise-connected quantum system. The process requires large temporal areas of the driving pulsed fields (pump and Stokes) in order to suppress the nonadiabatic couplings and thereby to make adiabatic evolution possible. To this end, in the present paper a variation of multistate STIRAP, which accelerates and improves the population transfer, is presented. In addition to the usual pump and Stokes fields it uses shortcut fields applied between the states, which form the dark state of the system. The shortcuts cancel the couplings between the dark state and the other adiabatic states thereby resulting (in the ideal case) in a unit transition probability between the two end states of the chain. Specific examples of five-state systems formed of the magnetic sublevels of the transitions between two degenerate levels with angular momenta $J_g=2$ and $J_e=1$ or $J_e=2$ are considered in detail, for which the shortcut fields are derived analytically. The proposed method is simpler than the usual"shortcuts to adiabaticity"recipe, which prescribes shortcut fields between all states of the system, while the present proposal uses shortcut fields between the sublevels forming the dark state only. The results are of potential interest in applications where high-fidelity quantum control is essential, e.g. quantum information, atom optics, formation of ultracold molecules, cavity QED, etc.
Light-Weighted CNN for Text Classification

For management, documents are categorized into a specific category, and to do these, most of the organizations use manual labor. In today's automation era, manual efforts on such a task are not justified, and to avoid this, we have so many software out there in the market. However, efficiency and minimal resource consumption is the focal point which is also creating a competition. The categorization of such documents into specified classes by machine provides excellent help. One of categorization technique is text classification using a Convolutional neural network(TextCNN). TextCNN uses multiple sizes of filters, as in the case of the inception layer introduced in Googlenet. The network provides good accuracy but causes high memory consumption due to a large number of trainable parameters. As a solution to this problem, we introduced a whole new architecture based on separable convolution. The idea of separable convolution already exists in the field of image classification but not yet introduces to text classification tasks. With the help of this architecture, we can achieve a drastic reduction in trainable parameters.
Too Many Claims to Fact-Check: Prioritizing Political Claims Based on Check-Worthiness

The massive amount of misinformation spreading on the Internet on a daily basis has enormous negative impacts on societies. Therefore, we need automated systems helping fact-checkers in the combat against misinformation. In this paper, we propose a model prioritizing the claims based on their check-worthiness. We use BERT model with additional features including domain-specific controversial topics, word embeddings, and others. In our experiments, we show that our proposed model outperforms all state-of-the-art models in both test collections of CLEF Check That! Lab in 2018 and 2019. We also conduct a qualitative analysis to shed light-detecting check-worthy claims. We suggest requesting rationales behind judgments are needed to understand subjective nature of the task and problematic labels.
Ultimate limit on time signal generation

The generation of time signals is a fundamental task in science. Here we study the relation between the quality of a time signal and the physics of the system that generates it. According to quantum theory, any time signal can be decomposed into individual quanta that lead to single detection events. Our main result is a bound on how sharply peaked in time these events can be, which depends on the dimension of the signal generator. This result promises applications in various directions, including information theory, quantum clocks, and process simulation.
Estimating Motion Uncertainty with Bayesian ICP

Accurate uncertainty estimation associated with the pose transformation between two 3D point clouds is critical for autonomous navigation, grasping, and data fusion. Iterative closest point (ICP) is widely used to estimate the transformation between point cloud pairs by iteratively performing data association and motion estimation. Despite its success and popularity, ICP is effectively a deterministic algorithm, and attempts to reformulate it in a probabilistic manner generally do not capture all sources of uncertainty, such as data association errors and sensor noise. This leads to overconfident transformation estimates, potentially compromising the robustness of systems relying on them. In this paper we propose a novel method to estimate pose uncertainty in ICP with a Markov Chain Monte Carlo (MCMC) algorithm. Our method combines recent developments in optimization for scalable Bayesian sampling such as stochastic gradient Langevin dynamics (SGLD) to infer a full posterior distribution of the pose transformation between two point clouds. We evaluate our method, called Bayesian ICP, in experiments using 3D Kinect data demonstrating that our method is capable of both quickly and accuractely estimating pose uncertainty, taking into account data association uncertainty as reflected by the shape of the objects.
Prevalence and Clinical Implications of the Mirror and TV Signs in Advanced Alzheimer's Disease and Dementia with Lewy Bodies

Objective: To explore the prevalence and clinical implications of the mirror and TV signs in the moderate to advanced stages of Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB). Methods: We retrospectively examined the prevalence of clinical and psychiatric symptoms including the mirror and TV signs in 200 subjects with AD and 200 with DLB and evaluated the relationships among the symptoms. Results: The mirror sign was found in 3.0% of AD and 4.5% of DLB subjects. The TV sign was found in 1.5% of AD and 4.0% of DLB subjects. The prevalence of the mirror and TV signs was not significantly different between the AD and DLB groups. Visual hallucination, visual illusion, misidentification of person, and sleep talking were significantly more frequent in DLB than in AD subjects. The mirror sign was significantly associated with lower Mini-Mental State Examination scores, whereas the TV sign was significantly associated with the misidentification of person. Conclusions: Both the mirror and TV signs were rare even in the moderate to advanced stages of AD and DLB. The mirror sign may be independent from other delusional misidentification syndromes (DMSs). Being associated mainly with global cognitive decline, the mirror sign is unlikely attributed to any specific cognitive impairment or the dysfunction of localized brain areas. In contrast, the TV sign was significantly more often coexistent with the misidentification of person, suggesting that the TV sign may partly share common neuropsychological mechanisms with DMSs.
Association between self-rated health and physical performance in middle-aged and older women from Northeast Brazil

Background Self-rated Health (SRH) is regarded as a simple and valid measure of a person’s health status, given its association to adverse health outcomes, including low physical performance in older populations. However, studies investigating these associations in low- and middle-income settings are scarce, especially for middle-aged populations. Understanding the validity of SRH in relation to objective health measures in low-income populations could assist in decision making about health policy and strategies, especially in under-resourced settings. Objective Assess the relationship between SRH and physical performance measures in middle-aged and older women in a low-income setting of Brazil. Methods This is a cross-sectional study of 571 middle-aged (40–59 years old) and older (60–80 years old) women living in Parnamirim and Santa Cruz in the Northeast region of Brazil. Participants reported their health status and were allocated to the “SRH good” or “SRH poor” groups. The physical performance evaluation included: handgrip strength, one-legged balance with eyes open and closed and chair stand test. The relationship between SRH and physical performance for middle-aged and older women was assessed by quantile regression (modeling medians) adjusted for potential confounders (age, socioeconomic variables, body mass index, menopause status, age at first birth, parity, chronic conditions and physical activity). Results Middle-aged women from the “SRH good” group presented better physical performance with 1.75 kgf stronger handgrip strength (95% CI [0.47–3.02]; p = 0.004), 1.31 s longer balance with eyes closed ([0.00–2.61]; p = 0.030), and they were 0.56 s faster in the chair stand test ([0.18–0.94]; p = 0.009) than those who reported “SRH poor”. No association was found for balance with eyes open. For older women, there was no evidence of associations between physical performance and SRH. Conclusion This study showed that SRH is significantly associated with objective measures of physical performance in a sample of low-income middle-aged women. SRH can be an important tool to indicate the need for further evaluation of physical performance among middle-aged women and can be particularly useful for low-income communities.
Primary Bone Lymphoma in Axial Skeleton in a Middle-Aged Female Presented as Recurrent Anemia

Primary bone lymphoma (PBL) is a peculiar extranodal presentation of non-Hodgkin’s lymphoma. Primary bone diffuse large B-cell lymphoma (DLBCL) is the most common pathological type, comprising about 80% of PBL. The diagnosis of PBL depends on the combined clinical examination and imaging studies and is confirmed with immunohistochemical examination. Due to the rarity of this disease, more relative studies and case reports are needed to provide insight into this obscure lymphoproliferative malignancy. Here, we report one rare case of primary bone DLBCL involving the axial skeleton in a 37-year-old female.
QSAR study of isonicotinamides derivatives as Alzheimr's disease inhibitors using PLS-R and ANN methods

The field of artificial intelligence such as artificial neural networks (ANNs) and partial least squares regression (PLS-R) are the methods of choice for quantitative structure activity relationship (QSAR) correlation. Here, we have applied 2D-QSAR approach on a series of isonicotinamides derivatives as Glycogen synthase kinase-3 beta (GSK-3β) inhibitors using PLS-R and ANN methods. The models were generated and validated using a data set of 35 molecules. The best predictive models by PLS-R and ANN gave highly significant square correlation coefficient (r2) values of 0.84 and 0.90 respectively. Based on the results of 2D-QSAR models, GCUT_PEOE_2, h_emd_C, PEOE_VSA_FPPOS, and SlogP_VSA6 are the main descriptors in controlling the activity of the molecules. The developed models could be used to design the new isonicotinamides derivatives with high inhibitory activity against GSK-3β enzyme.
An Acceleration Method Using CUDA based on ORB-SLAM2

In order to solve the problem of poor real-time performance and low position accuracy when mobile robots apply the technology of simultaneous localization and mapping, this paper proposed an improved algorithm using CUDA parallel computing to do the ORB feature extraction and matching portion to reduce the running time of the algorithm based on open source ORB - SLAM2 visual SLAM algorithm. After that. ,testing the algorithm with public stereo datasets by doing some comparative experiments using the laptop and camera. the experiment result shows that the proposed method by this paper can greatly reduce the time of extracting feature points, calculating feature descriptors and image matching, and also improve the accuracy of localization. Therefore, the improved algorithm can improve the operation efficiency of the algorithm and the robustness of mobile robot SLAM.
A Review on Different Modulation Schemes for Massive MIMO

The field of wireless communication is rapidly advancing and the latest technological addition is 5G. To account for the large heterogeneity in terms of devices and services that comes with 5G scheme, the efficient use of available spectrum which may be non-contiguous remains as the main research objective. Effective mapping of services to the available frequencies should be achieved. Waveform remains as the core component in any wireless technology. In this context, the various 5G waveform candidates are studied in this research work. The detailed comparative study on the most promising schemes like OFDM, UFMC and FBMC are carried out in this research work. The schemes are then compared by means of their spectral leakage values.
A Hybrid Anomaly Classification with Deep Learning (DL) and Binary Algorithms (BA) as Optimizer in the Intrusion Detection System (IDS)

Nowadays, along with network development, due to the threats of unknown sources, information communication is more vulnerable, and thus, more secured information is required. Intrusion Detection System (IDS) is very important for cybersecurity with the presence in particular of various networked computers' foundation. An efficient IDS apply machine learning method as computational technics to increase rates of detection to gain the high accuracy and low false alarms rate within the huge amounts of data. To increase the rate of detection, researcher usually implements the optimizer. Thus, in this research, a comprehensive experimental study is presented based on various binary to optimize the rate of detection and decrease the error. Moreover, Numerous researches have been conducted about intrusion detection systems with the old dataset such as Kddcup'99 dataset, and due to this reason, most of them did not get the potential accuracy to detect intrusion with the current intrusion as the old dataset is not covering the current attacks. Therefore, this research aims to A hybrid Anomaly Classification of IDS with Deep Learning (DL) and Binary Algorithms (BA) as Optimizer with the most updated dataset named “CICIDS2017” which can be used for the intrusion detection evaluation. Moreover, this research conducts the anomaly classification of IDS based on the deep neural network (DNN) as the Deep Learning (DL) platform and Binary Algorithms (BA) in terms of Binary Bat Algorithm (BBA), Binary Genetic Algorithm (BGA), Binary Gravitational Search Algorithm (BGSA) as optimizer to enhance the rates of detection. Some of the results which had been considered and achieved for DNN and the hybrid version (DNN and Binary Algorithms) are in terms of: Accuracy, Recall, Precision, Confusion Matrix, Sensitivity, Specificity, and Cost Error.
The Effect of Virtual Museum on Students' Cognitive and Non-Cognitive Abilities: From the Perspective of Multimedia Learning Theory

Based on the theory of multimedia learning, an artwork exhibition system that integrates digital media art instruction with virtual museum was designed as an auxiliary teaching tool for digital media major, and the cognitive and non-cognitive abilities of three groups of participants was explored: artwork exhibition, panoramic roaming and multimodal PPT. The participants were 81 students majoring in digital media arts, and a quasi-experimental research design was employed. Several cognitive ability factors of the students aided with different tools were evaluated, including information processing, perceptual expression, learning transfer and reflection. Several non-cognitive ability factors were also evaluated, including learning motivation, knowledge construction, and self-management. The results showed that compared with the other two groups of participants, the artwork exhibition assisted learning effectively improved students' non-cognitive abilities such as motivation and attitude in the learning process, deepened their understanding of knowledge, promoted learning transfer and enhanced practical skills. The presentation mode of the work exhibition based on the theory of multimedia learning is more beneficial to cultivating students' cognitive and non-cognitive abilities.