Miracle-dz/UniFAR-FTUs huggingface dataset:
Regression-enhanced random forests with personalized patching for covid-19 retweet prediction

In this report, we describe an ensemble approach with a set of enhanced random forest models for COVID-19 retweet prediction challenge at CIKM Analyticup 2020 held by the 29th ACM International Conference on Information and Knowledge Management. The proposed approach is based on a global model and a set of personalized models. The global model consists of a set of random forests enhanced by three different types of models such as linear regression, feed-forward neural networks, and factorization machines. In addition to this global model, we trained a number of personalized models for users that exist in both training and test sets and have a sufficient number of tweets for training. Our approach obtained a MSLE (Mean Squared Log Error) value of 0.149997 on the test set of the challenge and ranked 4th on the final leaderboard. © 2020 CEUR-WS. All rights reserved.
Supplementary Materials for ACTION-Net: Multipath Excitation for Action Recognition

MobileNet V2. Figure 2 and Table 3 summarize details of MobileNet V2 architecture and positions that insert TSM/ACTION. We insert TSM/ACTION into each bottleneck at the start. In order to keep consistent with adding TSM to MobileNet V2 in the original work [3], we insert TSM/ACTION into two blocks in stage4 and the first block in stage5, which results in 10 TSM/ACTION modules have been added into MobileNet V2.
Developing a supervisor identity through experiential learning: Narratives of three novice academics working in a South African University

One of the critical tasks of academics in any research-intensive university is the supervision of postgraduate students. Given the central role of this activity, how novice academics learn to supervise is significant. In this paper, we present our narratives as novice academics, and we focus on our learning of the supervision role. We drew from experiential learning and role identity theories to frame our understanding of this process. Using a narrative inquiry methodology, we shared and examined our narratives of lived experiences as novice supervisors. Upon examining our narratives, we identified four sources of learning that were significant in developing our supervisor identity: our current or previous supervisors; senior colleagues; meaningful corridor talks; and the practice of reflexivity. We recommend that institutions of higher learning pay added attention to the development of novice supervisors. Although structured programmes are significant, other learning avenues need to be explored to assist novice supervisors to develop a supervisor identity.
Microgrid Operational Optimization with Dynamic Voltage Security Constraints

Emergency islanding of a microgrid (MG) can be vital in ensuring continuity of power supply to a local network during major contingencies in the main grid. However, such an event will inadvertently cause large voltage transients except in the trivial case where zero power is exchanged. The under/over voltages can result in the disconnection of the local distributed generators within the MG, increasing the risk of a local blackout. In this paper, we present a MG operational optimization strategy that includes dynamic voltage constraints aiming at enhancing MG resilience during emergency situations. A dynamic optimization technique, based on sequential constraint transcription, is used to formulate the dynamic voltage security constraints applied to the steady-state problem in a computationally efficient manner. An iterative approach ensures that the dynamic constraints are updated in relation to the optimized operating point of the MG. The performance of the proposed approach is investigated on a 30-bus medium-voltage MG by considering a potential disconnection at each hour of the day, subject to daily load and generation profiles.
A Comparison of UWB CIR and WiFi CSI for Human Activity Recognition

—Human Activity Recognition (HAR) is becoming increasingly important in smart homes and in healthcare applications such as assisted-living and remote health monitoring, especially in elderly care homes. In this paper, we leverage the use of Ultra-Wideband (UWB) technology and WiFi Channel State Information (CSI) for device-free HAR. An experiment was performed where the transmitter and receiver were separated by a ﬁxed distance in a Line-of-Sight (LoS) setting. Then ﬁve activities were performed in between them, namely, sitting, standing, lying down, standing from the ﬂoor and walking. We use the high-resolution Channel Impulse Responses (CIRs) provided by the UWB modules as features in machine and deep learning algorithms for classifying the activities. Experimental results show that a classiﬁcation performance with an F1-score as high as 95.53% is achieved using processed UWB CIR data as features. Furthermore, we analysed the classiﬁcation performance of the ﬁve activities using WiFi CSI in the same physical layout. In this case, maximum F1-scores of 92.24% and 80.89% are obtained when amplitude CSI data and spectrograms are used as features, respectively. In this study it has been shown that in addition to the primary function of the UWB system which is localisation, it can also be used for HAR since it achieves excellent activity classiﬁcation performance.
User-Guided Line Art Flat Filling with Split Filling Mechanism

Flat filling is a critical step in digital artistic content creation with the objective of filling line arts with flat colors. We present a deep learning framework for user-guided line art flat filling that can compute the "influence areas" of the user color scribbles, i.e., the areas where the user scribbles should propagate and influence. This framework explicitly controls such scribble influence areas for artists to manipulate the colors of image details and avoid color leakage/contamination between scribbles, and simultaneously, leverages data-driven color generation to facilitate content creation. This framework is based on a Split Filling Mechanism (SFM), which first splits the user scribbles into individual groups and then independently processes the colors and influence areas of each group with a Convolutional Neural Network (CNN). Learned from more than a million illustrations, the framework can estimate the scribble influence areas in a content-aware manner, and can smartly generate visually pleasing colors to assist the daily works of artists. We show that our proposed framework is easy to use, allowing even amateurs to obtain professional-quality results on a wide variety of line arts.
Reinforcement Calculation of Circular and Annular Cross Section

Circular and annular cross-section is one of the more common cross-section forms, such as water towers, piers, etc. But reinforcement calculation of these crosssections is dual, the nonlinearity in width change and the nonlinear stress-strain relationship of concrete and reinforcement, which brings inconvenience to the calculation. In this paper, the corresponding calculation formulas are analytically derived and practical design charts (nomograms) are obtained. Based on stress-stain curve of concrete and reinforcement, five strain distribution regions were constructed. Via strains stress can be determined and then via the stress internal force can be computed, without application of the traditional equivalent rectangular stress block in concrete compression zone. The obtained dimensionless design charts can be applied to all load cases, such as uniform tension, tension with a small and large eccentricity, pure bending, compression with a large and small eccentricity and uniform compression. The calculation by using the design charts is simple and quick.
Data
mining approach for business value analysis in basketball

With the rapidly increasing volume of data, novel methods and technologies for their analysis, and opportunities to support decision-making processes emerge in the domain of sports analytics. This, in particular, applies to analysing athletes’ performance and calculating related business added value in major sports leagues such as the National Basketball Association (NBA). Specifically, the financial success of a team/franchise depends not only on the results of the games but also on the success of attracting marketable individuals who bring higher business value. In that regard, this paper aims to demonstrate the potential and importance of data mining methods to uncover the factors influencing the decisions related to the player selection based on individual results, physical characteristics, and professional contract salaries in the NBA. For the study, 22 datasets were integrated into three large datasets. The data covers the period from 1946 (when the league was founded) to 2017. Data mining models were developed in RapidMiner, enabling correlation, cluster and regression analysis. Change in the factors affecting the selection of new players in recent years was uncovered, while the classification revealed, for example, that more than 50% of players have below-average coefficients of efficiency and individual result contribution. An artificial neural network algorithm was used to identify discrepancies for players with high-salary contracts as many do not meet high-performance standards. The study demonstrates how classification and prediction models can serve sports analysts and managers in making decisions related to future professional contracts and predict future salaries for active players, among other contributions.
Investigations on V2C and V2CX2 (X = F, OH) Monolayer as a Promising Anode Material for Li Ion Batteries from First-Principles Calculations

First-principles calculations are performed to study the electronic properties and Li storage capability of V2C and its corresponding fluoride and hydroxide. We find that the V2C monolayer is metallic with antiferromagnetic configuration, while its derived V2CF2 and V2C(OH)2 in their the most stable configurations are small-gap antiferromagnetic semiconductors. Li adsorption could enhance the electric conductivity of V2C fluoride and hydroxide. The bare V2C monolayer shows fast Li diffusion with low diffusion barrier height and very high Li storage capacity (with theoretical value ∼940 mAh/g), while the passivated F or OH atoms on its surface tend to impede Li diffusion and largely reduce the Li storage capacity. Moreover, the average intercalation potentials for V2C-based materials are calculated to be relatively low. Our results suggest that V2C monolayer could be a promising anode material for Li-ion batteries.
A wrapper approach to supervised input selection using simulated annealing

Genomic data, and more generally biomedical data, are usually characterized by high dimensionality. A feature selection procedure can attain the two objectives of highlighting the relevant variables (genes) and improving classi(cid:12)cation results. In this paper we propose a wrapper approach to gene selection in classi(cid:12)cation of gene expression data using Simulated Annealing coupled with supervised classi(cid:12)ers. The proposed approach can perform global combinatorial searches through the space of all possible input subsets, can handle cases with numerical, categorical or mixed inputs, and is able to (cid:12)nd (sub-)optimal subsets of inputs giving very low classi(cid:12)cation errors. The method has been tested on publicly available bioinformatics data sets, and also on mixed type data, using Support Vector Machines or Classi(cid:12)cation Trees. Moreover we propose some heuristics able to speed-up the convergence. The experimental results highlight the ability of the method to select minimal sets of relevant genes.
Method of Moments on GPU and Shape Synthesis Using Machine Learning Techniques

This thesis aims to describe and implement the method of moments in electromagnetics using graphical processing units utilizing CUDA technology. Speedups are evaluated with respect to the already existing implementation in Antenna Toolbox for MATLAB and verified with respect to commercial software. An effective implementation of the method of moments allows us to speed up automated antenna design process. In the part focused on shape synthesis, we theoretically evaluate and describe its behavior together with experimental use of machine learning algorithms like genetic algorithms and artificial neural networks for data classification.
The relationship between generalized preconsolidation pressure and brittle-ductile transition pressure in sedimentary soft rock

Abstract In this study, we investigated the preconsolidation pressure (P C) of sedimentary soft rock and its relationship with brittle-ductile transition (BDT) pressure. We proposed a generalized preconsolidation pressure (P G) suitable for both soil and sedimentary soft rock, which is defined in terms of the yield stress of the initial sediment structure under confined lateral compression. We then explored the digenetic effect on P G using 24 sediment types. Next, P G and BDT pressure were verified using confined compression and conventional triaxial tests, respectively, on red-bed soft rock in the Dingxi region of China. Finally, we discussed the BDT mechanism and analyzed the relationship between the two characteristic pressures for a given initial yield surface considered structural strength. We found that the relationship was dependent on the strength parameters, the initial structural strength, and the coefficient of earth pressure at rest. The findings of this study will have a significant impact on the determination of BDT pressure of sedimentary rock in future studies.
Ultra-Deep Sequencing Reveals the Mutational Landscape of Classical Hodgkin Lymphoma

The malignant Hodgkin and Reed Sternberg (HRS) cells of classical Hodgkin lymphoma (cHL) are scarce in affected lymph nodes, creating a challenge to detect driver somatic mutations. As an alternative to cell purification techniques, we hypothesized that ultra-deep exome sequencing would allow genomic study of HRS cells, thereby streamlining analysis and avoiding technical pitfalls. To test this, 31 cHL tumor/normal pairs were exome sequenced to ~1000x median depth of coverage. An orthogonal error-corrected sequencing approach verified >95% of the discovered mutations. We identified mutations in genes novel to cHL including: CDH5 and PCDH7; novel mutations in IL4R, and a novel pattern of recurrent mutations in pathways regulating Hippo signaling. This study provides proof-of-principle that ultra-deep exome sequencing can be utilized to identify recurrent mutations in HRS cells, allowing for the analysis for clinically relevant genomic variants in large cohorts of cHL patients.
Algorithmic Determination of a Large Integer in the Two-Term Machin-like Formula for π

In our earlier publication we have shown how to compute by iteration a rational number u2,k in the two-term Machin-like formula for π of the kind π4=2k−1arctan1u1,k+arctan1u2,k,k∈Z,k≥1, where u1,k can be chosen as an integer u1,k=ak/2−ak−1 with nested radicals defined as ak=2+ak−1 and a0=0. In this work, we report an alternative method for determination of the integer u1,k. This approach is based on a simple iteration and does not require any irrational (surd) numbers from the set ak in computation of the integer u1,k. Mathematica programs validating these results are presented.
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation

We present an efficient low-rank approximation algorithm for non-negative tensors. The algorithm is derived from our two findings: First, we show that rank-1 approximation for tensors can be viewed as a mean-field approximation by treating each tensor as a probability distribution. Second, we theoretically provide a sufficient condition for distribution parameters to reduce Tucker ranks of tensors; interestingly, this sufficient condition can be achieved by iterative application of the mean-field approximation. Since the mean-field approximation is always given as a closed formula, our findings lead to a fast low-rank approximation algorithm without using a gradient method. We empirically demonstrate that our algorithm is faster than the existing non-negative Tucker rank reduction methods and achieves competitive or better approximation of given tensors.
The Highly Self-absorbed Blazar PKS 1351-018

PKS 1351-018 at a redshift of z = 3.71 is one of the most luminous, steady synchrotron sources with a luminosity >1047 erg s−1. The synchrotron luminosity does not seem to have varied by more than ∼25% over 35 yr. In order to appreciate this remarkable behavior, if it were at z = 0.5, it would have a flux density at 15 GHz in a range of 110–137 Jy over 11 yr. In spite of this steady behavior, two strong γ-ray flares ≲1049 erg s−1 were detected in 2011 and 2016. There is a blazar-like behavior coexisting with the steady behavior. This study is aimed at elucidating the dual nature of this source. We find that the radio source is extremely compact with a bright core and a steep spectrum secondary component, 12 mas away, that appears to be constant in position and flux density in six epochs from 1995–2018. We estimate that a jet with a time averaged power of (5.2 ± 3.2) × 1045 erg s−1 terminates in this lobe, which is advancing ≳0.9c at a deprojected distance of 1–3 kpc from the central engine. This is the rare case of a young (∼6000 yr), very powerful radio source that is viewed a few degrees from the jet axis. We find evidence of a high velocity (4000 km s−1), high ionization wind emanating from a luminous quasar. The young radio jet appears to experience modest bending as it navigates through the intense quasar environment.
A Business Model for Resource Sharing in Cell-Free UAVs-Assisted Wireless Networks

Unmanned aerial vehicles (UAVs) are widely deployed to enhance the wireless network capacity and to provide communication services to mobile users beyond the infrastructure coverage. Recently, with the help of a promising technology called network virtualization, multiple service providers (SPs) can share the infrastructures and wireless resources owned by the mobile network operators (MNOs). Then, they provide specific services to their mobile users using the resources obtained from MNOs. However, wireless resource sharing among SPs is challenging as each SP wants to maximize their utility/profit selfishly while satisfying the QoS requirement of their mobile users. Therefore, in this paper, we propose joint users association and wireless resource sharing problem in the cell-free UAVs-assisted wireless networks with the objective of maximizing the total network utility of the SPs while ensuring QoS constraints of their mobile users and the resource constraints of the UAVs deployed by MNOs. To solve the proposed mixed-integer non-convex problem, we decompose the proposed problem into two subproblems: users association, and resource sharing problems. Then, a two-sided matching algorithm is deployed in order to solve users association problem. We further deploy the whale optimization and Lagrangian relaxation algorithms to solve the resource sharing problem. Finally, extensive numerical results are provided in order to show the effectiveness of our proposed algorithm.
q-Paths: Generalizing the Geometric Annealing Path using Power Means

Many common machine learning methods involve the geometric annealing path, a sequence of intermediate densities between two distributions of interest constructed using the geometric average. While alternatives such as the moment-averaging path have demonstrated performance gains in some settings, their practical applicability remains limited by exponential family endpoint assumptions and a lack of closed form energy function. In this work, we introduce $q$-paths, a family of paths which is derived from a generalized notion of the mean, includes the geometric and arithmetic mixtures as special cases, and admits a simple closed form involving the deformed logarithm function from nonextensive thermodynamics. Following previous analysis of the geometric path, we interpret our $q$-paths as corresponding to a $q$-exponential family of distributions, and provide a variational representation of intermediate densities as minimizing a mixture of $\alpha$-divergences to the endpoints. We show that small deviations away from the geometric path yield empirical gains for Bayesian inference using Sequential Monte Carlo and generative model evaluation using Annealed Importance Sampling.
Sequential importance sampling for estimating expectations over the space of perfect matchings

This paper makes three contributions to estimating the number of perfect matching in bipartite graphs. First, we prove that the popular sequential importance sampling algorithm works in polynomial time for dense bipartite graphs. More carefully, our algorithm gives a $(1\pm\epsilon)$-approximation for the number of perfect matchings of a $\lambda$-dense bipartite graph, using $O(n^{\frac{1-2\lambda}{\lambda}\epsilon^{-2}})$ samples. With size $n$ on each side and for $\frac{1}{2}>\lambda>0$, a $\lambda$-dense bipartite graph has all degrees greater than $(\lambda+\frac{1}{2})n$. Second, practical applications of the algorithm require many calls to matching algorithms. A novel preprocessing step is provided which makes significant improvements. Third, three applications are provided. The first is for counting Latin squares, the second is a practical way of computing the greedy algorithm for a card-guessing game with feedback, and the third is for stochastic block models. In all three examples, sequential importance sampling allows treating practical problems of reasonably large sizes.
DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature

In this work, we present to the NLP community, and to the wider research community as a whole, an application for the diachronic analysis of research corpora. We open source an easy-to-use tool coined DRIFT, which allows researchers to track research trends and development over the years. The analysis methods are collated from well-cited research works, with a few of our own methods added for good measure. Succinctly put, some of the analysis methods are: keyword extraction, word clouds, predicting declining/stagnant/growing trends using Productivity, tracking bi-grams using Acceleration plots, finding the Semantic Drift of words, tracking trends using similarity, etc. To demonstrate the utility and efficacy of our tool, we perform a case study on the cs.CL corpus of the arXiv repository and draw inferences from the analysis methods. The toolkit and the associated code are available here: https://github.com/rajaswa/DRIFT.
Model checking C++ programs

In the last three decades, memory safety issues in system programming languages such as C or C++ have been one of the most significant sources of security vulnerabilities. However, there exist only a few attempts with limited success to cope with the complexity of C++ program verification. We describe and evaluate a novel verification approach based on bounded model checking (BMC) and satisfiability modulo theories (SMT) to verify C++ programs. Our verification approach analyses bounded C++ programs by encoding into SMT various sophisticated features that the C++ programming language offers, such as templates, inheritance, polymorphism, exception handling, and the Standard Template Libraries. We formalize these features within our formal verification framework using a decidable fragment of first‐order logic and then show how state‐of‐the‐art SMT solvers can efficiently handle that. We implemented our verification approach on top of ESBMC. We compare ESBMC to LLBMC and DIVINE, which are state‐of‐the‐art verifiers to check C++ programs directly from the LLVM bitcode. Experimental results show that ESBMC can handle a wide range of C++ programs, presenting a higher number of correct verification results. Additionally, ESBMC has been applied to a commercial C++ application in the telecommunication domain and successfully detected arithmetic‐overflow errors, which could potentially lead to security vulnerabilities.