Miracle-dz/UniFAR-FTUs huggingface dataset:
Who in the World Is Trying to Change Their Personality Traits? Volitional Personality Change Among College Students in Six Continents

Recent research conducted largely in the United States suggests that most people would like to change one or more of their personality traits. Yet almost no research has investigated the degree to which and in what ways volitional personality change (VPC), or individuals’ active efforts toward personality change, might be common around the world. Through a custom-built website, 13,278 college student participants from 55 countries and one of a larger country (Hong Kong, S.A.R.) using 42 different
Genomic Structure and Localization of the Human Protein Phosphatase 2A BR7 Regulatory Subunit

In the course of our analysis of genomic sequence from the human chromosome 4pl6.1 region harboring both the Wolfram and Ellis van Creveld syndrome genes' we have identified a sequence with high homology (98% at the amino acid level) to the rat cDNA coding for the protein phosphatase 2A BR7 (PP2ABR7) regulatory subunit. Although the human cDNAs for both the BRa and BR/3 isoforms have been described previously, the BR7 subunit has not yet been identified in humans. Here we describe the precise genomic organization and genetic localization of the human PP2ABR7 gene.
Using multivariate adaptive regression splines (MARS) in pavement roughness prediction

The paper presents the application of a new statistical technique, multivariate adaptive regression splines (MARS), to a flexible pavement roughness prediction model. MARS is a non-parametric function estimation technique that shows great promise for fitting non-linear multivariate functions. The MARS approach was used to develop a roughness equation, based on available input, and was able to identify the threshold values of each input and the most important variables contributing to the roughness equation. The MARS technique allows easy interpretation of the relative importance of pavement condition variables, environmental factors and traffic for the overall fit.
MOVING OBJECT DETECTION UNDER SIGNIFICANT CHANGES OF LIGHTING

Surveillance applications require reliable components with capabilities to work under disturbance conditions such as momentary power interruption. The research leading to this paper has been focused on implementing a robust algorithm to detect movement of foreground objects independent of illumination changes. An improved version combining time and space analysis is presented. Experimental results show a promising advance in the addressed problem
INTRODUCING A NOVEL INTEGRATED MODEL FOR THE ADOPTION OF INFORMATION SECURITY AWARENESS THROUGH CONTROL, PREDICTION, MOTIVATION, AND DETERRENCE FACTORS: A PILOT STUDY

Information security (IS) violations have a negative impact at both organisational and individual levels. Information security awareness (ISA) therefore plays a positive role in ensuring employees adhere to an organisation's security policies. The challenges that arise in protecting the information infrastructure in the Omani public sector are increasing because of a rise in cases of piracy and phishing attempts as well as a lack of adequate ISA among employees. This paper discusses a pilot study from our ongoing research that proposes an integrated model for adopting ISA as an effective method to improve the security behaviour of employees in Omani institutions. This study aims to validate the reliability of research instrument items prior to conducting a main survey on the adoption of the ISA model in Oman. PLS-SEM and SPSS software were used to conduct statistical analyses, which included a skewness test, a correlation analysis, a Cronbach’s alpha test, and tests for composite reliability (CR), average variance extracted (AVE), and discriminant validity. Three main theories were utilised in the formation of the integrated ISA model: the theory of planned behaviour (TPB), protection motivation theory (PMT), and general deterrence theory (GDT) – along with two facilitating condition-related constructs: organisational support and communication. The findings indicated that all construct items for all variables have acceptable Cronbach’s alpha values, composites reliability, AVE, and discriminant validity. The results of this study reveal that the questions were applicable, subject to one interpretation, to all participants. Hence, this study contributes to the body of ISA literature by discussing ISA in terms of the bases of control, prediction, motivation, and deterrence. This synthesis constitutes a new perspective that enables organisations to better manage the ISA process, particularly in Oman.
SENTIMENT ANALYSIS OF AMAZON’S REVIEWS USING MACHINE LEARNING ALGORITHMS

Sentiment analysis also called opinion mining, is the field of study that analyses people’s opinion, sentiment, evaluations, appraisals, attitudes and emotions towards entities such us services, organizations, individuals, issues, events, topics and products. The fast evolution of Internet-based applications like websites, social networks, and blogs, leads people to generate enormous heaps of opinions and reviews about products, services, and day-to-day activities. Sentiment analysis poses as a powerful tool for businesses, governments, and researchers to extract and analyze public mood and views, gain business insight, and make better decisions. There are many approaches to classify the sentiment, approaches based on machine learning or lexicon-based approach. In this article we will discuss the different approaches of sentiment analysis, and we will compare the performance of the different machine learning algorithms. In this comparative study we will use the naïve Bayes, Support vector Machine, the Decision Tree and the Logistic regression algorithms to analyze the sentiments in amazon’s reviews data. The main objective is to analyze the large number of reviews expressed in amazons in order to deduce the different feelings expressed in it, positive, negative or neutral. The main goal of this work is to achieve the best result of sentiment analysis. So, to analyze and classify the data we will start by preprocessing the data then the features extraction after that the sentiment classification using the machine learning algorithms and finally the evaluation of the algorithms, using Spark and Scala language for implementing the algorithms. The final results show that the SVM classifier achieved 100% accuracy, Naive Bayes classifier achieved 95% accuracy, the Logistic regression 97% and the Decision Tree classifier achieved 75% accuracy.
DEEP GRAPH EMBEDDINGS IN RECOMMENDER SYSTEMS: A SURVEY

In recent decades, the increasing quantity of products and services offered the possibility of collecting significant amounts of data, which require new techniques to sort it. Rather than manually filter these large quantities of information, it provides changes over time. Recommender systems help suggest articles to read on smartphones, posts to watch on Facebook, books to buy on Amazon. Their goal is to personalize data to increase the use of a service or to enable more sales. Their influence is not just a technical and commercial necessity. They have also become a part of the evolution of the human mass and his\her ideas. Because a book or a newspaper is not just a commercial object, many current recommendation techniques are challenged by information overload, which poses many issues like high cost, slow processing of data, and low time complexity. For this reason, many researchers in this field use graph embeddings algorithms in the recommendation area, as the last few years have also seen the success of these algorithms, especially the ones based on deep learning. Current recommender systems based on these algorithms have shown that they can obtain exciting results and improve the quality of recommendations offered to users. In this survey, we present an overview of recommender systems and graph embeddings based on deep learning. Then we provide a literature review of recent recommendations works based on deep graph embeddings to make a pragmatic analysis and showings common limitations.
Authors’ response to referee #1

General comments: This manuscript describes the further development of the AIOMFAC model to include additional chemical species, such as carbonates and iodine. Using literature thermodynamic data and new experimental observations, the authors derive estimates for the interaction parameters between ionic species and certain organic functional groups. The authors report excellent agreement between model and experimental results for inorganic mixtures, and good agreement with organic mixtures at high humidity. Overall, this work provides the community with a detailed description of the model development and will allow for a broader application of the AIOMFAC model to understanding the thermodynamics of chemically complex systems. It is appropriate for this journal, well-written and can be published with minimal changes, as detailed below.
PeopleSansPeople: A Synthetic Data Generator for Human-Centric Computer Vision

In recent years, person detection and human pose estimation have made great strides, helped by large-scale labeled datasets. However, these datasets had no guarantees or analysis of human activities, poses, or context diversity. Additionally, privacy, legal, safety, and ethical concerns may limit the ability to collect more human data. An emerging alternative to real-world data that alleviates some of these issues is synthetic data. However, creation of synthetic data generators is incredibly challenging and prevents researchers from exploring their usefulness. Therefore, we release a human-centric synthetic data generator PeopleSansPeople which contains simulation-ready 3D human assets, a parameterized lighting and camera system, and generates 2D and 3D bounding box, instance and semantic segmentation, and COCO pose labels. Using PeopleSansPeople, we performed benchmark synthetic data training using a Detectron2 Keypoint R-CNN variant [1]. We found that pre-training a network using synthetic data and fine-tuning on various sizes of real-world data resulted in a keypoint AP increase of $+38.03$ ($44.43 \pm 0.17$ vs. $6.40$) for few-shot transfer (limited subsets of COCO-person train [2]), and an increase of $+1.47$ ($63.47 \pm 0.19$ vs. $62.00$) for abundant real data regimes, outperforming models trained with the same real data alone. We also found that our models outperformed those pre-trained with ImageNet with a keypoint AP increase of $+22.53$ ($44.43 \pm 0.17$ vs. $21.90$) for few-shot transfer and $+1.07$ ($63.47 \pm 0.19$ vs. $62.40$) for abundant real data regimes. This freely-available data generator should enable a wide range of research into the emerging field of simulation to real transfer learning in the critical area of human-centric computer vision.
Fast Carrier Cancellation for UHF Near Field RFID Systems

Abstract A novel carrier cancelation circuit is presented for ultra high frequency (UHF) near field radio frequency identification applications.A directional coupler, logarithmic detector and controlling circuit are used for improving the sensitivity of the receiver. The cancelation circuit, which consists of vector multiplier and SCM (Single Chip Micyoco), can be adaptively tuned to generate an optimum canceling signal for carrier cancelation. In order to secure good and fast carrier cancelation, logarithmic detector is used as a feedback component to transmit power information of the tag signal channel. The implemented reconfigurable carrier cancelation exhibits good performance on reduction of carrier level in tag signal channel when the adaptive control voltage of SCM is applied. Furthermore, the carrier cancelation control system could be operated real time as well as applied for hopping-frequency systems.
Meteorites and the RNA World: Synthesis of Nucleobases in Carbonaceous Planetesimals and the Role of Initial Volatile Content

Prebiotic molecules, fundamental building blocks for the origin of life, have been found in carbonaceous chondrites. The exogenous delivery of these organic molecules onto the Hadean Earth could have sparked the polymerization of the first RNA molecules in Darwinian ponds during wet-dry cycles. Here, we investigate the formation of the RNA and DNA nucleobases adenine, uracil, cytosine, guanine, and thymine inside parent body planetesimals of carbonaceous chondrites. An up-to-date thermochemical equilibrium model coupled with a 1D thermodynamic planetesimal model is used to calculate the nucleobase concentrations. Different from previous studies, we assume the initial volatile concentrations more appropriate for the formation zone of carbonaceous chondrite parent bodies. This represents more accurately cosmochemical findings that these bodies have formed inside the inner, ∼2–5 au, warm region of the solar system. Due to these improvements, our model represents the concentrations of adenine and guanine measured in carbonaceous chondrites. Our model did not reproduce per se the measurements of uracil, cytosine, and thymine in these meteorites. This can be explained by transformation reactions between nucleobases and the potential decomposition of thymine. The synthesis of prebiotic organic matter in carbonaceous asteroids could be well explained by a combination of (i) radiogenic heating, (ii) aqueous chemistry involving a few key processes at a specific range of radii inside planetesimals where water can exist in the liquid phase, and (iii) a reduced initial volatile content (H2, CO, HCN , and CH2O) of the protoplanetary disk material in the parent body region compared to the outer region of comets.
Autonomous Reinforcement Learning: Formalism and Benchmarking

Reinforcement learning (RL) provides a naturalistic framing for learning through trial and error, which is appealing both because of its simplicity and effectiveness and because of its resemblance to how humans and animals acquire skills through experience. However, real-world embodied learning, such as that performed by humans and animals, is situated in a continual, non-episodic world, whereas common benchmark tasks in RL are episodic, with the environment resetting between trials to provide the agent with multiple attempts. This discrepancy presents a major challenge when attempting to take RL algorithms developed for episodic simulated environments and run them on real-world platforms, such as robots. In this paper, we aim to address this discrepancy by laying out a framework for Autonomous Reinforcement Learning (ARL): reinforcement learning where the agent not only learns through its own experience, but also contends with lack of human supervision to reset between trials. We introduce a simulated benchmark EARL around this framework, containing a set of diverse and challenging simulated tasks reflective of the hurdles introduced to learning when only a minimal reliance on extrinsic intervention can be assumed. We show that standard approaches to episodic RL and existing approaches struggle as interventions are minimized, underscoring the need for developing new algorithms for reinforcement learning with a greater focus on autonomy.
Design and Optimization of an UWB Antenna with 5.8 GHz Band Suppression Using Genetic Algorithm

This paper reports the optimization of a staircase ultra wideband antenna with an inverted U shaped structure for 5.8 GHz ISM band suppression. Conventionally antennas are optimized on trial and error techniques, which are time consuming and even after a number of iterations there is no guarantee of achieving optimum result. To address the optimization of microwave antenna with a large number of dependent variables genetic algorithm techniques has been proposed. The optimum band suppression has been achieved by using the algorithm. Twelve dependent antenna variables were optimized in a very efficient way by exploiting dependencies among them in terms of bandwidth, notch band selection and return loss. The optimization procedure was elaborated and the optimized antenna results are presented. The antenna covers frequencies from 2.2 to 8.5 GHz and is immune to 5.8 GHz communication by rejecting the frequency. The optimized antenna design prototype was fabricated and the measured results are verified with the simulated results.
Constraint-based graph network simulator

In the area of physical simulations, nearly all neural-network-based methods directly predict future states from the input states. However, many traditional simulation engines instead model the constraints of the system and select the state which satisfies them. Here we present a framework for constraint-based learned simulation, where a scalar constraint function is implemented as a graph neural network, and future predictions are computed by solving the optimization problem defined by the learned constraint. Our model achieves comparable or better accuracy to top learned simulators on a variety of challenging physical domains, and offers several unique advantages. We can improve the simulation accuracy on a larger system by applying more solver iterations at test time. We also can incorporate novel hand-designed constraints at test time and simulate new dynamics which were not present in the training data. Our constraint-based framework shows how key techniques from traditional simulation and numerical methods can be leveraged as inductive biases in machine learning simulators.
Detectable Quantum Byzantine Agreement for Any Arbitrary Number of Dishonest Parties

Reaching agreement in the presence of arbitrary faults is a fundamental problem in distributed computation, which has been shown to be unsolvable if one-third of the processes can fail, unless signed messages are used. In this paper, we propose a solution to a variation of the original BA problem, called Detectable Byzantine Agreement (DBA), that does not need to use signed messages. The proposed algorithm uses what we call $Q$-correlated lists, which are generated by a quantum source device. Once each process has one of these lists, they use them to reach the agreement in a classical manner. Although, in general, the agreement is reached by using $m+1$ rounds (where $m$ is the number of processes that can fail), if less than one-third of the processes fail it only needs one round to reach the agreement.
Symmetry-dependent ultrafast manipulation of nanoscale magnetic domains

Symmetry is a powerful concept in physics, but its applicability to far-from-equilibrium states is still being understood. Recent attention has focused on how far-from-equilibrium states lead to spontaneous symmetry breaking. Conversely, ultrafast optical pumping can be used to drastically change the energy landscape and quench the magnetic order parameter in magnetic systems. Here, we find a distinct symmetry-dependent ultrafast behaviour by use of ultrafast x-ray scattering from magnetic patterns with varying degrees of isotropic and anisotropic symmetry. After pumping with an optical laser, the scattered intensity reveals a radial shift exclusive to the isotropic component and exhibits a faster recovery time from quenching for the anisotropic component. These features arise even when both symmetry components are concurrently measured, suggesting a correspondence between the excitation and the magnetic order symmetry. Our results underline the importance of symmetry as a critical variable to manipulate the magnetic order in the ultrafast regime.
The Gelfand problem for the Infinity Laplacian

<abstract><p>We study the asymptotic behavior as $ p\to\infty $ of the Gelfand problem</p> <p><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ \begin{equation*} \left\{ \begin{aligned} -&\Delta_{p} u = \lambda\,e^{u}&& \text{in}\ \Omega\subset \mathbb{R}^n\\ &u = 0 && \text{on}\ \partial\Omega. \end{aligned} \right. \end{equation*} $\end{document} </tex-math></disp-formula></p> <p>Under an appropriate rescaling on $ u $ and $ \lambda $, we prove uniform convergence of solutions of the Gelfand problem to solutions of</p> <p><disp-formula> <label/> <tex-math id="FE2"> \begin{document}$ \left\{ \begin{aligned} &\min\left\{|\nabla{}u|-\Lambda\,e^{u}, -\Delta_{\infty}u\right\} = 0&& \text{in}\ \Omega,\\ &u = 0\ &&\text{on}\ \partial\Omega. \end{aligned} \right. $\end{document} </tex-math></disp-formula></p> <p>We discuss existence, non-existence, and multiplicity of solutions of the limit problem in terms of $ \Lambda $. </p></abstract>
Deep Impact: What Happens When Archaeological Sites are Built on?

Abstract Increasingly, developers and archaeologists search for ways to build on archaeological sites, while at the same time preserving the remains underneath. However, deciding which effects of construction on archaeological sites are acceptable and which are not is hampered by the lack of knowledge on the impacts of construction on archaeological sites. This paper provides an update on some of the effects of building on archaeological sites. Recent research had shown that displacement piles cause less disturbance in soft soils than previously thought. Moreover, replacement piles may be less benign that assumed up till now. Effects of loading on archaeological sites are described, and gaps in the knowledge on these effects are indicated. More research is needed in this field, especially on predicting soil disturbance and damage to weak materials. Impermeable surfaces and constructions on top of sites may affect the visibility of soil features due to decreased rainwater infiltration. Finally, a series of non-physical effects are mentioned that will have to be taken into account when deciding on allowing building on archaeological sites.
Marginalization in Bayesian Networks: Integrating Exact and Approximate Inference

Bayesian Networks are probabilistic graphical models that can compactly represent dependencies among random variables. Missing data and hidden variables require calculating the marginal probability distribution of a subset of the variables. While knowledge of the marginal probability distribution is crucial for various problems in statistics and machine learning, its exact computation is generally not feasible for categorical variables due to the NP-hardness of this task. We develop a divide-and-conquer approach using the graphical properties of Bayesian networks to split the computation of the marginal probability distribution into sub-calculations of lower dimensionality, reducing the overall computational complexity. Exploiting this property, we present an eﬃcient and scalable algorithm for estimating the marginal probability distribution for categorical variables. The novel method is compared against state-of-the-art approximate inference methods in a benchmarking study, where it displays superior performance. As an immediate application, we demonstrate how the marginal probability distribution can be used to classify incomplete data against Bayesian networks and use this approach for identifying the cancer subtype of kidney cancer patient samples. ∗