Miracle-dz/UniFAR-FTUs huggingface dataset:
Jordanian Adolescent Cannabis Use: Patterns, Risks, And Protective Factors

JORDANIAN ADOLESCENT CANNABIS USE: PATTERNS, RISKS, AND PROTECTIVE FACTORS by SUKAINA ALZYOUD May 2010 Advisor: Dr. Stephanie Schim Major: Nursing Degree: Doctor of Philosophy Background: Cannabis is considered to be the most commonly used substances worldwide. Its use is not only common among adolescents but is increasing in developing countries such as Jordan. Cannabis use among adolescents has been linked to unintentional injuries, physical fights, academic problems, and illegal behavior such as driving under the influence. Studies of cannabis use patterns, risks, and protective factors are limited in developing countries. Therefore, the purpose of this study is to determine the patterns, risks, and protective factors for cannabis use among Jordanian adolescents. Methods: A cross-sectional descriptive/correlational survey design was used to assess the patterns among Jordanian adolescents 13-18 years of age drawn from Zarka Governorate in Jordan. Zarka Governorate is located at the central region of Jordan and is home to 15% of the total Jordanian population. Sample size was estimated using a 95% confidence interval for p that is expected to be about 50% (0.50). The final sample was 384 Jordanian students who attend public schools and participated in the study. These participants were recruited using simple random sampling from three grades (8, 10, &12). Following permission from the Jordanian Ministry of Education and Wayne State University IRB, participant assent, and parental consent for those less than 18
Varying fundamental constants and dark energy in the ESPRESSO era

The observational evidence for the recent acceleration of the universe shows that canonical theories of cosmology and particle physics are incomplete and that new physics is out there, waiting to be discovered. A compelling task for astrophysical facilities is to search for, identify and ultimately characterize this new physics. I present very recent developments in tests of the stability of nature’s fundamental constants, as well as their impact on physics paradigms beyond the standard model. Specifically I discuss new observational constraints at low redshifts and at the BBN epoch, and highlight their different implications for canonical quintessence-type models and for non-canonical string-theory inspired models. Finally I also present new forecasts, based on realistic simulated data, of the gains in sensitivity for these constraints expected from ELT-HIRES, on its own and in combination with Euclid.
Improving Peer Assessment with Graph Convolutional Networks

Peer assessment systems are emerging in many social and multi-agent settings, such as peer grading in large (online) classes, peer review in conferences, peer art evaluation, etc. However, peer assessments might not be as accurate as expert evaluations, thus rendering these systems unreliable. The reliability of peer assessment systems is influenced by various factors such as assessment ability of peers, their strategic assessment behaviors, and the peer assessment setup (e.g., peer evaluating group work or individual work of others). In this work, we first model peer assessment as multi-relational weighted networks that can express a variety of peer assessment setups, plus capture conflicts of interest and strategic behaviors. Leveraging our peer assessment network model, we introduce a graph convolutional network which can learn assessment patterns and user behaviors to more accurately predict expert evaluations. Our extensive experiments on real and synthetic datasets demonstrate the efficacy of our proposed approach, which outperforms existing peer assessment methods.
Wrapped Haptic Display for Communicating Physical Robot Learning

Physical interaction between humans and robots can help robots learn to perform complex tasks. The robot arm gains information by observing how the human kinesthetically guides it throughout the task. While prior works focus on how the robot learns, it is equally important that this learning is transparent to the human teacher. Visual displays that show the robot's uncertainty can potentially communicate this information; however, we hypothesize that visual feedback mechanisms miss out on the physical connection between the human and robot. In this work we present a soft haptic display that wraps around and conforms to the surface of a robot arm, adding a haptic signal at an existing point of contact without significantly affecting the interaction. We demonstrate how soft actuation creates a salient haptic signal while still allowing flexibility in device mounting. Using a psychophysics experiment, we show that users can accurately distinguish inflation levels of the wrapped display with an average Weber fraction of 11.4%. When we place the wrapped display around the arm of a robotic manipulator, users are able to interpret and leverage the haptic signal in sample robot learning tasks, improving identification of areas where the robot needs more training and enabling the user to provide better demonstrations. See videos of our device and user studies here: https://youtu.be/tX-2Tqeb9Nw
Physics-Informed Neural Operator for Learning Partial Differential Equations

In this paper, we propose physics-informed neural operators (PINO) that combine training data and physics constraints to learn the solution operator of a given family of parametric Partial Differential Equations (PDE). PINO is the first hybrid approach incorporating data and PDE constraints at different resolutions to learn the operator. Specifically, in PINO, we combine coarse-resolution training data with PDE constraints imposed at a higher resolution. The resulting PINO model can accurately approximate the ground-truth solution operator for many popular PDE families and shows no degradation in accuracy even under zero-shot super-resolution, i.e., being able to predict beyond the resolution of training data. PINO uses the Fourier neural operator (FNO) framework that is guaranteed to be a universal approximator for any continuous operator and discretization convergent in the limit of mesh refinement. By adding PDE constraints to FNO at a higher resolution, we obtain a high-fidelity reconstruction of the ground-truth operator. Moreover, PINO succeeds in settings where no training data is available and only PDE constraints are imposed, while previous approaches, such as the Physics-Informed Neural Network (PINN), fail due to optimization challenges, e.g., in multi-scale dynamic systems such as Kolmogorov flows.
Practical, Fast and Robust Point Cloud Registration for 3D Scene Stitching and Object Localization

3D point cloud registration ranks among the most fundamental problems in remote sensing, photogrammetry, robotics and geometric computer vision. Due to the limited accuracy of 3D feature matching techniques, outliers may exist, sometimes even in very large numbers, among the correspondences. Since existing robust solvers may encounter high computational cost or restricted robustness, we propose a novel, fast and highly robust solution, named VOCRA (VOting with Cost function and Rotating Averaging), for the point cloud registration problem with extreme outlier rates. Our first contribution is to employ the Tukey’s Biweight robust cost to introduce a new voting and correspondence sorting technique, which proves to be rather effective in distinguishing true inliers from outliers even with extreme (99%) outlier rates. Our second contribution consists in designing a time-efficient consensus maximization paradigm based on robust rotation averaging, serving to seek inlier candidates among the correspondences. Finally, we apply Graduated Non-Convexity with Tukey’s Biweight (GNC-TB) to estimate the correct transformation with the inlier candidates obtained, which is then used to find the complete inlier set. Both standard benchmarking and realistic experiments with application to two real-data problems are conducted, and we show that our solver VOCRA is robust against over 99% outliers (exceeding traditional GNC by nearly 10% and RANSAC by nearly 4%) and more time-efficient than the state-of-the-art competitors.
Online Adaptation of Monocular Depth Prediction with Visual SLAM

— The ability of accurate depth prediction by a CNN is a major challenge for its wide use in practical visual SLAM applications, such as enhanced camera tracking and dense mapping. This paper is set out to answer the following question: Can we tune a depth prediction CNN with the help of a visual SLAM algorithm even if the CNN is not trained for the current operating environment in order to beneﬁt the SLAM performance? To this end, we propose a novel online adaptation framework consisting of two complementary processes: a SLAM algorithm that is used to generate keyframes to ﬁne-tune the depth prediction and another algorithm that uses the online adapted depth to improve map quality. Once the potential noisy map points are removed, we perform global photometric bundle adjustment (BA) to improve the overall SLAM performance. Experimental results on both benchmark datasets and a real robot in our own experimental environments show that our proposed method improves the SLAM reconstruction accuracy. We demonstrate the use of regularization in the training loss as an effective means to prevent catastrophic forgetting . In addition, we compare our online adaptation framework against the state-of-the-art pre-trained depth prediction CNNs to show that our online adapted depth prediction CNN outperforms the depth prediction CNNs that have been trained on a large collection of datasets.
Magnetic reconnection and plasmoid formation in three-dimensional accretion flows around black holes

Magnetic reconnection is thought to be one of the main energy-dissipation mechanisms fu-eling energy to the plasma in the vicinity of a black hole. Indeed, plasmoids formed through magnetic reconnection may play a key role in γ -ray, X-ray and near-infrared ﬂares from the black hole at the center of our galaxy, SgrA*. We report the results of three-dimensional general-relativistic ideal and resistive magnetohydrodynamics simulations modelling magnetic reconnection in accretion ﬂows around astrophysical black holes. As an important dif-ference with similar works, our accretion discs have an initial dipolar magnetic-ﬁeld conﬁguration with loops of alternating polarity. We show that current sheets are formed and destroyed rapidly in the turbulent environment of black-hole accretion. Plasmoids are formed from current sheets close to the event horizon, in a region of ∼ 2 − 15 gravitational radii. We further quantify the magnetic dissipation and the process of energy transfer to the plasmoids, reporting the reconnection rate, the relative current density with respect to the local magnetic ﬁeld, and the size of the plasmoids. We ﬁnd that plasmoids gain energy through reconnection and heat up to relativistic temperatures, with the largest ones being sufﬁciently energetic to leave the black hole near the polar regions. During their evolution, plasmoids are stretched and elongated, becoming disrupted when the shear is sufﬁciently large, although some plasmoids survive as well-distinguished structures at distances of ∼ 30 − 40 gravitational radii from the black hole. Finally, we ﬁnd that in some cases the plasmoids acquire a super-Keplerian azimuthal velocity, as suggested by recent observations of ﬂares from Sgr A*.
Flight Demand Forecasting with Transformers

Transformers have become the de-facto standard in the natural language processing (NLP) field. They have also gained momentum in computer vision and other domains. Transformers can enable artificial intelligence (AI) models to dynamically focus on certain parts of their input and thus reason more effectively. Inspired by the success of transformers, we adopted this technique to predict strategic flight departure demand in multiple horizons. This work was conducted in support of a MITRE-developed mobile application, Pacer, which displays predicted departure demand to general aviation (GA) flight operators so they can have better situation awareness of the potential for departure delays during busy periods. Field demonstrations involving Pacer's previously designed rule-based prediction method showed that the prediction accuracy of departure demand still has room for improvement. This research strives to improve prediction accuracy from two key aspects: better data sources and robust forecasting algorithms. We leveraged two data sources, Aviation System Performance Metrics (ASPM) and System Wide Information Management (SWIM), as our input. We then trained forecasting models with temporal fusion transformer (TFT) for five different airports. Case studies show that TFTs can perform better than traditional forecasting methods by large margins, and they can result in better prediction across diverse airports and with better interpretability.
Propagation-induced entanglement revival

The practical implementation of free-space quantum information tasks requires entanglement to be sustained over long distances and in the presence of turbulent and noisy environments. The transverse position-momentum entanglement of photon pairs produced by parametric down-conversion has found several uses in quantum information science, however, it is not suitable for applications involving long-distance propagation as the entanglement decays very rapidly when photons propagate away from their source. Entanglement is lost after a few centimetres of propagation, and the effect becomes even more pronounced in turbulent environments. In contrast, in this article, we show that entanglement in the angle-orbital angular momentum (OAM) bases exhibits a remarkably different behaviour. As with the position-momentum case, initially, the angle-OAM entanglement decays with propagation, but as the photons continue to travel further from the source, the photons regain their strongly correlated behaviour, and the entanglement returns. We theoretically and experimentally demonstrate this behaviour and show that entanglement returns even in the presence of strong turbulence. The only effect of turbulence is to increase the propagation distance for revival, but once revived, the two photons remain entangled up to an arbitrary propagation distance. This work highlights the role that OAM-angle entanglement will play in applications where quantum information is shared over long distances.
A flexible and open architecture for the user modeling shell system BG P-MS

Many of the user modeling shell systems of today have an architecture that is based on a one-to-one relationship between adaptive application and user modeling system. Therefore, they can hardly address interesting future scenarios in the field of user modeling where synergetic effects arising from the distributed use of a central user model might play an important role. We propose a generic architecture for the user modeling shell system BGP-MS which has been designed to cope with different possible user modeling scenarios and at the same time offers a high degree of transparency that is manifested in a uniform view of the available functionality.
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

Adversarial attacks on graphs have posed a major threat to the robustness of graph machine learning (GML) models. Naturally, there is an ever-escalating arms race between attackers and defenders. However, the strategies behind both sides are often not fairly compared under the same and realistic conditions. To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models. GRB standardizes the process of attacks and defenses by 1) developing scalable and diverse datasets, 2) modularizing the attack and defense implementations, and 3) unifying the evaluation protocol in refined scenarios. By leveraging the GRB pipeline, the end-users can focus on the development of robust GML models with automated data processing and experimental evaluations. To support open and reproducible research on graph adversarial learning, GRB also hosts public leaderboards across different scenarios. As a starting point, we conduct extensive experiments to benchmark baseline techniques. GRB is open-source and welcomes contributions from the community. Datasets, codes, leaderboards are available at https://cogdl.ai/grb/home.
When Cyber-Physical Systems Meet AI: A Benchmark, an Evaluation, and a Way Forward

Cyber-Physical Systems (CPS) have been broadly deployed in safety-critical domains, such as automotive systems, avionics, medical devices, etc. In recent years, Artificial Intelligence (AI) has been increasingly adopted to control CPS. Despite the popularity of AI-enabled CPS, few benchmarks are publicly available. There is also a lack of deep understanding on the performance and reliability of AI-enabled CPS across different industrial domains. To bridge this gap, we present a public benchmark of industry-level CPS in seven domains and build AI controllers for them via state-of-the-art deep reinforcement learning (DRL) methods. Based on that, we further perform a systematic evaluation of these AI-enabled systems with their traditional counterparts to identify current challenges and future opportunities. Our key findings include (1) AI controllers do not always outperform traditional controllers, (2) existing CPS testing techniques (falsification, specifically) fall short of analyzing AI-enabled CPS, and (3) building a hybrid system that strategically combines and switches between AI controllers and traditional controllers can achieve better performance across different domains. Our results highlight the need for new testing techniques for AI-enabled CPS and the need for more investigations into hybrid CPS to achieve optimal performance and reliability. Our benchmark, code, detailed evaluation results, and experiment scripts are available on https://sites.google. com/view/ai-cps-benchmark.
Hierarchical Segment-based Optimization for SLAM

This paper presents a hierarchical segment-based optimization method for Simultaneous Localization and Mapping (SLAM) system. First we propose a reliable trajectory segmentation method that can be used to increase efficiency in the back-end optimization. Then we propose a buffer mechanism for the first time to improve the robustness of the segmentation. During the optimization, we use global information to optimize the frames with large error, and interpolation instead of optimization to update well-estimated frames to hierarchically allocate the amount of computation according to error of each frame. Comparative experiments on the benchmark show that our method greatly improves the efficiency of optimization with almost no drop in accuracy, and outperforms existing high-efficiency optimization method by a large margin.
JaMIE: A Pipeline Japanese Medical Information Extraction System with Novel Relation Annotation

In the field of Japanese medical information extraction, few analyzing tools are available and relation extraction is still an under-explored topic. In this paper, we first propose a novel relation annotation schema for investigating the medical and temporal relations between medical entities in Japanese medical reports. We experiment with the practical annotation scenarios by separately annotating two different types of reports. We design a pipeline system with three components for recognizing medical entities, classifying entity modalities, and extracting relations. The empirical results show accurate analyzing performance and suggest the satisfactory annotation quality, the superiority of the latest contextual embedding models. and the feasible annotation strategy for high-accuracy demand.
Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments

Attitude control of fixed-wing unmanned aerial vehicles (UAVs) is a difficult control problem in part due to uncertain nonlinear dynamics, actuator constraints, and coupled longitudinal and lateral motions. Current state-of-the-art autopilots are based on linear control and are thus limited in their effectiveness and performance. Gls drl is a machine learning method to automatically discover optimal control laws through interaction with the controlled system that can handle complex nonlinear dynamics. We show in this article that deep reinforcement learning (DRL) can successfully learn to perform attitude control of a fixed-wing UAV operating directly on the original nonlinear dynamics, requiring as little as 3 min of flight data. We initially train our model in a simulation environment and then deploy the learned controller on the UAV in flight tests, demonstrating comparable performance to the state-of-the-art ArduPlane proportional-integral-derivative (PID) attitude controller with no further online learning required. Learning with significant actuation delay and diversified simulated dynamics were found to be crucial for successful transfer to control of the real UAV. In addition to a qualitative comparison with the ArduPlane autopilot, we present a quantitative assessment based on linear analysis to better understand the learning controller’s behavior.
Modeling of Graphene Oxide Memory based on Hybridization State Modulation

Graphene oxide (GO)-based resistive random access memory (RRAM) is one of the most promising emerging non-volatile memories for flexible electronics because of its simple structure and low fabrication cost. The reported switching mechanism can be classified into either localized or bulk effect. The localized effect is commonly observed in GO-RRAM with active electrode e.g. Cu and Al. The switching mechanism includes metallic conduction filament evolution or modification of interfacial resistance between GO layer and electrodes. On the other hand, the bulk effect involves hybridization state change of the entire GO layer and is typically observed in devices with inert electrode e.g. Pt and Au. Numerous compact models have been proposed for RRAM that are based on localized switching mechanism. However, compact modeling of GO-RRAM based on bulk mechanism is still lacking. Thus, this paper presents a compact model for GO-RRAM that relies on hybridization state modulation. The proposed model associates the resistance switching mechanism with the electricaldriven modification of sp clusters density in the switching layer. A SPICE model incorporating the proposed compact model is developed for parameter calibration. The simulated I-V characteristic exhibits strong correlation with the experimental data. Hereby, we regard the proposed compact model as an acceptable and close description of GO-RRAM operation that depends on the bulk switching mechanism.