Miracle-dz/UniFAR-FTUs huggingface dataset:
Less Than Human: How Different Users of Telepresence Robots Expect Different Social Norms

Does the norm of first-come-first-serve (FCFS) equally apply to those piloting a Mobile Remote Presence (MRP) system as to those who are physically present with it? While telepresence robots could make social interactions more accessible and enjoyable for geographically-constrained individuals, such an outcome requires both pilots and local users of MRPs to share the same social norm expectations that govern their use. To address this question, we conducted an online study $(N=903)$ involving simulated human-MRP interaction scenarios. Our results suggest that those remotely piloting the MRP-rather than local users-assign the robot to a lower social priority; they find it more appropriate when local users ignore queue order than when pilots ignore queue order. Furthermore, we provide significant empirical evidence that local users expect different social norms to be upheld depending on how they perceive the robot. Those who perceive MRPs simply as robots-rather than an extension of a person-do not expect the FCFS norm to be respected for MRPs.
An Inexpensive, Portable Shield to Improve Healthcare Worker Safety During Intubation Procedures*

Healthcare workers (HCW) are exposed to risk of infection during intubation procedures, in particular, in the prehospital setting. Here, we demonstrate a novel shield that can be used during intubation to block aerosols and droplets from reaching the HCW. The device is mounted on the patient’s head and provides a barrier between patient and HCW. It incorporates a self-sealing port through which an endotracheal tube can be inserted. The port "floats" in the plane of the shield to facilitate maneuvering of the endotracheal tube. The shield is fabricated from transparent materials to enable the HCW to visualize the procedure. Using two complementary imaging methods, background oriented Schlieren imaging and laser sheet droplet imaging, we show that the device prevents detectable transmission of gas flow and droplets through the shield both before and after endotracheal tube insertion.Clinical Relevance— This device has the potential to protect HCWs from infections during intubation procedures, especially in the prehospital setting.
Design and Characterization of Pressure Monitoring and Insertion system for Intraparenchymal Convection Enhanced Delivery

Neurological disorders are a significant societal and economic burden. Common pharmacological therapies often can only manage symptoms and have limited efficacy. Intraparenchymal convection enhanced delivery (IP CED) is a neurosurgical technique for direct brain delivery of therapeutics. Currently, the main applications of IP CED are targeted chemotherapy for glioblastoma and gene therapy. While IP CED has advantages over systemic approaches, its benefits can be drastically reduced by inadequate coverage as low as 21% of target anatomy, excessive infusion durations greater than 2 hours, and off-target effects. Addressing the limitations of IP CED requires thorough investigation and optimization of the relevant fluid dynamic and operational parameters. In this work, we present the design, fabrication, and characterization of low-cost, open-source, and fully automated CED cannula insertion control and pressure-monitoring systems. Using these automated CED control systems, we investigate the effects of pressure, insertion velocity, and flow rates on several outcome variables, including reflux, volume distribution, and infusion cloud morphology during CED infusions in brain phantoms.Clinical Relevance— CED pressure properties may be able to implicate reflux incidents and could provide clinicians with valuable, real-time information regarding ongoing infusions without the need for costly medical imaging modalities.
Optical Flow Glottovibrogram for the examination of vocal fold pathology

Laryngeal high-speed video endoscopy is performed to examine the cycles of vocal fold vibrations in detail and to diagnose voice abnormalities. One of the recent image processing techniques for visualizing vocal fold vibration is optical flow-based playbacks, which include optical flow kymograms (OFKG) for local dynamics, optical flow glottovibrogram (OFGVG) and glottal optical flow waveforms (GOFW) for global dynamics. In recent times, various optical flow computing algorithms have been developed. In this paper, we used four well-known optical flow algorithms Horn Schunk, Lucas Kanade, Gunnar Farneback, and TVL1 to construct the optical flow playbacks. The proposed playback reliability is examined by comparing them to traditional representations such as Phonovibrogram (PVG). Since PVG and OFGVG are interconnected, a comparison study was carried out to better comprehend their interaction.Clinical Relevance— Both OFGVG and PVG add to the precision of interpreting pathological conditions by offering complementary information to the conventional spatiotemporal representations.
Ambient Cardiovascular Monitoring with Metamaterial Textile Sensors

Contactless sensors embedded in the ambient environment have broad applications in unobtrusive, long-term health monitoring for preventative and personalized healthcare. Microwave radar sensors are an attractive candidate for ambient sensing due to their high sensitivity to physiological motions, ability to penetrate through obstacles and privacy-preserving properties, but practical applications in complex real-world environments have been limited because of challenges associated with background clutter and interference. In this work, we propose a thin and soft textile sensor based on microwave metamaterials that can be easily integrated into ordinary furniture for contactless ambient monitoring of multiple cardiovascular signals in a localized manner. Evaluations of our sensor’s performance in human subjects show high accuracy of heartbeat and arterial pulse detection, with ≥ 96.5% sensitivity and < 5% mean absolute relative error (MARE) across all subjects. We demonstrate our sensor’s utility for cuffless blood pressure monitoring on a human subject over a continuous 10-minute period. Our results highlight the potential of metamaterial textile sensors in ambient health and wellness monitoring applications.Clinical relevance—The contactless metamaterial textile sensors demonstrated in this paper provide unobtrusive, convenient and long-term monitoring of multiple cardiovascular health metrics, including heart rate, pulse rate and cuffless blood pressure, which can facilitate preventative and personalized healthcare.
Computer Vision for Determining Trajectory and Impact Position of Fighter-Aircraft Bombing

The motion detection process of bombing by fighter aircraft or combat drones is very important in the Weapon Impact Scoring System (WISS). Recently, the available WISS results are only based on the impact position of the fighter-aircraft bomb in the Air Weapon Range (AWR). The combat training safety regulation pushes observation which can only be made away from AWR. This paper presents the technique of computer vision to enhance WISS capability in observing fighter-aircraft bombs in the AWR. In this proposed method, not only the impact position is observed, but also the trajectory of the bomb gliding before exploding on the ground will be captured. This technique uses a digital video camera safely placed far from the AWR. The camera with a certain altitude on the tower is directed to the circle target to the source of the bombing. The proposed method processes the five-points position of the target circle like a clock (12-3-6-9-central point) while observing the bombing process prior to impact in the AWR. Based on real-time video processing, the image processing successfully makes a sequence of bomb trajectory and its impact position quickly. The results are very important to inform the pilot to improve the next bombing and to support precise analysis of more comprehensive bombing training.
Representation of subgrid-scale sea ice-ocean interactions in large-scale models

Polar oceans are partly covered with sea ice that results from the freezing of seawater. Sea ice is a leading player both in the climate of polar regions and for the Earth climate system as a whole. It acts as an insulating layer between the ocean and the atmosphere and it strongly influences the upper ocean physics. Because it is moved and deformed by winds and currents, the sea ice cover turns out to be an extremely heterogeneous medium. Sea ice models have been developed for decades, reaching nowadays a high level of complexity. In particular, the most advanced of them include a representation of the smallscale spatial variability in ice properties. The heterogeneous nature of sea ice-ocean exchanges has however been so far overlooked. This hampers the simulation of potentially important processes and introduces uncertainties in the predictions based on these models. The objective of this doctoral thesis was to investigate how subgrid-scale sea ice-ocean interactions can be repre.
Navigation Among Movable Obstacles Using Machine Learning Based Total Time Cost Optimization

Most navigation approaches treat obstacles as static objects and choose to bypass them. However, the detour could be costly or could lead to failures in indoor environments. The recently developed navigation among movable obstacles (NAMO) methods prefer to remove all the movable obstacles blocking the way, which might be not the best choice when planning and moving obstacles takes a long time. We propose a pipeline where the robot solves the NAMO problems by optimizing the total time to reach the goal. This is achieved by a supervised learning approach that can predict the time of planning and performing obstacle motion before actually doing it if this leads to faster goal reaching. Besides, a pose generator based on reinforcement learning is proposed to decide where the robot can move the obstacle. The method is evaluated in two kinds of simulation environments and the results demonstrate its advantages compared to the classical bypass and obstacle removal strategies.
Multi-disease Retinal Vessel Segmentation: A Deep Learning Approach

The structure of blood vessels in the retina is a crucial factor in identifying and forecasting various eye diseases like cardiovascular diseases, diabetes, and other diseases. Therefore, detecting the structure of blood vessels from retinal fundus images is a critical field of research in healthcare. This study employed a novel deep learning model to segment vessels for different diseases, including Glaucoma, Diabetic Retinopathy (DR), and Age-related Macular Degeneration (AMD). We considered multiple transfer learning-based models and discovered that the ResNet-based U-Net architecture was the most effective for vessel segmentation, achieving the highest Dice Score above 84% for disease-agnostic, and 82%-84% for disease-specific conditions. We believe the proposed methodology will help to advance retinal vessel segmentation process and enhance the screening process of diseases based on retinal fundus images in clinical settings of Qatar Biobank as well as other biobanks across the globe.
Cell Tracking in C. elegans with Cell Position Heatmap-Based Alignment and Pairwise Detection

3D cell tracking in a living organism has a crucial role in live cell image analysis. Cell tracking in C. elegans has two difficulties. First, cell migration in a consecutive frame is large since they move their head during scanning. Second, cell detection is often inconsistent in consecutive frames due to touching cells and low-contrast images, and these inconsistent detections affect the tracking performance worse. In this paper, we propose a cell tracking method to address these issues, which has two main contributions. First, we introduce cell position heatmap-based non-rigid alignment with test-time fine-tuning, which can warp the detected points to near the positions at the next frame. Second, we propose a pairwise detection method, which uses the information of detection results at the previous frame for detecting cells at the current frame. The experimental results demonstrate the effectiveness of each module, and the proposed method achieved the best performance in comparison.
Tightly-Coupled Visual-DVL Fusion For Accurate Localization of Underwater Robots

This paper proposes a tightly-coupled visual-Doppler-Velocity-Log (visual-DVL) fusion method for underwater robot localization through integrating the velocity measurements from a DVL into a visual odometry (VO). Considering that employing the DVL measurements in dead-reckoning systems easily leads to error accumulation and suboptimal results in previous works, we directly integrate them into the visual tracking process. Specifically, the velocity measurements are utilized to improve the initial estimation of camera pose during visual tracking, aiming to provide a better initial value for pose optimization. Thereafter, these velocity measurements are also directly employed to constrain the position change of the camera between two adjacent frames by constructing a novel DVL error term, which is optimized jointly with the visual constrains to obtain a more accurate camera pose. Various experiments are carried out in the datasets collected from several scenarios of the underwater simulation environment HoloOcean, and the results illustrate that the proposed fusion method can effectively improve the localization accuracy for underwater robots by about 20% compared to pure visual odometry. The proposed method provides valuable guidance for the accurate localization of underwater robots.
Run and Catch: Dynamic Object-Catching of Quadrupedal Robots

Quadrupedal robots are performing increasingly more real-world capabilities, but are primarily limited to locomotion tasks. To expand their task-level abilities of object acquisition, i.e., run-to-catch as frisbee catching for dogs, this paper developed a control pipeline using stereo vision for legged robots which allows for dynamic catching balls while the robot is in motion. To achieve high-frame-rate tracking, we designed a ball that can actively emit homogeneous infrared (IR) light and then located the flying ball based on binocular vision positioning using the onboard RealSense D450 camera with an additional IR bandpass filter. The camera was mounted on top of a 2-DoF head to gain a full view of the target ball. A state estimation module was developed to fuse the vision positioning, camera motor readings, localization result of RealSense T265 equipped on the back, and the legged odometry output altogether. With the use of a ballistic model, we achieved a robust estimation of both the ball and robot positions in an inertial coordinate. Additionally, we developed a close-loop catching strategy and employed trajectory prediction so that tracking and run-to-catch were performed simultaneously, which is critical for such drastically dynamic and precise tasks. The proposed approach was validated through both static testing and dynamic catch experiments conducted on the CyberDog robot with a high success rate.
Improving simultaneous and proportional control from EMG signals based on a Two-Stage Regression Structure

A two-stage regression structure is proposed in this paper to improve simultaneous and proportional control based on electromyography (EMG) signals. Instead of considering the conventional approach with a regression model per degree of freedom (DoF), the proposed method applies a regression model per direction of each DoF: the 1st stage detects DoFs and then is used to select the direction models of the 2nd stage. By using linear regression on the data from one healthy experienced participant for 2 DoFs of his wrist, the proposed structure was evaluated offline with cross-evaluation and online with a real-time control of a cursor to hit some targets. The evaluation results showed a clear improvement of the proposed method in terms of accuracy, ability to reach the boundaries, reaction speed, accurate control and reactive control compared to the conventional approach. The potential of this structure also lies in the fact that it can use different regression methods, work for 3 DoFs and more and use the 1st stage knowledge to improve performance of the 2nd stage.
Reinforcement Learning for Improved Guidance and Power Management of Unmanned Underwater Vehicles

We introduce a deep reinforcement learning framework that enables the generation of trained policy networks (agents) that incorporate local knowledge of current fields to provide energy-minimizing guidance for unmanned underwater vehicles (UUVs). Agents trained using this framework are capable of generating heading, depth, and speed commands that yield nearly-optimal, minimum-energy transits through spatially varying current fields. Numerical simulations in two and three spatial dimensions demonstrate that the energy cost of transits executed under the guidance of a trained agent are better than dead-reckoning and approach known optimal performance in a set of test scenarios.
Employee Watcher: A Machine Learning-based Hybrid Insider Threat Detection Framework

Insider threats refer to harmful actions carried out by authorized users within an organization, posing the most damaging risks. The increasing number of these threats has revealed the inadequacy of traditional methods for detecting and mitigating insider threats. These existing approaches lack the ability to analyze activity-related information in detail, resulting in delayed detection of malicious intent. To address this, our paper presents a hybrid insider threat detection framework. We enhance prediction accuracy by incorporating a layer of statistical criteria using information gain metrics on top of Machine Learning-based classification. We evaluate the performance of our framework using a real-life threat test dataset (CERT r4.2) and compare it to existing methods on the same dataset [7]. Our initial evaluation demonstrates that our proposed framework achieves an accuracy of 98.94% in detecting insider threats, surpassing the performance of existing methods. Additionally, our framework effectively handles potential bias and data imbalance issues that can arise in real-life scenarios.
Play fairway analysis of the eastern great basin extensional regime, Utah: Preliminary indications

We are assessing the geothermal potential including possible blind systems of the Eastern Great Basin extensional regime of western Utah through a Play Fairway Analysis (PFA) of existing geoscientific data. A PFA working model is adopted where magnetotelluric (MT) low resistivity upwellings suggesting geothermal fluids may coincide with dilatent geological structural settings and observed thermal fluids with deep high-temperature contributions. Unique to this setting is the superposition of active rifting with a N-S strike over E-W oriented belts of mid-Cenozoic plutonic rocks and possible NE-SW Precambrian shear trends. Data from ~470 high-quality MT sites have been undergoing 3D inversion using a new edge finite element formulation. Inversions show that several low resistivity upwellings project in the vicinity of Crater Bench, central Sevier Desert, Cove Fort, and east flank of the northern Mineral Mountains. The former three have known thermal expressions but the latter has no such association although it is along strike to the north of the Crater Knoll-Red Knoll Quaternary eruptions. There is a strong suggestion of control of resistivity structure by ENE-oriented structural trends possibly reactivated in E-W rifting. Reanalysis of seismicity shows that both swarm and non-swarm earthquake clusters locate in close proximity to the low resistivity upwellings near Cove Fort and the northern Mineral Mountains, while there is no seismic signature near the upwelling in the central Sevier Desert. Structural geology analysis based upon compilation of mapping, gravity and high-resolution (5 m often) DEM data has identified thirty-five areas of interest within and near the project fairway representing geometries that can produce critical stresses for dilatency, some of which overlap with MT structures. Major element geochemistry of thermal waters is dominated by chloride and sulphate compositions. Silica geothermometry values correspond well with direct reservoir temperatures, but the somewhat higher Na-K inferred temperatures may reflect deeper basement equilibration. Produced fluid compositions often are not compatible solely with the reservoir rocks of production, and imply substantially larger rock volumes are contributing. Also correlated with reservoir temperature are 3He (R/Ra) values, indicating that magmatic input is associated with convective flow. To date, results appear consistent with our PFA working model described above.
Deep Reinforcement Learning-Based Resource Management for 5G Networks: Optimizing eMBB Throughput and URLLC Latency

The rapid progression of 5G networks has ushered in a new era of communication, marked by challenges in optimizing enhanced Mobile Broadband (eMBB) throughput and Ultra-Reliable Low Latency Communications (URLLC) latency. This research delves into Deep Reinforcement Learning (DRL) to address these challenges, with a particular emphasis on the Proximal Policy Optimization (PPO) algorithm. By leveraging a judiciously crafted environment and reward structure, our DRL agents were trained to optimize eMBB throughput and URLLC latency concurrently. Using the Colosseum O-RAN COMMAG Dataset, our agents achieved an average eMBB throughput of 0.4451 Gbps and an average URLLC latency of 0.4719 ms. These outcomes highlight the potency of DRL as a tool for 5G optimization, presenting a promising avenue for future advancements in intelligent 5G network management.
Using Single Demonstrations to Define Autonomous Manipulation Contact Tasks in Unstructured Environments via Object Affordances

Performing a manipulation contact task in an unknown and unstructured environment is still a challenge. Learning from Demonstration (LfD) techniques provide an intuitive means to define difficult-to-model contact tasks, but have attributes that make them undesirable for novice users in uncertain environments. We present a novel end-to-end system that captures a single manipulation task demonstration from an augmented reality (AR) head-mounted display (HMD), computes an affordance primitive (AP) representation of the task, and sends the task parameters to a mobile manipulator for execution in realtime. Using an AR HMD for task demon-stration and APs for task representation has several distinct advantages. AR task demonstration is intuitive, practical, and can be accomplished without requiring sensor installment in the task environment. APs provide a compact and legible task representation, enabling scalability, generalization, and modification of the task without significant data processing overhead. In this effort, we demonstrate system generalization with 10 object manipulation tasks, confirming the computed parameters from all tasks fit within AP tolerances. Secondly, we evaluate a mobile manipulator robot's ability to perform human-demonstrated tasks using AP representation. To increase robustness, we devised and tested four methods to correct for inherent, irreducible position errors in the system. A final study shows the system has a manipulation success rate of 96 % from a single manipulation demonstration on an industrial wheel valve.
Health Literacy Drift in Online Health Communities

Online Health Communities (OHCs) have been used to improve personal health literacy. Based on data from a breast cancer OHC, we applied LIWC to quantify different dimensions of health literacy and applied the unsupervised learning algorithm to aggregate users’ health literacy and depicted individual health literacy trajectories in two years. The preliminary results indicate that the related ability of 71.1% of users flows among different health literacy dimensions. The findings of this study would be beneficial for the operation and management of OHCs.