Miracle-dz/UniFAR-FTUs huggingface dataset:
Specific Habitat Elements (Refuges and Leaf Litter) Are Better Predictors of Sceloporus Lizards in Central Mexico Than General Human Disturbance

Abstract: Human land transformation alters features of the landscape that may favor or eliminate biodiversity. Understanding habitat use among species in human-affected ecosystems can inform the management of habitats and conservation of species. The Trans-Mexican Volcanic Belt in central Mexico is a biological hot spot for lizard species diversity that is under considerable anthropogenic pressures including grazing, agriculture, urbanization, and climate change. Here, we used species-occupancy modeling to (1) identify habitat characteristics that are essential to predicting the presence of Sceloporus torquatus and S. grammicus lizards, (2) determine if disturbance predicts species occupancy, and (3) determine which features, if any, predict our ability to detect each species in the wild. We found that S. torquatus lizards were more likely to be present in areas with large boulders and abundant refuges, whereas S. grammicus lizards were more common in forests with leaf litter. Human disturbance and urban disruption did not predict the occupancy of either species, with lizards making use of artificial as well as natural refuges in human settlements as well as protected areas. Although we found only weak evidence that habitat and climate predicted detection probabilities, Sceloporus lizards (particularly S. grammicus) were somewhat more easily detected in high humidity, perhaps because of generally higher activity levels. Our results emphasize the importance of understanding the detailed physical characteristics that allow each species to persist, even in disturbed habitats. This can better inform conservation efforts so that resources are allocated to ensure that these characteristics, like rocks and trees, are readily available in both pristine and human-modified areas.
No Evidence of Long-Term Effects on Physiological Stress or Innate Immune Functioning in Northern Map Turtles a Decade After a Freshwater Oil Spill

Abstract: Environmental perturbations such as chemical spills often have immediate and obvious effects on the health of ecosystems and individual organisms. However, the long-term effects of such perturbations are less evident and often less well-understood. In July 2010, a ruptured pipeline spilled diluted bitumen into the Kalamazoo River in Michigan, USA, ultimately oiling 56 km of the river channel. During spill cleanup operations, the most common vertebrate species rescued from the oiled river channel was the Northern Map Turtle (Graptemys geographica), of which over 2000 individuals were captured, cleaned of oil, and released. We returned to areas along the 2010 spill gradient to determine whether turtles from locations that had experienced different magnitudes of oiling in 2010 differed in baseline stress levels or immune functioning in 2020, which was 10 yr after the oil spill. In 2020, we collected blood samples from 100 individual Northern Map Turtles along the 2010 spill gradient and subsequently quantified the heterophil:lymphocyte ratio, bactericidal capacity, and natural antibody agglutination. We found no correlation between turtles' 2020 capture locations relative to the origin of the 2010 oil spill and either mean stress level (heterophil:lymphocyte ratio) or immune functioning (bactericidal capacity and natural antibody agglutination ability). Thus, we found no evidence that Northern Map Turtles sampled along a gradient of oil exposure resulting from the 2010 Kalamazoo River oil spill experienced long-term effects on physiological stress levels or immune functioning. Importantly, our sampling did not include any individuals that may have died during 2010–2020 from poor health after the oil spill and therefore is biased toward healthy turtles remaining in the population. Interestingly, we found that a subset of turtles known to have been oiled in 2010 actually had higher natural antibody titers than turtles sampled from a control site that presumably had never been exposed to oil. We cannot exclude the possibility of other long-term effects of the oil spill on the Map Turtle population, such as decreased reproductive output. We recommend that long-term monitoring of populations and ecosystems be included in spill mitigation plans to track the recovery of populations and ecosystems over time and to document any effects that may become evident only years after the spill event.
A robust blind deconvolution based on estimation of point spread function parameters

At present, in the field of image processing, the main algorithm to restore the blurred image is the blind deconvolution. But most of the blind deconvolution methods have to iterate a large amount of times and the result is also unsatisfactory. In this paper, a new blind deconvolution algorithm is proposed, which, consisting of two steps, is based on simultaneous estimating the specimen function and the parameters of the point-spread function (PSF). Firstly, it uses the expectation maximization algorithm (EM) to iterate the specimen function; secondly it uses the conjugate gradient method to estimate the parameters of the PSF. The mathematical model ensures that all the constraints of the PSF are satisfied, and the maximum-likelihood approach ensures that the specimen is nonnegative. In this paper, the general Gauss function is used to be as the PSF. In the experiment, it can successfully restore both the two-dimensional and three-dimensional images within limited times of iteration.
Investigation of hydrodynamics of water impact and tail slamming of high-speed water entry with a novel immersed boundary method

Abstract High-speed water entry is a transient hydrodynamic process that is accompanied by strongly compressible flow, free surface splash, cavity evolution and other nonlinear hydrodynamic phenomena. To address these problems, a novel fluid–structure interaction (FSI) scheme based on the immersed boundary method is proposed which is suitable for strongly compressible multiphase flows. In this scheme, considering the multiphase interfaces at the immersed boundary, an improved immersed boundary method for effectively suppressing the non-physical force oscillation is proposed. Additionally, a quaternion-based six degrees of freedom motion system is used to describe rigid body motion, and the multiphase flow Eulerian finite element method is applied as the fluid solver. Using analytical solutions, experimental data and literature data, the accuracy and robustness of the FSI scheme are validated. Finally, the high-speed water entry of the slender body with different noses is investigated, and the hydrodynamic loads including the axial and normal drag forces and the bending moment are extensively discussed. The hydrodynamic load and motion trajectory are determined by the nose configuration. The tail slamming phenomenon is the primary focus, and it is revealed that its formation is primarily related to the pitch moment formed at the stage of crossing the free surface. Tail slamming also causes violent impact loads, especially bending moments, which may cause slender projectiles to break off. Finally, to combine the features of the flat and hemispherical noses, the water entry of the projectile with a truncated hemispherical nose is simulated and discussed.
Implementation of a color calibration method for liquid crystal displays

In this paper, we describe the implementation of a color calibration method for LCDs where we assume that the angle of vision is normal to the center of the screen. We change the procedure generally used with cathode ray tubes and take into account the high intensity black of an LCD, the variable chromaticities of its primaries and the less than perfect additivity of its colors. This is done by modifying the calibration data and using correction tables. We describe three rendering algorithms which can be used with this implementation. We test them on four IBM LCD models produced in the last four years and conclude that the simplest algorithm is adequate for the latest model.
Trust Estimation for Autonomous Vehicles by Measuring Pedestrian Behavior in VR

This study proposes a method to estimate pedestrian trust in an automated vehicle (AV) based on pedestrian behavior. It conducted experiments in a VR environment where an AV approached a crosswalk. Participants rated their trust in the AV at three levels before/while they crossed the road. The level can be estimated by deep learning using their skeletal coordinates, position, vehicle position, and speed during the past four seconds. The estimation accuracy was 61%.
Factors Impacting Diversity and Efectiveness of Evolved Modular Robots

In many natural environments, diferent forms of living organisms successfully accomplish the same task while being diverse in shape and behavior. This biodiversity is what made life capable of adapting to disrupting changes. Being able to reproduce biodiversity in artiicial agents, while still optimizing them for a particular task, might increase their applicability to scenarios where human response to unexpected changes is not possible. In this work, we focus on Voxel-based Soft Robots (VSRs), a form of robots that grants great freedom in the design of both morphology and controller and is hence promising in terms of biodiversity. We use evolutionary computation for optimizing, at the same time, morphology and controller of VSRs for the task of locomotion. We investigate experimentally whether three key factorsÐrepresentation, Evolutionary Algorithm (EA), and environmentÐimpact the emergence of biodiversity and if this occurs at the expense of efectiveness. We devise an automatic machine learning pipeline for systematically characterizing the morphology and behavior of robots resulting from the optimization process. We classify the robots into species and then measure biodiversity in populations of robots evolved in a multitude of conditions resulting from the combination of diferent morphology representations, controller representations, EAs, and environments. The experimental results suggest that, in general, EA and environment matter more than representation. We also propose a novel EA based on a speciation mechanism that operates on morphology and behavior descriptors and we show that it allows to jointly evolve morphology and controller of efective and diverse VSRs.
Thermoacoustic tomography using a fiber-based Fabry-Perot interferometer as an integrating line detector

Thermoacoustic tomography is based on the generation of acoustic waves by a bulk illumination of a sample with a short electromagnetic pulse. The thermal expansion of the sample generates acoustic waves. The absorption density inside the sample is reconstructed from the acoustic pressure signals measured outside of the sample. So far signals have been collected with small detectors that approximate point-detectors. In the present study we report on a novel measurement setup applying integrating detectors. With these detectors the pressure is integrated along one or two dimensions. This enables the use of numerically efficient algorithms, such as the inverse Radon transformation, for thermoacoustic tomography. To reconstruct a three-dimensional image, either a two-dimensional integrating detector has to be moved tangentially around a sphere enclosing the object or an array of line-detectors has to be rotated around a single axis. Implementations of line-detectors are demonstrated that are adaptations of a Fabry--Perot interferometer. As a novel approach for the implementation a Fabry-Perot interferometer consisting of a single mode fiber is demonstrated.
Socially Assistive Robotics for Anxiety Reduction

Prior work in HRI for domains such as exercise, rehabilitation, and autism has shown how socially assistive robots (SARs) can successfully support behavioral practices. Applying these insights to mental health is an opportunity to support a large and growing population that is actively struggling. My research investigates utilizing SARs for supporting the therapeutic behavior of deep breathing for anxiety reduction. My prior work to date has focused on the design affordances required for an anxious population through the development of a new, haptically-based robot, Ommie. Future work explores how SARs for anxiety-reducing behaviors can maximize long-term, in-the-wild use through haptic interactions, perception technologies, and personalized motivational mechanisms.
The Peg-Turning Dilemma: An Experimental Framework for Measuring Altruistic Behaviour Towards Robots

This paper presents the results of a preregistered pilot study testing an experimental framework to measure altruistic behaviour towards robots. We define altruistic behaviour as behaviour that benefits others at a personal cost to the behaving individual. The pilot study explores feelings of guilt and perceived agency and experience (i.e., mind) in the robot as potential predictors of altruistic behaviour. Using a within-subjects design (n=48), we compared the willingness to perform a dull, repetitive task (i.e., turning pegs in a pegboard) to avoid shaking (harming) either an emotion-simulating robot, or a non-responsive object. The results showed that participants felt significantly more guilty after shaking the robot than after shaking the object, and they perceived more agency and experience in the robot than in the object. Finally, even though participants felt significantly more bored after performing the peg-turning task than after subsequent tasks, they were significantly more willing to repeat this task to avoid shaking the robot again, compared to the object. An exploratory regression analysis showed that feelings of guilt were the only significant predictor of this behaviour.
Low-latency Classification of Social Haptic Gestures Using Transformers

Social touch, and its recognition and classification, is increasingly important in human-robot interaction. We present a Transformer-based model trained and evaluated on an open-source dataset. The dataset, the Human-Animal Affective Robot Touch (HAART) dataset, was collected for the 2015 Recognition of Touch Gesture Challenge (RTGC 2015) and contains different haptic actions directed at a robotic animal. The actions are recorded using a multi-resolution pressure sensor. We feed the output, containing the touch type to the Nao robot to make the robot sense the touch type. The proposed transformer-based gesture classification model achieved 72.8% classification accuracy in 2.67 seconds, which outperforms the best-submitted algorithm of the RTGC 2015 which has a test classification accuracy of 70.9 % and needed 8 seconds.
A Comparison of Multiscale GSI-Based EnKF and 3DVar Data Assimilation Using Radar and Conventional Observations for Midlatitude Convective-Scale Precipitation Forecasts

AbstractA GSI-based data assimilation (DA) system, including three-dimensional variational assimilation (3DVar) and ensemble Kalman filter (EnKF), is extended to the multiscale assimilation of both meso- and synoptic-scale observation networks and convective-scale radar reflectivity and velocity observations. EnKF and 3DVar are systematically compared in this multiscale context to better understand the impacts of differences between the DA techniques on the analyses at multiple scales and the subsequent convective-scale precipitation forecasts.Averaged over 10 diverse cases, 8-h precipitation forecasts initialized using GSI-based EnKF are more skillful than those using GSI-based 3DVar, both with and without storm-scale radar DA. The advantage from radar DA persists for ~5 h using EnKF, but only ~1 h using 3DVar.A case study of an upscale growing MCS is also examined. The better EnKF-initialized forecast is attributed to more accurate analyses of both the mesoscale environment and the storm-scale features.
Toward Balancing the Efficiency and Effectiveness in k-Facility Relocation Problem

Facility Relocation (FR), which is an effort to reallocate the placement of facilities to adapt to the changes of urban planning, has remarkable impact on many areas. Existing solutions fail to guarantee the result quality on relocating k > 1 facilities. As k-FR problem is NP-complete and is not submodular or non-decreasing, traditional greedy algorithm cannot be directly applied. We propose to transform k-FR into another facility placement problem, which is submodular and non-decreasing. We prove that the optimal solutions of both problems are equivalent. Accordingly, we present the first approximate solution toward the k-FR, FR2FP. Our extensive comparison over both FR2FP and the state-of-the-art solution shows that FR2FP, although it provides approximation guarantee, cannot necessarily given superior results. The comparison motivates us to present an advanced approximate solution, FR2FP-ex. Moreover, based on Lagrangian relaxation, we develop an algorithm that can adjust the approximation ratio. Extensive experiments verified that, FR2FP-ex demonstrates the best result quality, and it is very close to the optimal solution. In addition, we also unveil the scenarios when the state-of-the-art would fail. We further generalize the k-FR problem, considering the budget for relocation and the cost of each facility. We also present corresponding approximate solutions toward the new problem and prove the approximation ratio.
Video Captioning by Learning from Global Sentence and Looking Ahead

Video captioning aims to automatically generate natural language sentences describing the content of a video. Although encoder-decoder-based models have achieved promising progress, it is still very challenging to effectively model the linguistic behavior of humans in generating video captions. In this paper, we propose a novel video captioning model by learning from gLobal sEntence and looking AheaD, LEAD for short. Specifically, LEAD consists of two modules: a Vision Module (VM) and a Language Module (LM). Thereinto, VM is a novel attention network, which can map visual features to high-level language space and model entire sentences explicitly. LM can not only effectively make use of the information of the previous sequence when generating the current word, but also have a look at the future word. Therefore, based on VM and LM, LEAD can obtain global sentence information and future word information to make video captioning more like a fill-in-the-blank task than a word-by-word sentence generation. In addition, we also propose an autonomous strategy and a multi-stage training scheme to optimize the model, which can mitigate the problem of information leakage. Extensive experiments show that LEAD outperforms some state-of-the-art methods on MSR-VTT, MSVD, and VATEX, demonstrating the effectiveness of the proposed approach in video captioning. In addition, we release the code of our proposed model to be publicly available.1
Unintended Failures of Robot-Assisted Feeding in Social Contexts

Over 1.8 million Americans require assistance eating. Robot-assisted feeding is a promising way to empower people with motor impairments to eat independently. Yet, most robot-assisted feeding research has focused on individual dining (e.g., eating at home with a caregiver), but not social dining (e.g., family meals, friends' brunch, romantic dates). What happens when a robot developed for individual contexts gets used in social contexts? In this humorous video, we present unintended consequences that can arise from robot-assisted feeding in social settings. This video aims to raise awareness about the importance of accounting for social context when designing assistive robots.
Montessori-based Design of Long-term Child-Robot Interaction for Alphabet Learning

The transition of the Kazakh alphabet from Cyrillic to Latin, set to be fully implemented by 2031, poses unwanted challenges to early and continuous literacy development and acquisition of the new script. This creates a need to design innovative learning solutions to boost children's motivation in acquiring the new Kazakh Latin alphabet. The Montessori method has proven itself effective for young children to engage in self-directed and developmentally appropriate literacy acquisition. These core ideas have been carefully adopted to establish design principles for the robotic system that is adhering to the principles of the Montessori pedagogy. This paper proposes a robotic system named Moveable -l-pbi and details its interaction design life cycle from understanding users and establishing requirements to designing, and implementing robot behaviours and validating them with the Montessori practitioner. This process was iterative that involved several cycles of piloting the system with children of targeted age groups and redesigning the learning activities. With the aim to evaluate the proposed system and to find the most cognitively rewarding way of learning the alphabet, we conducted a mixed-subject design experiment with 60 Kazakh children aged 8-10 years old from a local public school where we compare the proposed Moveable -l-pbi robotic system with a baseline Montessori human teacher. The results demonstrate the potential of the robot as a Montessori teacher in providing foundational letter acquisition over multiple sessions. Implications for improving the interaction design and activities are discussed based on the findings.
The Warehouse Robot Interaction Sim: An Open-Source HRI Research Platform

The use of physical robots in real-world laboratories for the study of human-robot interaction is not without limitations and logistical challenges. In response, a wide range of studies have begun using virtual representations of robots. However, very few of these platforms are openly available to the HRI community. This limits reproducibility and the ability of the community to leverage existing resources for their own research. In response, this paper presents The Warehouse Robot Interaction Sim. The Warehouse Robot Interaction Sim is an open-source immersive virtual platform developed in the Unreal Engine with the goal of conducting research on trust repair in HRI. This paper summarizes the overall structure of the platform, how it can be modified, and briefly discuss how this platform has been leveraged for research. In doing so we hope to encourage other researchers in HRI to consider leveraging this platform for their own research questions and study designs.
Demonstrating TRAinAR: An Augmented Reality Tool that Helps Humans Teach Robots

We demonstrate TrainAR, an augmented reality (AR)-based tool that is designed to improve sim2real reinforcement learning (RL) for robots. Users with TRAinAR can tailor a virtual training environment with constraints to match the real-world, visualize training data to gain insights into an agent's learning process, and animate a robot's future actions before execution. The system described here enabled a robotic arm manipulator to learn how to navigate its end-effector toward a target object. The novelty of our AR application will hopefully enable users to better train a robot by quickly prototyping complex environments that are difficult to model.
Demonstrating the Potential of Interactive Product Packaging for Enriching Human-Robot Interaction

While social robots are increasingly introduced into domestic settings, few have explored the utility of the robots' packaging. Here we highlight the potential of product packaging in human-robot interaction to facilitate, expand, and enrich user experience with the robot. We present a social robot's box as interactive product packaging, designed to be reused as a "home'' for the robot. Through co-design sessions with children, an narrative-driven and socially engaging box was developed to support initial interactions between the child and the robot. Our findings emphasize the importance of packaging design to produce positive outcomes towards successful human-robot interaction.