Miracle-dz/UniFAR-FTUs huggingface dataset:
Low computational-cost detection and tracking of dynamic obstacles for mobile robots with RGB-D cameras

—Deploying autonomous robots in crowded indoor environments usually requires them to have accurate dynamic obstacle perception. Although plenty of previous works in the autonomous driving ﬁeld have investigated the 3D object detection problem, the usage of dense point clouds from a heavy LiDAR and their high computation cost for learning-based data processing make those methods not applicable to small robots, such as vision-based UAVs with small onboard computers. To address this issue, we propose a lightweight 3D dynamic obstacle detection and tracking (DODT) method based on an RGB-D camera, which is designed for low-power robots with limited computing power. Our method adopts a novel ensemble detection strategy, combining multiple computationally efﬁcient but low-accuracy detectors to achieve real-time high-accuracy obstacle detection. Besides, we introduce a new feature-based data association method to prevent mismatches and use the Kalman ﬁlter with the constant acceleration model to track detected obstacles. In addition, our system includes an optional and auxiliary learning-based module to enhance the obstacle detection range and dynamic obstacle identiﬁcation. The users can determine whether or not to run this module based on the available computation resources. The proposed method is implemented in a small quadcopter, and the experiments prove that the algorithm can make the robot detect dynamic obstacles and navigate dynamic environments safely.
Molecular Diversity of Human Respiratory Syncytial Virus before and during the COVID-19 Pandemic in Two Neighboring Japanese Cities

Understanding the molecular diversity of human respiratory syncytial viruses during pandemics caused by different viruses can provide insights that can guide public health decisions and vaccine development. ABSTRACT Human respiratory syncytial viruses (HRSVs) are divided into subgroups A and B, which are further divided based on the nucleotide sequence of the second hypervariable region (HVR) of the attachment glycoprotein (G) gene. Understanding the molecular diversity of HRSV before and during the coronavirus disease 2019 (COVID-19) pandemic can provide insights into the effects of the pandemic on HRSV dissemination and guide vaccine development. Here, we analyzed HRSVs isolated in Fukushima Prefecture from September 2017 to December 2021. Specimens from pediatric patients were collected at two medical institutions in neighboring cities. A phylogenetic tree based on the second HVR nucleotide sequences was constructed using the Bayesian Markov chain Monte Carlo method. HRSV-A (ON1 genotype) and HRSV-B (BA9 genotype) were detected in 183 and 108 specimens, respectively. There were differences in the number of HRSV strains within clusters prevalent at the same time between the two hospitals. The genetic characteristics of HRSVs in 2021 after the COVID-19 outbreak were similar to those in 2019. HRSVs within a cluster may circulate within a region for several years, causing an epidemic cycle. Our findings add to the existing knowledge of the molecular epidemiology of HRSV in Japan. IMPORTANCE Understanding the molecular diversity of human respiratory syncytial viruses during pandemics caused by different viruses can provide insights that can guide public health decisions and vaccine development.
An approach for including social impact measures in systems design exploration

Abstract Engineered products have economic, environmental, and social impacts, which comprise the major dimensions of sustainability. This paper seeks to explore interactions between design parameters when social impacts are incorporated into the concept development phase of the systems design process. Social impact evaluation is increasing in importance similar to what has happened in recent years with environmental impact consideration in the design of engineered products. Concurrently, research into new airship design has increased. Airships have yet to be reintroduced at a large scale or for a range of applications in society. Although airships have the potential for positive environmental and economic impacts, the social impacts are still rarely considered. This paper presents a case study of the hypothetical introduction of airships in the Amazon region of Brazil to help local farmers transport their produce to market. It explores the design space in terms of both engineering parameters and social impacts using a discrete-event simulation to model the system. The social impacts are found to be dependent not only on the social factors and airship design parameters but also on the farmer-airship system, suggesting that socio-technical systems design will benefit from integrated social impact metric analysis.
Special Section Guest Editorial: Thirty Years of Functional Near-Infrared Spectroscopy

Abstract. Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive optical technique that measures cerebral hemodynamics across multiple regions of interest, and thereby characterises brain functional activation. Since its first description in 1993, fNIRS has undergone substantial developments in hardware, analysis techniques, and applications. Thirty years later, this technique is significantly enchancing our understanding in diverse areas of neuroscience research such as neurodevelopment, cognitive neuroscience, psychiatric disorders, neurodegenerative conditions, and brain injury management in intensive care settings. This special issue outlines the latest progress in instrumentation and analysis techniques and showcases some applications within the expanding field of fNIRS over the past decade.
Trustworthy Swarms

Swarm robotics has begun to move from the laboratory to the real world. Potential applications include: logistics, environmental monitoring, search and rescue, and medicine. However, a key challenge for the deployment and use of these systems is the interrelated factor of ensuring technical, human and societal trust. To help address this complex issue, we first turn our attention to research on the technical properties of swarms, such as proficiency, scalability, robustness, and adaptability and their role in building trust. Second, we explore the area of research known as Human-Swarm Interaction, which studies how humans understand, monitor, control, and interact with swarms. Third, we focus on techniques used to specify, verify and validate swarms. Finally, we discuss the future of swarm robotics and conclude by suggesting areas of advancement which may help build human and society’s trust towards robot swarms.
Building Trust in Assistive Robotics: Insights From a Real-World Mobile Navigation Experiment

Assistive robotics can improve the lives of people with mobility impairments, but widespread acceptance depends on understanding how trust forms during human-robot interaction (HRI). This study reports on a real-world mobile navigation experiment involving 27 participants who used an autonomous wheelchair to deliver packages to predetermined locations. The performance of the wheelchair was manipulated to create good and bad performance conditions. The participants’ trust in the robot was measured using established trust questionnaires before and after the interaction. The results indicate that exposure to the robot and its performance significantly impact participants’ self-reported levels of trust in the robot. Specifically, scores on trust factors relating to the robot taken after the experiment were significantly higher than those taken before, suggesting that exposure to the robot is an essential element of the trust-building process. The study also found that poor robot performance during the interaction negatively affected participants’ perception of the robot’s behaviour and their attitudes towards it. The study concludes with implications for the design and development of autonomous assistive devices, highlighting the importance of potential users’ exposure to assistive robots for building trust in the system and the negative impact of bad robot performance on people’s attitudes towards the technology.
Smoothing the finite differences defining the Non-local Total Variation and application in image restoration

In the usual non-local variational models , such as the non-local total variations (NLTV), the image is regularized by minimizing an energy term that penalizes gray-levels discrepancy between some specified pairs of pixels. The pairs of pixels are interpreted as weighted edges joining nodes in a graph. The nodes correspond to the pixels of the image. The weights are ponderations. The graph structure is usually determined a-priori or, sometimes , is adjusted (most of the time empirically) during the iterative process. In this paper, we study the possibility to impose some regularity to the graph. More precisely, we study a model in which two edges whose starting nodes are near and such that the starting and ending points form a parallelogram to carry similar weights. In order to do so, we minimize a function involving a regularization term, analogous to an H 1 term, on the graph edges. Doing so, the finite differences defining the image regularity depend on their environment. They are therefore more stable. The model is also better suited to solve inverse problems for which the design of the graph is not straightforward (one can think of computerized to-. TZ is partially supported by NSFC 11271049, RGC 12302714 and RFGs of HKBU. mography problems, reconstruction from random measures, inpainting.) We provide all the details necessary for the implementation of a PALM algorithm with proved convergence. We illustrate the ability of the model to restore relevant unknown edges from the neighboring edges on an image inpainting problem. We also argue on inpainting and denoising problems that the model better recovers thin structures.
Meet your Maker: A Social Identity Analysis of Robotics Software Engineering

Software systems often reflect the values of the people that engineered them: it is vital to understand and engineer those values systematically. This is crucial for autonomous systems, where human interventions are not always possible. The software engineering community shows some positive values —like altruism— and lack others —like diversity. In this project, we propose to elicit the values of the engineers of autonomous systems by analysing the artefacts they produce. We propose to build on the social identity theory to identify encouraged and discouraged behaviours within this collective. Our goal is to understand, diagnose, and improve the engineering culture behind autonomous system development.
Improving the Efficiency of Quantum Circuits for Information Set Decoding

Code-based cryptosystems are a promising option for Post-Quantum Cryptography, as neither classical nor quantum algorithms provide polynomial time solvers for their underlying hard problem. Indeed, to provide sound alternatives to lattice-based cryptosystems, U.S. National Institute of Standards and Technology (NIST) advanced all round 3 code-based cryptosystems to round 4 of its Post-Quantum standardization initiative. We present a complete implementation of a quantum circuit based on the Information Set Decoding (ISD) strategy, the best known one against code-based cryptosystems, providing quantitative measures for the security margin achieved with respect to the quantum-accelerated key recovery on AES, targeting both the current state-of-the-art approach and the NIST estimates. Our work improves the state-of-the-art, reducing the circuit depth by 219 to 230 for all the parameters of the NIST selected cryptosystems, mainly due to an improved quantum Gauss–Jordan elimination circuit with respect to previous proposals. We show how our Prange’s-based quantum ISD circuit reduces the security margin with respect to its classical counterpart. Finally, we address the concern brought forward in the latest NIST report on the parameters choice for the McEliece cryptosystem, showing that its parameter choice yields a computational effort slightly below the required target level.
Socio-Technical Resilience for Community Healthcare

Older adults at home frequently rely on ‘circles of support’ which range from relatives and neighbours, to the voluntary sector, social workers, paid carers, and medical professionals. Creating, maintaining, and coordinating these circles of support has often been done manually and in an ad hoc manner. We argue that a socio-technical system that assists in creating, maintaining, and coordinating circles of support is a key enabler of community healthcare for older adults. In this paper we propose a framework called SERVICE (Socio-Technical Resilience for the Vulnerable) to help represent, reason about, and coordinate these circles of support and strengthen their capacity to deal with variations in care needs and environment. The objective is to make these circles resilient to changes in the needs and circumstances of older adults. Early results show that older adults appreciate the ability to represent and reflect on their circle of support.
Cooking Up Trust: Eye Gaze and Posture for Trust-Aware Action Selection in Human-Robot Collaboration

In Human-Robot Collaboration (HRC), trust is an essential factor that can change over time, and robots capable of estimating a human’s trust and using that information to select their actions can improve the quality of interaction. In this paper, we present our early-stage research on a trust-aware policy for HRC applied to an assistive cooking scenario. We propose to study how physiological signals, such as eye gaze and posture, can be used to estimate the human’s trust level. We describe our experimental platform and the evaluation scenarios used to test the effectiveness of the policy.
Probabilistic Modelling for Trustworthy Artificial Intelligence in Drone-Supported Autonomous Wheelchairs

In this work, we address the potential of probabilistic modelling approaches for ensuring trustworthy AI in sensing subsystems within drone-supported autonomous wheelchairs. The combination of drones and autonomous wheelchairs provides an innovative solution for enhancing mobility and independence of motion-impaired people. However, safety is a critical concern when deploying such systems in real-world scenarios. To address this challenge, probabilistic models can be used to capture uncertainty and non-stationarity in the environment and sensory system, enabling the device to make informed decisions while ensuring safe autonomy. The approach is being developed in the context of a recently started European project named REXASI-PRO, which addresses the modelling methodology, tools, reference architecture, design and implementation guidelines. In the project, relevant indoor and outdoor navigation use cases are addressed to demonstrate the effectiveness of the proposed approach in providing trustworthy autonomous wheelchairs in real-world environments.
Swarm Medicine: Developing guidance for in-human testing of emerging swarm-based cancer nanomedicines

Abstract: The development of in silico methodology, active controllable nanoparticles (nanorobots) and collective nanosystems that work cooperatively rather than individually (nanoswarms) are emerging as the new frontier in cancer nanomedicine. A multimethod qualitative study was undertaken to develop an understanding of how the first-in-human cancer nanoswarm trial should be regulated from the perspective of various stakeholders. This poster presents the initial findings of the ongoing study. 21 semi-structured interviews with regulators, nanomedicine researchers, healthcare professionals and patients were conducted, with data subjected to thematic analysis. Five themes emerged from the interviews: (1) Patient Cancer Journey; (2) Perceptions; (3) Regulation; (4) Future Thinking and; (5) Stakeholder Interaction. Overall the results demonstrate a willingness from oncology patients to develop nanoswarm treatments, as well highlight concerns from all stakeholders around trust and transparency. In the next stage, we need to further explore with focus groups if there is a level of acceptance and awareness for regulating this technology amongst stakeholders before making final recommendations for a framework of what the first-in-human cancer nanoswarm clinical trial should look like.
Least distortion Euclidean embeddings of flat tori

Lattices (discrete subgroups of n-dimensional Euclidean spaces) are ubiquitous objects in mathematics. One typical algorithmic problem for lattices is the closest vector problem (given a lattice and a point in Euclidean space, which lattice vector is closest to this given point? ). In general the closest vector problem is NP-hard. One potential approach to derive efficient approximation algorithm for the closest vector problem could go via the concept of least distortion metric embeddings. In this paper we derive and analyze an infinite-dimensional semidefinite program which computes least distortion embeddings of flat tori , where L is an n-dimensional lattice, into Hilbert spaces. This enables us to derive some optimal embeddings of flat tori given by certain important lattices, especially of the hexagonal lattice A2 and the root lattice E8. For this we apply and modify the linear programming method for spherical designs.
WVC: Towards Secure Device Paring for Mobile Augmented Reality

In mobile augmented reality applications, how to build a secure device connection between two previously unassociated devices without prior set-up is challenging, which also refers to the problem of device pairing. Most existing approaches to device pairing either have certain limits to be utilized in the environment of augmented reality or lack considerations of security issues. In this article, we design WVC, an intuitive, user-friendly, and secure device paring system for mobile augmented reality. It enables users to connect to a neighboring device by waving a finger to click towards it in the air. The system uses critical features from finger tracking trajectories to understand which device a user wants to interact with. Then, it designs key generation and correction algorithms to enhance security in device communication. In addition, we present solutions to defend against malicious attacks. We implement and test the system with 10 volunteers. The experimental results demonstrate the feasibility and effectiveness of the system under varied scenarios in which devices are close to each other at a small angle or located at different heights within 2m.
The stability of C-peptide and insulin in plasma and serum samples under different storage conditions

Abstract Objectives C-peptide and insulin are peptide hormones and their stability is affected by a number of pre-analytical factors. The study aimed to investigate the impact of sample type, storage temperature and time delays before centrifugation and analysis on the stability of C-peptide and insulin. Methods Ten healthy non-diabetic adults in fasting and non-fasting state were enrolled. 40 mL of blood was collected from each participant into SST and dipotassium EDTA tubes. Samples were centrifuged immediately or at timed intervals (8, 12, 48 and 72 h). After baseline measurements on the Roche Cobas e602 analyzer using electrochemiluminescence immunoassays, aliquots were stored at room temperature (RT), 2–8 and −20 °C for 4 h to 30 days. The percentage deviation (PD) from baseline was calculated and a change greater than desirable biological variation total error was considered clinically significant. Results C-peptide was more stable in separated serum than plasma (PD of −5 vs. −13 %) samples stored at 2–8 °C for 7 days and was most unstable at RT when centrifugation was delayed (PD −46 % in plasma and −74 % in serum after 48 h). Insulin was more stable in plasma than in serum under the different storage conditions with a minimum PD of −1% when stored at −20 °C for 30 days. When samples were kept unspun at RT for 72 h, PD was −23 and −80 % in plasma and serum, respectively. Conclusions C-peptide was more stable in serum provided the sample was centrifuged immediately and stored in the fridge or freezer while insulin was found to be more stable in EDTA plasma.
Plant-Parasitic Nematodes Associated with Cannabis sativa in Florida

Abstract The subtropical climate of Florida allows for a wide range of crops to be grown. With the classification of hemp (Cannabis sativa L., <0.3% delta-9-tetrahydrocannabinol) as an agricultural commodity, hemp has become a potential alternative crop in Florida. Hemp cultivars of different geographies (Europe, China, and North America), and uses (fiber, oil and CBD), were evaluated in three field experiments. The field experiments evaluated a total of 26 cultivars and were conducted for two consecutive seasons at three different locations (soil types) in North (sandy loam), Central (fine sand), and South Florida (gravelly loam). Nematode soil populations were measured at the end of each season. A diverse population of plant-parasitic nematodes was found, with reniform nematodes (RN, Rotylenchulus reniformis) the dominant species in North and South Florida (up to 27.5 nematodes/cc soil), and RKN (Meloidogne javanica) the main species in central Florida (up to 4.7 nematodes/cc soil). Other nematodes that were commonly found in south Florida (and to a lesser extent north Florida) were spiral (Helicotylenchus spp.), stunt (Tylenchorhynchus spp.) and ring nematodes (Criconemoids), while in central Florida, stubby root (Nanidorus minor) and sting nematodes (Belonolaimus longicaduatus) were found. No significant difference among hemp cultivars was noted at any of the locations. RKN were found in all three regions and soils, while RN were only found in North and South Florida. This is the first report on plant-parasitic nematodes associated with hemp in Florida fields. Natural nematode populations varied greatly, depending on where in Florida hemp was grown. Growers who wish to include hemp in their crop rotation need to be aware of potential pest pressure from nematodes. More research is needed to determine to what extent nematodes, especially RKN and RN, can reduce hemp growth and yield.
Supervised Evaluation of the Quality of BinaryPartition Trees based on Uncertain Semantic Ground-Truth for Image Segmentation Purpose

The binary partition tree (BPT) is a hierarchical data-structure that models the content of an image in a multiscale way. In particular, a cut of the BPT of an image provides a segmentation, as a partition of the image support. Actually, building a BPT allows for dramatically reducing the search space for segmentation purposes, based on intrinsic (image signal) and extrinsic (construction metric) information. A large literature has been devoted to the construction on such metrics, and the associated choice of criteria (spectral, spatial , geometric, etc.) for building relevant BPTs, in particular in the challenging context of remote sensing. But, surprisingly, there exists few works dedicated to evaluate the quality of BPTs, i.e. their ability to further provide a satisfactory segmentation. In this paper , we propose a framework for BPT quality evaluation, in a supervised paradigm. Indeed, we assume that ground-truth segments are provided by an expert, possibly with a semantic labelling and a given uncertainty. Then, we describe local evaluation metrics, BPT nodes / ground-truth segments fitting strategies, and global quality score computation considering semantic information, leading to a complete evaluation framework. This framework is illustrated in the context of BPT segmentation of multispectral satellite images.