Ising Field Theory on a Pseudosphere

We show how the symmetries of the Ising field theory on a pseudosphere can be exploited to derive the form factors of the spin fields as well as the non-linear differential equations satisfied by the corresponding two-point correlation functions. The latter are studied in detail and, in particular, we present a solution to the so-called connection problem relating two of the singular points of the associated Painleve VI equation. A brief discussion of the thermodynamic properties is also presented.Comment: 39 pages, 6 eps figures, uses harvma

Space and habitat selection by female European wild cats (Felis silvestris silvestris)

Studies on the use of space and habitat selection of threatened species are useful for identifying factors that influence fitness of individuals and population viability. However, there is a considerable lack of published information regarding these factors for the European wildcat ( Felis silvestris ). Serra da Malcata Nature Reserve (SMNR), a mountainous area in the eastern centre of Portugal, hosts a stable wildcat population which constitutes a priority in terms of conservation. We studied space use and habitat selection of female wildcats in SMNR with the following objectives: 1) to describe seasonal space use and habitat selection and 2) to obtain information on priority habitats for wildcats in order to develop a proper conservation strategy. We used radio-telemetry as the basic tool for our study and we analysed habitat selection using an Euclidean distance-based approach to investigate seasonal and annual habitat selection by wildcats. We detected that during spring females exhibit smaller home ranges and core areas. Females exhibited habitat selection for establishing home ranges from the available habitats within the study area. In fact, females selected Quercus pyrenaica forests and Quercus rotundifolia and Arbutus unedo forests positively and avoided Erica spp. and Cistus ladanifer scrubland and other habitats. Quercus pyrenaica forests and Quercus rotundifolia and Arbutus unedo forests are important habitats for female wildcats because they provide shelter and food resources, such as small mammals. They also contain elevated tree cavities which can be use as dens. In contrast, Erica spp. and Cistus ladanifer scrubland is an extremely dense habitat with low associated biodiversity and so wildcats avoid it. We believe that this habitat, as well as pine stands, do not provide food and cover resources for wildcats. Home ranges with higher percentage of these habitat types tend to be larger, since females are required to use larger areas to meet their resource requirements. Our results emphasize the importance of the remaining autochthonous forests in wildcat conservation. Therefore, we recommend that current habitat policy for restoration and conservation should be continued and expanded in order to substantially increase the amount of natural forested land in Serra da Malcata

Sound production in the Meagre, Argyrosomus regius (Asso, 1801): intraspecific variability associated with size, sex and context

Many fish taxa produce sound in voluntary and in disturbance contexts but information on the full acoustic repertoire is lacking for most species. Yet, this knowledge is critical to enable monitoring fish populations in nature through acoustic monitoring.Portuguese Foundation for Science and Technology: PTDC/BIA-BMA/30517/2017; SFRH/BD/115562/2016; UID/MAR/04292/2019; UID/BIA/00329/2019; PTDC/BIA-BMA/29662/2017.info:eu-repo/semantics/publishedVersio

Collision avoidance on unmanned aerial vehicles using neural network pipelines and flow clustering techniques

UIDB/04111/2020 PCIF/SSI/0102/2017 IF/00325/2015Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.publishersversionpublishe

The Use of Respiratory Effort Improves an ECG-Based Deep Learning Algorithm to Assess Sleep-Disordered Breathing

BACKGROUND: Sleep apnea is a prevalent sleep-disordered breathing (SDB) condition that affects a large population worldwide. Research has demonstrated the potential of using electrocardiographic (ECG) signals (heart rate and ECG-derived respiration, EDR) to detect SDB. However, EDR may be a suboptimal replacement for respiration signals.METHODS: We evaluated a previously described ECG-based deep learning algorithm in an independent dataset including 198 patients and compared performance for SDB event detection using thoracic respiratory effort versus EDR. We also evaluated the algorithm in terms of apnea-hypopnea index (AHI) estimation performance, and SDB severity classification based on the estimated AHI.RESULTS: Using respiratory effort instead of EDR, we achieved an improved performance in SDB event detection (F1 score = 0.708), AHI estimation (Spearman's correlation = 0.922), and SDB severity classification (Cohen's kappa of 0.62 was obtained based on AHI).CONCLUSION: Respiratory effort is superior to EDR to assess SDB. Using respiratory effort and ECG, the previously described algorithm achieves good performance in a new dataset from an independent laboratory confirming its adequacy for this task.</p

Lower limb kinematic, kinetic, and EMG data from young healthy humans during walking at controlled speeds

Understanding the lower limb kinematic, kinetic, and electromyography (EMG) data interrelation in controlled speeds is challenging for fully assessing human locomotion conditions. This paper provides a complete dataset with the above-mentioned raw and processed data simultaneously recorded for sixteen healthy participants walking on a 10 meter-flat surface at seven controlled speeds (1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0km/h). The raw data include 3D joint trajectories of 24 retro-reflective markers, ground reaction forces (GRF), force plate moments, center of pressures, and EMG signals from Tibialis Anterior, Gastrocnemius Lateralis, Biceps Femoris, and Vastus Lateralis. The processed data present gait cycle-normalized data including filtered EMG signals and their envelope, 3D GRF, joint angles, and torques. This study details the experimental setup and presents a brief validation of the data quality. The presented dataset may contribute to (i) validate and enhance human biomechanical gait models, and (ii) serve as a reference trajectory for personalized control of robotic assistive devices, aiming an adequate assistance level adjusted to the gait speed and user's anthropometry.This work has been supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant 2020.05711.BD, and in part by the FEDER Funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868, and by FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020

Deep learning-based energy expenditure estimation in assisted and non-assisted gait using inertial, EMG, and heart rate wearable sensors

Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ > 0.85) between target and estimation (R¯2 = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control.This work has been supported in part by the FEDER Funds through the COMPETE 2020-Programa Operacional Competitividade e Internacionalizacao (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868, and by FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020, under the FCT scholarship with reference 2020.05708.BD, and under the Stimulus of Scientific Employment with the grant 2020.03393.CEECIND