Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine

Activity-Based Computing aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject’s body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.Peer ReviewedPostprint (author's final draft

Gyrokinetic treatment of a grazing angle magnetic field

>We develop a gyrokinetic treatment for ions in the magnetic presheath, close to the plasma-wall boundary. We focus on magnetic presheaths with a small magnetic field to wall angle, α ⟪ 1. Characteristic lengths perpendicular to the wall in such a magnetic presheath scale with the typical ion Larmor orbit size, pi. The smallest scale length associated with variations parallel to the wall is taken to be across the magnetic field, and ordered l = ρi/δ, where δ ⟪ 1 is assumed. The scale lengths along the magnetic field line are assumed so long that variations associated with this direction are neglected. These orderings are consistent with what we expect close to the divertor target of a tokamak. We allow for a strong electric field E in the direction normal to the electron repelling wall, with strong variation in the same direction. The large change of the electric field over an ion Larmor radius distorts the orbit so that it is not circular. We solve for the lowest order orbits by identifying coordinates, which consist of constants of integration, an adiabatic invariant and a gyrophase, associated with periodic ion motion in the system with α = δ = 0. By using these new coordinates as variables in the limit α ~ δ ⟪ 1, we obtain a generalized ion gyrokinetic equation. We find another quantity that is conserved to first order and use this to simplify the gyrokinetic equation, solving it in the case of a collisionless magnetic presheath. Assuming a Boltzmann response for the electrons, a form of the quasineutrality equation that exploits the change of variables is derived. The gyrokinetic and quasineutrality equations give the ion distribution function and electrostatic potential in the magnetic presheath if the entrance boundary condition is specified

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Human activity recognition on smartphones for mobile context awareness

Activity-Based Computing [1] aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject’s body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic. In addition to the clear computational advantages of fixed-point arithmetic, it is easy to show the regularization effect of the number of bits and then the connections with the Statistical Learning Theory. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.Peer ReviewedPostprint (published version

Energy efficient smartphone-based activity recognition using fixed-point arithmetic

In this paper we propose a novel energy efficient approach for the recognition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre- serve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experiments show comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of the proposed method.Peer ReviewedPostprint (published version

Mapping Climate Change Research via Open Repositories & AI: advantages and limitations for an evidence-based R&D policy-making

In the last few years, several initiatives have been starting to offer access to research outputs data and metadata in an open fashion. The platforms developed by those initiatives are opening up scientific production to the wider public and they can be an invaluable asset for evidence-based policy-making in Science, Technology and Innovation (STI). These resources can indeed facilitate knowledge discovery and help identify available R&D assets and relevant actors within specific research niches of interest. Ideally, to gain a comprehensive view of entire STI ecosystems, the information provided by each of these resources should be combined and analysed accordingly. To ensure so, at least a certain degree of interoperability should be guaranteed across data sources, so that data could be better aggregated and complemented and that evidence provided towards policy-making is more complete and reliable. Here, we study whether this is the case for the case of mapping Climate Action research in the whole Denmark STI ecosystem, by using 4 popular open access STI data sources, namely OpenAire, Open Alex, CORDIS and Kohesio.Comment: This is an extended version of paper 10.1007/978-3-031-16802-4_52, which was accepted at the International Conference on Theory and Practice of Digital Libraries (TPDL) 2022. arXiv admin note: text overlap with arXiv:2209.0892

Insulin-Like Growth Factor 2 mRNA-Binding Protein 3 Modulates Aggressiveness of Ewing Sarcoma by Regulating the CD164-CXCR4 Axis

Ewing sarcoma (EWS) is the second most common bone and soft tissue-associated malignancy in children and young adults. It is driven by the fusion oncogene EWS/FLI1 and characterized by rapid growth and early metastasis. We have previously discovered that the mRNA binding protein IGF2BP3 constitutes an important biomarker for EWS as high expression of IGF2BP3 in primary tumors predicts poor prognosis of EWS patients. We additionally demonstrated that IGF2BP3 enhances anchorage-independent growth and migration of EWS cells suggesting that IGF2BP3 might work as molecular driver and predictor of EWS progression. The aim of this study was to further define the role of IGF2BP3 in EWS progression. We demonstrated that high IGF2BP3 mRNA expression levels correlated with EWS metastasis and disease progression in well-characterized EWS tumor specimens. EWS tumors with high IGF2BP3 levels were characterized by a specific gene signature enriched in chemokine-mediated signaling pathways. We also discovered that IGF2BP3 regulated the expression of CXCR4 through CD164. Significantly, CD164 and CXCR4 colocalized at the plasma membrane of EWS cells upon CXCL12 stimulation. We further demonstrated that IGF2BP3, CD164, and CXCR4 expression levels correlated in clinical samples and the IGF2BP3/CD164/CXCR4 signaling pathway promoted motility of EWS cells in response to CXCL12 and under hypoxia conditions. The data presented identified CD164 and CXCR4 as novel IGF2BP3 downstream functional effectors indicating that the IGF2BP3/CD164/CXCR4 oncogenic axis may work as critical modulator of EWS aggressiveness. In addition, IGF2BP3, CD164, and CXCR4 expression levels may constitute a novel biomarker panel predictive of EWS progression

Spatial beam cleaning in multimode GRIN fibers. Polarization effects

The beam self-cleaning effect in graded-index multimode optical fibers has several interesting potential applications, such as high-resolution nonlinear imaging and mode-locked fiber lasers. Most experimental and theoretical studies of beam selfcleaning have neglected the role of the state of polarization of light. In this work, we fill this gap by reporting extensive experimental investigations of beam self-cleaning in multimode fibers, for beams with different input states of light polarization. We found that the state of polarization undergoes a complex evolution, which may lead either to full conservation of the input state of polarization, or to nearly complete light depolarization at the fiber output. The former outcome is compelling for applications based on the beam self-cleaning effect, such as multimode mode-locked fiber lasers for micromachining, and multimode devices for microscopy and endoscopy. The latter result, instead, permits to test the limits of validity of current purely scalar theoretical approaches for describing nonlinear propagation in multimode fibers

IMPACT OF PHYSICAL EXERCISE ON PSYCHOLOGICAL WELL-BEING AND PSYCHIATRIC DISORDERS

Background: Physical exercise is one of the major features of human health, as it is involved in several physiological processes and related to major benefits in reducing body fat, myocardial infarction, hypertension and insulin resistance risk. Physical exercise also plays a positive role in achieving psychological well-being that can be defined as a state of happiness and serenity, with low levels of distress, overall good physical and mental health and outlook and a good quality of life. Aim of the paper: To review the positive effects of physical activity on psychological well-being and its possible neurobiological underpinnings, as well as its impact on several neuropsychiatric disorders, such as depression, anxiety, eating disorders, obsessive-compulsive disorder, post-traumatic stress disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, schizophrenia and some neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease. Methods: The PubMed, Scopus, Embase, PsycINFO and Google Scholar databases were searched for full text articles published in the latest thirty years on the benefits that physical activity exerts on psychological well-being. Objectives: This study aims to identify the common and differential elements of the DLD (SLI) and LD through a quantitative and qualitative analysis. Results: An impressive amount of data support the positive role of physical activity on psychological well-being and a large amount of research has focused on its beneficial effects in improving the symptoms of the main neuropsychiatric disorders, while highlighting its usefulness as an adjuvant option to psychopharmacological treatments and psychotherapy. In particular, exercise would deeply affect CNS morphology and function, through heterogeneous mechanisms including, amongst the others, the production of hormones, neurotransmitters and neurotrophins, the promotion of angiogenesis and neuroplasticity, and the regulation of gene expression. Conclusion: Literature indicates that the promotion of physical activity may work like an adjunctive and/or augmentation strategy to enhance drugs or psychological treatments, or even as an alternative option in major depression