PREDICTING GROUND REACTION FORCES FROM TRUNK KINEMATICS: A MASS-SPRING-DAMPER MODEL APPROACH

The purpose of this study was to explore if a mass-spring-damper model can simulate trunk kinematics during running with the purpose of predicting ground reaction forces (GRF). Vertical GRF and trunk kinematics was measured for 16 participants during running at 2-5 m·s-1. The vertical trunk acceleration were used to simulate the acceleration of a mass-spring-damper model’s upper mass and generate the eight model parameters required to predict GRF. Mean squared errors between 0.8 ± 0.4 and 0.9 ± 0.5 m·s-2 and between 19.1 ± 7.0 and 27.9 ± 14.5 N·kg-1 were observed for the trunk acceleration and GRF respectively. Despite excellent trunk acceleration simulations, poor GRF predictions indicated that a simple mass-spring-damper model is shortcoming to predict variations in distinct loading features across different running speeds

Bioinformatics and Medicine in the Era of Deep Learning

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic

FEW : file management for portable devices

Comunicação apresentada ao International Workshop on Software Support for Portable Storage (IWSSPS), San Francisco, 2005.In recent years, an increasing number of portable devices with large amounts of storage have become widely used. In this paper, we present the early design of the FEW system, a system that aims to ease file management in the new mobile environment. To this end, the system will manage file replicas stored in fixed and portable storage devices. It will provide an automatic mechanism to establish new file replicas by analyzing file system activity. The system will automatically and incrementally synchronize all file replicas exploring the available network connectivity and the availability of portable storage devices. To merge concurrent updates, operational transformation techniques will be used.FCT/MCES POSI/FEDER - Project #59064/2004

Societal issues in machine learning: when learning from data is not enough

It has been argued that Artificial Intelligence (AI) is experiencing a fast process of commodification. Such characterization is on the interest of big IT companies, but it correctly reflects the current industrialization of AI. This phenomenon means that AI systems and products are reaching the society at large and, therefore, that societal issues related to the use of AI and Machine Learning (ML) cannot be ignored any longer. Designing ML models from this human-centered perspective means incorporating human-relevant requirements such as safety, fairness, privacy, and interpretability, but also considering broad societal issues such as ethics and legislation. These are essential aspects to foster the acceptance of ML-based technologies, as well as to ensure compliance with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. The ESANN special session for which this tutorial acts as an introduction aims to showcase the state of the art on these increasingly relevant topics among ML theoreticians and practitioners. For this purpose, we welcomed both solid contributions and preliminary relevant results showing the potential, the limitations and the challenges of new ideas, as well as refinements, or hybridizations among the different fields of research, ML and related approaches in facing real-world problems involving societal issues.Peer ReviewedPostprint (published version

Enhancement of optical absorption by modulation of the oxygen flow of TiO2 films deposited by reactive sputtering

Oxygen-deficient TiO2 films with enhanced visible and near-infrared optical absorption have been deposited by reactive sputtering using a planar diode radio frequency magnetron configuration. It is observed that the increase in the absorption coefficient is more effective when the O2 gas supply is periodically interrupted rather than by a decrease of the partial O2 gas pressure in the deposition plasma. The optical absorption coefficient at 1.5 eV increases from about 1 102 cm 1 to more than 4 103 cm 1 as a result of the gas flow discontinuity. A red-shift of 0.24 eV in the optical absorption edge is also observed. High resolution transmission electron microscopy with composition analysis shows that the films present a dense columnar morphology, with estimated mean column width of 40 nm. Moreover, the interruptions of the O2 gas flow do not produce detectable variations in the film composition along its growing direction. X-ray diffraction and micro-Raman experiments indicate the presence of the TiO2 anatase, rutile, and brookite phases. The anatase phase is dominant, with a slight increment of the rutile and brookite phases in films deposited under discontinued O2 gas flow. The increase of optical absorption in the visible and near-infrared regions has been attributed to a high density of defects in the TiO2 films, which is consistent with density functional theory calculations that place oxygen-related vacancy states in the upper third of the optical bandgap. The electronic structure calculation results, along with the adopted deposition method and experimental data, have been used to propose a mechanism to explain the formation of the observed oxygen-related defects in TiO2 thin films. The observed increase in sub-bandgap absorption and the modeling of the corresponding changes in the electronic structure are potentially useful concerning the optimization of efficiency of the photocatalytic activity and the magnetic doping of TiO2 films

Convex non-negative matrix factorization for brain tumor delimitation from MRSI data

Background: Pattern Recognition techniques can provide invaluable insights in the field of neuro-oncology. This is because the clinical analysis of brain tumors requires the use of non-invasive methods that generate complex data in electronic format. Magnetic Resonance (MR), in the modalities of spectroscopy (MRS) and spectroscopic imaging (MRSI), has been widely applied to this purpose. The heterogeneity of the tissue in the brain volumes analyzed by MR remains a challenge in terms of pathological area delimitation. Methodology/Principal Findings: A pre-clinical study was carried out using seven brain tumor-bearing mice. Imaging and spectroscopy information was acquired from the brain tissue. A methodology is proposed to extract tissue type-specific sources from these signals by applying Convex Non-negative Matrix Factorization (Convex-NMF). Its suitability for the delimitation of pathological brain area from MRSI is experimentally confirmed by comparing the images obtained with its application to selected target regions, and to the gold standard of registered histopathology data. The former showed good accuracy for the solid tumor region (proliferation index (PI)>30%). The latter yielded (i) high sensitivity and specificity in most cases, (ii) acquisition conditions for safe thresholds in tumor and non-tumor regions (PI>30% for solid tumoral region; ≤5% for non-tumor), and (iii) fairly good results when borderline pixels were considered. Conclusions/Significance: The unsupervised nature of Convex-NMF, which does not use prior information regarding the tumor area for its delimitation, places this approach one step ahead of classical label-requiring supervised methods for discrimination between tissue types, minimizing the negative effect of using mislabeled voxels. Convex-NMF also relaxes the non-negativity constraints on the observed data, which allows for a natural representation of the MRSI signal. This should help radiologists to accurately tackle one of the main sources of uncertainty in the clinical management of brain tumors, which is the difficulty of appropriately delimiting the pathological area

Reproducible k-means clustering in galaxy feature data from the GAMA survey

A fundamental bimodality of galaxies in the local Universe is apparent in many of the features used to describe them. Multiple sub-populations exist within this framework, each representing galaxies following distinct evolutionary pathways. Accurately identifying and characterizing these sub-populations requires that a large number of galaxy features be analysed simultaneously. Future galaxy surveys such as LSST and Euclid will yield data volumes for which traditional approaches to galaxy classification will become unfeasible. To address this, we apply a robust k-means unsupervized clustering method to feature data derived from a sample of 7338 local-Universe galaxies selected from the Galaxy And Mass Assembly (GAMA) survey. This allows us to partition our sample into k clusters without the need for training on pre-labelled data, facilitating a full census of our high-dimensionality feature space and guarding against stochastic effects. We find that the local galaxy population natively splits into 2, 3, 5, and a maximum of six sub-populations, with each corresponding to a distinct ongoing evolutionary mechanism. Notably, the impact of the local environment appears strongly linked with the evolution of low-mass (M* \u3c 1010 M⊙) galaxies, with more massive systems appearing to evolve more passively from the blue cloud on to the red sequence. With a typical run time of ~3 min per value of k for our galaxy sample, we show how k-means unsupervized clustering is an ideal tool for future analysis of large extragalactic data sets, being scalable, adaptable, and providing crucial insight into the fundamental properties of the local galaxy population