Segmentation and supervised classification of image objects in Epo doping-control

Abstract A software system Gel Analysis System for Epo (GASepo) has been developed within an international WADA project. As recent WADA criteria of rEpo positivity are based on identification of each relevant object (band) in Epo images, development of suitable methods of image segmentation and object classification were needed for the GASepo system. In the paper we address two particular problems: segmentation of disrupted bands and classification of the segmented objects into three or two classes. A novel band projection operator is based on convenient object merging measures and their discrimination analysis using specifically generated training set of segmented objects. A weighted ranks classification method is proposed, which is new in the field of image classification. It is based on ranks of the values of a specific criterial function. The weighted ranks classifiers proposed in our paper have been evaluated on real samples of segmented objects of Epo images and compared t

Pair-Wise Temporal Pooling Method for Rapid Training of the HTM Networks Used in Computer Vision Applications

In the paper, several modifications to the conventional learning algorithms of the Hierarchical Temporal Memory (HTM) -- a biologically inspired large-scale model of the neocortex by Numenta -- have been proposed. Firstly, an alternative spatial pooling method has been introduced, which makes use of a random pattern generator exploiting the Metropolis-Hastings algorithm. The original inference algorithm by Numenta has been reformulated, in order to reduce a number of tunable parameters and to optimize its computational efficiency. The main contribution of the paper consists in the proposal of a novel temporal pooling method -- the pair-wise explorer -- which allows faster and more reliable training of the HTM networks using data without inherent temporal information (e.g., static images). While the conventional temporal pooler trains the HTM network on a finite segment of the smooth Brownian-like random walk across the training images, the proposed method performs training by means of the pairs of patterns randomly sampled (in a special manner) from a virtually infinite smooth random walk. We have conducted a set of experiments with the single-layer HTM network applied to the position, scale, and rotation-invariant recognition of geometric objects. The obtained results provide a clear evidence that the pair-wise method yields significantly faster convergence to the theoretical maximum of the classification accuracy with respect to both the length of the training sequence (defined by the maximum allowed number of updates of the time adjacency matrix -- TAM) and the number of training patterns. The advantage of the proposed explorer manifested itself mostly in the lower range of TAM updates where it caused up to 10 % relative accuracy improvement over the conventional method. Therefore we suggest to use the pair-wise explorer, instead of the smooth explorer, always when the HTM network is trained on a set of static images, especially when the exhaustive training is impossible due to the complexity of the given task

Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO

Applications of Silicon Retinas: from Neuroscience to Computer Vision

Traditional visual sensor technology is firmly rooted in the concept of sequences of image frames. The sequence of stroboscopic images in these "frame cameras" is very different compared to the information running from the retina to the visual cortex. While conventional cameras have improved in the direction of smaller pixels and higher frame rates, the basics of image acquisition have remained the same. Event-based vision sensors were originally known as "silicon retinas" but are now widely called "event cameras." They are a new type of vision sensors that take inspiration from the mechanisms developed by nature for the mammalian retina and suggest a different way of perceiving the world. As in the neural system, the sensed information is encoded in a train of spikes, or so-called events, comparable to the action potential generated in the nerve. Event-based sensors produce sparse and asynchronous output that represents in- formative changes in the scene. These sensors have advantages in terms of fast response, low latency, high dynamic range, and sparse output. All these char- acteristics are appealing for computer vision and robotic applications, increasing the interest in this kind of sensor. However, since the sensor’s output is very dif- ferent, algorithms applied for frames need to be rethought and re-adapted. This thesis focuses on several applications of event cameras in scientific scenarios. It aims to identify where they can make the difference compared to frame cam- eras. The presented applications use the Dynamic Vision Sensor (event camera developed by the Sensors Group of the Institute of Neuroinformatics, University of Zurich and ETH). To explore some applications in more extreme situations, the first chapters of the thesis focus on the characterization of several advanced versions of the standard DVS. The low light condition represents a challenging situation for every vision sensor. Taking inspiration from standard Complementary Metal Oxide Semiconductor (CMOS) technology, the DVS pixel performances in a low light scenario can be improved, increasing sensitivity and quantum efficiency, by using back-side illumination. This thesis characterizes the so-called Back Side Illumination DAVIS (BSI DAVIS) camera and shows results from its application in calcium imaging of neural activity. The BSI DAVIS has shown better performance in the low light scene due to its high Quantum Efficiency (QE) of 93% and proved to be the best type of technology for microscopy application. The BSI DAVIS allows detecting fast dynamic changes in neural fluorescent imaging using the green fluorescent calcium indicator GCaMP6f. Event camera advances have pushed the exploration of event-based cameras in computer vision tasks. Chapters of this thesis focus on two of the most active research areas in computer vision: human pose estimation and hand gesture classification. Both chapters report the datasets collected to achieve the task, fulfilling the continuous need for data for this kind of new technology. The Dynamic Vision Sensor Human Pose dataset (DHP19) is an extensive collection of 33 whole-body human actions from 17 subjects. The chapter presents the first benchmark neural network model for 3D pose estimation using DHP19. The network archives a mean error of less than 8 mm in the 3D space, which is comparable with frame-based Human Pose Estimation (HPE) methods using frames. The gesture classification chapter reports an application running on a mobile device and explores future developments in the direction of embedded portable low power devices for online processing. The sparse output from the sensor suggests using a small model with a reduced number of parameters and low power consumption. The thesis also describes pilot results from two other scientific imaging applica- tions for raindrop size measurement and laser speckle analysis presented in the appendices

Document summarization with neural query modeling

Document summarization is a natural language processing task that aims to produce a short summary that concisely delivers the most important information of a document or multiple documents. Over the last few decades, the task has drawn much attention from both academia and industry, as it provides effective tools to manage and access text information. For example, through a newswire summarization engine, users can quickly digest a cluster of news articles by reading a short summary of the topic. Such summaries can, meanwhile, be used by news recommendation and question answering engines. Depending on the users’ role in the summarization process, document summarization falls into two broad categories: generic summarization and query focused summarization (QFS). The former focuses on information intrinsically salient in the input text, while the latter also caters to requests explicitly specified by users. Despite the difference between generic summarization and QFS in their task formulations, we argue that all summaries address queries, even if they are not formulated explicitly. In this thesis, we introduce query modeling in the document summarization context as a critical objective for incorporating observed or latent user intent. We investigate different approaches that explore this theme with deep neural networks. We develop novel systems with neural query modeling for both extractive summarization, where summaries are composed of salient segments (e.g., sentences) from the original document(s), and abstractive summarization, where summaries are made up of words or phrases that do not exist in the input. The recent availability of large-scale datasets has driven the development of neural models that create generic summaries. However, training data in the form of queries, documents, and summaries for QFS is scarce. As most existing research in QFS has employed an extractive approach, we first consider better modeling query-cluster interactions for low-resource extractive QFS. In contrast to previous work with retrieval-style methods for assembling query-relevant summaries, we propose a framework that progressively estimates whether text segments should be included in the summary. Notably, modules of this framework can be independently developed and can leverage training data if available. We present an instantiation of this framework with distant supervision from question answering where various resources exist to identify segments which are likely to answer the query. Experiments on benchmark datasets show that our framework achieves competitive results and is robust across domains. Ideally, summaries should be abstracts, and the hidden costs incurred by annotating QA pairs should be avoided in query modeling. The second part of this thesis focuses on the low-resource challenge in abstractive QFS, and builds an abstractive QFS system which is trained query-free. Concretely, we propose to decompose the task into query modeling and conditional language modeling. For query modeling, we first introduce a unified representation for summaries and queries to exploit training resources in generic summarization, on top of which a weakly supervised model is optimized for evidence estimation. The proposed framework achieves state-of-the-art performance in generating query focused abstracts across existing benchmarks. Finally, the third part of this thesis moves beyond QFS. We provide a unified modeling framework for any kind of summarization, under the assumption that all summaries are a response to a query, which is observed in the case of QFS and latent in the case of generic summarization. We model queries as discrete latent variables over document tokens, and learn representations compatible with observed and unobserved query verbalizations. Requiring no further optimization on downstream summarization tasks, experiments show that our approach outperforms strong comparison systems across benchmarks, query types, document settings, and target domains

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

V Jornadas de Investigación de la Facultad de Ciencia y Tecnología. 2016

171 p.I. Abstracts. Ahozko komunikazioak / Comunicaciones orales: 1. Biozientziak: Alderdi Molekularrak / Biociencias: Aspectos moleculares. 2. Biozientziak: Ingurune Alderdiak / Biociencias: Aspectos Ambientales. 3. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 4. Geología / Geología. 5. Matematika / Matemáticas. 6. Kimika / Química. 7. Ingenieritza Kimikoa eta Kimika / Ingeniería Química y Química. II. Abstracts. Idatzizko Komunikazioak (Posterrak) / Comunicaciones escritas (Pósters): 1. Biozientziak / Biociencias. 2. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 3. Geologia / Geologia. 4. Matematika / Matemáticas. 5. Kimika / Química. 6. Ingenieritza Kimikoa / Ingeniería Química

The search for human skeletal muscle memory : exercise effects on the transcriptome and epigenome

Regular physical activity is an environmental stimulus that is highly associated to many health benefits, while physical inactivity is detrimental for health and physical function. Regular exercise training is used in the prevention and treatment of a large number of disease conditions, including obesity, type II diabetes, cardiovascular disease and cancer, and reduces the risk for premature death. Most tissues adapt to exercise training, not least skeletal muscle tissue, which is highly plastic. The local adaptation of muscle is important not only for muscle function but also the health effects of training that affect the whole body. The cellular adaptations in skeletal muscle are driven by extra- and intracellular signals arising from the exercise stimulus, for example changes in shear stress, oxygen tension, energy levels, pH and temperature. Ultimately, these cellular perturbations lead to gene expression and protein alterations that improve muscle function. Thus, it is important from a clinical, as well as basic science perspective to understand the regulation of skeletal muscle gene activity and how activity changes contribute to the many health benefits of a physically active lifestyle. The understanding of training-induced changes in gene expression and the underlying mechanisms have progressed extensively over the past 20 years. Still, many key mechanisms remain to be investigated. The overall purpose of this thesis was to investigate the influence of epigenetic mechanisms, i.e. DNA methylation and post-translational modifications of histones, on endurance training adaptation. Epigenetic mechanisms are important for cellular memory. Thus, another objective was to investigate if there were any residual intrinsic memory effects of previous endurance training, and if that could induce different responses to a repeated training period after detraining. The results in this thesis are based on skeletal muscle biopsies from the vastus lateralis, taken before and after three months, or six weeks, of endurance training, or at rest in elite athletes and sedentary individuals. In the first study, the baseline skeletal muscle transcriptome was investigated. Studies using repeated skeletal muscle sampling regularly assume that potential changes are due to the intervention and not inherent variability between samples. The results showed, using global RNA sequencing analysis, that tissue homogeneity was remarkably high within a muscle and in the corresponding muscle of the contralateral leg of an individual, while the transcriptome difference between male and female skeletal muscle was substantial. This study also found 23 000 isoforms expressed in skeletal muscle at baseline, together with almost 2500 previously unannotated, novel transcripts, out of which at least five were protein-coding. The transcriptome changes induced by three months of one-legged knee extension training were very significant. Over 3000 isoforms were found to be differentially expressed, as well as 34 of the novel transcripts discovered at baseline. The one-legged training regime meant that the other leg was included as an intraindivdual control leg, which was exposed to the same other environmental factors such as diet, stress, sleep etc. We found that the training response of the trained leg was very specific, although significant but markedly smaller changes occurred also in the untrained leg. At the protein level, a specific investigation of HIF (hypoxia inducible factor) was performed. HIF is activated by acute exercise, but was hypothesized to be attenuated by long-term training due to its inhibitory effect on mitochondrial energy production. A comparison of skeletal muscle from elite athletes with normally active individuals, showed that the negative regulators of HIF were higher in the elite athletes, indicating a reduced HIF activity in that group. This was supported by similar findings in a six-week bicycle training study. Three months of endurance training induced changes in DNA methylation at almost 5000 specific sites across the human skeletal muscle genome that were associated to functionally relevant transcriptional changes. Many of these changes occurred in regulatory enhancer regions and the differentially methylated sites were associated to transcription factor binding sites for myogenic regulatory factors (increases in methylation) and the ETS family (decreases in methylation). Six weeks of bicycle training showed a strong trend towards a global downregulation of trimethylation of histone H3, lysine 27, previously described as a dynamic and predominantly inhibitory modification. The specific genes potentially affected by this histone modification in response to training are currently being analyzed using chromatin immunoprecipitation followed by sequencing. After the initial three months of one-legged endurance training, a subset of the subjects came back after nine months of detraining and performed a second three-month training period. This time, they trained both legs in the exact same way as one leg was trained in the first period. One leg had thus been previously well-trained, while the other was previously untrained. Potential residual effects were investigated by comparing biopsies obtained from both legs before starting the second training period. At the transcriptome level, there were no indications of remaining effects, although the exertion perceived in the first training session of period 2 was lower in the previously trained leg. Repeated training induced similar changes physiologically and at the global transcriptome level between the two legs. There were specific differences in the gene activity changes between the legs, but with the current approach, we found no overall significant differences in the response to a repeated training period. Collectively, the results in this thesis show that endurance exercise training induced associated changes in the epigenome and transcriptome of human skeletal muscle. The data included an in-depth analysis of the human skeletal muscle transcriptome at baseline and how it changes in response to repeated endurance training periods, with no detectable muscle memory of previous training at the transcriptome level. The results contribute to a better understanding of the molecular pathways involved in physiological adaptation to endurance training and can potentially be used to describe how training prevents disease development and different dysfunctions

Training for Optimal Sports Performance and Health

In this book, the emphasis is on various training interventions. Types of exercises that can help improve performance in athletes and health in people facing poor movement diseases.Also, we have presented a variety of strength training interventions in the form of various types of research. On the other hand, we continue to monitor internal and external loads related to non-contact injuries and performance analysis