Identifying cross country skiing techniques using power meters in ski poles

Power meters are becoming a widely used tool for measuring training and racing effort in cycling, and are now spreading also to other sports. This means that increasing volumes of data can be collected from athletes, with the aim of helping coaches and athletes analyse and understanding training load, racing efforts, technique etc. In this project, we have collaborated with Skisens AB, a company producing handles for cross country ski poles equipped with power meters. We have conducted a pilot study in the use of machine learning techniques on data from Skisens poles to identify which "gear" a skier is using (double poling or gears 2-4 in skating), based only on the sensor data from the ski poles. The dataset for this pilot study contained labelled time-series data from three individual skiers using four different gears recorded in varied locations and varied terrain. We systematically evaluated a number of machine learning techniques based on neural networks with best results obtained by a LSTM network (accuracy of 95% correctly classified strokes), when a subset of data from all three skiers was used for training. As expected, accuracy dropped to 78% when the model was trained on data from only two skiers and tested on the third. To achieve better generalisation to individuals not appearing in the training set more data is required, which is ongoing work.Comment: Presented at the Norwegian Artificial Intelligence Symposium 201

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Convolutional LSTM Networks for Subcellular Localization of Proteins

Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) model on the other hand are designed to handle sequences. In this study we demonstrate that LSTM networks predict the subcellular location of proteins given only the protein sequence with high accuracy (0.902) outperforming current state of the art algorithms. We further improve the performance by introducing convolutional filters and experiment with an attention mechanism which lets the LSTM focus on specific parts of the protein. Lastly we introduce new visualizations of both the convolutional filters and the attention mechanisms and show how they can be used to extract biological relevant knowledge from the LSTM networks

The 3′ end of the heavy chain constant region locus enhances germline transcription and switch recombination of the four γ genes

The switch in immunoglobulin (Ig) heavy chain class is preceded by germline transcription and then mediated by a DNA recombination event. To study germline transcription and class switch recombination we used transgenic mice with a 230-kilobase bacterial artificial chromosome that included a rearranged VDJ gene and the entire heavy chain constant region locus. In addition to several lines with intact transgenes, we identified two lines in which the heavy chain locus transgene lacked Cα and everything 3′ of it, including the regulatory elements HS3a, HS1-2, HS3b, and HS4. B cells from both lines with the truncated transgenes make abundant transgenic (Tg) VDJCμ transcripts and IgM protein. Deletion of the 3′ end of the locus results in dramatically reduced expression of both germline transcripts and switched VDJCH transcripts of the γ3, γ2b, γ2a, and ɛ genes. In addition, the transgenes lacking the 3′ end of the locus express reduced amounts of γ1 germline transcripts and 2–3% of the amount of Tg IgG1 in tissue culture compared with intact transgenes. Finally, switch recombination to γ1 is undetectable in the transgenes lacking the 3′ elements, as measured by digestion circularization–polymerase chain reaction or by the expression of VDJCγ1 transcripts

Imaging Carotid Atherosclerosis Plaque Ulceration: Comparison of Advanced Imaging Modalities and Recent Developments.

Atherosclerosis remains the leading cause of long-term mortality and morbidity worldwide, despite remarkable advancement in its management. Vulnerable atherosclerotic plaques are principally responsible for thromboembolic events in various arterial territories such as carotid, coronary, and lower limb vessels. Carotid plaque ulceration is one of the key features associated with plaque vulnerability and is considered a notable indicator of previous plaque rupture and possible future cerebrovascular events. Multiple imaging modalities have been used to assess the degree of carotid plaque ulceration for diagnostic and research purposes. Early diagnosis and management of carotid artery disease could prevent further cerebrovascular events. In this review, we highlight the merits and limitations of various imaging techniques for identifying plaque ulceration

PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model

Andean Land Use And Biodiversity: Humanized Landscapes In A Time Of Change

Some landscapes Cannot be understood without reference., to the kinds. degrees, kinds, degrees, and history of human-caused modifications to the Earth's surface. The tropical latitudes of the Andes represent one such place, with agricultural land-use systems appearing in the Early Holocene. Current land use includes both intensive and extensive grazing and crop- or tree-based agricultural systems found across virtually the, entire range of possible elevations and humidity regimes. Biodiversity found in or adjacent to such humanized landscapes will have been altered in abundance. composition, and distribution in relation to the resiliency of the native Species to harvest, hold cover modifications, and other deliberate or inadvertent human land uses. In addition, the geometries of land cover, resulting flout difference among the shapes, sizes, connectivities, and physical structures of the patches, corridors, and matrices that compose landscape mosaics, will constrain biodiversity, often in predictable ways. This article proposes a conceptual model that alter ins that the Continued persistence of native species may depend as much oil the shifting Of Andean landscape mosaics as on species characteristics, themselves. Furthermore, mountains such as the Andes display long gradients of environmental Conditions that after in relation to latitude, soil moisture, aspect, and elevation. Global environmental change will shift these, especially temperature and humidity regimes along elevational gradients, causing Changes outside the historical range of variation for some species. Both land-use systems and Conservation efforts will need to respond spatially to these shifts in the future, at both landscape and regional scales.Geography and the Environmen

Strategic toolkits: seniority, usage and performance in the German SME machinery and equipment sector

This paper examines the strategic tool kit, from a human resource management (HRM) perspective, in terms of usage and impact. Research to date has tended to consider usage, assuming to a certain extent that knowledge and understanding of particular tools suggest that practitioners value them. The research on which this paper is based builds upon the idea that usage indicates satisfaction, but develops the usage theme to investigate which decision-makers are actually engaged in both tool appliance and the strategic process. Of particular interest to the researchers are the educational background, age and seniority of the decision-makers. In addition, potential links with HRM and organizational performance are also explored. The context of the research, the German machinery and equipment sector, provides an insight into the industry's ability to sustain growth in face of increasing international competition. The paper calls for a greater awareness, from a human resource perspective, and utilization of strategic management practice and associated decision-making aids