Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

What do brain network interactions tell about cognitive processes? How to seek causality?

Optimal adjustment of brain networks allows the biased processing of information in response to the demand of environments and is therefore prerequisite for adaptive behaviour. It is widely shown that a biased state of networks is associated with a particular cognitive process. However, those associations were identified by backward categorization of trials and cannot provide a causal association with cognitive processes. This problem still remains a big obstacle to advance the state of our field in particular human cognitive neuroscience. In my talk, I will present two approaches to address the causal relationships between brain network interactions and behaviour. Firstly, we combined connectivity analysis of fMRI data and a machine leaning method to predict inter-individual differences of behaviour and responsiveness to environmental demands. The connectivity-based classification approach outperforms local activation-based classification analysis, suggesting that interactions in brain networks carry information of instantaneous cognitive processes. Secondly, we have recently established a brand new method combining transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and EEG. We use the method to measure signal transmission between brain areas while introducing extrinsic oscillatory brain activity and to study causal association between oscillatory activity and behaviour. We show that phase-matched oscillatory activity creates the phase-dependent modulation of signal transmission between brain areas, while phase-shifted oscillatory activity blunts the phase-dependent modulation. The results suggest that phase coherence between brain areas plays a cardinal role in signal transmission in the brain networks. In sum, I argue that causal approaches will provide more concreate backbones to cognitive neuroscience.

A Flow Trace Generator using Graph-based Traffic Classification Techniques

We propose a novel methodology to generate realistic network flow traces to enable systematic evaluation of network monitoring systems in various traffic conditions. Our technique uses a graph-based approach to model the communication structure observed in real-world traces and to extract traffic templates. By combining extracted and user-defined traffic templates, realistic network flow traces that comprise normal traffic and customized conditions are generated in a scalable manner. A proof-of-concept implementation demonstrates the utility and simplicity of our method to produce a variety of evaluation scenarios. We show that the extraction of templates from real-world traffic leads to a manageable number of templates that still enable accurate re-creation of the original communication properties on the network flow level.

A landscape mosaics approach for characterizing swidden systems from a REDD+ perspective

Swidden agriculture is often deemed responsible for deforestation and forest degradation in tropical regions, yet swidden landscapes are commonly not visible on land cover/use maps, making it difficult to prove this assertion. For a future REDD+ scheme, the correct identification of deforestation and forest degradation and linking these processes to land use is crucial. However, it is a key challenge to distinguish degradation and deforestation from temporal vegetation dynamics inherent to swiddening. In this article we present an approach for spatial delineation of swidden systems based on landscape mosaics. Furthermore we introduce a classification for change processes based on the change matrix of these landscape mosaics. Our approach is illustrated by a case study in Viengkham district in northern Laos. Over a 30-year time period the swidden landscapes have increased in extent and they have degraded, shifting from long crop–fallow cycles to short cycles. From 2007 to 2009 degradation within the swidden system accounted for half of all the landscape mosaics change processes. Pioneering shifting cultivation did not prevail. The landscape mosaics approach could be used in a swidden compatible monitoring, reporting and verification (MRV) system of a future REDD+ framework.

Pathogenesis and classification of eosinophil disorders: a review of recent developments in the field

Eosinophils and their products play an essential role in the pathogenesis of various reactive and neoplastic disorders. Depending on the underlying disease, molecular defect and involved cytokines, hypereosinophilia may develop and may lead to organ damage. In other patients, persistent eosinophilia is accompanied by typical clinical findings, but the causative role and impact of eosinophilia remain uncertain. For patients with eosinophil-mediated organ pathology, early therapeutic intervention with agents reducing eosinophil counts can be effective in limiting or preventing irreversible organ damage. Therefore, it is important to approach eosinophil disorders and related syndromes early by using established criteria, to perform all appropriate staging investigations, and to search for molecular targets of therapy. In this article, we review current concepts in the pathogenesis and evolution of eosinophilia and eosinophil-related organ damage in neoplastic and non-neoplastic conditions. In addition, we discuss classifications of eosinophil disorders and related syndromes as well as diagnostic algorithms and standard treatment for various eosinophil-related disorders.

A novel 2D-based approach to the discovery of candidate substrates for the metalloendopeptidase meprin

In the past, protease-substrate finding proved to be rather haphazard and was executed by in vitro cleavage assays using singly selected targets. In the present study, we report the first protease proteomic approach applied to meprin, an astacin-like metalloendopeptidase, to determine physiological substrates in a cell-based system of Madin-Darby canine kidney epithelial cells. A simple 2D IEF/SDS/PAGE-based image analysis procedure was designed to find candidate substrates in conditioned media of Madin-Darby canine kidney cells expressing meprin in zymogen or in active form. The method enabled the discovery of hitherto unknown meprin substrates with shortened (non-trypsin-generated) N- and C-terminally truncated cleavage products in peptide fragments upon LC-MS/MS analysis. Of 22 (17 nonredundant) candidate substrates identified, the proteolytic processing of vinculin, lysyl oxidase, collagen type V and annexin A1 was analysed by means of immunoblotting validation experiments. The classification of substrates into functional groups may propose new functions for meprins in the regulation of cell homeostasis and the extracellular environment, and in innate immunity, respectively.

Posterolateral approach to the displaced posterior malleolus: functional outcome and local morbidity

BACKGROUND: Stable anatomical reconstruction of the joint surface in ankle fractures is essential to successful recovery. However, the functional outcome of fractures involving the posterior tibial plafond is often poor. We describe the morbidity and functional outcome for plate fixation of the displaced posterior malleolus using a posterolateral approach. MATERIALS AND METHODS: The posterolateral approach was used for osteosynthesis of the posterior malleolus in 45 consecutive patients (median age 54 years) with AO/Muller-classification type 44-A3 (n = 1), 44-B3 (n = 35), 44-C1 (n = 7), and 44-C2 (n = 2) ankle fractures. Thirty-three of the patients suffered complete fracture dislocation. Functional outcome at followup was measured using the modified Weber protocol and the standardized AAOS foot and ankle questionnaire. Radiological evaluation employed standardized anterior-posterior and lateral views. RESULTS: The fragment comprised a median of 24% (range, 10% to 48%) of the articular surface. Postoperative soft tissue problems were encountered in five patients (11%), one of whom required revision surgery. Two patients (4%) developed Stage I complex regional pain syndrome. Clinical and radiological followup at 25 months disclosed no secondary displacement of the fixed fragment. The median foot and ankle score was 93 (range, 58 to 100), shoe comfort score was 77 (range, 0 to 100). A median score of 7 (range, 5 to 16) was documented using the modified Weber protocol. CONCLUSION: The posterolateral approach allowed good exposure and stable fixation of a displaced posterior malleolar fragment with few local complications. The anatomical repositioning and stable fixation led to good functional and subjective outcome.

A comparison of pixel and object-based land cover classification. A case study of the Asmara region, Eritrea

In this paper we compare the performance of two image classification paradigms (object- and pixel-based) for creating a land cover map of Asmara, the capital of Eritrea and its surrounding areas using a Landsat ETM+ imagery acquired in January 2000. The image classification methods used were maximum likelihood for the pixel-based approach and Bhattacharyya distance for the object-oriented approach available in, respectively, ArcGIS and SPRING software packages. Advantages and limitations of both approaches are presented and discussed. Classifications outputs were assessed using overall accuracy and Kappa indices. Pixel- and object-based classification methods result in an overall accuracy of 78% and 85%, respectively. The Kappa coefficient for pixel- and object-based approaches was 0.74 and 0.82, respectively. Although pixel-based approach is the most commonly used method, assessment and visual interpretation of the results clearly reveal that the object-oriented approach has advantages for this specific case-study.

A systematic approach to analyse the sequelae of LCPD

The analysis and treatment of hips with healed Legg-Calvé-Perthes disease (LCPD) differs substantially from the treatment in the acute phase of the disease. More specifically, the treating orthopaedic surgeon is often faced with a complex three-dimensional pathomorphology of the hip that is difficult to understand and correct. To date, none of the current classification systems provide a useful decision-making algorithm with regards to the type of surgical intervention necessary to improve hip function in patients with sequelae of LCPD. The conceptual recognition of the femoroacetabular impingement (FAI) and the ability to safely dislocate the hip have revolutionised our diagnostic and therapeutic algorithm for joint-preserving surgery of hips with structural residuals of LCPD. We present a systematic approach to analyse femoral and acetabular pathomorphologic features. The resulting pathomechanisms and the surgical treatment options are presented.

A Texture-based land cover classification for the delineation of a shifting cultivation landscape in the Lao PDR using landscape metrics

The delineation of shifting cultivation landscapes using remote sensing in mountainous regions is challenging. On the one hand, there are difficulties related to the distinction of forest and fallow forest classes as occurring in a shifting cultivation landscape in mountainous regions. On the other hand, the dynamic nature of the shifting cultivation system poses problems to the delineation of landscapes where shifting cultivation occurs. We present a two-step approach based on an object-oriented classification of Advanced Land Observing Satellite, Advanced Visible and Near-Infrared Spectrometer (ALOS AVNIR) and Panchromatic Remote-sensing Instrument for Stereo Mapping (ALOS PRISM) data and landscape metrics. When including texture measures in the object-oriented classification, the accuracy of forest and fallow forest classes could be increased substantially. Based on such a classification, landscape metrics in the form of land cover class ratios enabled the identification of crop-fallow rotation characteristics of the shifting cultivation land use practice. By classifying and combining these landscape metrics, shifting cultivation landscapes could be delineated using a single land cover dataset.

Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery

Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability. Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to the majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks, SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of conducted analyses proved fine detection skills of the PCM method with results comparable to or better than the reference PPS algorithm.

Tissue metabolomics of hepatocellular carcinoma: Tumor energy metabolism and the role of transcriptomic classification

Hepatocellular carcinoma (HCC) is one of the commonest causes of death from cancer. A plethora of metabolomic investigations of HCC have yielded molecules in biofluids that are both up- and down-regulated but no real consensus has emerged regarding exploitable biomarkers for early detection of HCC. We report here a different approach, a combined transcriptomics and metabolomics study of energy metabolism in HCC. A panel of 31 pairs of HCC tumors and corresponding nontumor liver tissues from the same patients was investigated by gas chromatography-mass spectrometry (GCMS)-based metabolomics. HCC was characterized by ∼2-fold depletion of glucose, glycerol 3- and 2-phosphate, malate, alanine, myo-inositol, and linoleic acid. Data are consistent with a metabolic remodeling involving a 4-fold increase in glycolysis over mitochondrial oxidative phosphorylation. A second panel of 59 HCC that had been typed by transcriptomics and classified in G1 to G6 subgroups was also subjected to GCMS tissue metabolomics. No differences in glucose, lactate, alanine, glycerol 3-phosphate, malate, myo-inositol, or stearic acid tissue concentrations were found, suggesting that the Wnt/β-catenin pathway activated by CTNNB1 mutation in subgroups G5 and G6 did not exhibit specific metabolic remodeling. However, subgroup G1 had markedly reduced tissue concentrations of 1-stearoylglycerol, 1-palmitoylglycerol, and palmitic acid, suggesting that the high serum α-fetoprotein phenotype of G1, associated with the known overexpression of lipid catabolic enzymes, could be detected through metabolomics as increased lipid catabolism. Conclusion: Tissue metabolomics yielded precise biochemical information regarding HCC tumor metabolic remodeling from mitochondrial oxidation to aerobic glycolysis and the impact of molecular subtypes on this process.

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

Classification of interstitial lung disease patterns using local DCT features and random forest.

Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.

Petrov classification and holographic reconstruction of spacetime

Using the asymptotic form of the bulk Weyl tensor, we present an explicit approach that allows us to reconstruct exact four-dimensional Einstein spacetimes which are algebraically special with respect to Petrov’s classification. If the boundary metric supports a traceless, symmetric and conserved complex rank-two tensor, which is related to the boundary Cotton and energy-momentum tensors, and if the hydrodynamic congruence is shearless, then the bulk metric is exactly resummed and captures modes that stand beyond the hydrodynamic derivative expansion. We illustrate the method when the congruence has zero vorticity, leading to the Robinson-Trautman spacetimes of arbitrary Petrov class, and quote the case of non-vanishing vorticity, which captures the Plebański-Demiański Petrov D family.