Data set

Related publications (100)

Quantification and Classification of Handprint-based Net Positive Resource Efficiency Measures in the Swiss Industry Sector

Florian Elias Landerer

The task of the master project is to analyse a data set with information of several hundred companies from Switzerland. The goal is to find in the Swiss industry sector a classification/typology for the nature of resource efficiency of innovating companies and comparing their impacts in Switzerland and abroad.

2018

A multi-class framework for a pedestrian cell transmission model accounting for population heterogeneity

Guy Alexander Cooper

This work is an extension of PedCTM, an aggregate and transient cell transmission model for multidirectional pedestrian flows in which pedestrian characteristics are assumed to be homogeneous across the population considered. Critically, one fundamental diagram relating pedestrian speed and flow to local pedestrian density is employed across the entire population. This work extends the model to population heterogeneity with a multi-class approach wherein each sub-population is assigned its own characteristic fundamental diagram. The model presented requires the implementation of an update cycle to minimize numerical dis- persion of pedestrians across all classes. In addition, multi-class dynamics introduces an element of competition in determining the flow constraints of the various classes. A priority scheme is implemented that allows for static, dynamic and stochastic determination of flow priorities throughout the network over the course of simulation. An attempt was made to base the class-specific fundamental diagrams off of inference from a dataset related to the PedFlux collaboration. Ultimately, however, the implementation made use of the Kladek formula for the speed-density relation. Preliminary simulations and results are presented to serve as a proof of concept.

2014

A Graph Signal Processing Framework for the Classification of Temporal Brain Data

Sarah Itani

Graph Signal Processing (GSP) addresses the analysis of data living on an irregular domain which can be modeled with a graph. This capability is of great interest for the study of brain connectomes. In this case, data lying on the nodes of the graph are considered as signals (e.g., fMRI time-series) that have a strong dependency on the graph topology (e.g., brain structural connectivity). In this paper, we adopt GSP tools to build features related to the frequency content of the signals. To make these features highly discriminative, we apply an extension of the Fukunaga-Koontz transform. We then use these new features to train a decision tree for the prediction of autism spectrum disorder. Interestingly, our framework outperforms state-of-the-art methods on the publicly available ABIDE dataset.

IEEE2020

Vulnérabilité sismique à grande échelle de la ville de Lausanne

Loris Silva Brites

The assessment of the risk maps for the seismic vulnerability at large scale is based on the vulnerability of each building. In order to determine these vulnerabilities, it is first required to assign to each building its construction class. The construction class is needed to define the seismic behavior of the building. Since the structures have been built in different times, this construction classes are different between them, and their vulnerability values have been pre-defined in order to simplify the analysis. If, for a small city the task of assign to each building its construction class can be done by hand, in a big city, with thousands of buildings, this process can result long and hard. In this Master project the possibility of assigning the construction classes by using a machine learning method will be studied. This method use as a data set of the city of Lausanne from previous works which contains around 1000 buildings analyzed by hand with their construction classes. The goal of this method is to find the relationships between the attributes of the buildings to their construction classes. Thanks to the statistical offices, it is possible to obtain the attributes in an automatic way for every building in a city. Based on the relations that the machine learning method finds (and using the statistical attributes) the construction class for every building can finally be determined, in a quick and efficient manner. Once obtained the vulnerability’s values for each building, the risk maps can be drawn. In this project there will be studied three maps, two with the Europeans’ typologies (LM1, LM2), and one with the Swiss’ typologies (UniGE). Some comparisons will be made between the maps, in order to highlight the differences. Some other comparisons will be made to show the impact of the new micro zonation of Lausanne and of the new optimized method for the determination of the seismic displacement demand (N2 optimized method) on the maps. This is done for the mechanical methods (LM2 and UniGE) only. Finally, there will be shown the relationships between the soil’s characteristics (micro zonation), the building’s attributes and the city’s historical evolution with the risk maps.

2020

Seismic Vulnerability Assessment at Large Scale – City of Geneva

Babak Haftgoli Bakhtiari

In order to evaluate the seismic vulnerability at large scale, it is necessary to gain awareness of the soil properties, types of materials used in the construction of buildings during different periods, the construction standards of each period, different techniques of construction, the epicentre of the earthquake, etc. An ideal evaluation would take into account all the mentioned variables above; however, in this study only the building typologies are considered (Classification according to EMS-98). More precisely, the typologies are identified for a small data set manually. Each building possesses four attributes such as: 1) construction period, 2) number of floors, 3) surface of the building, and 4) Roof shape. In order to obtain the attributes above, data sets from Statistic Federal Office (OFS) and territorial information system in Geneva (SITG) were acquired. In addition, these data which embodied many imprecisions as well as irregularities were examined and treated. Furthermore, machine learning techniques are applied and will result in the acquisition of the building typologies for the whole city of Geneva. These machine learning methods are trained on a learning set collected manually, and then applied to the testing set which corresponds to the whole attribute combinations of Geneva. All in the interest of obtaining the corresponding typologies.

2020

Identification of parameters affecting the outcome of biochemical methane potential (BMP) tests

Jonas Stöckli

Anaerobic digestion of organic waste to methane gas (CH4) is an attractive method to produce renewable energy. Due to the trend towards the reuse of waste and away from fossil energy sources, this technology saw a worldwide development in the recent years. A fundamental parameter in this domain is the Biochemical Methane Potential (BMP), which defines the amount of CH4 which can be produced out of a certain organic substrate. Such information is essential in order to plan and optimise anaerobic digestion plants, and to evaluate the reactor feed with a new substrate or a new substrate combination (co-digestion). BMPs are assessed in batch test, which can be regarded as the simulation of a full-scale biogas plant. Two experiments are required, the test in which the test substrate is digested on the inoculum, and the blank, in which the inoculum is digested alone. The part of the inoculum which can be digested is called endogenous substrate. Since the test experiment produced not only on the test substrate but only on the endogenous substrate, the CH4 production on the test substrate alone is obtained by the subtraction of the test production minus the blank production. The units of a BMP are generally indicated in litre of CH4 per gram of volatile solids (VS) test substrate [L/gVS]. Despite the importance and the wide application of this parameter, its exact determination remains a challenging issue and results are often not consistent in between (inter-) and within (intra-) laboratories. Experimental protocols exist but do not lead to a satisfactory BMP tests consistency neither. The aim of this study was to identify parameters affecting the outcome of BMP tests, based on the investigation of two different data sets. An inter-laboratory study of BMP tests providing the final BMP values of 327 experiments and information about 40 related experimental parameters, and a second data set containing the complete CH4 production curves of 136 BMP experiments provided by one single laboratory. The method consisted in graphical and statistical analysis (Mixed Effect Modelling), using [R] programming language and software environment. This study found out, that a significant part of the inter-laboratory BMP inconsistency can be explained by an imprecise assessment of the VS, which was not expected. As the VS of the substrate are directly implied in the computation of the BMP, the impact was significant and therefore the BMP were corrected regarding the VS imprecision. With these data, up to 70% of the inconsistency of BMPs could be explained by inter-laboratory effects. The statistical analysis led to the conclusion that the concentration of the endogenous substrate and the moisture content in the digestate would be the principal factors affecting the outcome of BMP tests. An effort was made to identify and correct errors contained in CH4 production curves, which turned out to be delicate for certain cases. Also indications regarding the precision and the reliability of BMPs were formulated. Further, the investigation of the CH4 production curves led to the development of a new method in order to compute the BMP result. This method was based on the fact, that a certain inoculum reaches always the same slope toward the end of the experiment, no matter what was digested before. The advantage of this method would be that the experiment end-point could be set clearly and that the concentration of the endogenous substrate would not have an impact on the outcome of a BMP test. According to the findings of this report, it was proposed to add the following requirements into experimental protocols for BMP tests: Tests, blanks and the analysis of VS should be carried out in triplicates and their standard deviation should be indicated for each of them, together with the BMP result If triplicates contain set-up errors, these experiments must be repeated, including the corresponding blank/test In the annex: An experimental protocol for the TS and VS analyses An indication of a required moisture content of the digestate (mechanism to investigate in detail first) An indication of the range of required VS concentrations or a maximal production rate for blank tests according to certain experimental conditions (mechanism to investigate in detail first). This report identified several parameters, which contribute to the inter-laboratory inconsistency of BMPs. These findings should be investigated further in order to prove and quantify their impact. An effort should be made to demonstrate the newly developed BMP-computation-method, which could eventually lead to more consistent results. The limitation of this study was, that a relatively low amount of data was available compared to their characteristics. Consequently, the findings could only be proven on a few examples and should therefore only be seen as evidences. Further, this led also to the risk of overfitting, since a relative high amount of parameters needed to be included into the statistical model.

2012

Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic

Marcel Salathé

COVID-19 represents the most severe global crisis to date whose public conversation can be studied in real time. To do so, we use a data set of over 350 million tweets and retweets posted by over 26 million English speaking Twitter users from January 13 to June 7, 2020. We characterize the retweet network to identify spontaneous clustering of users and the evolution of their interaction over time in relation to the pandemic's emergence. We identify several stable clusters (super-communities), and are able to link them to international groups mainly involved in science and health topics, national elites, and political actors. The science- and health-related super-community received disproportionate attention early on during the pandemic, and was leading the discussion at the time. However, as the pandemic unfolded, the attention shifted towards both national elites and political actors, paralleled by the introduction of country-specific containment measures and the growing politicization of the debate. Scientific super-community remained present in the discussion, but experienced less reach and became more isolated within the network. Overall, the emerging network communities are characterized by an increased self-amplification and polarization. This makes it generally harder for information from international health organizations or scientific authorities to directly reach a broad audience through Twitter for prolonged time. These results may have implications for information dissemination along the unfolding of long-term events like epidemic diseases on a world-wide scale.

NATURE PORTFOLIO2021

Tests of mutual independence among several random vectors using univariate and multivariate ranks of nearest neighbours

Soham Sarkar

Testing mutual independence among several random vectors of arbitrary dimensions is a challenging problem in Statistics, and it has gained considerable interest in recent years. In this article, we propose some nonparametric tests based on different notions of ranks of nearest neighbour. These proposed tests can be conveniently used for high dimensional data, even when the dimensions of the random vectors are larger than the sample size. We investigate the performance of these tests on several simulated and real data sets and also use them in identifying causal relationships among the random vectors. Our numerical results show that they can outperform state-of-the-art tests in a wide variety of examples.

TAYLOR & FRANCIS LTD2021

On Perfect Clustering of High Dimension, Low Sample Size Data

Soham Sarkar

Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have adverse effects on their performance. In this article, we use a new data-driven dissimilarity measure, called MADD, which takes care of these problems. MADD uses the distance concentration phenomenon to its advantage, and as a result, clustering algorithms based on MADD usually perform well for high dimensional data. We establish it using theoretical as well as numerical studies. We also address the problem of estimating the number of clusters. This is a challenging problem in cluster analysis, and several algorithms are available for it. We show that many of these existing algorithms have superior performance in high dimensions when they are constructed using MADD. We also construct a new estimator based on a penalized version of the Dunn index and prove its consistency in the HDLSS asymptotic regime. Several simulated and real data sets are analyzed to demonstrate the usefulness of MADD for cluster analysis of high dimensional data.

IEEE COMPUTER SOC2020

On some graph-based two-sample tests for high dimension, low sample size data

Soham Sarkar

Testing for equality of two high-dimensional distributions is a challenging problem, and this becomes even more challenging when the sample size is small. Over the last few decades, several graph-based two-sample tests have been proposed in the literature, which can be used for data of arbitrary dimensions. Most of these test statistics are computed using pairwise Euclidean distances among the observations. But, due to concentration of pairwise Euclidean distances, these tests have poor performance in many high-dimensional problems. Some of them can have powers even below the nominal level when the scale-difference between two distributions dominates the location-difference. To overcome these limitations, we introduce some new dissimilarity indices and use them to modify some popular graph-based tests. These modified tests use the distance concentration phenomenon to their advantage, and as a result, they outperform the corresponding tests based on the Euclidean distance in a wide variety of examples. We establish the high-dimensional consistency of these modified tests under fairly general conditions. Analyzing several simulated as well as real data sets, we demonstrate their usefulness in high dimension, low sample size situations.

SPRINGER2020