Mapping financial stability

The ongoing global financial crisis has demonstrated the importance of a systemwide, or macroprudential, approach to safeguarding financial stability. An essential part of macroprudential oversight concerns the tasks of early identification and assessment of risks and vulnerabilities that eventually may lead to a systemic financial crisis. Thriving tools are crucial as they allow early policy actions to decrease or prevent further build-up of risks or to otherwise enhance the shock absorption capacity of the financial system. In the literature, three types of systemic risk can be identified: i ) build-up of widespread imbalances, ii ) exogenous aggregate shocks, and iii ) contagion. Accordingly, the systemic risks are matched by three categories of analytical methods for decision support: i ) early-warning, ii ) macro stress-testing, and iii ) contagion models. Stimulated by the prolonged global financial crisis, today's toolbox of analytical methods includes a wide range of innovative solutions to the two tasks of risk identification and risk assessment. Yet, the literature lacks a focus on the task of risk communication. This thesis discusses macroprudential oversight from the viewpoint of all three tasks: Within analytical tools for risk identification and risk assessment, the focus concerns a tight integration of means for risk communication. Data and dimension reduction methods, and their combinations, hold promise for representing multivariate data structures in easily understandable formats. The overall task of this thesis is to represent high-dimensional data concerning financial entities on lowdimensional displays. The low-dimensional representations have two subtasks: i ) to function as a display for individual data concerning entities and their time series, and ii ) to use the display as a basis to which additional information can be linked. The final nuance of the task is, however, set by the needs of the domain, data and methods. The following ve questions comprise subsequent steps addressed in the process of this thesis: 1. What are the needs for macroprudential oversight? 2. What form do macroprudential data take? 3. Which data and dimension reduction methods hold most promise for the task? 4. How should the methods be extended and enhanced for the task? 5. How should the methods and their extensions be applied to the task? Based upon the Self-Organizing Map (SOM), this thesis not only creates the Self-Organizing Financial Stability Map (SOFSM), but also lays out a general framework for mapping the state of financial stability. This thesis also introduces three extensions to the standard SOM for enhancing the visualization and extraction of information: i ) fuzzifications, ii ) transition probabilities, and iii ) network analysis. Thus, the SOFSM functions as a display for risk identification, on top of which risk assessments can be illustrated. In addition, this thesis puts forward the Self-Organizing Time Map (SOTM) to provide means for visual dynamic clustering, which in the context of macroprudential oversight concerns the identification of cross-sectional changes in risks and vulnerabilities over time. Rather than automated analysis, the aim of visual means for identifying and assessing risks is to support disciplined and structured judgmental analysis based upon policymakers' experience and domain intelligence, as well as external risk communication.

Prediction of market switching and delisting events from OMX First North Nordic multilateral stock exchange

This thesis studies the predictability of market switching and delisting events from OMX First North Nordic multilateral stock exchange by using financial statement information and market information from 2007 to 2012. This study was conducted by using a three stage process. In first stage relevant theoretical framework and initial variable pool were constructed. Then, explanatory analysis of the initial variable pool was done in order to further limit and identify relevant variables. The explanatory analysis was conducted by using self-organizing map methodology. In the third stage, the predictive modeling was carried out with random forests and support vector machine methodologies. It was found that the explanatory analysis was able to identify relevant variables. The results indicate that the market switching and delisting events can be predicted in some extent. The empirical results also support the usability of financial statement and market information in the prediction of market switching and delisting events.

Visual analytics for behavioral and niche market segmentation

Companies require information in order to gain an improved understanding of their customers. Data concerning customers, their interests and behavior are collected through different loyalty programs. The amount of data stored in company data bases has increased exponentially over the years and become difficult to handle. This research area is the subject of much current interest, not only in academia but also in practice, as is shown by several magazines and blogs that are covering topics on how to get to know your customers, Big Data, information visualization, and data warehousing. In this Ph.D. thesis, the Self-Organizing Map and two extensions of it – the Weighted Self-Organizing Map (WSOM) and the Self-Organizing Time Map (SOTM) – are used as data mining methods for extracting information from large amounts of customer data. The thesis focuses on how data mining methods can be used to model and analyze customer data in order to gain an overview of the customer base, as well as, for analyzing niche-markets. The thesis uses real world customer data to create models for customer profiling. Evaluation of the built models is performed by CRM experts from the retailing industry. The experts considered the information gained with help of the models to be valuable and useful for decision making and for making strategic planning for the future.

Effect of scanner acoustic background noise on strict resting-state fMRI

Functional MRI (fMRI) resting-state experiments are aimed at identifying brain networks that support basal brain function. Although most investigators consider a ‘resting-state’ fMRI experiment with no specific external stimulation, subjects are unavoidably under heavy acoustic noise produced by the equipment. In the present study, we evaluated the influence of auditory input on the resting-state networks (RSNs). Twenty-two healthy subjects were scanned using two similar echo-planar imaging sequences in the same 3T MRI scanner: a default pulse sequence and a reduced “silent” pulse sequence. Experimental sessions consisted of two consecutive 7-min runs with noise conditions (default or silent) counterbalanced across subjects. A self-organizing group independent component analysis was applied to fMRI data in order to recognize the RSNs. The insula, left middle frontal gyrus and right precentral and left inferior parietal lobules showed significant differences in the voxel-wise comparison between RSNs depending on noise condition. In the presence of low-level noise, these areas Granger-cause oscillations in RSNs with cognitive implications (dorsal attention and entorhinal), while during high noise acquisition, these connectivities are reduced or inverted. Applying low noise MR acquisitions in research may allow the detection of subtle differences of the RSNs, with implications in experimental planning for resting-state studies, data analysis, and ergonomic factors.

FUTURES OF FINNISH FOOD SYSTEM BY 2050. The perspective of resilience

Modern food systems face complex global challenges such as climate change, resource scarcities, population growth, concentration and globalization. It is not possible to forecast how all these challenges will affect food systems, but futures research methods provide possibilities to enable better understanding of possible futures and that way increases futures awareness. In this thesis, the two-round online Delphi method was utilized to research experts’ opinions about the present and the future resilience of the Finnish food system up to 2050. The first round questionnaire was constructed based on the resilience indicators developed for agroecosystems. Sub-systems in the study were primary production (main focus), food industry, retail and consumption. Based on the results from the first round, the future images were constructed for primary production and food industry sub-sections. The second round asked experts’ opinion about the future images’ probability and desirability. In addition, panarchy scenarios were constructed by using the adaptive cycle and panarchy frameworks. Furthermore, a new approach to general resilience indicators was developed combining “categories” of the social ecological systems (structure, behaviors and governance) and general resilience parameters (tightness of feedbacks, modularity, diversity, the amount of change a system can withstand, capacity of learning and self- organizing behavior). The results indicate that there are strengths in the Finnish food system for building resilience. According to experts organic farms and larger farms are perceived as socially self-organized, which can promote innovations and new experimentations for adaptation to changing circumstances. In addition, organic farms are currently seen as the most ecologically self-regulated farms. There are also weaknesses in the Finnish food system restricting resilience building. It is important to reach optimal redundancy, in which efficiency and resilience are in balance. In the whole food system, retail sector will probably face the most dramatic changes in the future, especially, when panarchy scenarios and the future images are reflected. The profitability of farms is and will be a critical cornerstone of the overall resilience in primary production. All in all, the food system experts have very positive views concerning the resilience development of the Finnish food system in the future. Sometimes small and local is beautiful, sometimes large and international is more resilient. However, when probabilities and desirability of the future images were questioned, there were significant deviations. It appears that experts do not always believe desirable futures to materialize.

FUTURES OF FINNISH FOOD SYSTEM BY 2050. The perspective of resilience

Modern food systems face complex global challenges such as climate change, resource scarcities, population growth, concentration and globalization. It is not possible to forecast how all these challenges will affect food systems, but futures research methods provide possibilities to enable better understanding of possible futures and that way increases futures awareness. In this thesis, the two-round online Delphi method was utilized to research experts’ opinions about the present and the future resilience of the Finnish food system up to 2050. The first round questionnaire was constructed based on the resilience indicators developed for agroecosystems. Sub-systems in the study were primary production (main focus), food industry, retail and consumption. Based on the results from the first round, the future images were constructed for primary production and food industry sub-sections. The second round asked experts’ opinion about the future images’ probability and desirability. In addition, panarchy scenarios were constructed by using the adaptive cycle and panarchy frameworks. Furthermore, a new approach to general resilience indicators was developed combining “categories” of the social ecological systems (structure, behaviors and governance) and general resilience parameters (tightness of feedbacks, modularity, diversity, the amount of change a system can withstand, capacity of learning and self- organizing behavior). The results indicate that there are strengths in the Finnish food system for building resilience. According to experts organic farms and larger farms are perceived as socially self-organized, which can promote innovations and new experimentations for adaptation to changing circumstances. In addition, organic farms are currently seen as the most ecologically self-regulated farms. There are also weaknesses in the Finnish food system restricting resilience building. It is important to reach optimal redundancy, in which efficiency and resilience are in balance. In the whole food system, retail sector will probably face the most dramatic changes in the future, especially, when panarchy scenarios and the future images are reflected. The profitability of farms is and will be a critical cornerstone of the overall resilience in primary production. All in all, the food system experts have very positive views concerning the resilience development of the Finnish food system in the future. Sometimes small and local is beautiful, sometimes large and international is more resilient. However, when probabilities and desirability of the future images were questioned, there were significant deviations. It appears that experts do not always believe desirable futures to materialize.

Conformational analysis of antibody binding site using EDMA methods /

Euclidean distance matrix analysis (EDMA) methods are used to distinguish whether or not significant difference exists between conformational samples of antibody complementarity determining region (CDR) loops, isolated LI loop and LI in three-loop assembly (LI, L3 and H3) obtained from Monte Carlo simulation. After the significant difference is detected, the specific inter-Ca distance which contributes to the difference is identified using EDMA.The estimated and improved mean forms of the conformational samples of isolated LI loop and LI loop in three-loop assembly, CDR loops of antibody binding site, are described using EDMA and distance geometry (DGEOM). To the best of our knowledge, it is the first time the EDMA methods are used to analyze conformational samples of molecules obtained from Monte Carlo simulations. Therefore, validations of the EDMA methods using both positive control and negative control tests for the conformational samples of isolated LI loop and LI in three-loop assembly must be done. The EDMA-I bootstrap null hypothesis tests showed false positive results for the comparison of six samples of the isolated LI loop and true positive results for comparison of conformational samples of isolated LI loop and LI in three-loop assembly. The bootstrap confidence interval tests revealed true negative results for comparisons of six samples of the isolated LI loop, and false negative results for the conformational comparisons between isolated LI loop and LI in three-loop assembly. Different conformational sample sizes are further explored by combining the samples of isolated LI loop to increase the sample size, or by clustering the sample using self-organizing map (SOM) to narrow the conformational distribution of the samples being comparedmolecular conformations. However, there is no improvement made for both bootstrap null hypothesis and confidence interval tests. These results show that more work is required before EDMA methods can be used reliably as a method for comparison of samples obtained by Monte Carlo simulations.

Le projet organisant : vers une ontologie du projet d'aménagement

Codirection: Dr. Gonzalo Lizarralde

Offline Emergence Engineering For Agent Societies

Many examples for emergent behaviors may be observed in self-organizing physical and biological systems which prove to be robust, stable, and adaptable. Such behaviors are often based on very simple mechanisms and rules, but artificially creating them is a challenging task which does not comply with traditional software engineering. In this article, we propose a hybrid approach by combining strategies from Genetic Programming and agent software engineering, and demonstrate that this approach effectively yields an emergent design for given problems.

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme führt zu einer weiteren Erhöhung der Komplexität und damit auch zu einer weiteren Zunahme von Sicherheitslücken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-Lösungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegenüber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungewöhnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. Während signatur-basierte Ansätze nur bereits bekannte Angriffsmuster detektieren können, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) frühzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen lösen zu können, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten für das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell für Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das künstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ansätze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten bösartigen Verbindungen aufzudecken, die Stabilität der Wachstumstopologie durch neuartige Ansätze für die Initialisierung der Gewichtvektoren und durch die Stärkung der Winner Neuronen erhöht, und ein selbst-adaptives Verfahren eingeführt, um das Modell ständig aktualisieren zu können. Darüber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der Überprüfung, ob sie normal sind. Jedoch, ändern sich die Netzverkehrsdaten wegen des Concept drif Phänomens ständig, was in Echtzeit zur Erzeugung nicht stationärer Netzdaten führt. Dieses Phänomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die Änderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzuschätzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie präzise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ansätzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ansätze übertrifft. Dies lässt sich auf folgende Kernpunkte zurückführen: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.

Einsatz von rückgekoppelten mehrstufigen selbstorganisierenden Karten in der Windenergie Leistungsprognose

In dieser Arbeit wird ein Verfahren zum Einsatz neuronaler Netzwerke vorgestellt, das auf iterative Weise Klassifikation und Prognoseschritte mit dem Ziel kombiniert, bessere Ergebnisse der Prognose im Vergleich zu einer einmaligen hintereinander Ausführung dieser Schritte zu erreichen. Dieses Verfahren wird am Beispiel der Prognose der Windstromerzeugung abhängig von der Wettersituation erörtert. Eine Verbesserung wird in diesem Rahmen mit einzelnen Ausreißern erreicht. Verschiedene Aspekte werden in drei Kapiteln diskutiert: In Kapitel 1 werden die verwendeten Daten und ihre elektronische Verarbeitung vorgestellt. Die Daten bestehen zum einen aus Windleistungshochrechnungen für die Bundesrepublik Deutschland der Jahre 2011 und 2012, welche als Transparenzanforderung des Erneuerbaren Energiegesetzes durch die Übertragungsnetzbetreiber publiziert werden müssen. Zum anderen werden Wetterprognosen, die der Deutsche Wetterdienst im Rahmen der Grundversorgung kostenlos bereitstellt, verwendet. Kapitel 2 erläutert zwei aus der Literatur bekannte Verfahren - Online- und Batchalgorithmus - zum Training einer selbstorganisierenden Karte. Aus den dargelegten Verfahrenseigenschaften begründet sich die Wahl des Batchverfahrens für die in Kapitel 3 erläuterte Methode. Das in Kapitel 3 vorgestellte Verfahren hat im modellierten operativen Einsatz den gleichen Ablauf, wie eine Klassifikation mit anschließender klassenspezifischer Prognose. Bei dem Training des Verfahrens wird allerdings iterativ vorgegangen, indem im Anschluss an das Training der klassenspezifischen Prognose ermittelt wird, zu welcher Klasse der Klassifikation ein Eingabedatum gehören sollte, um mit den vorliegenden klassenspezifischen Prognosemodellen die höchste Prognosegüte zu erzielen. Die so gewonnene Einteilung der Eingaben kann genutzt werden, um wiederum eine neue Klassifikationsstufe zu trainieren, deren Klassen eine verbesserte klassenspezifisch Prognose ermöglichen.

Ciencias de la complejidad: estado actual

Los aportes teóricos y aplicados de la complejidad en economía han tomado tantas direcciones y han sido tan frenéticos en las últimas décadas, que no existe un trabajo reciente, hasta donde conocemos, que los compile y los analice de forma integrada. El objetivo de este proyecto, por tanto, es desarrollar un estado situacional de las diferentes aplicaciones conceptuales, teóricas, metodológicas y tecnológicas de las ciencias de la complejidad en la economía. Asimismo, se pretende analizar las tendencias recientes en el estudio de la complejidad de los sistemas económicos y los horizontes que las ciencias de la complejidad ofrecen de cara al abordaje de los fenómenos económicos del mundo globalizado contemporáneo.

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions.

Improved SOM learning using simulated annealing

Self-Organizing Map (SOM) algorithm has been extensively used for analysis and classification problems. For this kind of problems, datasets become more and more large and it is necessary to speed up the SOM learning. In this paper we present an application of the Simulated Annealing (SA) procedure to the SOM learning algorithm. The goal of the algorithm is to obtain fast learning and better performance in terms of matching of input data and regularity of the obtained map. An advantage of the proposed technique is that it preserves the simplicity of the basic algorithm. Several tests, carried out on different large datasets, demonstrate the effectiveness of the proposed algorithm in comparison with the original SOM and with some of its modification introduced to speed-up the learning.

Multimodal semantic-associative collateral labelling and indexing of still images

A novel framework for multimodal semantic-associative collateral image labelling, aiming at associating image regions with textual keywords, is described. Both the primary image and collateral textual modalities are exploited in a cooperative and complementary fashion. The collateral content and context based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix, of the visual keywords, A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. Finally, we use Self Organising Maps to examine the classification and retrieval effectiveness of the proposed high-level image feature vector model which is constructed based on the image labelling results.