On Distributed Storage Codes

Distributed storage systems are studied. The interest in such system has become relatively wide due to the increasing amount of information needed to be stored in data centers or different kinds of cloud systems. There are many kinds of solutions for storing the information into distributed devices regarding the needs of the system designer. This thesis studies the questions of designing such storage systems and also fundamental limits of such systems. Namely, the subjects of interest of this thesis include heterogeneous distributed storage systems, distributed storage systems with the exact repair property, and locally repairable codes. For distributed storage systems with either functional or exact repair, capacity results are proved. In the case of locally repairable codes, the minimum distance is studied. Constructions for exact-repairing codes between minimum bandwidth regeneration (MBR) and minimum storage regeneration (MSR) points are given. These codes exceed the time-sharing line of the extremal points in many cases. Other properties of exact-regenerating codes are also studied. For the heterogeneous setup, the main result is that the capacity of such systems is always smaller than or equal to the capacity of a homogeneous system with symmetric repair with average node size and average repair bandwidth. A randomized construction for a locally repairable code with good minimum distance is given. It is shown that a random linear code of certain natural type has a good minimum distance with high probability. Other properties of locally repairable codes are also studied.Siirretty Doriast

Computational methods and tools for protein phosphorylation analysis

Signaling pathways represent a central regulatory mechanism of biological systems where a key event in their correct functioning is the reversible phosphorylation of proteins. Protein phosphorylation affects at least one-third of all proteins and is the most widely studied posttranslational modification. Phosphorylation analysis is still perceived, in general, as difficult or cumbersome and not readily attempted by many, despite the high value of such information. Specifically, determining the exact location of a phosphorylation site is currently considered a major hurdle, thus reliable approaches are necessary for the detection and localization of protein phosphorylation. The goal of this PhD thesis was to develop computation methods and tools for mass spectrometry-based protein phosphorylation analysis, particularly validation of phosphorylation sites. In the first two studies, we developed methods for improved identification of phosphorylation sites in MALDI-MS. In the first study it was achieved through the automatic combination of spectra from multiple matrices, while in the second study, an optimized protocol for sample loading and washing conditions was suggested. In the third study, we proposed and evaluated the hypothesis that in ESI-MS, tandem CID and HCD spectra of phosphopeptides can be accurately predicted and used in spectral library searching. This novel strategy for phosphosite validation and identification offered accuracy that outperformed the other currently existing popular methods and proved applicable to complex biological samples. And finally, we significantly improved the performance of our command-line prototype tool, added graphical user interface, and options for customizable simulation parameters and filtering of selected spectra, peptides or proteins. The new software, SimPhospho, is open-source and can be easily integrated in a phosphoproteomics data analysis workflow. Together, these bioinformatics methods and tools enable confident phosphosite assignment and improve reliable phosphoproteome identification and reportin

Effects of regular use of scalable, technology enhanced solution for primary mathematics education

Mathematics is one of the key subjects in any school curriculum and most teachers agree that mathematical skills are important for students to master. There is an abundance of research in learning mathematics and a consensus exists among researchers that technology can enhance the learning process. However, many factors need to be taken into consideration when introducing technology into teaching mathematics. Developing a more natural collaboration between learning technology experts, teachers, and students ensures all stakeholders are considered. Involving teachers early on helps develop enduring commitment to innovations and practical solutions. Moreover, creating a culture of collaboration between experts in the field and teachers brings to bear the best of what both worlds have to offer. This thesis synthesizes six papers and offers additional findings that focus on how technology experts can collaborate with elementary teachers to improve student learning outcomes. We focus on managing educational change in ways that improve the sustainability of innovations. We also explore how technical and teaching experts co-create effective lesson plans. In one of the six papers we collected and reported teachers’ responses to survey questions covering typical usage patterns on a platform. Teachers’ direct feedback was collected and incorporated to improve technical solutions. Moreover, one study was conducted abroad to measure the effect of culture on the teaching and learning process. Evidence of effectiveness of technologically enhanced lessons and corresponding homework was based on multiple studies in grades 1 - 3, covering 379 students. The effectiveness of educational technology was measured based on two variables: student performance in mathematics, based on the learning objectives specified in the curriculum, and arithmetic fluency measured by how rapidly and accurately students solved basic arithmetic operations. Statistically significant findings show that educational technology can improve two target variables when comparing students who did not use educational technology to students who did. An additional effect size analysis was conducted to verify and compare results with previous research. Based on these results, platform use produced the same or better effect than previous studies. Based on teacher feedback and user growth on the platform, we managed to integrate technology into the regular school classroom in meaningful and sustainable ways. We were clearly able to support teachers in their practice in a manner that resulted in noticeable student achievement gains. A survey revealed a need to emphasize new features that were introduced to the platform in teacher training programs. Teachers also reported having a positive attitude towards the platform and the initiative gained wide acceptance among their peers.Matematiikka on yksi tärkeimmistä kouluaineista pelkästään tuntimääräisesti mitattunakin. Matematiikan osaamista ja oppimista pidetään yleisesti tärkeänä ja arvostettuna taitona. Matematiikan oppimisesta on valtavasti tutkimusta ja tutkijoiden keskuudessa vallitsee yhteisymmärrys tietotekniikan positiivisista mahdollisuuksista edistää matematiikan oppimista. Tietotekniikan ja oppimisen vuorovaikutus on kuitenkin monisyinen vyyhti ja sen onnistunut hyödyntäminen vaatii tutkijoiden, opettajien ja oppilaiden välistä tiivistä ja vuorovaikutteista yhteistyötä. Uusien innovaatioiden ja kokeilujen onnistumiselle ja niihin sitoutumiselle luodaan vahva pohja, kun opettajat otetaan mukaan kehitystyöhön ensimetreiltä lähtien. Tällaisen tiiviin yhteistyökulttuurin vaaliminen mahdollistaa käytännön työn ja teorian vahvuuksien hyödyntämisen. Tämä väitöstyö koostuu kuudesta artikkelista. Artikkelit kuvaavat, kuinka tutkijat ja opettajat työskentelivät yhdessä parantaakseen oppilaiden matematiikan oppimista. Tavoitteenamme oli muuttaa koulun käytänteitä pitkäjänteisesti ja kestävällä tavalla. Tutkimme kuinka tutkijat ja opettajat pystyivät yhdessä luomaan onnistuneita ja tehokkaita oppimiskokonaisuuksia. Opettajat olivat koko ajan kehitystyön keskiössä. Yhdessä kuudesta artikkelista tutkittiin kyselytutkimuksen avulla opettajien kokemuksia ja käyttötottumuksia. Näitä vastauksia hyödynnettiin teknisessä kehitystyössä ja hyvien käytänteiden hiomisessa. Yksi väitöskirjan tutkimuksista tehtiin ulkomailla opetus- ja oppimiskulttuureista vaikutusten huomioimiseksi. Sähköisten oppituntien ja kotitehtävien vaikuttavuuden arviointi perustuu useisiin 1.-3. luokilla tehtyihin tutkimuksiin ja kaikkiaan 379 oppilaan vastauksiin. Sähköisten oppituntien vaikuttavuutta arvioitiin kahden eri mittarin perusteella. Ensin matematiikan taitojen perusteella, eli kuinka hyvin kunkin luokka-asteen oppimistavoitteet olivat täyttyneet ja myöhemmin myös laskusujuvuuden perusteella, eli kuinka nopeasti ja tarkasti oppilaat pystyivät laskemaan peruslaskutoimituksia. Tulokset osoittavat, että opetusteknologian avulla pystytään parantamaan oppilaiden suoriutumista edellä mainittujen osa-alueiden osalta verrattuna oppilaisiin, jotka eivät käyttäneet opetusteknologiaa. Tulokset olivat tilastollisesti merkitseviä. Näiden tulosten varmistamiseksi laskettiin vaikuttavuuden suuruus ja sitä verrattiin aiempiin alan tutkimuksiin. Tulosten perusteella sähköisillä oppitunneilla oli sama tai parempi vaikuttavuus kuin aiemmissa tutkimuksissa. Opettajien palautteiden ja kasvavan käyttäjämäärän perusteella voidaan sanoa, että onnistuimme tavoitteessamme integroida opetusteknologiaa mielekkäällä tavalla osaksi koulutyötä. Onnistuimme myös tukemaan ja auttamaan opettajia opetustyössään ja samalla merkittävästi parantamaan oppilaiden suoriutumista. Kyselytutkimuksen perusteella huomasimme, että uusien ominaisuuksien kouluttamiseen tulee kiinnittää enemmän huomiota. Samassa tutkimuksessa opettajat raportoivat olevansa tyytyväisiä alustaan ja sähköiset oppitunnit näyttävät saaneen vankan jalansijan suomalaisessa opettajakunnassa

The formulation of competitive actions in practice

This is a study of what managers do in relation to the formulation of competitive actions. The study started with Project 1 (P1), a literature review that looked at managers’ cognitions in respect of competitive positioning and competitive strategy. A gap was found in how individual competitive actions are formulated and executed. A gap was also found concerning what managers do in response to interpretations of their competitive environments. Following the literature review, a series of semi-structured interviews were undertaken with managers and 26 individual competitive actions were recorded and analysed in Project 2 (P2). A structure to the formulation of competitive actions was discerned and developed into a processual model that is triggered by a stimulus, followed by the manager envisaging desired outcome and setting objectives, then deciding which levers to use, developing the action and refining it. Its application to practice was developed in Project 3 (P3) through an aide memoir tool to assist managers. The study makes a contribution to theory by providing a framework that captures the way in which managers construe and formulate competitive actions. In P2 it was found that managers tend to follow a largely homogenous process and that the tools and techniques offered in the extant literature are seldom used. The managers interviewed in mature industries were far more aware of who their competitors were in more when compared to nascent industries. This had a bearing on the formulation of competitive actions insofar as companies operating in mature industries formulated competitive actions to fend off or compete with their competitors more effectively while companies operating in nascent industries tended to formulate competitive actions with the aim of exploiting gaps in the market. It was found in P2 that managers’ backgrounds, including their functional and educational, as well as their nationalistic and cultural backgrounds, had a bearing on how they construed their competitors and the competitive actions they formulated. It was also found that competitive actions were formulated and executed on an iterative process, whereby managers would refine their actions applying the learnings from previous actions. Managers, particularly those with more experience, relied heavily on intuition and tacit knowledge, as well as input from colleagues and customers, when formulating competitive actions. Contrary to the assertions many in much of the extant literature about companies not deviating from industry norms when formulating competitive actions, the study found that managers would often do so in search of abnormal profits. The study makes a contribution to practice by providing a guide to assist in formulating competitive actions. The guide is based on the processual model developed in P2 and was summarised in five key steps, comprising Stimulus, Objectives, Levers, Actions and Refinement, and abbreviated ‘SOLAR’

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.Siirretty Doriast

Scheduling of guarded command based models

Formal methods provide a means of reasoning about computer programs in order to prove correctness criteria. One subtype of formal methods is based on the weakest precondition predicate transformer semantics and uses guarded commands as the basic modelling construct. Examples of such formalisms are Action Systems and Event-B. Guarded commands can intuitively be understood as actions that may be triggered when an associated guard condition holds. Guarded commands whose guards hold are nondeterministically chosen for execution, but no further control flow is present by default. Such a modelling approach is convenient for proving correctness, and the Refinement Calculus allows for a stepwise development method. It also has a parallel interpretation facilitating development of concurrent software, and it is suitable for describing event-driven scenarios. However, for many application areas, the execution paradigm traditionally used comprises more explicit control flow, which constitutes an obstacle for using the above mentioned formal methods. In this thesis, we study how guarded command based modelling approaches can be conveniently and efficiently scheduled in different scenarios. We first focus on the modelling of trust for transactions in a social networking setting. Due to the event-based nature of the scenario, the use of guarded commands turns out to be relatively straightforward. We continue by studying modelling of concurrent software, with particular focus on compute-intensive scenarios. We go from theoretical considerations to the feasibility of implementation by evaluating the performance and scalability of executing a case study model in parallel using automatic scheduling performed by a dedicated scheduler. Finally, we propose a more explicit and non-centralised approach in which the flow of each task is controlled by a schedule of its own. The schedules are expressed in a dedicated scheduling language, and patterns assist the developer in proving correctness of the scheduled model with respect to the original one

Algorithmic Analysis Techniques for Molecular Imaging

This study addresses image processing techniques for two medical imaging modalities: Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), which can be used in studies of human body functions and anatomy in a non-invasive manner. In PET, the so-called Partial Volume Effect (PVE) is caused by low spatial resolution of the modality. The efficiency of a set of PVE-correction methods is evaluated in the present study. These methods use information about tissue borders which have been acquired with the MRI technique. As another technique, a novel method is proposed for MRI brain image segmen- tation. A standard way of brain MRI is to use spatial prior information in image segmentation. While this works for adults and healthy neonates, the large variations in premature infants preclude its direct application. The proposed technique can be applied to both healthy and non-healthy premature infant brain MR images. Diffusion Weighted Imaging (DWI) is a MRI-based technique that can be used to create images for measuring physiological properties of cells on the structural level. We optimise the scanning parameters of DWI so that the required acquisition time can be reduced while still maintaining good image quality. In the present work, PVE correction methods, and physiological DWI models are evaluated in terms of repeatabilityof the results. This gives in- formation on the reliability of the measures given by the methods. The evaluations are done using physical phantom objects, correlation measure- ments against expert segmentations, computer simulations with realistic noise modelling, and with repeated measurements conducted on real pa- tients. In PET, the applicability and selection of a suitable partial volume correction method was found to depend on the target application. For MRI, the data-driven segmentation offers an alternative when using spatial prior is not feasible. For DWI, the distribution of b-values turns out to be a central factor affecting the time-quality ratio of the DWI acquisition. An optimal b-value distribution was determined. This helps to shorten the imaging time without hampering the diagnostic accuracy.Siirretty Doriast

Energy and Performance: Management of Virtual Machines: Provisioning, Placement, and Consolidation

Cloud computing is a new computing paradigm that offers scalable storage and compute resources to users on demand through Internet. Public cloud providers operate large-scale data centers around the world to handle a large number of users request. However, data centers consume an immense amount of electrical energy that can lead to high operating costs and carbon emissions. One of the most common and effective method in order to reduce energy consumption is Dynamic Virtual Machines Consolidation (DVMC) enabled by the virtualization technology. DVMC dynamically consolidates Virtual Machines (VMs) into the minimum number of active servers and then switches the idle servers into a power-saving mode to save energy. However, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations concerning performance. Therefore, the main challenge is to minimize the data center energy consumption while maintaining the required QoS. This thesis address this challenge by presenting novel DVMC approaches to reduce the energy consumption of data centers and improve resource utilization under workload independent quality of service constraints. These approaches can be divided into three main categories: heuristic, meta-heuristic and machine learning. Our first contribution is a heuristic algorithm for solving the DVMC problem. The algorithm uses a linear regression-based prediction model to detect over-loaded servers based on the historical utilization data. Then it migrates some VMs from the over-loaded servers to avoid further performance degradations. Moreover, our algorithm consolidates VMs on fewer number of server for energy saving. The second and third contributions are two novel DVMC algorithms based on the Reinforcement Learning (RL) approach. RL is interesting for highly adaptive and autonomous management in dynamic environments. For this reason, we use RL to solve two main sub-problems in VM consolidation. The first sub-problem is the server power mode detection (sleep or active). The second sub-problem is to find an effective solution for server status detection (overloaded or non-overloaded). The fourth contribution of this thesis is an online optimization meta-heuristic algorithm called Ant Colony System-based Placement Optimization (ACS-PO). ACS is a suitable approach for VM consolidation due to the ease of parallelization, that it is close to the optimal solution, and its polynomial worst-case time complexity. The simulation results show that ACS-PO provides substantial improvement over other heuristic algorithms in reducing energy consumption, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) architecture based on a three-tier data center topology which is very common use in data centers. HiVM has the ability to scale across many thousands of servers with energy efficiency. Our sixth contribution is a Utilization Prediction-aware Best Fit Decreasing (UP-BFD) algorithm. UP-BFD can avoid SLA violations and needless migrations by taking into consideration the current and predicted future resource requirements for allocation, consolidation, and placement of VMs. Finally, the seventh and the last contribution is a novel Self-Adaptive Resource Management System (SARMS) in data centers. To achieve scalability, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource management by dynamically adjusting the utilization thresholds for each server in data centers.Siirretty Doriast

Energy-Efficient and Reliable Computing in Dark Silicon Era

Dark silicon denotes the phenomenon that, due to thermal and power constraints, the fraction of transistors that can operate at full frequency is decreasing in each technology generation. Moore’s law and Dennard scaling had been backed and coupled appropriately for five decades to bring commensurate exponential performance via single core and later muti-core design. However, recalculating Dennard scaling for recent small technology sizes shows that current ongoing multi-core growth is demanding exponential thermal design power to achieve linear performance increase. This process hits a power wall where raises the amount of dark or dim silicon on future multi/many-core chips more and more. Furthermore, from another perspective, by increasing the number of transistors on the area of a single chip and susceptibility to internal defects alongside aging phenomena, which also is exacerbated by high chip thermal density, monitoring and managing the chip reliability before and after its activation is becoming a necessity. The proposed approaches and experimental investigations in this thesis focus on two main tracks: 1) power awareness and 2) reliability awareness in dark silicon era, where later these two tracks will combine together. In the first track, the main goal is to increase the level of returns in terms of main important features in chip design, such as performance and throughput, while maximum power limit is honored. In fact, we show that by managing the power while having dark silicon, all the traditional benefits that could be achieved by proceeding in Moore’s law can be also achieved in the dark silicon era, however, with a lower amount. Via the track of reliability awareness in dark silicon era, we show that dark silicon can be considered as an opportunity to be exploited for different instances of benefits, namely life-time increase and online testing. We discuss how dark silicon can be exploited to guarantee the system lifetime to be above a certain target value and, furthermore, how dark silicon can be exploited to apply low cost non-intrusive online testing on the cores. After the demonstration of power and reliability awareness while having dark silicon, two approaches will be discussed as the case study where the power and reliability awareness are combined together. The first approach demonstrates how chip reliability can be used as a supplementary metric for power-reliability management. While the second approach provides a trade-off between workload performance and system reliability by simultaneously honoring the given power budget and target reliability

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presentedSiirretty Doriast