Lightweight integer compression algorithms are frequently applied in in-memory database systems to tackle the growing gap between processor speed and main memory bandwidth. In recent years, the vectorization of basic techniques such as delta coding and null suppression has considerably enlarged the corpus of available algorithms. As a result, today there is a large number of algorithms to choose from, while different algorithms are tailored to different data characteristics. However, a comparative evaluation of these algorithms with different data and hardware characteristics has never been sufficiently conducted in the literature. To close this gap, we conducted an exhaustive experimental survey by evaluating several state-of-the-art lightweight integer compression algorithms as well as cascades of basic techniques. We systematically investigated the influence of data as well as hardware properties on the performance and the compression rates. The evaluated algorithms are based on publicly available implementations as well as our own vectorized reimplementations. We summarize our experimental findings leading to several new insights and to the conclusion that there is no single-best algorithm. Moreover, in this article, we also introduce and evaluate a novel cost model for the selection of a suitable lightweight integer compression algorithm for a given dataset.

Der Sprechakt teilt sich in zwei Vorgänge: Zunächst muss das richtige Wort aus dem mentalen Lexikon abgerufen werden und anschließend wird es in der Kommunikation verwendet. Zur Erforschung des ersteren Vorgangs werden oft Ein-Personen-Studien verwendet, in denen durch Beobachten der Reaktion auf Stimuli (z. B. Bilder) die Mikrostruktur des lexikalisch- semantischen Systems beleuchtet wird. Für die Anwendung von Sprache in der Kommunikation hingegen nutzt man Partnerexperimente, um die Koordination zwischen den Gesprächspartnern zu beobachten und zu ergründen, wie sich gegenseitiges Verstehen und biografisches Wissen darauf auswirken. Wenig erforscht ist aber, wie ein von einem Gesprächspartner eingebrachter Bedeutungskontext die traditionell in Ein-Personen-Studien untersuchten lexikalisch-semantischen Effekte beeinflusst. Im Rahmen meiner Dissertation möchte ich die Lücke zwischen den beiden Forschungsansätzen schließen, indem ich einen kommunikativen Kontext in etablierte Paradigmen der Bildbenennung integriere. Hierzu betrachte ich zunächst klassische semantische Kontexteffekte, die durch nähere oder entferntere kategorische Relationen zwischen Begriffen hervorgerufen werden (Studie 1), um anschließend lose thematische Beziehungen zu untersuchen, die mit alltäglichen Ereignissen verbunden sind (Studie 2 & Studie 3). Um die hochgradig verflochtenen Ebenen der lexikalischen und semantischen Verarbeitung voneinander zu trennen, habe ich ereigniskorrelierte Hirnpotentiale (ERPs) eingesetzt, um die elektrophysiologischen Signaturen des konzeptuellen Primings und der lexikalischen Auswahl zu verfolgen. Die vorliegende Arbeit liefert sowohl theoretische als auch praktische Beiträge. Erstens stützen unsere Ergebnisse die theoretischen Annahmen, dass sich semantisches Priming und lexikalische Interferenz vorübergehend überschneiden und gemeinsam das Benennungsverhalten in einem Trade-off beeinflussen. Auch die Gegenwart eines Kommunikationspartners kann Auswirkungen auf dieses Zusammenspiel haben. Zweitens ergänzen diese Ergebnisse die aktuelle Literatur zu verschiedenen Arten von semantischen Beziehungen, wie z. B. Nulleffekte für entfernte Beziehungen und Kontexteffekte, die systematisch mit der Stärke der Verwandtschaft zunehmen. Und schließlich bietet unser neuartiges Design eines kommunikativen Kontextes ein praktisches Instrument, um die Lücke zwischen Ein-Personen-Studien und Kommunikationsstudien zu schließen. Alles in allem tragen diese Ergebnisse zu einem besseren Verständnis der neuronalen Mechanismen unseres Sprachproduktionssystems bei, das in der Lage ist, sich flexibel sowohl an sprachliche als auch an soziale Kontexte anzupassen. / Speaking could be divided into two processes: first, the correct word must be retrieved from the mental lexicon, and then it is used in communication. To study the former process, single-person studies are often used, in which the microstructure of the lexical-semantic system is illuminated by observing reaction times to name stimuli (e.g., pictures). For the language use in communication, on the other hand, partner experiments are used to observe coordination between interlocutors and to explore how mutual understanding and biographical knowledge affect it. However, how a meaningful context brought by an interlocutor influences the established lexical-semantic effects from single-person studies remains underexplored. Within the scope of my dissertation, I aim to bridge the gap between these two research approaches by integrating a communicative context into well-established picture naming paradigms. To this end, I first investigate classic semantic context effects induced by close or distant categorical relations (Study 1), and then examine loose thematic relations associated with everyday events (Study 2 & Study 3). To separate the highly intertwined strata of lexical and semantic processing, I used event-related brain potentials (ERPs) to track the electrophysiological signatures of conceptual priming and lexical selection. The present work makes both theoretical and practical contributions. First, our results support the theoretical assumptions that semantic priming and lexical interference temporarily overlap, and jointly modulate naming behavior in a trade-off. Such interplay may be greatly influenced by the presence of a communicating partner. Second, these findings add to the current literature on different types of semantic relations, such as null effects for distant relations and context effects that systematically increase with the strength of relatedness. Finally, our novel design of a communicative context provides a practical tool to bridge the gap between single-person studies and communication studies. All in all, these findings advance our understanding of the neural mechanisms of our speech production system, which is capable of flexibly adapting to both linguistic and social contexts.

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A two-step model order reduction method is investigated in order to overcome problems of certain one-step methods. Not only optimal combinations of one-step reductions are considered but also the selection of a suitable intermediate dimension (ID) is described. Several automated selection methods are presented and their application tested on a gear box model. The implementation is realized using a Matlab-based Software MORPACK. Several recommendations are given towards the selection of a suitable ID, and problems in Model Order Reduction (MOR) combinations are pointed out. A pseudo two-step is suggested to reduce the full system without any modal information. A new node selection approach is proposed to enhance the SEREP approximation of the system’s response for small reduced representations. / Mehrschrittverfahren der Modellreduktion werden untersucht, um spezielle Probleme konventioneller Einschrittverfahren zu lösen. Eine optimale Kombination von strukturmechanischen Reduktionsverfahren und die Auswahl einer geeigneten Zwischendimension wird untersucht. Dafür werden automatische Verfahren in Matlab implementiert, in die Software MORPACK integriert und anhand des Finite Elemente Modells eines Getriebegehäuses ausgewertet. Zur Auswahl der Zwischendimension werden Empfehlungen genannt und auf Probleme bei der Kombinationen bestimmter Reduktionsverfahren hingewiesen. Ein Pseudo- Zweischrittverfahren wird vorgestellt, welches eine Reduktion ohne Kenntnis der modalen Größen bei ähnlicher Genauigkeit im Vergleich zu modalen Unterraumverfahren durchführt. Für kleine Reduktionsdimensionen wird ein Knotenauswahlverfahren vorgeschlagen, um die Approximation des Frequenzganges durch die System Equivalent Reduction Expansion Process (SEREP)-Reduktion zu verbessern.

A two-step model order reduction method is investigated in order to overcome problems of certain one-step methods. Not only optimal combinations of one-step reductions are considered but also the selection of a suitable intermediate dimension (ID) is described. Several automated selection methods are presented and their application tested on a gear box model. The implementation is realized using a Matlab-based Software MORPACK. Several recommendations are given towards the selection of a suitable ID, and problems in Model Order Reduction (MOR) combinations are pointed out. A pseudo two-step is suggested to reduce the full system without any modal information. A new node selection approach is proposed to enhance the SEREP approximation of the system’s response for small reduced representations.:Contents Kurzfassung..........................................................................................iv Abstract.................................................................................................iv Nomenclature........................................................................................ix 1 Introduction........................................................................................1 1.1 Motivation........................................................................................1 1.2 Objectives........................................................................................1 1.3 Outline of the Thesis........................................................................2 2 Theoretical Background.......................................................................3 2.1 Finite Element Method......................................................................3 2.1.1 Modal Analysis...............................................................................4 2.1.2 Frequency Response Function.......................................................4 2.2 Model Order Reduction.....................................................................5 2.3 Physical Subspace Reduction Methods.............................................7 2.3.1 Guyan Reduction...........................................................................7 2.3.2 Improved Reduced System Method...............................................8 2.4 Modal Subspace Reduction Methods...............................................10 2.4.1 Modal Reduction...........................................................................11 2.4.2 Exact Modal Reduction..................................................................11 2.4.3 System Equivalent Reduction Expansion Process.........................13 2.5 Krylov Subspace Reduction Methods...............................................14 2.6 Hybrid Subspace Reduction Methods..............................................15 2.6.1 Component Mode Synthesis........................................................16 2.6.2 Hybrid Exact Modal Reduction......................................................19 2.7 Model Correlation Methods.............................................................21 2.7.1 Normalized Relative Frequency Difference...................................21 2.7.2 Modified Modal Assurance Criterion.............................................22 2.7.3 Pseudo-Orthogonality Check.......................................................22 2.7.4 Comparison of Frequency Response Function.............................23 3 Selection of Active Degrees of Freedom............................................25 3.1 Non-Iterative Methods...................................................................26 3.1.1 Modal Kinetic Energy and Variants..............................................26 3.1.2 Driving Point Residue and Variants..............................................27 3.1.3 Eigenvector Component Product..................................................28 3.2 Iterative Reduction Methods...........................................................29 3.2.1 Effective Independence Distribution.............................................29 3.2.2 Mass-Weighted Effective Independence.......................................32 3.2.3 Variance Based Selection Method.................................................33 3.2.4 Singular Value Decomposition Based Selection Method................34 3.2.5 Stiffness-to-Mass Ratio Selection Method.....................................34 3.3 Iterative Expansion Methods...........................................................35 3.3.1 Modal-Geometrical Selection Criterion...........................................36 3.3.2 Triaxial Effective Independence Expansion...................................36 3.4 Measure of Goodness for Selected Active Set..................................39 3.4.1 Determinant and Rank of the Fisher Information Matrix................39 3.4.2 Condition Number of the Partitioned Modal Matrix........................40 3.4.3 Measured Energy per Mode..........................................................40 3.4.4 Root Mean Square Error of Pseudo-Orthogonality Check.............41 3.4.5 Eigenvalue Comparison................................................................41 4 Two-Step Reduction in MORPACK.......................................................42 4.1 Structure of MORPACK.....................................................................42 4.2 Selection of an Intermediate Dimension.........................................43 4.2.1 Intermediate Dimension Requirements........................................44 4.2.2 Implemented Selection Methods..................................................45 4.2.3 Recommended Selection of an Intermediate Dimension...............48 4.3 Combination of Reduction Methods.................................................49 4.3.1 Overview of All Candidates..........................................................50 4.3.2 Combinations with Modal Information.........................................54 4.3.3 Combinations without Modal Information....................................54 5 Applications........................................................................................57 5.1 Gear Box Model...............................................................................57 5.2 Selection of Additional Active Nodes................................................58 5.3 Optimal Intermediate Dimension......................................................64 5.4 Two-Step Model Order Reduction Results........................................66 5.5 Comparison to One-Step Model Order Reduction Methods..............70 5.6 Comparison to One-Step Hybrid Model Order Reduction Methods...72 5.7 Proposal of a New Approach for Additional Node Selection..............73 6 Summary and Conclusions...................................................................77 7 Zusammenfassung und Ausblick..........................................................79 Bibliography............................................................................................81 List of Tables..........................................................................................86 List of Figures.........................................................................................88 A Appendix.............................................................................................89 A.1 Results of Two-Step Model Order Reduction.....................................89 A.2 Data CD............................................................................................96 / Mehrschrittverfahren der Modellreduktion werden untersucht, um spezielle Probleme konventioneller Einschrittverfahren zu lösen. Eine optimale Kombination von strukturmechanischen Reduktionsverfahren und die Auswahl einer geeigneten Zwischendimension wird untersucht. Dafür werden automatische Verfahren in Matlab implementiert, in die Software MORPACK integriert und anhand des Finite Elemente Modells eines Getriebegehäuses ausgewertet. Zur Auswahl der Zwischendimension werden Empfehlungen genannt und auf Probleme bei der Kombinationen bestimmter Reduktionsverfahren hingewiesen. Ein Pseudo- Zweischrittverfahren wird vorgestellt, welches eine Reduktion ohne Kenntnis der modalen Größen bei ähnlicher Genauigkeit im Vergleich zu modalen Unterraumverfahren durchführt. Für kleine Reduktionsdimensionen wird ein Knotenauswahlverfahren vorgeschlagen, um die Approximation des Frequenzganges durch die System Equivalent Reduction Expansion Process (SEREP)-Reduktion zu verbessern.:Contents Kurzfassung..........................................................................................iv Abstract.................................................................................................iv Nomenclature........................................................................................ix 1 Introduction........................................................................................1 1.1 Motivation........................................................................................1 1.2 Objectives........................................................................................1 1.3 Outline of the Thesis........................................................................2 2 Theoretical Background.......................................................................3 2.1 Finite Element Method......................................................................3 2.1.1 Modal Analysis...............................................................................4 2.1.2 Frequency Response Function.......................................................4 2.2 Model Order Reduction.....................................................................5 2.3 Physical Subspace Reduction Methods.............................................7 2.3.1 Guyan Reduction...........................................................................7 2.3.2 Improved Reduced System Method...............................................8 2.4 Modal Subspace Reduction Methods...............................................10 2.4.1 Modal Reduction...........................................................................11 2.4.2 Exact Modal Reduction..................................................................11 2.4.3 System Equivalent Reduction Expansion Process.........................13 2.5 Krylov Subspace Reduction Methods...............................................14 2.6 Hybrid Subspace Reduction Methods..............................................15 2.6.1 Component Mode Synthesis........................................................16 2.6.2 Hybrid Exact Modal Reduction......................................................19 2.7 Model Correlation Methods.............................................................21 2.7.1 Normalized Relative Frequency Difference...................................21 2.7.2 Modified Modal Assurance Criterion.............................................22 2.7.3 Pseudo-Orthogonality Check.......................................................22 2.7.4 Comparison of Frequency Response Function.............................23 3 Selection of Active Degrees of Freedom............................................25 3.1 Non-Iterative Methods...................................................................26 3.1.1 Modal Kinetic Energy and Variants..............................................26 3.1.2 Driving Point Residue and Variants..............................................27 3.1.3 Eigenvector Component Product..................................................28 3.2 Iterative Reduction Methods...........................................................29 3.2.1 Effective Independence Distribution.............................................29 3.2.2 Mass-Weighted Effective Independence.......................................32 3.2.3 Variance Based Selection Method.................................................33 3.2.4 Singular Value Decomposition Based Selection Method................34 3.2.5 Stiffness-to-Mass Ratio Selection Method.....................................34 3.3 Iterative Expansion Methods...........................................................35 3.3.1 Modal-Geometrical Selection Criterion...........................................36 3.3.2 Triaxial Effective Independence Expansion...................................36 3.4 Measure of Goodness for Selected Active Set..................................39 3.4.1 Determinant and Rank of the Fisher Information Matrix................39 3.4.2 Condition Number of the Partitioned Modal Matrix........................40 3.4.3 Measured Energy per Mode..........................................................40 3.4.4 Root Mean Square Error of Pseudo-Orthogonality Check.............41 3.4.5 Eigenvalue Comparison................................................................41 4 Two-Step Reduction in MORPACK.......................................................42 4.1 Structure of MORPACK.....................................................................42 4.2 Selection of an Intermediate Dimension.........................................43 4.2.1 Intermediate Dimension Requirements........................................44 4.2.2 Implemented Selection Methods..................................................45 4.2.3 Recommended Selection of an Intermediate Dimension...............48 4.3 Combination of Reduction Methods.................................................49 4.3.1 Overview of All Candidates..........................................................50 4.3.2 Combinations with Modal Information.........................................54 4.3.3 Combinations without Modal Information....................................54 5 Applications........................................................................................57 5.1 Gear Box Model...............................................................................57 5.2 Selection of Additional Active Nodes................................................58 5.3 Optimal Intermediate Dimension......................................................64 5.4 Two-Step Model Order Reduction Results........................................66 5.5 Comparison to One-Step Model Order Reduction Methods..............70 5.6 Comparison to One-Step Hybrid Model Order Reduction Methods...72 5.7 Proposal of a New Approach for Additional Node Selection..............73 6 Summary and Conclusions...................................................................77 7 Zusammenfassung und Ausblick..........................................................79 Bibliography............................................................................................81 List of Tables..........................................................................................86 List of Figures.........................................................................................88 A Appendix.............................................................................................89 A.1 Results of Two-Step Model Order Reduction.....................................89 A.2 Data CD............................................................................................96

Das arabische Wort „Khalifa“ in der Bedeutung „Stellvertreter“ oder „Nachfolger“ wird im Koran, dem heiligen Buch der Muslime an zwei Stellen verwendet (Sure 7, Vers 69 und Sure 38, Vers 26). Darin wird der Mensch als der Stellvertreter Gottes auf Erden bezeichnet. Im historischen Kontext entsteht der Begriff nach dem Tod des Propheten Muhammad im Jahr 632. Die ersten vier Nachfolger in der politischen Führung der Gemeinde werden in der sunnitischen Geschichtsschreibung als die „Raschidun“ (die Rechtgeleiteten) bezeichnet. In dieser Zeit, also zwischen 632 und 661, entsteht auch der Begriff „Amir al-Muminin“ (Beherrscher der Gläubigen) als Titel des Kalifen, mit dem die Herrscher auch angeredet wurden. Die Frage der Nachfolge des Propheten Muhammad entwickelte sich zu einem grundlegenden Streitpunkt innerhalb der jungen muslimischen Gemeinde. Aus diesen Auseinandersetzungen heraus entstand dann die konfessionelle Spaltung der muslimischen Welt in die sunnitische Mehrheit und die schiitische Minderheit. Grundlegend gibt es Gemeinsamkeiten sowie Unterschiede zwischen den zwei Hauptströmungen der islamischen Gemeinde, den Sunniten und Schiiten. Der Umfang der Meinungsverschiedenheiten zwischen Sunniten und Schiiten sind mehr als deren Ähnlichkeiten, obwohl diese Unterschiede auf den ersten Blick nicht erkennbar sind. Die Wurzeln all dieser Differenzen sind darauf zurückzuführen, dass die Schiiten nach dem Hinscheiden des Propheten die Dogmen ihres Glaubens von den Ahl al-Bayt (Angehörige des Hauses Man darf das Thema Kalifat als den Schwerpunkt aller anderen Diskrepanzen der Glaubensauffassungen der sunnitischen und schiitischen Gelehrten betrachten. / The Arabic word "Khalifa" in the meaning "deputy" or "successor" has been used in the Coran, the holy book of the Muslims at two locations: sura 7, verse 69 and sura 38, verse 26. In These verses is the human being known as God''s representative on earth. In historical context, the term arises after the death of Prophet Muhammad in 632. The first four successors of him in the political leadership of the community calls in the Sunni historiography as the "Rashidun" means (the rightly guided). During this time, i. e. from 632 to 661, the term "Amir al-Mu''minin" (Commander of the Faithful) was created as the title for the Caliphs; thereby the rulers were also addressed. The question of the succession of the Prophet Muhammad became a fundamental point of conflicts within the young Muslim community. From these contentions arose then the confessional division of the Muslim world in the Sunni majority and the Shia minority. Basically, there are similarities and differences between the two mainstreams of the Islamic community, the Sunnis and Shiites. The dimensions of disagreements between Sunnis and Shiites are more than their similarities, although these differences at first glance are not recognizable. The roots of all these differences are due to the fact that the Shiites after the passing away of the Prophet adopted the dogmas of their faith from the Ahl al-Bayt (solely 13 members of the house of the Prophet) and the Sunnis from others. It must be considered, that the issue Caliphate is the focus of all other discrepancies in the beliefs of the Sunni and Shiite scholars.

In neural implants and biohybrid research systems, the integration of electrode recording and stimulation front-ends with pre-processing circuitry promises a drastic increase in real-time capabilities [1,6]. In our proposed neural recording system, constant sampling with a bandwidth of 9.8kHz yields 6.73μV input-referred noise (IRN) at a power-per-channel of 0.34μW for the time-continuous ΔΣ−modulator, and 0.52μW for the digital filters and spike detectors. We introduce dynamic current/bandwidth selection at the ΔΣ and digital filter to reduce recording bandwidth at the absence of spikes (i.e. local field potentials). This is controlled by a two-level spike detection and adjusted by adaptive threshold estimation (ATE). Dynamic bandwidth selection reduces power by 53.7%, increasing the available channel count at a low heat dissipation. Adaptive back-gate voltage tuning (ABGVT) compensates for PVT variation in subthreshold circuits. This allows 1.8V input/output (IO) devices to operate at 0.4V supply voltage robustly. The proposed 64-channel neural recording system moreover includes a 16-channel adaptive compression engine (ACE) and an 8-channel on-chip current stimulator at 3.3V. The stimulator supports field-shaping approaches, promising increased selectivity in future research.