This thesis presents a statistical method to identify the test escapes. Test often acquires parametric measurements as a function of logical state of a chip. The usual method of classifying chips as pass or fail is to compare each state measurement to a test limit. Subtle manufacturing defects are escaping the test limits due to process variations in deep sub-micron technologies which results in mixing of healthy and faulty parametric test measurements. This thesis identifies the chips with subtle defects by using rank order of the parametric measurements. A hypothesis is developed that a defect is likely to disturb the defect-free ranking, whereas a shift caused by process variations will not affect the rank. The hypothesis does not depend on a-priori knowledge of a defect-free ranking of parametric measurements. This thesis introduces a modified Estimation Maximization (EM) algorithm to separate the healthy and faulty tau components calculated from parametric responses of die pairs on a wafer. The modified EM uses generalized beta distributions to model the two components of tau mixture distribution. The modified EM estimates the faulty probability of each die on a wafer. The sensitivity of the modified EM is evaluated using Monte Carlo simulations. The modified EM is applied on production product A. An average 30% reduction in DPPM (defective parts per million) is observed in Product A across all lots.

Aggressive scaling of CMOS circuits in recent times has lead to dramatic increase in leakage currents. Previously, sub-threshold leakage current was the only leakage current taken into account in power estimation. But now gate leakage and reverse biased junction band-to-band-tunneling leakage currents have also become significant. Together all the three types of leakages namely sub-threshold leakage, gate leakage and reverse bias junction band-to-band tunneling leakage currents contribute to more than 25% of power consumption in the current generation of leading edge designs. Different sources of leakage can affect each other by interacting through resultant intermediate node voltages. This is called loading effect and it leads to further increase in leakage current. On the other hand, sub-threshold leakage current decreases as more number of transistors is stacked in series. This is called stack effect. Previous works have been done that analyze each type of leakage current and its effect in detail but independent of each other. In this work, a pattern dependent steady state leakage estimation technique was developed that incorporates loading effect and accounts for all three major leakage components, namely the gate leakage, band to band tunneling leakage and sub-threshold leakage. It also considers transistor stack effect when estimating sub-threshold leakage. As a result, a coherent leakage current estimator tool was developed. The estimation technique was implemented on 65nm and 45nm CMOS circuits and was shown to attain a speed up of more than 10,000X compared to HSPICE. This work also extends the leakage current estimation technique in Field Programmable Gate Arrays (FPGAs). A different version of the leakage estimator tool was developed and incorporated into the Versatile Place & Route CAD tool to enable leakage estimation of design after placement and routing. Leakage current is highly dependent on the steady state terminal voltage of the transistor, which depends on the logic state of the CMOS circuit as determined by the input pattern. Consequently, there exists a pattern that will produce the highest leakage current. This work considers all leakage sources together and tries to find an input pattern(s) that will maximize the composite leakage current made up of all three components. This work also analyzes leakage power in presence of dynamic power in a unique way. Current method of estimating total power is to sum dynamic power which is ½&#;CLVDD2f and sub-threshold leakage power. The dynamic power in this case is probabilistic and pattern independent. On the other hand sub-threshold leakage is pattern dependent. This makes the current method very inaccurate for calculating total power. In this work, it is shown that leakage current can vary by more than 8% in time in presence of switching current.

Background: In today's society, there is more pressure to be sustainable and not least in the financial world. Several agreements, such as the Paris Agreement, have been created to steer countries towards more sustainability. When it comes to the economy, several SRI strategies have been developed to serve the same purpose. However, the problem that emerges is that investors who invest sustainably and use these strategies can lose returns and thus depart from their main goal of maximizing profits.   Purpose: The purpose of this paper is to examine how SRI strategies are used by investors when constructing their portfolios in terms of profit maximization. The paper will thus conclude if the underlying motivation behind the choice of strategy is affected by maximizing profit.   Method and implementation: By conducting a qualitative study and interviewing several fund managers at the largest banks and fund companies in Sweden, the authors aim to answer the research question. The answers provided by the respondents are presented and analyzed in the empirical section and linked to the study's theory.   Conclusion: In this study, there is clearly shown that by investing, according to SRI, a professional investor is still able to profit maximize. The authors, therefore, see that the new way of being rational as an investor is to include SRI strategies. The relationship with being both sustainable and profit-maximizing can be seen as a significant motivating factor. The same can be said about reduced ESG risk and creating legitimacy towards customers. Furthermore, a combination of strategies can be seen as a way to create an optimal portfolio by the investors. This further proves that sustainable investing is the most rational way of investing and a way to achieve an investors main goal to profit maximize.

Digital marketing is constantly adapting and evolving in line with technological advances. One of these advances is the digitalization, which has given rise to Programmatic Advertising (PA). In order to practice PA, the companies need to collect data about consumers' preferences and personal interests. On the other hand, consumers have a need to protect their privacy. The needs of these two parties cross each other which creates the tension, called the Personalization-privacy paradox. In this study, we intend to investigate consumers' attitudes towards PA and whether personalization gives rise to improved browsing experiences or evenviolates their privacy. A quantitative study was conducted, where the independent variable was called Personalization and the dependent variables - Attractiveness, Annoyance, Invasiveness and Trade-off. The results of bivariate regression analysis showed that all of the dependent variables of the study were statistically significant. The results also illustrated that the majority of the respondents experienced PA ads as beneficial, but also invasive. These results are in line with the Utility maximization theory, as PA ads were considered both beneficial and risky. Therefore, consumers would have incentives to disclose their personal information as long as the percieved benefits would outweigh perceived risks, generated by PA. We concluded that there are no clear, predetermined answers to what attitudes consumers have towards PA, but this can vary from case to case, which is in line with both the Privacy calculus theory and the Utility maximization theory. This involves a risk-benefit analysis, performed by consumers, where perceived benefits exceeding perceived risks would generate positive attitudes and vice versa

No description available.

Rather than being randomly wired together, the components of complex network systems are recently reported to represent a scale-free architecture, in which the node degree distribution follows power-law. While social networks are scale-free, it is natural to utilize their structural properties in some social network applications. As a result, this dissertation explores social network architectures, and in turn, leverages these architectures to facilitate some influence and information propagation applications. Social network architectures are analyzed in two different aspects. The first aspect focuses on the node degree snowballing effects (i.e., degree growth effects) in social networks, which is based on an age-sensitive preferential attachment model. The impact of the initial links is explored, in terms of accelerating the node degree snowballing effects. The second aspect focuses on Nested Scale-Free Architectures (NSFAs) for social networks. The scale-free architecture is a classic concept, which means that the node degree distribution follows the power-law distribution. `Nested' indicates that the scale-free architecture is preserved when low-degree nodes and their associated connections are iteratively removed. NSFA has a bounded hierarchy. Based on the social network structure, this dissertation explores two influence propagation applications for the Social Influence Maximization Problem (SIMP). The first application is a friend recommendation strategy with the perspective of social influence maximization. For the system provider, the objective is to recommend a fixed number of new friends to a given user, such that the given user can maximize his/her social influence through making new friends. This problem is proved to be NP-hard by reduction from the SIMP. A greedy friend recommendation algorithm with an approximation ratio of $1-e^{-1}$ is proposed. The second application studies the SIMP with the crowd influence, which is NP-hard, monotone, non-submodular, and inapproximable in general graphs. However, since user connections in Online Social Networks (OSNs) are not random, approximations can be obtained by leveraging the structural properties of OSNs. The modularity, denoted by $\Delta$, is proposed to measure to what degree this problem violates the submodularity. Two approximation algorithms are proposed with ratios of $\frac{1}{\Delta+2}$ and $1-e^{-1/(\Delta+1)}$, respectively. Beside the influence propagation applications, this dissertation further explores three different information propagation applications. The first application is a social network quarantine strategy, which can eliminate epidemic outbreaks with minimal isolation costs. This problem is NP-hard. An approximation algorithm with a ratio of 2 is proposed through utilizing the problem properties of feasibility and minimality. The second application is a rating prediction scheme, called DynFluid, based on the fluid dynamics. DynFluid analogizes the rating reference among the users in OSNs to the fluid flow among containers. The third application is an information cascade prediction framework: given the social current cascade and social topology, the number of propagated users at a future time slot is predicted. To reduce prediction time complexities, the spatiotemporal cascade information (a larger size of data) is decomposed to user characteristics (a smaller size of data) for subsequent predictions. All these three applications are based on the social network structure. / Computer and Information Science

As a clean, abundant, and renewable source of energy with a strategic location in close proximity to global population regions, ocean wave energy shows major promise. Although much wave energy converter development has focused on large-scale power generation, there is also increasing interest in small-scale applications for powering the blue economy. In this thesis, the objective was to optimize the performance of small-sized, portable, oscillating-body wave energy converters (WECs). Two types of oscillating body WECs were studied: bottom hinged and two-body attenuator. For the bottom-hinged device, the goal was to show the feasibility of an oscillating surge WEC and desalination system using numerical modeling to estimate the system performance. For a 5-day test period, the model estimated 517 L of freshwater production with 711 ppm concentration and showed effective brine discharge, agreeing well with preliminary experimental results. The objective for the two-body attenuator was to develop a method of power maximization through resonance tuning and numerical simulation. Three different geometries of body cross sections were used for the study with four different drag coefficients for each geometry. Power generation was maximized by adjusting body dimensions to match the natural frequency with the wave frequency. Based on the time domain simulation results, there was not a significant difference in power between the geometries when variation in drag was not considered, but the elliptical geometry had the highest power when using approximate drag coefficients. Using the two degree-of-freedom (2DOF) model with approximate drag coefficients, the elliptical cross section had a max power of 27.1 W and 7.36% capture width ratio (CWR) for regular waves and a max power of 8.32 W and 2.26% CWR for irregular waves. Using the three degree-of-freedom (3DOF) model with approximate drag coefficients, the elliptical cross section had a max power of 22.5 W and 6.12% CWR for regular waves and 6.18 W and 1.68% CWR for irregular waves. A mooring stiffness study was performed with the 3DOF model, showing that mooring stiffness can be increased to increase relative motion and therefore increase power. / Master of Science / As a clean, abundant, and renewable source of energy with a strategic location in close proximity to global population centers, ocean wave energy shows major promise. Although much wave energy converter development has focused on large-scale power generation, there is also increasing interest in small-scale applications for powering the blue economy. There are many situations where large-scale wave energy converter (WEC) devices are not necessary or practical, but easily-portable, small-sized WECs are suitable, including navigation signs, illumination, sensors, survival kits, electronics charging, and portable desalination. In this thesis, the objective was to optimize the performance of small-sized, oscillating body wave energy converters. Oscillating body WECs function by converting a device's wave-driven oscillating motion into useful power. Two types of oscillating body WECs were studied: bottom hinged and two-body attenuator. For the bottom-hinged device, the goal was to show the feasibility of a WEC and desalination system using numerical modeling to estimate the system performance. Based on the model results, the system will produce desirable amounts of fresh water with suitably low concentration and be effective at discharging brine. The objective for the two-body attenuator was to develop a method of power maximization through resonance tuning and numerical simulation. Based on the two- and three-degree-of-freedom model results with approximate drag coefficients, the elliptical cross section had the largest power absorption out of three different geometries of body cross sections. A mooring stiffness study with the three-degree-of-freedom model showed that mooring stiffness can be increased to increase power absorption.

RNA-Seq is an increasingly popular approach to transcriptome profiling that uses the capabilities of next generation sequencing technologies and provides better measurement of levels of transcripts and their isoforms. In this thesis, we apply RNA-Seq protocol and transcriptome quantification to estimate gene expression and pathway activity levels. We present a novel method, called IsoDE, for differential gene expression analysis based on bootstrapping. In the first version of IsoDE, we compared the tool against four existing methods: Fisher's exact test, GFOLD, edgeR and Cuffdiff on RNA-Seq datasets generated using three different sequencing technologies, both with and without replicates. We also introduce the second version of IsoDE which runs 10 times faster than the first implementation due to some in-memory processing applied to the underlying gene expression frequencies estimation tool and we also perform more optimization on the analysis. The second part of this thesis presents a set of tools to differentially analyze metabolic pathways from RNA-Seq data. Metabolic pathways are series of chemical reactions occurring within a cell. We focus on two main problems in metabolic pathways differential analysis, namely, differential analysis of their inferred activity level and of their estimated abundance. We validate our approaches through differential expression analysis at the transcripts and genes levels and also through real-time quantitative PCR experiments. In part Four, we present the different packages created or updated in the course of this study. We conclude with our future work plans for further improving IsoDE 2.0.

Der kontinuierliche Entwicklungsprozess der modernen Informations- und Kommunikationstechnologie (IKT) bietet in rascher Folge immer wieder neue Handlungsalternativen für die Unternehmen. Als ein Resultat dieser Entwicklung ist der inzwischen hohe Grad der Globalisierung der Wirtschaft zu interpretieren. Derartige Veränderungen der wirtschaftlichen Rahmenbedingungen zwingen insbesondere kleine und mittlere Unternehmen (KMU) zu Überlegungen, wie mit dieser Entwicklung mitgehalten werden kann. Durch die i.d.R. sehr begrenzten finanziellen Möglichkeiten, einer tendenziell schlechteren Infrastruktur sowie die Beschränkung auf nur wenige Kernkompetenzen haben viele KMU Schwierigkeiten, mit den großen Konzernen zu konkurrieren. Aus diesem Grund gilt es, dass KMU auf ihre spezifischen Stärken wie schnelle Reaktionsfähigkeit und schlanke Organisationsstruktur setzen. Daher entscheiden sich zahlreiche KMU für eine Teilnahme in Netzwerken. Das Ziel derartiger unternehmensübergreifender Wertschöpfungsverbünde besteht in der Herstellung weltweit konkurrenzfähiger Produkte durch intensive kompetenzorientierte Kooperation. Dieser Ansatz wurde in der Realität bereits in zahlreichen Fällen verwirklicht. Neben dieser praxisorientierten Sicht stellen Netzwerke (und insbesondere Produktionsnetzwerke) auch ein interessantes und vielschichtiges Forschungsgebiet dar. Diesbezügliche Arbeiten konzentrieren sich entweder auf einzelne Teilbereiche wie zum Beispiel Organisation, Recht, Technik, Logistik oder Controlling oder aber stellen eine umfassende generelle Konzeption in den Mittelpunkt der Betrachtungen. Vorliegender Beitrag stellt einführend für ein theoretisch fundiertes Basiskonzept das Modell des „Hierarchielosen Regionalen Produktionsnetzes“ sowie das dafür entwickelte Betreiberkonzept „Extended Value Chain Management“ (EVCM) vor. Auf der Basis dieses Ansatzes erfolgt die konzeptionelle Beschreibung eines geeigneten Gewinnverteilungsmodells unter Berücksichtigung von Anreiz- und Sanktionsmechanismen für die Netzwerkteilnehmer. Als Hauptziel wird in diesem Zusammenhang die Maximierung des Nutzens für das gesamte Produktionsnetz formuliert.

Dashiell Hammett's The Maltese Falcon and Red Harvest are examples of iconic hard-boiled detective literature that reflect on the anxieties and tensions of the 1930s-1940s. With the Great Depression looming over these decades, the genre uses the hard-boiled detective as a way to communicate with and understand this time period. In our analysis of game theory, we look at how Dashiell Hammett's characters make decisions based on the actions of other players in the game, illustrating the influences of bargaining power and manipulation. With characters that oftentimes find themselves in situations where they must collude to reach maximum utility, the novels explore the various ways in which one player takes advantage of another, almost always leading towards the detective's best payoff. Game theory provides us with a unique method to looking at literature, hard-boiled fiction particularly, as a reflection of the historical period of its conception and prime.