What are the Most Effective Executive Compensation Strategies for Levels not Eligible for Long Term Incentive?

The trend toward incentive-based, long-term compensation has increasingly strengthened as companies seek to align shareholder, management, and executive interests, especially in light of the financial crisis of 2008. Privately held companies face a unique challenge because they are not in a position to easily offer stock options as a means of a long-term incentive plan; although possible, it is both cumbersome and dilutes the owners’ control of the company. We have identified two alternative means of providing incentives to managers and executives: deferred compensation and the use of perquisites. In addition, we provide a closer at look at the impact and efficacy of the trend toward incentive-based pay on employee and business performance

Semantic image retrieval using relevance feedback and transaction logs

Due to the recent improvements in digital photography and storage capacity, storing large amounts of images has been made possible, and efficient means to retrieve images matching a user’s query are needed. Content-based Image Retrieval (CBIR) systems automatically extract image contents based on image features, i.e. color, texture, and shape. Relevance feedback methods are applied to CBIR to integrate users’ perceptions and reduce the gap between high-level image semantics and low-level image features. The precision of a CBIR system in retrieving semantically rich (complex) images is improved in this dissertation work by making advancements in three areas of a CBIR system: input, process, and output. The input of the system includes a mechanism that provides the user with required tools to build and modify her query through feedbacks. Users behavioral in CBIR environments are studied, and a new feedback methodology is presented to efficiently capture users’ image perceptions. The process element includes image learning and retrieval algorithms. A Long-term image retrieval algorithm (LTL), which learns image semantics from prior search results available in the system’s transaction history, is developed using Factor Analysis. Another algorithm, a short-term learner (STL) that captures user’s image perceptions based on image features and user’s feedbacks in the on-going transaction, is developed based on Linear Discriminant Analysis. Then, a mechanism is introduced to integrate these two algorithms to one retrieval procedure. Finally, a retrieval strategy that includes learning and searching phases is defined for arranging images in the output of the system. The developed relevance feedback methodology proved to reduce the effect of human subjectivity in providing feedbacks for complex images. Retrieval algorithms were applied to images with different degrees of complexity. LTL is efficient in extracting the semantics of complex images that have a history in the system. STL is suitable for query and images that can be effectively represented by their image features. Therefore, the performance of the system in retrieving images with visual and conceptual complexities was improved when both algorithms were applied simultaneously. Finally, the strategy of retrieval phases demonstrated promising results when the query complexity increases

Learning Desirable Matchings From Partial Preferences

We study the classic problem of matching $n$ agents to $n$ objects, where the agents have ranked preferences over the objects. We focus on two popular desiderata from the matching literature: Pareto optimality and rank-maximality. Instead of asking the agents to report their complete preferences, our goal is to learn a desirable matching from partial preferences, specifically a matching that is necessarily Pareto optimal (NPO) or necessarily rank-maximal (NRM) under any completion of the partial preferences. We focus on the top-$k$ model in which agents reveal a prefix of their preference rankings. We design efficient algorithms to check if a given matching is NPO or NRM, and to check whether such a matching exists given top-$k$ partial preferences. We also study online algorithms to elicit partial preferences adaptively, and prove bounds on their competitive ratio

NEJ2: Identifizierung und Charakterisierung eines neuen Proteins in Säugern und Saccharomyces cerevisiae, assoziiert mit Telomerstrukturen und der Nicht-Homologen Rekombination von DNA-Doppelstrangbrüchen

DNA-Doppelstrangbrüche (DSB) sind die schwerwiegendsten Formen von DNA-Schäden. Unrepariert können sie zum Zelltod führen. Die Nicht-Homologe Rekombination (NHEJ) ist einer der beiden Hauptmechanismen zur Reparatur von DSB. Der DNA-Ligase IV/XRCC4-Komplex in Säugern bzw. der Dnl4/Lif1p-Komplex in Hefe ist für den zusammenführenden Schritt des NHEJ, aber auch für pathologisch bedingte Telomerfusionen verantwortlich. Diese Arbeit beschreibt die Identifikation und Charakterisierung des neuen humanen Proteins hNej2 und seines Hefeorthologs scNej2p, welches ursprünglich von unserer AG als Interaktionspartner von Lif1p identifiziert wurde. Im Kontrast zu anderen NHEJ-Faktoren, ist eine scnej2-Deletion letal, was auf weitere essentielle Funktionen des scNej2p in Hefen hindeutet. ScNej2p interferiert mit der Formation des Dnl4/Lif1p-Komplexes und Überexpression von scNej2p reduziert die NHEJ-Effizienz in Hefen. Wir konnten zeigen, dass NEJ2 in Eukaryonten konserviert vorliegt, es in humanen Zellen ubiquitär exprimiert wird und dass die Interaktion zwischen Nej2 mit dem humanen XRCC4 konserviert ist. Die Überexpression von hNej2 in humanen Zellen ist letal. Durch eine transiente Expression konnte ein punktförmiges Muster im Nukleus von humanen und Hefe-Zellen gezeigt werden, welches in beiden Organismen an das Muster von Telomer- assoziierten Proteinen erinnert. Wir konnten hNej2 durch Colokalisierungen mit Telomer- assoziierten Proteinen, wie TRF2 (Telomer Repeat Faktor) und Mre11 in humanen Fibroblasten und der Interaktion mit PinX1, einem Telomeraseinhibitor, mit Telomerstrukturen assoziieren. Durch die Colokalisierung von hNej2 und PML-Körperchen, welche eine Reihe von DNA-Reparaturfaktoren beherbergen und mit der DNA-Schadensantwort, der alternativen Verlängerung der Telomere und der zellulären Alterung in Zusammenhang gebracht werden, konnte Nej2 in die DSB-Reparatur eingeordnet werden. Zusammengefasst gibt es Evidenzen dafür, dass NEJ2 ein neuer NHEJ- und Telomer- assoziierter Faktor ist, welcher möglicherweise als Verbindungsstelle zwischen DSB-Reparatur und dem Telomermetabolismus agiert. Weitere Studien sind nötig, um zu prüfen, ob NEJ2 der Faktor ist, der unter normalen Bedingungen NHEJ- vermittelte Telomerfusionen von freien Telomeren verhindert