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    \u3cem\u3eState v. Cheetham\u3c/em\u3e: Montana Supreme Court Refuses to Substitute \u3cem\u3eStrickland\u3c/em\u3e Standard When Analyzing Substitution of Counsel Claims

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    Distinct from a criminal defendant’s right to effective assistance of counsel is a defendant’s right to counsel of choice. In State v. Cheetham, the Montana Supreme Court erroneously analyzed the defendant’s substitution of counsel claim under a standard that blends substitution of counsel with ineffective assistance of counsel

    Comity in the Free Trade Zone

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    Meditation - Losing Kanye

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    Losing My Dad

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    After Losing You

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    Losing Face

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    When person A takes an action that can be interpreted as �making an offer� to person B and B �rejects the offer,� then A may �lose face.� This loss of face (LoF) and consequent disutility will occur only if these actions are common knowledge to A and B. While under some circumstances this LoF can be rationalized by the consequences for future reputation, we claim it also enters directly into the utility function. LoF concerns can lead to fewer offers and inefficiency in markets that involve matching, discrete transactions, and offers/proposals in both directions. This pertains to the marriage market, certain types of labor markets, admissions to colleges and universities, and certain types of joint ventures and collaborations. We offer a simple model of this, and show that under some circumstances welfare can be improved by a mechanism that only reveals offers when both parties say �yes.�

    Losing Face

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    When person A makes an offer to person B and B rejects it, then A may "lose face". This loss of face is assumed to occur only if B knows for sure of A's offer. While under some circumstances loss of face can be rationalized by the consequences for future reputation, it may also enter directly into the utility function. Loss of face concerns can lead to fewer offers and inefficiency in markets that involve matching, discrete transactions, and offers/proposals in both directions, such as the marriage market, certain types of labor markets, admissions to colleges and universities, and joint ventures and collaborations. We offer a simple model of this, and show that under some circumstances welfare can be improved by a mechanism that only reveals offers when both parties say "yes".Matching, marriage markets, anonymity, reputation, adverse selection, Bayesian games, emotions.

    Memory Models for Incremental Learning Architectures

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    Losing V. Memory Models for Incremental Learning Architectures. Bielefeld: Universität Bielefeld; 2019.Technological advancement leads constantly to an exponential growth of generated data in basically every domain, drastically increasing the burden of data storage and maintenance. Most of the data is instantaneously extracted and available in form of endless streams that contain the most current information. Machine learning methods constitute one fundamental way of processing such data in an automatic way, as they generate models that capture the processes behind the data. They are omnipresent in our everyday life as their applications include personalized advertising, recommendations, fraud detection, surveillance, credit ratings, high-speed trading and smart-home devices. Thereby, batch learning, denoting the offline construction of a static model based on large datasets, is the predominant scheme. However, it is increasingly unfit to deal with the accumulating masses of data in given time and in particularly its static nature cannot handle changing patterns. In contrast, incremental learning constitutes one attractive alternative that is a very natural fit for the current demands. Its dynamic adaptation allows continuous processing of data streams, without the necessity to store all data from the past, and results in always up-to-date models, even able to perform in non-stationary environments. In this thesis, we will tackle crucial research questions in the domain of incremental learning by contributing new algorithms or significantly extending existing ones. Thereby, we consider stationary and non-stationary environments and present multiple real-world applications that showcase merits of the methods as well as their versatility. The main contributions are the following: One novel approach that addresses the question of how to extend a model for prototype-based algorithms based on cost minimization. We propose local split-time prediction for incremental decision trees to mitigate the trade-off between adaptation speed versus model complexity and run time. An extensive survey of the strengths and weaknesses of state-of-the-art methods that provides guidance for choosing a suitable algorithm for a given task. One new approach to extract valuable information about the type of change in a dataset. We contribute a biologically inspired architecture, able to handle different types of drift using dedicated memories that are kept consistent. Application of the novel methods within three diverse real-world tasks, highlighting their robustness and versatility. Investigation of personalized online models in the context of two real-world applications
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