Christian Witness on the Plateau Vivarais-Lignon: Narrative, Nonviolence, and the Formation of Character

This dissertation analyzes the theological and ethical convictions that led the people of the Plateau Vivarais-Lignon to shelter thousands of refugees between 1939 and 1945. It does so by examining the themes of narrative identity, hospitality, character formation, nonviolence, and the contextual witness of church tradition. Though a number of studies have been published about the rescue activity in this region of France during World War II, none have thoroughly analyzed the theological nature of this activity. Using the Plateau Vivarais-Lignon as a case study in theological ethics, the dissertation draws on historical sources as well as the work of contemporary theologians and ethicists to understand, interpret, and analyze the witness of this community. After situating its rescue and resistance work within the Huguenot narrative of persecution and exile, I examine the theological convictions of the Reformed pastor of Le Chambon-sur-Lignon, André Trocmé, who played a key role in making the Plateau a place of refuge during the Holocaust. The study highlights the importance of narrative in the actions of this community and discusses the relationship between narrative, character, and ethics. It then examines the nonviolent commitments of key leaders of the rescue effort, using this analysis as a springboard to engage in broader theological reflection about the ethics of nonviolence. After examining the radical hospitality practiced on the Plateau in light of biblical narratives and Reformed history, I investigate the counter-cultural nature of Christian hospitality. The study concludes by analyzing the nature and witness of the church in light of the legacy of the Plateau Vivarais-Lignon. The dissertation suggests that increased academic and ecclesial attention be given to the relationship between narrative and character, the counter-cultural shape of Christian hospitality, and the active nature of nonviolence. It presents an in-depth analysis of the theological and ethical convictions of the people of the Plateau Vivarais-Lignon, arguing that their witness has ongoing significance for communities of faith as they grapple with how to form disciples, relate to the wider society, welcome strangers, and communicate God's shalom in a world of violence.

Online and Offline Character Recognition Using Alignment to Prototypes

Nearest neighbor classifiers are simple to implement, yet they can model complex non-parametric distributions, and provide state-of-the-art recognition accuracy in OCR databases. At the same time, they may be too slow for practical character recognition, especially when they rely on similarity measures that require computationally expensive pairwise alignments between characters. This paper proposes an efficient method for computing an approximate similarity score between two characters based on their exact alignment to a small number of prototypes. The proposed method is applied to both online and offline character recognition, where similarity is based on widely used and computationally expensive alignment methods, i.e., Dynamic Time Warping and the Hungarian method respectively. In both cases significant recognition speedup is obtained at the expense of only a minor increase in recognition error.

A Local Circuit Model of Learned Straitial and Dopamine Cell Responses under Probabilistic Schedules of Reward

Before choosing, it helps to know both the expected value signaled by a predictive cue and the associated uncertainty that the reward will be forthcoming. Recently, Fiorillo et al. (2003) found the dopamine (DA) neurons of the SNc exhibit sustained responses related to the uncertainty that a cure will be followed by reward, in addition to phasic responses related to reward prediction errors (RPEs). This suggests that cue-dependent anticipations of the timing, magnitude, and uncertainty of rewards are learned and reflected in components of the DA signals broadcast by SNc neurons. What is the minimal local circuit model that can explain such multifaceted reward-related learning? A new computational model shows how learned uncertainty responses emerge robustly on single trial along with phasic RPE responses, such that both types of DA responses exhibit the empirically observed dependence on conditional probability, expected value of reward, and time since onset of the reward-predicting cue. The model includes three major pathways for computing: immediate expected values of cures, timed predictions of reward magnitudes (and RPEs), and the uncertainty associated with these predictions. The first two model pathways refine those previously modeled by Brown et al. (1999). A third, newly modeled, pathway is formed by medium spiny projection neurons (MSPNs) of the matrix compartment of the striatum, whose axons co-release GABA and a neuropeptide, substance P, both at synapses with GABAergic neurons in the SNr and with the dendrites (in SNr) of DA neurons whose somas are in ventral SNc. Co-release enables efficient computation of sustained DA uncertainty responses that are a non-monotonic function of the conditonal probability that a reward will follow the cue. The new model's incorporation of a striatal microcircuit allowed it to reveals that variability in striatal cholinergic transmission can explain observed difference, between monkeys, in the amplitutude of the non-monotonic uncertainty function. Involvement of matriceal MSPNs and striatal cholinergic transmission implpies a relation between uncertainty in the cue-reward contigency and action-selection functions of the basal ganglia. The model synthesizes anatomical, electrophysiological and behavioral data regarding the midbrain DA system in a novel way, by relating the ability to compute uncertainty, in parallel with other aspects of reward contingencies, to the unique distribution of SP inputs in ventral SN.

Comparative Performance Measures of Fuzzy ARTMAP, Learned Vector Quantization, and Back Propagation for Handwritten Character Recognition

This article compares the performance of Fuzzy ARTMAP with that of Learned Vector Quantization and Back Propagation on a handwritten character recognition task. Training with Fuzzy ARTMAP to a fixed criterion used many fewer epochs. Voting with Fuzzy ARTMAP yielded the highest recognition rates.