Family religious involvement and the quality of family relationships for early adolescents

Examination of association between the religious involvement (number of family religious activities, parental worship service attendance and parental prayer) and quality of family relationships with results indicating that religiously involved families of adolescents (ages 12-14) living in the U.S. are more like to have stronger family relationships than families that are not religiously active.

Family religious involvement and the quality of family relationships for early adolescents

Examination of association between the religious involvement (number of family religious activities, parental worship service attendance and parental prayer) and quality of family relationships with results indicating that religiously involved families of adolescents (ages 12-14) living in the U.S. are more like to have stronger family relationships than families that are not religiously active.

A Theological Challenge: Coordinating Biological, Social, and Religious Visions of Humanity

This paper attempts two tasks. First, it sketches how the natural sciences (including especially the biological sciences), the social sciences, and the scientific study of religion can be understood to furnish complementary, consonant perspectives on human beings and human groups. This suggests that it is possible to speak of a modern secular interpretation of humanity (MSIH) to which these perspectives contribute (though not without tensions). MSIH is not a comprehensive interpretation of human beings, if only because it adopts a posture of neutrality with regard to the reality of religious objects and the truth of theological claims about them. MSIH is certainly an impressively forceful interpretation, however, and it needs to be reckoned with by any perspective on human life that seeks to insert its truth claims into the arena of public debate. Second, the paper considers two challenges that MSIH poses to specifically theological interpretations of human beings. On the one hand, in spite of its posture of religious neutrality, MSIH is a key element in a class of wider, seemingly antireligious interpretations of humanity, including especially projectionist and illusionist critiques of religion. It is consonance with MSIH that makes these critiques such formidable competitors for traditional theological interpretations of human beings. On the other hand, and taking the religiously neutral posture of MSIH at face value, theological accounts of humanity that seek to coordinate the insights of MSIH with positive religious visions of human life must find ways to overcome or manage such dissonance as arises. The goal of synthesis is defended as important, and strategies for managing these challenges, especially in light of the pluralism of extant philosophical and theological interpretations of human beings, are advocated.

Across the prairie in a motor caravan; a 3,000 mile tour by two Englishwomen on behalf of religious education,

http://www.archive.org/details/acrosstheprairie00haseuoft

Modern religious movements in India.

http://www.archive.org/details/modernreligiousm025064mbp

Rambo's Interdisciplinary Approach to Religious Conversion: The Case of St. Augustine

This dissertation illustrates the merits of an interdisciplinary approach to religious conversion by employing Lewis Rambo’s systemic stage model to illumine the process of St. Augustine’s conversion. Previous studies of Augustine’s conversion have commonly explored his narrative of transformation from the perspective of one specific discipline, such as theology, history, or psychology. In doing so, they have necessarily restricted attention to a limited set of questions and problems. By bringing these disciplines into a structured, critical conversation, this study demonstrates how formulating and responding to the interplay among personal, social, cultural, and religious dimensions of Augustine’s conversion process may eventuate in the consideration of issues previously unarticulated and thus unaddressed. Rambo (1993) formulates a model of religious change that consists of what he calls context, crisis, quest, encounter, interaction, commitment, and consequences. Change is explained by drawing upon the research and scholarship of psychologists, sociologists, anthropologists, and religionists, in conjunction with the contributions of theologians. This study unfolds in the following chapters: I. Introduction; II. Literature review of scholarship about conversion, with emphasis on explication of Rambo’s model; III. A description of the case of Augustine, drawn from a close reading of the Confessions; IV. Literature review of scholarship about Augustine’s conversion; V. Interdisciplinary interpretation of Augustine’s conversion; and VI. Implications for scholars of conversion, and for pastoral caregivers, as well as recommendations for future research. This dissertation demonstrates how Augustine’s conversion experience was deeply influenced by 1) psychological distress and crisis; 2) the quest to know himself and the divine; 3) interactions with significant others; 4) participation in Christian communities; 5) philosophical and cultural changes; and 6) the encounter with the divine. As such, this study reveals the value of interpreting Augustine’s conversion as an evolving process constituted in multiple factors that can be differentiated from one another, yet clearly interact with one another. It examines the implications of constructing an interdisciplinary approach to Augustine’s conversion narrative for both the academy and the Christian community, and recommends the use of Rambo’s model in studies of other cases of religious change.

The Specialized Mappings Architecture

A probabilistic, nonlinear supervised learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA employs a set of several forward mapping functions that are estimated automatically from training data. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). The SMA can model ambiguous, one-to-many mappings that may yield multiple valid output hypotheses. Once learned, the mapping functions generate a set of output hypotheses for a given input via a statistical inference procedure. The SMA inference procedure incorporates an inverse mapping or feedback function in evaluating the likelihood of each of the hypothesis. Possible feedback functions include computer graphics rendering routines that can generate images for given hypotheses. The SMA employs a variant of the Expectation-Maximization algorithm for simultaneous learning of the specialized domains along with the mapping functions, and approximate strategies for inference. The framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human’s body or hands, given silhouettes from a single image. The accuracy and stability of the SMA are also tested using synthetic images of human bodies and hands, where ground truth is known.

Efficient End-Host Architecture for High Performance Communication Using User-level Sandboxing

Current low-level networking abstractions on modern operating systems are commonly implemented in the kernel to provide sufficient performance for general purpose applications. However, it is desirable for high performance applications to have more control over the networking subsystem to support optimizations for their specific needs. One approach is to allow networking services to be implemented at user-level. Unfortunately, this typically incurs costs due to scheduling overheads and unnecessary data copying via the kernel. In this paper, we describe a method to implement efficient application-specific network service extensions at user-level, that removes the cost of scheduling and provides protected access to lower-level system abstractions. We present a networking implementation that, with minor modifications to the Linux kernel, passes data between "sandboxed" extensions and the Ethernet device without copying or processing in the kernel. Using this mechanism, we put a customizable networking stack into a user-level sandbox and show how it can be used to efficiently process and forward data via proxies, or intermediate hosts, in the communication path of high performance data streams. Unlike other user-level networking implementations, our method makes no special hardware requirements to avoid unnecessary data copies. Results show that we achieve a substantial increase in throughput over comparable user-space methods using our networking stack implementation.

Specialized Mappings Architecture with Applications to Vision-based Estimation of Articulated Body Pose

A fundamental task of vision systems is to infer the state of the world given some form of visual observations. From a computational perspective, this often involves facing an ill-posed problem; e.g., information is lost via projection of the 3D world into a 2D image. Solution of an ill-posed problem requires additional information, usually provided as a model of the underlying process. It is important that the model be both computationally feasible as well as theoretically well-founded. In this thesis, a probabilistic, nonlinear supervised computational learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human body or human hands, given images obtained via one or more uncalibrated cameras. The SMA consists of several specialized forward mapping functions that are estimated automatically from training data, and a possibly known feedback function. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). A probabilistic model for the architecture is first formalized. Solutions to key algorithmic problems are then derived: simultaneous learning of the specialized domains along with the mapping functions, as well as performing inference given inputs and a feedback function. The SMA employs a variant of the Expectation-Maximization algorithm and approximate inference. The approach allows the use of alternative conditional independence assumptions for learning and inference, which are derived from a forward model and a feedback model. Experimental validation of the proposed approach is conducted in the task of estimating articulated body pose from image silhouettes. Accuracy and stability of the SMA framework is tested using artificial data sets, as well as synthetic and real video sequences of human bodies and hands.

Assessing the Security of a Clean-Slate Internet Architecture

The TCP/IP architecture was originally designed without taking security measures into consideration. Over the years, it has been subjected to many attacks, which has led to many patches to counter them. Our investigations into the fundamental principles of networking have shown that carefully following an abstract model of Interprocess Communication (IPC) addresses many problems [1]. Guided by this IPC principle, we designed a clean-slate Recursive INternet Architecture (RINA) [2]. In this paper, we show how, without the aid of cryptographic techniques, the bare-bones architecture of RINA can resist most of the security attacks faced by TCP/IP. We also show how hard it is for an intruder to compromise RINA. Then, we show how RINA inherently supports security policies in a more manageable, on-demand basis, in contrast to the rigid, piecemeal approach of TCP/IP.

Estimating Human Body Pose from a Single Image via the Specialized Mappings Architecture

A non-linear supervised learning architecture, the Specialized Mapping Architecture (SMA) and its application to articulated body pose reconstruction from single monocular images is described. The architecture is formed by a number of specialized mapping functions, each of them with the purpose of mapping certain portions (connected or not) of the input space, and a feedback matching process. A probabilistic model for the architecture is described along with a mechanism for learning its parameters. The learning problem is approached using a maximum likelihood estimation framework; we present Expectation Maximization (EM) algorithms for two different instances of the likelihood probability. Performance is characterized by estimating human body postures from low level visual features, showing promising results.