Shape design optimization of parametric flume sections.

This research presents a shape design analysis and optimization methodology for parametric surface flume sections. Flume sections or flow channels have been used widely in many areas such as water chutes in civil and agricultural engineering and water slides and bob sled tracks in the recreational and sports fields. Designing such a flow channel in a CAD environment can provide many advantages such as time and resource savings to the designer. In addition, optimizing such flow channels in a virtual environment is especially efficient.In this research, geometric modeling was addressed first. Two types of parametric surface flume section models were created: B-Spline-based flume sections and parametric CAD-based flume sections as used in water chutes or water slides. The B-Spline-based flume sections were based on the dimensions of fifteen flume sections, provided by a commercial water slide firm. These flume sections presented the basis for building up realistic flume section configurations. In addition, three different kinds of CAD-based flume sections were developed by the author. Sets of differential equations based on Lagrange's equations of motion were derived that describe the motion of an object traveling in the flow channel. These ordinary differential equations were solved using MathematicaRTM. Continuity requirements were derived from the equations of motion. An analytical shape design sensitivity analysis (DSA) methodology was developed and employed to support the optimization of the B-Spline-based and the CAD-based flume section models.Optimization of parametric surfaces is a reasonably new area. Although research has been done in this area, most of it has been focused on developing better parametric surfaces, i.e., surface fitting schemes. Here, the B-Spline-based models, based on bi-cubic B-Spline surfaces, were optimized first. The control point positions were used as design variables in the optimization. Using the B-Spline control points as design variables provides more flexibility and allows for local design changes. However, the fact that no CAD software provides the control points as design parameters significantly limits their usefulness in a real design environment. The CAD-based flume section models consisted of sets of key dimensions, which were again defined as design variables for optimization. Using dimensions as design variables provided an easy and realistic avenue for design changes. However, the limited number of dimensions also limits the flexibility of design changes in the CAD-based flume section models. (Abstract shortened by UMI.

Does Optimal Distinctiveness Contribute to Group Polarization?

Group polarization occurs when group members have more extreme views after learning others in the group have similar attitudes. This effect has been found in numerous studies (e.g., Stoner, 1969 Mackie, 1986). Several theories, such as self-categorization theory and social comparison theory have been used to explain the phenomenon of group polarization. In the current research, an alternative framework based on optimal distinctiveness theory was proposed as a way to predict group polarization. This theory claims that individuals have two conflicting needs- the need to belong and the need to be distinct. When one of these needs is unmet, people act in specific ways so that the need can be addressed. Because these are conflicting needs, it can be difficult to achieve a balance where both needs are satisfied. There are many different strategies, depending on the context, that people use to establish equilibrium. One goal of the current study is to see if people in groups alter their attitudes as a way to establish optimal distinctiveness. To see if optimal distinctiveness plays a role in group polarization, specific experimental conditions were created where optimal distinctiveness would predict a particular pattern of results that differed from what existing explanations would expect. In moderate group norm condition, optimal distinctiveness and other explanations would predict polarization when needs are unmet. In extreme group norm condition, only optimal distinctiveness would predict less extreme attitudes when the need to be distinct is high. To activate particular needs and explore the role of optimal distinctiveness, a 2 (Group composition: homogeneous vs. heterogeneous) X 2 (Strength of group norm: extreme vs. moderate) mixed experiment was created, with the first factor being between-participants and the second within-participants. Participants read two essays, were given feedback about group norms, and provided their attitudes at multiple points in time. While the primary analyses failed to support for

Digital Divide and Growth Gap: A Cumulative Relationship

IT, growth gap, cumulative relationship

Study of Abnormal Group Velocities in Flexural Metamaterials

Generally, it has been known that the optical branch of a simple one-dimensional periodic structure has a negative group velocity at the first Brillouin zone due to the band-folding effect. However, the optical branch of the flexural wave in one-dimensional periodic structure doesn't always have negative group velocity. The problem is that the condition whether the group velocity of the flexural optical branch is negative, positive or positive-negative has not been studied yet. In consequence, who try to achieve negative group velocity has suffered from trial-error process without an analytic guideline. In this paper, the analytic investigation for this abnormal behavior is carried out. In particular, we discovered that the group velocity of the optical branch in flexural metamaterials is determined by a simple condition expressed in terms of a stiffness ratio and inertia ratio of the metamaterial. To derive the analytic condition, an extended mass-spring system is used to calculate the wave dispersion relationship in flexural metamaterials. For the validation, various numerical simulations are carried out, including a dispersion curve calculation and three-dimensional wave simulation. The results studied in this paper are expected to provide new guidelines in designing flexural metamaterials to have desired wave dispersion curves

Relaxing coherence for modern learning applications

The main objective of this research is to efficiently execute learning (model training) of modern machine learning (ML) applications. The recent explosion in data has led to the emergence of data-intensive ML applications whose key phase is learning that requires significant amounts of computation. A unique characteristic of learning is that it is iterative- convergent, where a consistent view of memory does not always need to be guaranteed such that parallel workers are allowed to compute using stale values in intermediate computations to relax certain read-after-write data dependencies. While multiple workers read-and- modify shared model parameters multiple times during learning, incurring multiple data communication between workers, most of the data communication is redundant, due to the stale value tolerant characteristic. Relaxing coherence for these learning applications has the potential to provide extraordinary performance and energy benefits but requires innovations across the system stack from hardware and software. While considerable effort has utilized the stale value tolerance on distributed learning, still inefficient utilization of the full performance potential of this characteristic has caused modern ML applications to have low execution efficiency on the state-of-the-art systems. The inefficiency mainly comes from the lack of architectural considerations and detailed understanding of the different stale value tolerance of different ML applications. Today’s architecture, designed to cater to the needs of more traditional workloads, incurs high and often unnecessary overhead. The lack of detailed understanding has led to ambiguity for the stale value tolerance thus failing to take the full performance potential of this characteristic. This dissertation presents several innovations regarding this challenge. First, this dissertation proposes Bounded Staled Sync (BSSync), hardware support for the bounded staleness consistency model, which accompanies simple logic layers in the memory hierarchy, for reducing atomic operation overhead on data synchronization intensive workloads. The long latency and serialization caused by atomic operations have a significant impact on performance. The proposed technique overlaps the long latency atomic operation with the main computation. Compared to previous work that allows stale values for read operations, BSSync utilizes staleness for write operations, allowing stale- writes. It reduces the inefficiency coming from the data movement between where they are stored and where they are processed. Second, this dissertation presents StaleLearn, a learning acceleration mechanism to reduce the memory divergence overhead of GPU learning with sparse data. Sparse data induces divergent memory accesses with low locality, thereby consuming a large fraction of total execution time on transferring data across the memory hierarchy. StaleLearn trans- forms the problem of divergent memory accesses into the synchronization problem by replicating the model, and reduces the synchronization overhead by asynchronous synchronization on Processor-in-Memory (PIM). The stale value tolerance makes possible to clearly decompose tasks between the GPU and PIM, which can effectively exploit parallelism be- tween PIM and GPU cores by overlapping PIM operations with the main computation on GPU cores. Finally, this dissertation provides a detailed understanding of the different stale value tolerance of different ML applications. While relaxing coherence can reduce the data communication overhead, its complicated impact on the progress of learning has not been well studied thus leading to ambiguity for domain experts and modern systems. We define the stale value tolerance of ML training with the effective learning rate. The effective learning rate can be defined by the implicit momentum hyperparameter, the update density, the activation function selection, RNN cell types, and learning rate adaptation. Findings of this work will open further exploration of asynchronous learning including improving the findings laid out in this dissertation.Ph.D