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

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

Technical efficiency of small-scale honey producers in Ethiopia: A stochastic frontier analysis

In this paper, a study is presented of the dynamic behavior of an automatic transmit power control (ATPC) loop in a single fixed wireless system (FWS) link subject to multipath fading and an uncorrelated co-channel interferer that does not use ATPC (this represents a so-called non-ATPC FWS link or a fixed satellite link). Fundamental questions include the sensitivity of an ATPC link to multipath interference and the co-channel interference that may be caused by a non-ATPC interferer. In the context of the present project, a good example of a non-ATPC interferer is a fixed satellite to which one antenna in a fixed microwave link has partial view. A computer model was developed that constitutes a useful tool in describing; simulating and analyzing an ATPC loop in a single FWS link. With the aid of this model, results are presented on the sensitivity of an ATPC loop in a FWS link with respect to channel conditions, non-ATPC interference and parameter settings

One-dimensional broadband phononic crystal filter with unit cells made of two non-uniform impedance-mirrored elements

A one-dimensional finite-sized phononic crystal(PC) made of a specially-configured unit cell is proposed to realize broad bandpass, high-performance filtering. The unit cell is specially-configured with two elements having mirrored impedance distributions of each other. One element has a non-uniform impedance distribution that is so engineered as to maximize wave transmission in the pass band and to minimize transmission in the adjacent stop band while the other, exactly the mirrored distribution. The mirroring approach naturally yields the overall impedance contrast within the resulting unit cell, necessary to form stop bands in a PC of the unit cells. More importantly, the good transmission performance of the orginally-engineered element can be preserved by the approach because no additional impedance mismatch is introduced along the interface of the two impedance-mirrored elements. Extraordinary performance of the PC filter made of the proposed unit cell, such as high transmission, large bandwidth and sharp roll-off, is demonstrated by using one-dimensional longitudinal elastic wave problems. Copyright 2013 Author(s). This article is distributed under a Creative Commons Attribution 3.0 Unported License. [http://dx.doi.org/10.1063/1.4790638ope