Defining a Community in Exile: Vietnam War Resister Communication and Identity in AMEX, 1968–1973

AMEX, the largest and longest-running American Vietnam War resister magazine published in Canada, served as an essential communication channel for war resisters. It provided practical information and helped build a sense of community both for individuals who produced the publication and for its readers. However, fractures of difference challenged the magazine’s aspiration to represent war resisters, as the common experience of leaving the United States to avoid the Vietnam War was automatically not enough to unify all who left. During the first five years of its publication, the pages of AMEX reveal a fragile community engaged in the challenging process of debating its collective identity through print. AMEX, la plus importante et la plus longuement publiée des revues canadiennes d’opposition à la guerre du Vietnam, était un outil de communication essentiel pour les opposants à la guerre. Il renfermait de l’information pratique et aidait tant ses producteurs que ses lecteurs à se forger un sentiment d’appartenance. Mais des scissions ont mis en péril l’aspiration de la revue de représenter les opposants à la guerre puisque l’expérience commune de quitter les États-Unis pour éviter la guerre du Vietnam ne suffisait pas d’office pour unifier toute la gauche. Durant les cinq premières années de sa publication, les pages d’AMEX révèlent une fragile communauté engagée dans le difficile processus de débattre de son identité collective à travers la presse

Effects of Training Data Variation and Temporal Representation in a QSR-Based Action Prediction System

Understanding of behaviour is a crucial skill for Artificial Intelligence systems expected to interact with external agents – whether other AI systems, or humans, in scenarios involving co-operation, such as domestic robots capable of helping out with household jobs, or disaster relief robots expected to collaborate and lend assistance to others. It is useful for such systems to be able to quickly learn and re-use models and skills in new situations. Our work centres around a behaviourlearning system utilising Qualitative Spatial Relations to lessen the amount of training data required by the system, and to aid generalisation. In this paper, we provide an analysis of the advantages provided to our system by the use of QSRs. We provide a comparison of a variety of machine learning techniques utilising both quantitative and qualitative representations, and show the effects of varying amounts of training data and temporal representations upon the system. The subject of our work is the game of simulated RoboCup Soccer Keepaway. Our results show that employing QSRs provides clear advantages in scenarios where training data is limited, and provides for better generalisation performance in classifiers. In addition, we show that adopting a qualitative representation of time can provide significant performance gains for QSR systems

Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

Deep networks thrive when trained on large scale data collections. This has given ImageNet a central role in the development of deep architectures for visual object classification. However, ImageNet was created during a specific period in time, and as such it is prone to aging, as well as dataset bias issues. Moving beyond fixed training datasets will lead to more robust visual systems, especially when deployed on robots in new environments which must train on the objects they encounter there. To make this possible, it is important to break free from the need for manual annotators. Recent work has begun to investigate how to use the massive amount of images available on the Web in place of manual image annotations. We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy. By combining these two results, we obtain a method for learning powerful deep object models automatically from the Web. We confirm the effectiveness of our approach through object categorization experiments using our Web-derived version of ImageNet on a popular robot vision benchmark database, and on a lifelong object discovery task on a mobile robot.Comment: 8 pages, 7 figures, 3 table

Reading list of selected PASM-related publications

Prepared for a chapter to be published in the forthcoming Encyclopedia of Parallel Computing by Springer Publishing Company. The Encyclopedia will contain a broad coverage of the field and will include entries on machine organization, programming, algorithms, and applications. The broad coverage, together with extensive pointers to the literature for in-depth study, is expected to make the Encyclopedia a useful reference tool in parallel computing

Spatiomechanical Modulation of EphB4-Ephrin-B2 Signaling in Neural Stem Cell Differentiation.

Interactions between EphB4 receptor tyrosine kinases and their membrane-bound ephrin-B2 ligands on apposed cells play a regulatory role in neural stem cell differentiation. With both receptor and ligand constrained to move within the membranes of their respective cells, this signaling system inevitably experiences spatial confinement and mechanical forces in conjunction with receptor-ligand binding. In this study, we reconstitute the EphB4-ephrin-B2 juxtacrine signaling geometry using a supported-lipid-bilayer system presenting laterally mobile and monomeric ephrin-B2 ligands to live neural stem cells. This experimental platform successfully reconstitutes EphB4-ephrin-B2 binding, lateral clustering, downstream signaling activation, and neuronal differentiation, all in a configuration that preserves the spatiomechanical aspects of the natural juxtacrine signaling geometry. Additionally, the supported bilayer system allows control of lateral movement and clustering of the receptor-ligand complexes through patterns of physical barriers to lateral diffusion fabricated onto the underlying substrate. The results from this study reveal a distinct spatiomechanical effect on the ability of EphB4-ephrin-B2 signaling to induce neuronal differentiation. These observations parallel similar studies of the EphA2-ephrin-A1 system in a very different biological context, suggesting that such spatiomechanical regulation may be a common feature of Eph-ephrin signaling

Hepatitis B among Pacific Islanders in Southern California: how is health information associated with screening and vaccination?

We measured Hepatitis B virus (HBV) transmission knowledge and self-reported screening/testing behavior among Pacific Islanders (Guamanians/Chamorros, Samoans, and Tongans) in Southern California. We also examined access and trust by Pacific Islanders of varying health information sources. We administered and analyzed survey data (N = 297), using a convenience sample in Los Angeles, Orange, and San Diego Counties in spring 2009. We found that while Pacific Islander respondents reported that they receive health information from physicians, and largely trust this source, information from and trust in physicians were not statistically significant in explaining whether respondents sought HBV screening or vaccination