Thermally Activated Magnetization and Resistance Decay during Near Ambient Temperature Aging of Co Nanoflakes in a Confining Semi-metallic Environment

We report the observation of magnetic and resistive aging in a self assembled nanoparticle system produced in a multilayer Co/Sb sandwich. The aging decays are characterized by an initial slow decay followed by a more rapid decay in both the magnetization and resistance. The decays are large accounting for almost 70% of the magnetization and almost 40% of the resistance for samples deposited at 35 $^oC$. For samples deposited at 50 $^oC$ the magnetization decay accounts for $\sim 50$ of the magnetization and 50% of the resistance. During the more rapid part of the decay, the concavity of the slope of the decay changes sign and this inflection point can be used to provide a characteristic time. The characteristic time is strongly and systematically temperature dependent, ranging from $\sim1$x$10^2 s$ at 400K to $\sim3$x$10^5 s$ at 320K in samples deposited at $35 ^oC$. Samples deposited at 50 $^oC$ displayed a 7-8 fold increase in the characteristic time (compared to the $35 ^oC$ samples) for a given aging temperature, indicating that this timescale may be tunable. Both the temperature scale and time scales are in potentially useful regimes. Pre-Aging, Scanning Tunneling Microscopy (STM) reveals that the Co forms in nanoscale flakes. During aging the nanoflakes melt and migrate into each other in an anisotropic fashion forming elongated Co nanowires. This aging behavior occurs within a confined environment of the enveloping Sb layers. The relationship between the characteristic time and aging temperature fits an Arrhenius law indicating activated dynamics

The utility of twins in developmental cognitive neuroscience research: How twins strengthen the ABCD research design

The ABCD twin study will elucidate the genetic and environmental contributions to a wide range of mental and physical health outcomes in children, including substance use, brain and behavioral development, and their interrelationship. Comparisons within and between monozygotic and dizygotic twin pairs, further powered by multiple assessments, provide information about genetic and environmental contributions to developmental associations, and enable stronger tests of causal hypotheses, than do comparisons involving unrelated children. Thus a sub-study of 800 pairs of same-sex twins was embedded within the overall Adolescent Brain and Cognitive Development (ABCD) design. The ABCD Twin Hub comprises four leading centers for twin research in Minnesota, Colorado, Virginia, and Missouri. Each site is enrolling 200 twin pairs, as well as singletons. The twins are recruited from registries of all twin births in each State during 2006–2008. Singletons at each site are recruited following the same school-based procedures as the rest of the ABCD study. This paper describes the background and rationale for the ABCD twin study, the ascertainment of twin pairs and implementation strategy at each site, and the details of the proposed analytic strategies to quantify genetic and environmental influences and test hypotheses critical to the aims of the ABCD study. Keywords: Twins, Heritability, Environment, Substance use, Brain structure, Brain functio

An Australian twin study of cannabis and other illicit drug use and misuse, and other psychopathology

Cannabis is the most widely used illicit drug throughout the developed world and there is consistent evidence of heritable influences on multiple stages of cannabis involvement including initiation of use and abuse/dependence. In this paper, we describe the methodology and preliminary results of a large-scale interview study of 3,824 young adult twins (born 1972–1979) and their siblings. Cannabis use was common with 75.2% of males and 64.7% of females reporting some lifetime use of cannabis while 24.5% of males and 11.8% of females reported meeting criteria for DSM-IV cannabis abuse or dependence. Rates of other drug use disorders and common psychiatric conditions were highly correlated with extent of cannabis involvement and there was consistent evidence of heritable influences across a range of cannabis phenotypes including early (≤15 years) opportunity to use (h(2) = 72%), early (≤16 years) onset use (h(2) = 80%), using cannabis 11+ times lifetime (h(2) = 76%), and DSM abuse/dependence (h(2) = 72%). Early age of onset of cannabis use was strongly associated with increased rates of subsequent use of other illicit drugs and with illicit drug abuse/dependence; further analyses indicating that some component of this association may have been mediated by increasing exposure to and opportunity to use other illicit drugs

Tweets as data: Demonstration of TweeQL and TwitInfo

Microblogs such as Twitter are a tremendous repository of user-generated content. Increasingly, we see tweets used as data sources for novel applications such as disaster mapping, brand sentiment analysis, and real-time visualizations. In each scenario, the workflow for processing tweets is ad-hoc, and a lot of unnecessary work goes into repeating common data processing patterns. We introduce TweeQL, a stream query processing language that presents a SQL-like query interface for unstructured tweets to generate structured data for downstream applications. We have built several tools on top of TweeQL, most notably TwitInfo, an event timeline generation and exploration interface that summarizes events as they are discussed on Twitter. Our demonstration will allow the audience to interact with both TweeQL and TwitInfo to convey the value of data embedded in tweets

TwitInfo: Aggregating and Visualizing Microblogs for Event Exploration

Microblogs are a tremendous repository of user-generated content about world events. However, for people trying to understand events by querying services like Twitter, a chronological log of posts makes it very difficult to get a detailed understanding of an event. In this paper, we present TwitInfo, a system for visualizing and summarizing events on Twitter. TwitInfo allows users to browse a large collection of tweets using a timeline-based display that highlights peaks of high tweet activity. A novel streaming algorithm automatically discovers these peaks and labels them meaningfully using text from the tweets. Users can drill down to subevents, and explore further via geolocation, sentiment, and popular URLs. We contribute a recall-normalized aggregate sentiment visualization to produce more honest sentiment overviews. An evaluation of the system revealed that users were able to reconstruct meaningful summaries of events in a small amount of time. An interview with a Pulitzer Prize-winning journalist suggested that the system would be especially useful for understanding a long-running event and for identifying eyewitnesses. Quantitatively, our system can identify 80-100% of manually labeled peaks, facilitating a relatively complete view of each event studied

Processing and visualizing the data in tweets

Microblogs such as Twitter provide a valuable stream of diverse user-generated data. While the data extracted from Twitter is generally timely and accurate, the process by which developers extract structured data from the tweet stream is ad-hoc and requires reimplementation of common data manipulation primitives. In this paper, we present two systems for querying and extracting structure from Twitter-embedded data. The first, TweeQL, provides a streaming SQL-like interface to the Twitter API, making common tweet processing tasks simpler. The second, TwitInfo, shows how end-users can interact with and understand aggregated data from the tweet stream, in addition to showcasing the power of the TweeQL language. Together these systems show the richness of content that can be extracted from Twitter