No abstract

Presented are investigations into the spatial structure of teleconnections between both the winter El Niño-Southern Oscillation (ENSO) and global sea surface temperatures (SSTs), and a measure of continental U.S. summer drought during the twentieth century. Potential nonlinearities and nonstationarities in the relationships are noted. During the first three decades of this century, summer drought teleconnections in response to SST patterns linked to ENSO are found to be strongest in the southern regions of Texas, with extensions into regions of the Midwest. From the 1930s through the 1950s, the drought teleconnection pattern is found to extend into southern Arizona. The most recent three decades show weak teleconnections between summer drought over southern Texas and Arizona, and winter SSTs, which is consistent with previous findings. Instead, the response to Pacific SSTs shows a clear shift to the western United States and southern regions of California. These epochal variations are consistent with epochal variations observed in ENSO and other low-frequency climate indicators. This changing teleconnection response complicates statistical forecasting of drought.

We consider the asymptotic characteristics of the periodogram ordinates of a fractionally integrated process having memory parameter d 3 0.5, for which the process is nonstationary, or d -0.5, for which the process is noninvertible. Series having d outside the range (-0.5,O.S) may arise in practice when a raw series is modeled without preliminary consideration of the stationarity and invertibility of the series or when a wrong decision is made concerning the stationarity and invertibility of the series. We find that the periodogram of a nonstationary or noninvertible fractionally integrated process at the jth Fourier frequency wj = 2xj/n, where n is the sample size, suffers from an asymptotic relative bias which depends on j . We also examine the impact of periodogram bias on the regression estimator of d proposed by Geweke and Porter-Hudak (1983) in finite samples. The results indicate that the bias in the periodogram ordinates can strongly affect the GPH estimator even when the number of Fourier frequencies used in the regression is allowed to depend on the length of the series. We find that data tapering and elimination of the first periodogram ordinate in the regression can reduce this bias, at the cost of an increase in variance for nonstationary series. Additionally, we find for nonstationary series that the GPH estimator is more nearly invariant to first-differencing when a data taper is used.

he mobile social networking revolution is upon us and could have as profound an effect in enriching local social interaction as the Internet has had in enriching online information access and discourse. The key observation in this article is that the explosive phenomenon of online social networks can be harnessed using mobile devices to answer the compelling question that frequently appears in local social contexts: "Who's that?" It is often the case that people want to find out more about those who are around them; for example, who is that speaking to a group of people in a corner of the room, or who is that who just walked into the room? Standard solutions include asking those around you, looking at name tags, introducing yourself, and so on, none of which leverage the power of technology to help answer these compelling questions and thereby enrich the social interaction.Online social networks have exploded in popularity [1-3]. As of December 2007, Facebook had over 59 million users [4]. It is estimated that over 85 percent of four-year college students have a Facebook profile, presenting a very usable penetration rate and providing an incredible resource for applications that might leverage this data. These online social networks provide a wealth of personal contextual information, including name, picture, contact information, gender, relationship status/interests, activities/hobbies, musical preferences, literature interests, group membership, and, of course, friendship information concerning user interconnection. Social networks provide a variety of mechanisms for users to share these rich sets of contextual data with other users, including searching for other users with similar interests, as well as a means to establish and maintain communication with other users. Social networks can be seen as a natural evolution of the Internet, where the first big wave facilitated a person's access to information; for example, Web servers and peer-topeer networks providing news and information content, as well as ways to buy products, whereas this next big wave is focused on facilitating person-to-person communication.WhozThat is motivated by the idea that bringing this rich contextual information from online social networks into the real world of local human interactions substantially enriches local social interaction. Imagine if you knew more about the people around you in a social gathering, such that you could more easily strike up a conversation with someone with whom you were interested in talking. By being informed via mobile technology of the identity of the person with whom you are seeking to interact and consulting information obtained from that person's public social networking profile, you could more easily initiate a conversation, perhaps introducing yourself and saying, "I noticed we have a shared interest in this hobby or that cause." The ability of mobile social networking (MoSoNet) technology to substantially lower the barriers to social discourse by minimizing unfamiliarity could revolutionize human soc...