"May I borrow Your Filter?" Exchanging Filters to Combat Spam in a Community

Leveraging social networks in computer systems can be effective in dealing with a number of trust and security issues. Spam is one such issue where the "wisdom of crowds" can be harnessed by mining the collective knowledge of ordinary individuals. In this paper, we present a mechanism through which members of a virtual community can exchange information to combat spam. Previous attempts at collaborative spam filtering have concentrated on digest-based indexing techniques to share digests or fingerprints of emails that are known to be spam. We take a different approach and allow users to share their spam filters instead, thus dramatically reducing the amount of traffic generated in the network. The resultant diversity in the filters and cooperation in a community allows it to respond to spam in an autonomic fashion. As a test case for exchanging filters we use the popular SpamAssassin spam filtering software and show that exchanging spam filters provides an alternative method to improve spam filtering performance

Wasted Talent and Broken Dreams: The Lost Potential of Undocumented Students

The current political debate over undocumented immigrants in the United States has largely ignored the plight of undocumented children. Yet children account for 1.8 million, or 15 percent, of the undocumented immigrants now living in this country. These children have, for the most part, grown up in the United States and received much of their primary and secondary educations here. But without a means to legalize their status, they are seldom able to go on to college and cannot work legally in this country. Moreover, at any time, they can be deported to countries they barely know. This wasted talent imposes economic and emotional costs on undocumented students themselves and on U.S. society as a whole. Denying undocumented students, most of whom are Hispanic, the opportunity to go to college and join the skilled workforce sends the wrong message to Hispanics about the value of a college education-and the value that U.S. society places on their education-at a time when raising the educational attainment of the Hispanic population is increasingly important to the nation's economic health

Optical properties of tensilely strained Ge nanomembranes

Group-IV semiconductors, which provide the leading materials platform of micro- electronics, are generally unsuitable for light emitting device applications because of their indirect- bandgap nature. This property currently limits the large-scale integration of electronic and photonic functionalities on Si chips. The introduction of tensile strain in Ge, which has the effect of lowering the direct conduction-band minimum relative to the indirect valleys, is a promising approach to address this challenge. Here we review recent work focused on the basic science and technology of mechanically stressed Ge nanomembranes, i.e., single-crystal sheets with thicknesses of a few tens of nanometers, which can sustain particularly large strain levels before the onset of plastic deformation. These nanomaterials have been employed to demonstrate large strain-enhanced photoluminescence, population inversion under optical pumping, and the formation of direct-bandgap Ge. Furthermore, Si-based photonic-crystal cavities have been developed that can be combined with these Ge nanomembranes without limiting their mechanical flexibility. These results highlight the potential of strained Ge as a CMOS-compatible laser material, and more in general the promise of nanomembrane strain engineering for novel device technologies.The Ge nanomembrane fabrication and characterization efforts were supported initially by DOE under Grant DE-FG02-03ER46028, and subsequently by AFOSR under Grant FA9550-14-1-0361. The development of the photonic-crystal cavities was supported by NSF under Grant ECCS-1308534. The initial photoluminescence studies were funded by NSF under Grant DMR-0907296. The contribution from several students and research scientists involved in this research at Boston University and the University of Wisconsin-Madison (including Cicek Boztug, Francesca Cavallo, Feng Chen, Xiaorui Cui, RB Jacobson, Debbie Paskiewicz, Jose Sanchez-Perez, Pornsatit Sookchoo, Faisal Sudradjat, Xiaowei Wang, and Jian Yin) is also gratefully acknowledged. (DE-FG02-03ER46028 - DOE; FA9550-14-1-0361 - AFOSR; ECCS-1308534 - NSF; DMR-0907296 - NSF)Published versio

Parametric frailty and shared frailty survival models

Frailty models are the survival data analog to regression models, which account for heterogeneity and random effects. A frailty is a latent multiplicative effect on the hazard function and is assumed to have unit mean and variance theta, which is estimated along with the other model parameters. A frailty model is an heterogeneity model where the frailties are assumed to be individual- or spell-specific. A shared frailty model is a random effects model where the frailties are common (or shared) among groups of individuals or spells and are randomly distributed across groups. Parametric frailty models were made available in Stata with the release of Stata 7, while parametric shared frailty models were made available in a recent series of updates. This article serves as a primer to those fitting parametric frailty models in Stata via the streg command. Frailty models are compared to shared frailty models, and both are shown to be equivalent in certain situations. The user-specified form of the distribution of the frailties (whether gamma or inverse Gaussian) is shown to subtly affect the interpretation of the results. Methods for obtaining predictions that are either conditional or unconditional on the frailty are discussed. An example that analyzes the time to recurrence of infection after catheter insertion in kidney patients is studied. Copyright 2002 by Stata Corporation.parametric survival analysis, frailty, random effects, overdispersion, heterogeneity

Frailty in survival analysis models (parametric frailty, parametric shared frailty, and frailty in Cox models

Frailty models are used to model survival times in the presence of overdispersion or group-specific random effects. The latter are distinguished from the former by the term "shared" frailty models. With the release of Stata 7, estimation of parametric non-shared frailty models is now possible, and the new models appear as extensions to the six parametric survival models previously available. The overdispersion in this case is represented by an unobservable multiplicative effect on the hazard, or frailty. For purposes of estimation this frailty is then assumed to either follow a gamma or inverse-Gaussian distribution. Parametric shared frailty models are the next logical step in the development in this area, and will soon be available as an update to Stata 7. For these models, the random unobservable frailty effects are assumed to follow either a gamma or inverse-Gaussian distribution, but are constrained to be equal over those observations from a given group or panel. Frailty models and shared frailty models for parametric regression with survival data will be discussed, along with avenues for future development at Stata Corp. in this area, in particular, an application of the frailty principle to Cox regression. Series: United Kingdom Stata Users' Group Meeting, 2001

Genericity of nondegenerate geodesics with general boundary conditions

Let M be a possibly noncompact manifold. We prove, generically in the C^k-topology (k=2,...,\infty), that semi-Riemannian metrics of a given index on M do not possess any degenerate geodesics satisfying suitable boundary conditions. This extends a result of Biliotti, Javaloyes and Piccione for geodesics with fixed endpoints to the case where endpoints lie on a compact submanifold P of the product MxM that satisfies an admissibility condition. Such condition holds, for example, when P is transversal to the diagonal of MxM. Further aspects of these boundary conditions are discussed and general conditions under which metrics without degenerate geodesics are C^k-generic are given.Comment: LaTeX2e, 21 pages, no figure