Georgetown University

About: Georgetown University is a education organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 23377 authors who have published 43718 publications receiving 1748598 citations. The organization is also known as: GU & Georgetown.

Liberalizing Electoral Outcomes in Competitive Authoritarian Regimes

Abstract: In the wake of the third wave of democratization, competitive authoritarianism has emerged as a prominent regime type. These regimes feature regular, competitive elections between a government and an opposition, but the incumbent leader or party typically resorts to coercion, intimidation, and fraud to attempt to ensure electoral victory. Despite the incumbent’s reliance on unfair practices to stay in power, such elections occasionally result in what we call a “liberalizing electoral outcome” (LEO), which often leads to a new government that is considerably less authoritarian than its predecessor. Using a “nested” research design that employs both cross-national statistical analysis and a case study of Kenya, we seek to explain how and why LEOs occur. Our findings highlight in particular the importance of the choices made by opposition elites to form a strategic coalition for the purpose of mounting a credible challenge to the ruling party or candidate in national elections.

Malaria in pregnancy: pathogenesis and immunity.

Abstract: Understanding of the biological basis for susceptibility to malaria in pregnancy was recently advanced by the discovery that erythrocytes infected with Plasmodium falciparum accumulate in the placenta through adhesion to molecules such as chondroitin sulphate A. Antibody recognition of placental infected erythrocytes is dependent on sex and gravidity, and could protect from malaria complications. Moreover, a conserved parasite gene-var2csa-has been associated with placental malaria, suggesting that its product might be an appropriate vaccine candidate. By contrast, our understanding of placental immunopathology and how this contributes to anaemia and low birthweight remains restricted, although inflammatory cytokines produced by T cells, macrophages, and other cells are clearly important. Studies that unravel the role of host response to malaria in pathology and protection in the placenta, and that dissect the relation between timing of infection and outcome, could allow improved targeting of preventive treatments and development of a vaccine for use in pregnant women.

A Genomic Predictor of Response and Survival Following Taxane-Anthracycline Chemotherapy for Invasive Breast Cancer

Abstract: Results Patients in the independent validation cohort (99% clinical stage II-III) who were predicted to be treatment sensitive (28%) had 56% (95% CI, 31%-78%) probability of excellent pathologic response and DRFS of 92% (95% CI, 85%-100%), with an ARR of 18% (95% CI, 6%-28%). Survival was predicted in ER-positive (30% predicted sensitive; DRFS, 97% [95% CI, 91%-100%]; ARR, 11% [95% CI, 0.1%21%]) and ER-negative (26% predicted sensitive; DRFS, 83% [95% CI, 68%100%]; ARR, 26% [95% CI, 4%-48%]) subsets and was significant in multivariate analysis. Other genomic predictors showed paradoxically worse survival for patients predicted to be responsive to chemotherapy.

Hidden voice commands

Abstract: Voice interfaces are becoming more ubiquitous and are now the primary input method for many devices. We explore in this paper how they can be attacked with hidden voice commands that are unintelligible to human listeners but which are interpreted as commands by devices. We evaluate these attacks under two different threat models. In the black-box model, an attacker uses the speech recognition system as an opaque oracle. We show that the adversary can produce difficult to understand commands that are effective against existing systems in the black-box model. Under the white-box model, the attacker has full knowledge of the internals of the speech recognition system and uses it to create attack commands that we demonstrate through user testing are not understandable by humans. We then evaluate several defenses, including notifying the user when a voice command is accepted; a verbal challenge-response protocol; and a machine learning approach that can detect our attacks with 99.8% accuracy.