583,989 research outputs found
Mother's Day Awards - 2
No full textCampus Scenes: Miscellaneousphotograph date: ca. 1939-1940Mother's Day Parad
Retelling racialized violence, remaking white innocence: the politics of interlocking oppressions in transgender day of remembrance
No full textTransgender Day of Remembrance has become a significant political event among those resisting violence against gender-variant persons. Commemorated in more than 250 locations worldwide, this day honors individuals who were killed due to anti-transgender hatred or prejudice. However, by focusing on transphobia as the definitive cause of violence, this ritual potentially obscures the ways in which hierarchies of race, class, and sexuality constitute such acts. Taking the Transgender Day of Remembrance/Remembering Our Dead project as a case study for considering the politics of memorialization, as well as tracing the narrative history of the Fred F. C. Martinez murder case in Colorado, the author argues that deracialized accounts of violence produce seemingly innocent White witnesses who can consume these spectacles of domination without confronting their own complicity in such acts. The author suggests that remembrance practices require critical rethinking if we are to confront violence in more effective ways. Description from publisher's site: http://caliber.ucpress.net/doi/abs/10.1525/srsp.2008.5.1.2
AI3SD Video: Accelerating structure prediction models for materials discovery
No full textThe discovery of new functional materials can be guided by computational screening, particularly if the structure of a material can be reliably predicted from its chemical composition. For this application, we have been developing the use energy-structure-function maps [1], which summarise the crystal structures available to a given molecule and the relevant properties that are predicted for these structures. The use of these methods is still limited by the computational cost of crystal structure prediction (CSP). Most of the cost of CSP is associated with the calculation of the relative energies of predicted crystal structures using energy models that are sufficiently accurate to provide reliable energetic rankings. To speed up these methods, we have been developing machine learning approaches to predict high quality energies (e.g. from solid state density functional theory) from structures that have been generated with computationally efficient energy models [2-4]. The talk will discuss the performance of these methods, which use Gaussian Process Regression based on descriptors of local environments of atoms within crystal structures. I will also describe how these descriptors can be used to more quickly navigate the structure-property landscapes of molecular crystals [5] and how fast CSP can be applied to screen chemical space for the most promising molecules for a given application [6].[1] Functional materials discovery using energy–structure–function maps, A. Pulido et al, Nature 2017, 543, 657.[2] Machine learning for the structure–energy–property landscapes of molecular crystals, F. Musil, S. De, J. Yang, J. E. Campbell, G. M. Day and M Ceriotti, Chem. Sci. 2018, 9, 1289-1300.[3] Machine-Learned Fragment-Based Energies for Crystal Structure Prediction, D. McDonagh, C.-K. Skylaris and G. M. Day, J. Chem. Theory Comput. 2019, 15, 2743–2758[4] Multi-fidelity Statistical Machine Learning for Molecular Crystal Structure Prediction, O. Egorova, R. Hafizi, D. C. Woods and G. M. Day, J. Phys. Chem. A 2020, 124, 39, 8065–8078.[5] Distributed Multi-Objective Bayesian Optimization for the Intelligent Navigation of Energy Structure Function Maps For Efficient Property Discovery, E. Pyzer-Knapp, G. M. Day, L. Chen, A. I. Cooper, ChemRxiv 2020, https://doi.org/10.26434/chemrxiv.13019960.v1[6] Evolutionary chemical space exploration for functional materials: computational organic semiconductor discovery, C. Y. Cheng, J. E. Campbell and G. M. Day, Chem. Sci. 2020, 11, 4922-4933
Foreign Policy Analysis: Classic and Contemporary Theory
No full textThis comprehensive introduction to Foreign Policy Analysis (FPA) is geared toward advanced undergraduate and beginning graduate students. Cogently written, clearly organized, and filled with illuminating examples, the third edition has been thoroughly revised and updated. Beginning with an overview of this broad field of study, Hudson and Day consider theory and research at multiple levels of analysis, including personality and psychology of foreign policy decision makers, small group dynamics, the organizational process, bureaucratic politics, domestic politics, cultural and societal influences, national attributes, and system-level effects on foreign policy. The authors also examine the promise and frustration of theoretical integration in FPA and overview promising new work by non-North American scholars
- …
