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OpenBU is Boston University’s digital institutional repository for scholarly articles, theses and dissertations, preprints, and grey literature. This repository enables BU researchers to share, disseminate, and preserve their scholarship, and makes their research more accessible
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Recent Submissions
Almost sure central limit theorem for the hyperbolic Anderson model with Lévy white noise
(American Mathematical Society) Zheng, Guangqu; Balan, Raluca; Xia, Panqiu
Global firms in large devaluations
(Oxford University Press (OUP), 2024-11-01) Blaum, Joaquin
The manuscript has a revise and resubmit
Horatio’s ‘mote’: mining a metaphor in Q2 Hamlet
(OpenEdition, 2024) Walsh, Brian
The weakness of authoritarian regimes: Rwanda as a difficult but convincing case
(Oxford University Press (OUP), 2024-10-07) Longman, Timothy
The lack of academic attention to the functioning of authoritarian regimes has allowed an erroneous impression that dictatorships are inherently strong and stable. Marie-Eve Desrosiers uses the difficult case of Rwanda, whose 1994 genocide against the Tutsi has widely been seen as a sign of state strength, to demonstrate the fragility of authoritarian rule. Looking at the First and Second Republics, which governed Rwanda from 1962 until 1994, Desrosiers explores both the vulnerability of the regimes and how they adjusted over time in attempts to strengthen control. Desrosiers argues for greater awareness of shifting strategies and changes in governance across time, what she calls “authoritarian trajectories,” to better understand how authoritarian regimes actually work and how the public responds to them. Although not focused on the 1994 genocide, Desrosiers' analysis helps explain why genocide emerged as a strategy to shore up Rwanda's failing regime.
Black women felt energized in 2024 – and frustrated
(2024-11-27) Slaughter, Christine; Brown, Nadia
Destabilizing happily ever after: Dickens’s conflation of the false bride/fairy bride motifs in David Copperfield
(2024-12-11) Bennett-Zendzian, Amy
The universal law of generalization holds for naturalistic stimuli
(American Psychological Association (APA), 2024-03) Marjieh, Raja; Jacoby, Nori; Peterson, Joshua C.; Griffiths, Thomas L.
Large language models assume people are more rational than we really are
Liu, Ryan; Geng, Jiayi; Peterson, Joshua; Sucholutsky, Ilia; Griffiths, Thomas
Aggregative efficiency of Bayesian learning in networks
(Elsevier BV) Dasaratha, Krishna; He, Kevin
PRIME‐SH: a data‐driven probabilistic model of Earth's magnetosheath
(American Geophysical Union (AGU), 2024-09) O’Brien, C.; Walsh, B.M.; Zou, Y.; Qudsi, R.; Tasnim, S.; Zhang, H.; Sibeck, D.G.
A data‐driven model of Earth's magnetosheath is developed by training a recurrent neural network (RNN) with probabilistic outputs to reproduce Magnetospheric MultiScale (MMS) measurements of the magnetosheath plasma and magnetic field using measurements from the Wind spacecraft upstream of Earth at the first Earth‐Sun Lagrange point (L1). This model, called Probabilistic Regressor for Input to the Magnetosphere Estimation‐magnetosheath (PRIME‐SH) in reference to its progenitor algorithm PRIME, is shown to predict spacecraft observations of magnetosheath conditions accurately in a statistical sense with a continuous rank probability score of 0.227σ (dimensionless standard deviation units). PRIME‐SH is shown to be more accurate than many current analytical models of the magnetosheath. Furthermore, PRIME‐SH is shown to reproduce physics not explicitly enforced during training, such as field line draping, the dayside plasma depletion layer, the magnetosheath flow stagnation point, and the Rankine‐Hugoniot MHD shock jump conditions. PRIME‐SH has the additional benefits of being computationally inexpensive relative to global MHD simulations, being capable of reproducing difficult‐to‐model physics such as temperature anisotropy, and being capable of reliably estimating its own uncertainty to within 3.5%.