14,503 research outputs found
A Prison of Education: The School-to-Prison Pipeline in Low-Income Schools
This paper examines the relationship between prisons and education in American culture, comparing public schools in California cities to wealthier private schools. The essay critiques the American dream’s notions of social stratification and success of the individual in racialized areas. The first section compares funding disparities between education and prison and argues that while funding is an integral part of the inner-city’s problem, the curriculum itself is ineffective. The second section takes a closer look at differences in the curricula and educational settings of an inner-city school and a private school. It offers ethnic studies in secondary education as a potential solution for re-thinking the way schools are taught in order to allow students to learn about their educational agency. The essay builds upon the genealogy of ethnic studies movements on college campuses in order to show how a similar curriculum in secondary education will offer a different educational discourse for students and allow them to break away from traditional rigid paths of education. The paper then moves to describe the relationship between the school-to- prison pipeline and the prison-industrial complex as a result of inner-city schools’ failure to provide a proper education to students. Law and normalization of surveillance are analyzed to argue that inner-city schools produce docile prisoners
Pulsation Frequencies and Modes of Giant Exoplanets
We calculate the eigenfrequencies and eigenfunctions of the acoustic
oscillations of giant exoplanets and explore the dependence of the
characteristic frequency and the eigenfrequencies on several parameters: the
planet mass, the planet radius, the core mass, and the heavy element mass
fraction in the envelope. We provide the eigenvalues for degree up to 8 and
radial order n up to 12. For the selected values of l and n, we find that the
pulsation eigenfrequencies depend strongly on the planet mass and radius,
especially at high frequency. We quantify this dependence through the
calculation of the characteristic frequency which gives us an estimate of the
scale of the eigenvalue spectrum at high frequency. For the mass range 0.5 <
M_P < 15 M_J, and fixing the planet radius to the Jovian value, we find that
the characteristic frequency is ~164.0 * (M_P/M_J)^(0.48) microHz, where M_P is
the planet mass and M_J is Jupiter's mass. For the radius range from 0.9 to 2.0
R_J, and fixing the planet's mass to the Jovian value, we find that the
characteristic frequency is ~164.0 * (R_P/R_J)^(-2.09) microHz, where R_P is
the planet radius and R_J is Jupiter's radius. We explore the influence of the
presence of a dense core on the pulsation frequencies and on the characteristic
frequency of giant exoplanets. We find that the presence of heavy elements in
the envelope affects the eigenvalue distribution in ways similar to the
presence of a dense core. Additionally, we apply our formalism to Jupiter and
Saturn and find results consistent with both the observationnal data of Gaulme
et al. (2011) and previous theoretical work.Comment: Accepted for publication in the Astrophysical Journal; 15 Figures and
11 Table
Scaling Deep Learning on GPU and Knights Landing clusters
The speed of deep neural networks training has become a big bottleneck of
deep learning research and development. For example, training GoogleNet by
ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training
process, the current deep learning systems heavily rely on the hardware
accelerators. However, these accelerators have limited on-chip memory compared
with CPUs. To handle large datasets, they need to fetch data from either CPU
memory or remote processors. We use both self-hosted Intel Knights Landing
(KNL) clusters and multi-GPU clusters as our target platforms. From an
algorithm aspect, current distributed machine learning systems are mainly
designed for cloud systems. These methods are asynchronous because of the slow
network and high fault-tolerance requirement on cloud systems. We focus on
Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original
EASGD used round-robin method for communication and updating. The communication
is ordered by the machine rank ID, which is inefficient on HPC clusters.
First, we redesign four efficient algorithms for HPC systems to improve
EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD
are faster \textcolor{black}{than} their existing counterparts (Async SGD,
Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design
Sync EASGD, which ties for the best performance among all the methods while
being deterministic. In addition to the algorithmic improvements, we use some
system-algorithm codesign techniques to scale up the algorithms. By reducing
the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x
speedup over original EASGD on the same platform. We get 91.5% weak scaling
efficiency on 4253 KNL cores, which is higher than the state-of-the-art
implementation
Synthesis and spectral properties of novel Singapore Green analogues for protease detection
Herein we describe the synthesis, characterisation and determination of fluorescence and photophysical properties of various novel analogues of the orphan fluorophore class Singapore Green. We equate the fluorescence properties of these novel fluorophores to their molecular structure and address the mechanisms through which their fluorescence is quenched and the effect this has on their quantum yields of fluorescence. Fluorescence quenching via acylation was also achieved, thereby providing conceptual proof of their utility as cores for future fluorescent probes. Additionally, we have produced and examined a number of unexpected acyl intermediates of variable photolytic stability. Furthermore, we have obtained proof of concept that the use of Singapore Greens for protease probe generation is feasible via demonstration of proteolytic cleavage of one of the acylated analogues
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