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 $l$ 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