Surface functionalization in combination with confinement for crystallization from undersaturated solutions

Crystallization from undersaturated solutions was demonstrated using functionalized nanoporous silica (Zorbax® chromatographic media). The silica matrix acts as a source of bound groups within the nanopores which act to reduce the solubility within the small pore volume resulting in the formation of nanocrystals within the pores. Experiments were conducted within sealed capillaries which were monitored via X-ray powder diffraction. Experiments were conducted for a number of solutes and concentrations and clearly demonstrated a critical concentration in the undersaturated region below which crystals would not form. Concentrations above this critical concentration would form crystals. Batch experiments confirmed that the crystallization yield could be calculated from the difference between the initial concentration before the addition of Zorbax® and the critical (effective saturation) concentration.United States. Defense Advanced Research Projects Agency (Grant W911NF-16-2-0023)Novartis-MIT Center for Continuous Manufacturin

Extracting spectra in the shell model Monte Carlo method using imaginary-time correlation matrices

Conventional diagonalization methods to calculate nuclear energy levels in the framework of the configuration-interaction (CI) shell model approach are prohibited in very large model spaces. The shell model Monte Carlo (SMMC) is a powerful technique for calculating thermal and ground-state observables of nuclei in very large model spaces, but it is challenging to extract nuclear spectra in this approach. We present a novel method to extract low-lying energy levels for given values of a set of good quantum numbers such as spin and parity. The method is based on imaginary-time one-body density correlation matrices that satisfy asymptotically a generalized eigenvalue problem. We validate the method in a light nucleus that allows comparison with exact diagonalization results of the CI shell model Hamiltonian. The method is applicable to other finite-size quantum many-body systems that can be described within a CI shell model approach.Comment: 5 pages, 2 figure

Goodwill Going Green: How Green Branding Can Generate $1M in Revenue

Goodwill Industries is a nonprofit organization. Not only do they specialize in charity work, but they also have a chain of stores that focus on thrift shopping. Additionally, Goodwill provides other services, such as recycling clothing, electronics, and other goods. Recently, Goodwill of Akron, Ohio received a grant of five million dollars, where the money for the grant would have to be utilized in five years. The main objective of this project was to find a way for Goodwill to use grant money efficiently, to generate revenue for a new transportation initiative. Our team has recommended utilizing the most popular social media platforms a little more, along with improving the Goodwill app, and utilizing in-store marketing to increase awareness about green branding. Overall, Goodwill should focus on green branding when using their grant

Monte Carlo Study of Real Time Dynamics on the Lattice

Monte Carlo studies involving real time dynamics are severely restricted by the sign problem that emerges from a highly oscillatory phase of the path integral. In this Letter, we present a new method to compute real time quantities on the lattice using the Schwinger-Keldysh formalism via Monte Carlo simulations. The key idea is to deform the path integration domain to a complex manifold where the phase oscillations are mild and the sign problem is manageable. We use the previously introduced "contraction algorithm" to create a Markov chain on this alternative manifold. We substantiate our approach by analyzing the quantum mechanical anharmonic oscillator. Our results are in agreement with the exact ones obtained by diagonalization of the Hamiltonian. The method we introduce is generic and, in principle, applicable to quantum field theory albeit very slow. We discuss some possible improvements that should speed up the algorithm

Vamsa: Automated Provenance Tracking in Data Science Scripts

There has recently been a lot of ongoing research in the areas of fairness, bias and explainability of machine learning (ML) models due to the self-evident or regulatory requirements of various ML applications. We make the following observation: All of these approaches require a robust understanding of the relationship between ML models and the data used to train them. In this work, we introduce the ML provenance tracking problem: the fundamental idea is to automatically track which columns in a dataset have been used to derive the features/labels of an ML model. We discuss the challenges in capturing such information in the context of Python, the most common language used by data scientists. We then present Vamsa, a modular system that extracts provenance from Python scripts without requiring any changes to the users' code. Using 26K real data science scripts, we verify the effectiveness of Vamsa in terms of coverage, and performance. We also evaluate Vamsa's accuracy on a smaller subset of manually labeled data. Our analysis shows that Vamsa's precision and recall range from 90.4% to 99.1% and its latency is in the order of milliseconds for average size scripts. Drawing from our experience in deploying ML models in production, we also present an example in which Vamsa helps automatically identify models that are affected by data corruption issues

Search for the standard model Higgs boson in the H to ZZ to 2l 2nu channel in pp collisions at sqrt(s) = 7 TeV

A search for the standard model Higgs boson in the H to ZZ to 2l 2nu decay channel, where l = e or mu, in pp collisions at a center-of-mass energy of 7 TeV is presented. The data were collected at the LHC, with the CMS detector, and correspond to an integrated luminosity of 4.6 inverse femtobarns. No significant excess is observed above the background expectation, and upper limits are set on the Higgs boson production cross section. The presence of the standard model Higgs boson with a mass in the 270-440 GeV range is excluded at 95% confidence level.Comment: Submitted to JHE