Public Articles
Advection-Diffusiuon: weak form
\begin{align} R(u) = 1 + \frac{\rho}{n} \times \frac{\partial S}{\partial u} \end{align}
\begin{align} S = k_p u^b \\ \frac{\partial S}{\partial u} = b k_p u^{b-1} \end{align}
\begin{align} \vec{v} = \frac{\vec{v_e}}{R(u)} \end{align}
\begin{align} D = \frac{D_l}{R(u)} \end{align}
Constants: b, kp, Dl
Velocity field: $\vec{v_e}$
Mass conservation law, assuming that $\vec{v}$ and D are functions of u.
\begin{align} \frac{\partial u}{\partial t} = - \nabla \cdot \left( \vec{v} u \right) + \nabla \cdot \left( D \nabla u \right) \\ \end{align}
Where u is concentration and t is time.
Integrate on volume Ω and multiply by the test function s.
\begin{align} \int_{\Omega}\frac{\partial u}{\partial t} s d\Omega + \int_{\Omega}\nabla \cdot \left(\vec{v} u \right) s d\Omega - \int_{\Omega} \nabla \cdot \left( D \nabla u \right) s d\Omega = 0 \end{align}
Integrate by parts the last term of the left-had side.
\begin{align} \int_{\Omega} \nabla \cdot \left( D \nabla u \right) s d\Omega = \int_{\Omega} \nabla \cdot \left( s D \nabla u \right) d\Omega - \int_{\Omega} D \nabla u \nabla s d\Omega \end{align}
Apply Gauss divergence theorem on the first part of the left-hand side.
\begin{align} \int_{\Omega} \nabla \cdot \left( s D \nabla u \right) d\Omega = \int_{\Gamma_{N} \cup \Gamma_{D}} s D \nabla u \cdot n d\Gamma \end{align}
Because s = 0 on ΓD.
\begin{align} \int_{\Omega} \nabla \cdot \left( s D \nabla u \right) d\Omega = \int_{\Gamma_{N}} s D \nabla u \cdot n d\Gamma \end{align}
Put the pieces together. \begin{align} \int_{\Omega}\frac{\partial u}{\partial t} s d\Omega + \int_{\Omega}\nabla \cdot \left(\vec{v} u \right) s d\Omega - \int_{\Gamma_{N}} s D \nabla u \cdot n d\Gamma + \int_{\Omega} D \nabla u \nabla s d\Omega = 0 \end{align}
GP SN LCVS
and 1 collaborator
We want to interpolate SN lightcurves with gaussian processes. The interpolation will be used to construct templates, which are then used to construct bolometric lightcurves, filling in for missing data.
The lightcurves have diverse sampling, diverse noise, generally a smooth behavior, with more variability at early times, rather than late times (after the 64Ni decay starts dominating).
Here is an example of a very good, very well sampled lightcurve, with photometry from 2 telescopes, which is not consistent within the tiny tiny, probably underestimated observational uncertainties:
Improving Information Flow on Neural Networks
and 1 collaborator
Part One: Small World - Random Watts Strogatz Graphs as Neural Networks
[VM: just to say I see it now!]
Intro: Small World Networks and Human Neural Networks Small world networks have been studied relatively extensively as a practical way to model the neural network of the human brain, thus we may use applications of graph theory to investigate the connections between neurons and ability for information flow. \cite{Hilgetag_2015} In the late 2000’s a collaborative study was released to provide experimental evidence of the small-world nature of the human brain via Magnetic Resonance Imaging on human subjects. In this study, it was established that although the human neural network in its entirety is not necessarily a small-world network in itself, the various sections of the human neural network do exhibit small-world characteristics \cite{Wang_2009}. Although the small world network is not necessarily the most accurate model for the entire brain, it is an acceptable place to start our investigation, as many other animals with similar DNA sequences to humans exhibit these small world characteristics.\cite{SPORNS_2004}. With that in mind, in many applications, small world networks have been considered moderately useful as basic graph theoretical models to serve as a neural network, and we can then incorporate this model to advanced computing applications. To begin the investigation, we will jump in and consider simple, random small world networks of the Watts Strogatz type, as well as random, dense GNM digraphs, as small testing models for the neural networks we will test in a later part. On these random network simulations, we assign a random weight value to each edge selected randomly from a Gaussian distribution 0 ≤ x ≤ 1 in the network. The random edge weight value is essentially a part of a larger data set, a column vector with n rows according to the number of edges. In a later investigation, we will see that the column vector we feed onto the network is a large data set that we will use to train the network with. We will also repeat the above simulations with edge weights assigned a value chosen at random from a Gaussian distribution 0 ≤ n ≤ 100 and observe any differences in the results.
So, each edge weight is a part of our random data set of interest that we are applying to the network in order to obtain a feel for manipulating and updating weights in Python, and how the ability for information flow across the network is affected by the edge weights, connectivity, clustering, and other parameters. As a warm up, we will look at how the assortativity in weight values (std. dev) affects the aforementioned properties. The reason why we are using these simple simulations as a warm up, is to assure that we have a strong grasp of the NetworkX package, and because it has been observed in several other experiments (cite papers) that different data sets exhibit varying degrees of training success on different neural network structures, hence why we are considering standard deviations of randomly generated data sets on these networks. Plots of standard deviation of edge weights and various parameters included below. The first round of trials was carried out with constant capacity cap = 1 and random weights, while the second round of trials was carried out with constant weight weight = 1 and random capacity.
Spatial Layouts of Playgrounds in New York City
and 5 collaborators
Our project is aimed at children growing up in NYC, by investigating the information of recreation facilities like playgrounds around zip codes zones and residential neighborhoods. Through this project we want to supply information of the playgrounds within walking distance in neighborhood units, the amounts for average children living in communities, the crime rates around the playgrounds, the transportation situation, the restaurants and schools around the playgrounds for parents to consider that when they plan to take children out.
Conteo y Grafos
Traditional Norwegian Ale Yeasts (Kveik) are Thermotolerant, Domesticated Saccharomyces Strains
gui_es_06_strumenti_per_il_web_Axure
and 2 collaborators
Determining Factors that Affect a Restaurant’s Yelp Rating
and 3 collaborators
Keywords: Logistic regression, principal component analysis, lasso regression, hot spot analysis, kernel density
Welcome to Authorea!
Hey, welcome. Double click anywhere on the text to start writing. In addition to simple text you can also add text formatted in boldface, italic, and yes, math too: E = mc2! Add images by drag’n’drop or click on the “Insert Figure” button.
The First Radial Velocity measurements of B[e] Supergiant LHA 115-S 18
and 1 collaborator
For a long time S18 has been noted as a strange source, with the first studies taking place in the 1950s \cite{Henize_1956}. It is a supergiant B[e] star in the Small Magellanic Cloud (SMC) that doubles as an X-ray source and a variable star. In addition, the star has in few cases exhibited an extreme and unexplained He II emission \cite{2015PhDT........39L}. My collaborators and I utilize the Southern Observatory for Astronomical Research (SOAR) telescope to produce high resolution spectra of S18. In the proceeding we examine spectra of this system and attempt the first period determination by measuring radial velocities from these spectra. Such a measurement would put a constraint on the nature of the companion.
Currently the proposed secondary star is unclassified. A white dwarf is however favored because of observed X-ray, visual, and UV luminosity \cite{1987A&A...176...59S}. This system is of interest to us because of the debate surrounding the classification of the system. Confirmation of a secondary object will help indicate the sources of many of the mysterious properties of the system. We are also interested in the classification of the companion object seeing as how both of these companion types have interesting implications for a B[e] system with such a range of physical parameters. Together this information will allow for better categorization of the central star and the system as a whole.
Exploring the Relationship between the Hour of Day and CitiBike Ridership
and 3 collaborators
In this analysis, we explore whether if there is a difference between the number of CitiBike rides during the rush hours of New York City and during non-rush hours. We define the rush hours of New York City to be the hours between 7 to 9 A.M. and 4 to 9 P.M during business days. We state our hypothesis and test it using a two-sided t-test. The test indicates that there is indeed a difference.
TECHO SI
contenido del abstract: Resumen del articulo, mostrando de manera muy breve: una motivación, descripción del problema, enfoque para abordar su solución, resultados/insights más relevantes y conclusión. Máximo 300 palabras.
Resumen
Reinventing airspace: spectatorship, fluidity, intimacy
Airports are relatively recent architectural conceptions. Early airports, that appeared at the beginning of the twentieth century in Europe and in the United States, were merely open, spacious, grassy fields. They were built around their functional premise – letting aircrafts land and take off – and thus consisted, essentially, of a runway. Since then, the architecture of airports has gone a long way. In modern airports, functional design requirements are addressed alongside myriad technological, institutional, political and economical requirements that define the modern practice of air travel: airports nowadays “accommodate a growing number of facilities that have nothing to do with aviation”\cite{ibelings:1998}.
wordpress_basic_esame_finale Enrico Peruselli
and 1 collaborator
wordpress_basic_esame_finale_Matteo_Bertozzo
and 1 collaborator
wordpress_basic_esame_finale_giulio_bergamo
and 1 collaborator
wordpress_basic_esame_finale_danielezerilli
and 1 collaborator
wordpress_basic_esame_finale_Aurora_Cappello
and 1 collaborator
wordpress_basic_esame_finale_Erica_Bontempo
and 1 collaborator
wordpress_basic_esame_finale_marlène_maisonniaux
and 1 collaborator
wordpress_basic_esame_finale_Caimi_Filippo
and 1 collaborator
The Role of Axinite and Boric Acid in Pegmatite Formation