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Laboratorios Windows Server 2016

The Antarctic Impulsive Transient Antenna (ANITA) is a NASA long-duration balloon experiment with the primary goal of detecting ultra-high-energy (\(> 10^{18}\)eV) neutrinos via the Askaryan Effect. In the fourth ANITA mission, the Tunable Universal Filter Frontend (TUFF) boards were deployed for mitigation of narrow-band, anthropogenic noise with tunable, switchable notch filters. They contributed to a factor of 2.8 higher total instrument livetime in ANITA-4 compared to ANITA-3. A search for a diffuse flux of ultra-high-energy neutrinos was conducted using the data collected during the ANITA-3 flight with a new approach where the Antarctic ice area is sectioned off into bins and a search is performed with different thresholds in different bins. The binned analysis methods were extended to the development of a search for neutrinos from Gamma Ray Bursts, implementing constraints in time, and for the first time, in direction. Lower analysis thresholds were achieved in a feasibility search even when extending the search to include longer afterglow periods. Authored with osudiss-2.cls (v0.9.1)

Dhatrik Nikhil Kumar's CV

Shivam Singh's CV

This is my first Survey paper and topic is from DBMS: Concurrency control solutions. I have compared different methods in this paper. As this is my first paper so critical review is highly needed

My Resume, forked from DD

\(y=\frac{c}{x}\)

El análisis estadístico de alta dimensión y tamaño de muestra pequeño (HDLSS) se está aplicando cada vez más en una amplia gama de contextos. En tales situaciones, se ve que el popular método de la Máquina de Vectores Soporte (SVM) sufre de ''Acumulación de datos'' en el margen, lo que puede disminuir la capacidad de generalización del modelo. Esto conduce al desarrollo de la Distance Weighted Discrimination para encontrar un hiperplano separador . En el presente trabajo se revisa y reproduce, con detalle en la derivación y solución de la función de pérdida que se resuelve usando SOCP, del método desarrollado en e implementado en el entorno R\cite{R}. Basado en el trabajo e implementación de se aplica y comparan resultados a conjuntos de datos reales y simulados (en medida de lo posible los mismos conjuntos de datos utilizados que en) Palabras clave: SVM, kernel, R (el ambiente de cómputo estadístico) y datos de alta dimensión con tamaño de muestra pequeño (data High Dimension Low Sample Size).

A dinâmica topológica de inversões geométricas foi estudada em [6]. O espa ̧co de parâmetros das medidas de Markov com suporte no atrator do sistema é um aberto de R3 folheado por superfícies de nível compactas definidas pela entropia métrica: superfícies isentrópicas [7]. Neste artigo abordaremos o aspecto geométrico dessas superfícies. Em particular, classificaremos suas geodésicas e pontos umbílicos.