Pycobra: A Python Toolbox for Ensemble Learning and Visualisation

We introduce \texttt{pycobra}, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Python libraries (as long as a \texttt{predict} method is given), and visualisation tools such as Voronoi tessellations. \texttt{pycobra} is fully \texttt{scikit-learn} compatible and is released under the MIT open-source license. \texttt{pycobra} can be downloaded from the Python Package Index (PyPi) and Machine Learning Open Source Software (MLOSS). The current version (along with Jupyter notebooks, extensive documentation, and continuous integration tests) is available at \href{https://github.com/bhargavvader/pycobra}{https://github.com/bhargavvader/pycobra} and official documentation website is \href{https://modal.lille.inria.fr/pycobra}{https://modal.lille.inria.fr/pycobra}

Effect of Surface Morphology on Adsorption-Induced Bending of Microcantilevers

Microcantilevers undergo bending due to molecular adsorption when adsorption is confined to a single surface. The origin of the adsorption-induced force is assumed to be surface stress variation due to molecular adsorption. Single crystal silicon cantilevers were etched for a series of different time periods using two different types of Potassium Hydroxide solutions in order to obtain a rough and a smooth finish on the cantilever surface. Cantilevers that approximately had the same resonance frequency in the rough and smooth etched categories were chosen for comparison in the experiment. Liquid phase adsorption of 1-Do-decan-thiol on the cantilevers having a thin gold receptor was investigated with optical beam deflection method. The surface roughness of the cantilevers was quantified using atomic force microscopy imaging of the cantilever. Our results indicate that an increase in surface area does not increase the bending of a microcantilever, a smoother surface provides a better platform for the formation of a Self Assembled Monolayer. The un-etched cantilevers were used as the control and had the least deflection. . Self assembly of alkanethiols closely follows Langmuir type kinetics up to a single monolayer assembly. My results demonstrate that surface stress and adsorption kinetics of alkanethiols on the gold layer is considerably affected by its structural conformation

Hybrid Cooling Systems for Low-Temperature Geothermal Power Production

The overall objective of this investigation is to identify and evaluate methods by which the net power output of an air-cooled geothermal power plant can be enhanced during hot ambient conditions using minimal amounts of water. Geothermal power plants that use air-cooled heat rejection systems experience a decrease in power production during hot periods of the day. This decrease in power output typically coincides with the time when utilities need power to address high air conditioning loads. Hybrid cooling options, which use both air and water, have been studied for this report to assess how they might mitigate the net power decrease

Eigenvalue Interlacing of Bipartite Graphs and Construction of Expander Code using Vertex-split of a Bipartite Graph

The second largest eigenvalue of a graph is an important algebraic parameter which is related with the expansion, connectivity and randomness properties of a graph. Expanders are highly connected sparse graphs. In coding theory, Expander codes are Error Correcting codes made up of bipartite expander graphs. In this paper, first we prove the interlacing of the eigenvalues of the adjacency matrix of the bipartite graph with the eigenvalues of the bipartite quotient matrices of the corresponding graph matrices. Then we obtain bounds for the second largest and second smallest eigenvalues. Since the graph is bipartite, the results for Laplacian will also hold for Signless Laplacian matrix. We then introduce a new method called vertex-split of a bipartite graph to construct asymptotically good expander codes with expansion factor $\frac{D}{2}<\alpha < D$ and $\epsilon<\frac{1}{2}$ and prove a condition for the vertex-split of a bipartite graph to be $k-$connected with respect to $\lambda_{2}.$ Further, we prove that the vertex-split of $G$ is a bipartite expander. Finally, we construct an asymptotically good expander code whose factor graph is a graph obtained by the vertex-split of a bipartite graph.Comment: 17 pages, 2 figure

Kernel-Based Ensemble Learning in Python

We propose a new supervised learning algorithm, for classification and regression problems where two or more preliminary predictors are available. We introduce \texttt{KernelCobra}, a non-linear learning strategy for combining an arbitrary number of initial predictors. \texttt{KernelCobra} builds on the COBRA algorithm introduced by \citet{biau2016cobra}, which combined estimators based on a notion of proximity of predictions on the training data. While the COBRA algorithm used a binary threshold to declare which training data were close and to be used, we generalize this idea by using a kernel to better encapsulate the proximity information. Such a smoothing kernel provides more representative weights to each of the training points which are used to build the aggregate and final predictor, and \texttt{KernelCobra} systematically outperforms the COBRA algorithm. While COBRA is intended for regression, \texttt{KernelCobra} deals with classification and regression. \texttt{KernelCobra} is included as part of the open source Python package \texttt{Pycobra} (0.2.4 and onward), introduced by \citet{guedj2018pycobra}. Numerical experiments assess the performance (in terms of pure prediction and computational complexity) of \texttt{KernelCobra} on real-life and synthetic datasets.Comment: 11 page

How Climate Effects the Tick Vector of Lyme Disease: A Critical and Systematic Review of the Literature

Background Lyme disease (LD) is a common vector-borne disease in North America. Understanding the causes behind inter-annual fluctuations of LD incidence can help warn healthcare providers of upcoming outbreaks. Objective Identifying what specific climate variables affects the vector, Ixodes scapularis ticks, and ultimately LD incidence. Methods A systematic review was carried out to understand how climate variables affect the tick population variables that are related to LD. Results Twenty-one studies met the inclusion criteria. Risk of bias was generally rated “low” or “probably low” and quality of evidence was rated “moderate”. Strength of evidence was assessed for tick abundance, a proxy for LD. The relationship with climatic moisture was rated as “sufficient”, but was rated “inadequate” for temperature and temperature+moisture. A positive, moderate-strong relationship between prior climatic moisture and tick abundance (r=0.82; r2=0.56–0.64) was observed in 50% of studies. The relationship was observed in 75% of nymph-only abundance studies. While relationships were observed between tick abundance and temperature (70% of studies, r=(-0.89)–0.93; r2=(-0.56)–0.34) and temperature+moisture (38% of studies, 75% negative relationships), direction and magnitude could not be determined. Conclusion Higher climatic moisture (yearly or 0.5–2 years prior) increases tick abundance and, by proxy, LD incidence. Nymph-only abundance studies, a more accurate proxy, was more likely to show this relationship. Climate change is predicted to increase precipitation in Northeast US/Canada, which appears likely to increase LD incidence