Abstract
Individual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well-established, but often make data collection and analyses time-consuming, or limit the contexts in which data can be collected. Recent computational advances, specifically deep learning, can help overcome the limitations of collecting large-scale data across contexts. However, one of the bottlenecks preventing the application of deep learning for individual identification is the need to collect and identify hundreds to thousands of individually labelled pictures to train convolutional neural networks (CNNs). Here we describe procedures for automating the collection of training data, generating training datasets, and training CNNs to allow identification of individual birds. We apply our procedures to three small bird species, the sociable weaverPhiletairus socius,the great titParus majorand the zebra finchTaeniopygia guttata, representing both wild and captive contexts. We first show how the collection of individually labelled images can be automated, allowing the construction of training datasets consisting of hundreds of images per individual. Second, we describe how to train a CNN to uniquely re-identify each individual in new images. Third, we illustrate the general applicability of CNNs for studies in animal biology by showing that trained CNNs can re-identify individual birds in images collected in contexts that differ from the ones originally used to train the CNNs. Finally, we present a potential solution to solve the issues of new incoming individuals. Overall, our work demonstrates the feasibility of applying state-of-the-art deep learning tools for individual identification of birds, both in the laboratory and in the wild. These techniques are made possible by our approaches that allow efficient collection of training data. The ability to conduct individual recognition of birds without requiring external markers that can be visually identified by human observers represents a major advance over current methods.

Abstract
In this work we seek clusters of genomic words in human DNA by studying their inter-word lag distributions. Due to the particularly spiked nature of these histograms, a clustering procedure is proposed that first decomposes each distribution into a baseline and a peak distribution. An outlier-robust fitting method is used to estimate the baseline distribution (the 'trend'), and a sparse vector of detrended data captures the peak structure. A simulation study demonstrates the effectiveness of the clustering procedure in grouping distributions with similar peak behavior and/or baseline features. The procedure is applied to investigate similarities between the distribution patterns of genomic words of lengths 3 and 5 in the human genome. These experiments demonstrate the potential of the new method for identifying words with similar distance patterns.

Abstract
This paper provides a six-level integrated optimization framework for a distribution system that transacts energy with upward electricity market and downward active microgrids in day-ahead and real-time horizons. The proposed method uses a risk-averse formulation and the distribution system utilizes multiple combined heating and power units, distributed generation, plug-in electric vehicles parking lots, and electric and thermal storage units. Demand response program alternatives are also utilized by the distribution system. A three-stage uncertainty modeling is proposed to model six sources of uncertainties that are consist of energy resource power generations, loads and prices, active microgrids contributions and contingencies. Two case studies evaluate the proposed algorithm for the 123-bus test system that multiple 33-bus microgrid systems are transacting energy and ancillary services with the main grid. Further, different sensitivity analyses are performed to evaluate the effect of energy and ancillary services prices on the simulation results.

Abstract
One of the most important aspects of the development of Electric Vehicles (EVs) is the optimal sizing and allocation of charging stations. Due to the interactions between the electricity and transportation systems, the key features of these systems (such as traffic network characteristics, charging demands and power system constraints) should be taken into account for the optimal planning. This paper addressed the optimal sizing and allocation of the fast-charging stations in a distribution network. The traffic flow of EVs is modeled using the User Equilibrium-based Traffic Assignment Model (UETAM). Moreover, a stochastic framework is developed based on the Queuing Theory (QT) to model the load levels (EVs' charging demand). The objective function of the problem is to minimize the annual investment cost, as well as the energy losses that are optimized through chance-constrained programming. The probabilistic aspects of the proposed problem are modeled by using the point estimation method and Gram-Charlier expansion. Furthermore, the probabilistic dominance criteria are employed in order to compare the uncertain alternatives. Finally, the simulation results are provided for both the distribution and traffic systems to illustrate the performance of the proposed problem.

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