Option Strategies with linear programming

In practice, all option strategies are decided in advance, given the investor’s belief of the stock price. In this paper, instead of deciding in advance the most appropriate hedging option strategy, an LP problem is formulated, by considering all significant Greek parameters of the Black-Scholes formula, such as delta, gamma, theta, rho and kappa. The optimal strategy to select will be simply decided by the solution of that model. The LP model is applied to Ericsson’s call and puts options.Finance, option portfolios, Linear programming

Asymptotically Optimal Message Dissemination with Applications to Blockchains

Messages in large-scale networks such as blockchain systems are typically disseminated using flooding protocols, in which parties send the message to a random set of peers until it reaches all parties. Optimizing the communication complexity of such protocols and, in particular, the per-party communication complexity is of primary interest since nodes in a network are often subject to bandwidth constraints. Previous flooding protocols incur a per-party communication complexity of $\Omega(l\cdot \gamma^{-1} \cdot (\log(n) + \kappa))$ bits to disseminate an $l$-bit message among $n$ parties with security parameter~$\kappa$ when it is guaranteed that a $\gamma$ fraction of the parties remain honest. In this work, we present the first flooding protocols with a per-party communication complexity of $O(l\cdot \gamma^{-1})$ bits. We further show that this is asymptotically optimal and that our protocols can be instantiated provably securely in the usual setting for proof-of-stake blockchains. To demonstrate that one of our new protocols is not only asymptotically optimal but also practical, we perform several probabilistic simulations to estimate the concrete complexity for given parameters. Our simulations show that our protocol significantly improves the per-party communication complexity over the state-of-the-art for practical parameters. Hence, for given bandwidth constraints, our results allow to, e.g., increase the block size, improving the overall throughput of a blockchain

Multi-Objective Genetic Programming for Feature Extraction and Data Visualization

Feature extraction transforms high dimensional data into a new subspace of lower dimensionalitywhile keeping the classification accuracy. Traditional algorithms do not consider the multi-objective nature of this task. Data transformations should improve the classification performance on the new subspace, as well as to facilitate data visualization, which has attracted increasing attention in recent years. Moreover, new challenges arising in data mining, such as the need to deal with imbalanced data sets call for new algorithms capable of handling this type of data. This paper presents a Pareto-basedmulti-objective genetic programming algorithm for feature extraction and data visualization. The algorithm is designed to obtain data transformations that optimize the classification and visualization performance both on balanced and imbalanced data. Six classification and visualization measures are identified as objectives to be optimized by the multi-objective algorithm. The algorithm is evaluated and compared to 11 well-known feature extraction methods, and to the performance on the original high dimensional data. Experimental results on 22 balanced and 20 imbalanced data sets show that it performs very well on both types of data, which is its significant advantage over existing feature extraction algorithms

2018 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

Excerpt: As an academic institution, we strive to meet and exceed the expectations for graduate programs and laud our values and contributions to the academic community. At the same time, we must recognize, appreciate, and promote the unique non-academic values and accomplishments that our faculty team brings to the national defense, which is a priority of the Federal Government. In this respect, through our diverse and multi-faceted contributions, our faculty, as a whole, excel, not only along the metrics of civilian academic expectations, but also along the metrics of military requirements, and national priorities

Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods-A Case Study from Dak Nong, Vietnam

Information on land use and land cover (LULC) including forest cover is important for the development of strategies for land planning and management. Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to produce estimates with a greater degree of confidence than traditional inventory estimates. However, use of these data has always been a challenge in tropical regions owing to the complexity of the biophysical environment, clouds, and haze, and atmospheric moisture content, all of which impede accurate LULC classification. We tested a parametric classifier (logistic regression) and three non-parametric machine learning classifiers (improved k-nearest neighbors, random forests, and support vector machine) for classification of multi-temporal Sentinel 2 satellite imagery into LULC categories in Dak Nong province, Vietnam. A total of 446 images, 235 from the year 2017 and 211 from the year 2018, were pre-processed to gain high quality images for mapping LULC in the 6516 km(2) study area. The Sentinel 2 images were tested and classified separately for four temporal periods: (i) dry season, (ii) rainy season, (iii) the entirety of the year 2017, and (iv) the combination of dry and rainy seasons. Eleven different LULC classes were discriminated of which five were forest classes. For each combination of temporal image set and classifier, a confusion matrix was constructed using independent reference data and pixel classifications, and the area on the ground of each class was estimated. For overall temporal periods and classifiers, overall accuracy ranged from 63.9% to 80.3%, and the Kappa coefficient ranged from 0.611 to 0.813. Area estimates for individual classes ranged from 70 km(2) (1% of the study area) to 2200 km(2) (34% of the study area) with greater uncertainties for smaller classes.Peer reviewe

Hyperspectral Reflectance as a Basis to Discriminate Olive Varieties—A Tool for Sustainable Crop Management

Worldwide sustainable development is threatened by current agricultural land change trends, particularly by the increasing rural farmland abandonment and agricultural intensification phenomena. In Mediterranean countries, these processes are affecting especially traditional olive groves with enormous socio-economic costs to rural areas, endangering environmental sustainability and biodiversity. Traditional olive groves abandonment and intensification are clearly related to the reduction of olive oil production income, leading to reduced economic viability. Most promising strategies to boost traditional groves competitiveness—such as olive oil differentiation through adoption of protected denomination of origin labels and development of value-added olive products—rely on knowledge of the olive varieties and its specific properties that confer their uniqueness and authenticity. Given the lack of information about olive varieties on traditional groves, a feasible and inexpensive method of variety identification is required. We analyzed leaf spectral information of ten Portuguese olive varieties with a powerful data-mining approach in order to verify the ability of satellite’s hyperspectral sensors to provide an accurate olive variety identification. Our results show that these olive varieties are distinguishable by leaf reflectance information and suggest that even satellite open-source data could be used to map them. Additional advantages of olive varieties mapping were further discussed