Many simulation practitioners can get more from their analyses by using the statistical theory on design of experiments (DOE) developed specifically for exploring computer models. We discuss a toolkit of designs for simulators with limited DOE expertise who want to select a design and an appropriate analysis for their experiments. Furthermore, we provide a research agenda listing problems in the design of simulation experiments-as opposed to real-world experiments-that require more investigation. We consider three types of practical problems: (1) developing a basic understanding of a particular simulation model or system, (2) finding robust decisions or policies as opposed to so-called optimal solutions, and (3) comparing the merits of various decisions or policies. Our discussion emphasizes aspects that are typical for simulation, such as having many more factors than in real-world experiments, and the sequential nature of the data collection. Because the same problem type may be addressed through different design types, we discuss quality attributes of designs, such as the ease of design construction, the flexibility for analysis, and efficiency considerations. Moreover, the selection of the design type depends on the metamodel (response surface) that the analysts tentatively assume; for example, complicated metamodels require more simulation runs. We present several procedures to validate the metamodel estimated from a specific design, and we summarize a case study illustrating several of our major themes. We conclude with a discussion of areas that merit more work to achieve the potential benefits-either via new research or incorporation into standard simulation or statistical packages.

No abstract

We propose "Areas of Attention", a novel attentionbased model for automatic image captioning. Our approach models the dependencies between image regions, caption words, and the state of an RNN language model, using three pairwise interactions. In contrast to previous attentionbased approaches that associate image regions only to the RNN state, our method allows a direct association between caption words and image regions. During training these associations are inferred from image-level captions, akin to weakly-supervised object detector training. These associations help to improve captioning by localizing the corresponding regions during testing. We also propose and compare different ways of generating attention areas: CNN activation grids, object proposals, and spatial transformers nets applied in a convolutional fashion. Spatial transformers give the best results. They allow for image specific attention areas, and can be trained jointly with the rest of the network. Our attention mechanism and spatial transformer attention areas together yield state-of-the-art results on the MSCOCO dataset.

An n-dimensional (n = 2, 3) inverse problem for the parabolic/diffusion equationThe problem consists of determining the function a(x) inside of a bounded domain ⊂ R n given the values of the solution u(x, t) for a single source location x 0 ∈ ∂ on a set of detectors {x i } m i=1 ⊂ ∂ , where ∂ is the boundary of . A novel numerical method is derived and tested. Numerical tests are conducted for n = 2 and for ranges of parameters which are realistic for applications to early breast cancer diagnosis and the search for mines in murky shallow water using ultrafast laser pulses. The main innovation of this method lies in a new approach for a novel linearized problem (LP). Such a LP is derived and reduced to a well-posed boundary-value problem for a coupled system of elliptic partial differential equations. A principal advantage of this technique is in its speed and accuracy, since it leads to the factorization of well conditioned, sparse matrices with non-zero entries clustered in a narrow band near the diagonal. The authors call this approach the elliptic systems method (ESM). The ESM can be extended to other imaging modalities.

Computer vision has been studied from many persective. It expands from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The wide usage has attracted many scholars to integrate with many disciplines and fields. This paper provide a survey of the recent technologies and theoretical concept explaining the development of computer vision especially related to image processing using different areas of their field application. Computer vision helps scholars to analyze images and video to obtain necessary information,    understand information on events or descriptions, and scenic pattern. It used method of multi-range application domain with massive data analysis. This paper provides contribution of recent development on reviews related to computer vision, image processing, and their related studies. We categorized the computer vision mainstream into four group e.g., image processing, object recognition, and machine learning. We also provide brief explanation on the up-to-date information about the techniques and their performance.

There continues to be increasing interest from a broad range of disciplines in agent-based and artificial life simulations. This includes the Department of Defense-which uses simulations heavily in its decision making process. Indeed, military conflicts can have many attributes that are consistent with complex adaptive systems-such as many entities interacting with some degree of autonomy, each of which is continually making decisions to satisfy a variety of sometimes conflicting objectives. In this paper, we present three applications of agent-based simulations used to analyze military problems. The first uses the MANA model to explore the ability of the U.S. Army's networkbased Future Force to perform with degraded communications. The second studies how unmanned surface vehicles can be used in force protection missions with the Pythagoras model. The last example examines the standard Army squad size with an integrated effort using MANA, Pythagoras, and the high-resolution simulation JANUS.

No abstract

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.