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الجماعة التي تسمي نفسها دولة : فهم تطور تنظيم الدولة الإسلامية وتحدياتة
في يونيو 2014، فوجئ العالم قاطبة بحدث مدو تمثل بسقوط مدينة الموصل العراقية بيد التنظيم الذي كان يعرف بأسم «الدولة الإسلامية في العراق والشام (داعش)»، والذي غير تاليا أسمه إلى «الدولة الإسلامية». وأصبح التنظيم منذئذ لا يفرض سيطرته العسكرية على مناطق واسعة في العراق وسوريا فحسب، بل إنه أخذ يحكم هذه المناطق ويخضعها لإدارته وسلطته. هذا الكتاب، يبحث الجذور التنظيمية والخلفيات التاريخية لتنظيم «الدولة الإسلامية»، وأسباب صعوده، ومجالات أنشطته، ونقاط قوته وضعفه، وأوجه إخفاقاته ونجاحاته. ويبحث الكتاب أيضا، على المستوى الاستراتيجي، التحديات الماثلة والفرص المتاحة لمواجهة التنظيم ومكافحته. أعد الكتاب باحثون في مركز مكافحة الإرهاب التابع للأكاديمية العسكرية الأمريكية في ويست بوينت بولاية نيويورك الأمريكية، وهو يستند إلى مصادر بيانات فريدة من نوعها ؛ بما في ذلك تلك التي استقيت من مواد التنظيم نفسه، وخصوصا الوثائق التي تم الاستحواذ عليها وكذلك المراسلات عبر شبكة الإنترنت. والكتاب يقدم صورة شاملة عن تنظيم «الدولة الإسلامية»، ولا شك في أنه يسد ثغرة قلة المعلومات المتوافرة عن التنظيم، كما أنه من أبرز الدراسات البحثية الجادة عن هذه الجماعة.
BOOK
AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses
Flow cytometry is a useful and efficient method for the rapid characterization of a cell population based on the optical and fluorescence properties of individual cells. Ideally, the cell population would consist of only healthy viable cells as dead cells can confound the analysis. Thus, separating out healthy cells from dying and dead cells, and any potential debris, is an important first step in analysis of flow cytometry data. While gating of debris can be conducted using measured optical properties, identifying dead and dying cells often requires utilizing fluorescent stains (e.g. Sytox, a nucleic acid stain that stains cells with compromised cell membranes) to identify cells that should be excluded from downstream analyses. These stains prolong the experimental preparation process and use a flow cytometer’s fluorescence channels that could otherwise be used to measure additional fluorescent markers within the cells (e.g. reporter proteins). Here we outline a stain-free method for identifying viable cells for downstream processing by gating cells that are dying or dead. AutoGater is a weakly supervised deep learning model that can separate healthy populations from unhealthy and dead populations using only light-scatter channels. In addition, AutoGater harmonizes different measurements of dead cells such as Sytox and CFUs.
Journal Article
A Tutorial on Planning Graph–Based Reachability Heuristics
The primary revolution in automated planning in the last decade has been the very impressive scale‐up in planner performance. A large part of the credit for this can be attributed squarely to the invention and deployment of powerful reachability heuristics. Most, if not all, modern reachability heuristics are based on a remarkably extensible data structure called the planning graph, which made its debut as a bit player in the success of GraphPlan, but quickly grew in prominence to occupy the center stage. Planning graphs are a cheap means to obtain informative look‐ahead heuristics for search and have become ubiquitous in state‐of‐the‐art heuristic search planners. We present the foundations of planning graph heuristics in classical planning and explain how their flexibility lets them adapt to more expressive scenarios that consider action costs, goal utility, numeric resources, time, and uncertainty.
Journal Article
Planning Graph Heuristics for Belief Space Search
Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A* search. The second, POND, is a conditional progression planner that uses AO* search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several state of the art approaches in conditional planning.
Journal Article
Grit and Frustration
A gritty person can choose a goal and work toward completion of said goal despite any obstacles or setbacks that may potentially present themselves. In the current study, the relationship between grit and frustration was assessed. Grit was compared with other related constructs (i.e., growth mindset, resilience, self-control, self-efficacy, task-specific self-efficacy) to determine if grit was the best predictor of frustration. Gritty participants were also expected to experience lower frustration, lower perceived task difficulty, lower negative affect, and higher positive affect. Participants (N = 82) first completed an online questionnaire comprised of scales related to grit and the five covariates. Afterwards, participants performed an in-person anagram task across two conditions. The low frustration condition involved eight solvable anagrams and the high frustration condition involved one solvable anagram and seven unsolvable anagrams. Participants were given two minutes to complete each individual anagram. Lastly, participants completed a post-test questionnaire to measure positive and negative affect (PANAS), self-reported frustration, perceived task difficulty, and willingness to continue. While grit did not manifest a significant effect across any of the five dependent variables, a significant effect for frustration was observed across all DVs except for willingness to continue.
Dissertation
The E² Bathe Subspace Iteration Method
Since its development in 1971, the Bathe subspace iteration method has been widely-used to solve the generalized symmetric-definite eigenvalue problem. The method is particularly useful for solving large eigenvalue problems when only a few of the least dominant eigenpairs are sought. In reference, an enriched subspace iteration method was proposed that accelerated the convergence of the basic method by replacing some of the iteration vectors with more effective turning vectors. In this thesis, we build upon this recent acceleration effort and further enrich the subspace of each iteration by replacing additional iteration vectors with our new turning-of-turning vectors.We begin by reviewing the underpinnings of the subspace iteration methodology. Then, we present the steps of our new algorithm, which we refer to as the Enriched- Enriched (E2 ) Bathe subspace iteration method. This is followed by a tabulation of the number of floating point operations incurred during a general iteration of the E2 algorithm. Additionally, we perform a simplified convergence analysis showing that the E2 method converges asymptotically at a faster rate than the enriched method. Finally, we examine the results from several test problems that were used to illustrate the E2 method and to assess its potential computational savings compared to the enriched method.The sample results for the E2 method are consistent with the theoretical asymptotic convergence rate that was obtained in our convergence analysis. Further, the results from the CPU time tests suggest that the E2 method can often provide a useful reduction in computational effort compared to the enriched method, particularly when relatively few iteration vectors are used in comparison with the number of eigenpairs that are sought.
Dissertation