We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Thank you for reading experimental and quasi experimental designs for research. Maybe you have knowledge that, people have search numerous times for their favorite readings like this experimental and quasi experimental designs for research, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some infectious virus inside their laptop.

Experimental And Quasi Experimental Designs For Research

Scholars of culture, humanities and social sciences have increasingly come to an appreciation of the importance of the legal domain in social life, while critically engaged socio-legal scholars around the world have taken up the task of understanding "Law's Empire" in all of its cultural, political, and economic dimensions. The questions arising from these intersections, and addressing imperialisms past and present forms the subject matter of a special symposium issue of Social Identities under the editorship of Griffith University's Rob McQueen, and UBC's Wes Pue and with contributions from McQueen, Ian Duncanson, Renisa Mawani, David Williams, Emma Cunliffe, Chidi Oguamanam, W. Wesley Pue, Fatou Camara, and Dianne Kirkby. This paper introduces the volume, forthcoming in late 2007. The central problematique of this issue has previously been explored through the 2005 Law's Empire conference, an informal but vibrant postcolonial legal studies network.

Law's Empire

American Bankruptcy Institute Law Review 17 Am. Bankr. Inst. L. Rev., No. 1, Spring, 2009. Boston College Law Review 50 B.C. L. Rev., No. 3, May, 2009. Boston University Public Interest Law Journal 18 B.U. Pub. Int. L.J., No. 2, Spring, 2009. Cardozo Journal of Conflict Resolution 10 Cardozo J. Conflict Resol., No. 2, Spring, 2009. Cardozo Public Law, Policy, & Ethics Journal 7 Cardozo Pub. L. Pol’y & Ethics J., No. 3, Summer, 2009. Chicago Journal of International Law 10 Chi. J. Int’l L., No. 1, Summer, 2009. Colorado Journal of International Environmental Law and Policy 20 Colo. J. Int’l Envtl. L. & Pol’y, No. 2, Winter, 2009. Columbia Journal of Law & the Arts 32 Colum. J.L. & Arts, No. 3, Spring, 2009. Connecticut Public Interest Law Journal 8 Conn. Pub. Int. L.J., No. 2, Spring-Summer, 2009. Cornell Journal of Law and Public Policy 18 Cornell J.L. & Pub. Pol’y, No. 1, Fall, 2008. Cornell Law Review 94 Cornell L. Rev., No. 5, July, 2009. Creighton Law Review 42 Creighton L. Rev., No. 3, April, 2009. Criminal Law Forum 20 Crim. L. Forum, Nos. 2-3, Pp. 173-394, 2009. Delaware Journal of Corporate Law 34 Del. J. Corp. L., No. 2, Pp. 433-754, 2009. Environmental Law Reporter News & Analysis 39 Envtl. L. Rep. News & Analysis, No. 7, July, 2009. European Journal of International Law 20 Eur. J. Int’l L., No. 2, April, 2009. Family Law Quarterly 43 Fam. L.Q., No. 1, Spring, 2009. Georgetown Journal of International Law 40 Geo. J. Int’l L., No. 3, Spring, 2009. Georgetown Journal of Legal Ethics 22 Geo. J. Legal Ethics, No. 2, Spring, 2009. Golden Gate University Law Review 39 Golden Gate U. L. Rev., No. 2, Winter, 2009. Harvard Environmental Law Review 33 Harv. Envtl. L. Rev., No. 2, Pp. 297-608, 2009. International Review of Law and Economics 29 Int’l Rev. L. & Econ., No. 1, March, 2009. Journal of Environmental Law and Litigation 24 J. Envtl. L. & Litig., No. 1, Pp. 1-201, 2009. Journal of Legislation 34 J. Legis., No. 1, Pp. 1-98, 2008. Journal of Technology Law & Policy 14 J. Tech. L. & Pol’y, No. 1, June, 2009. Labor Lawyer 24 Lab. Law., No. 3, Winter/Spring, 2009. Michigan Journal of International Law 30 Mich. J. Int’l L., No. 3, Spring, 2009. New Criminal Law Review 12 New Crim. L. Rev., No. 2, Spring, 2009. Northern Kentucky Law Review 36 N. Ky. L. Rev., No. 4, Pp. 445-654, 2009. Ohio Northern University Law Review 35 Ohio N.U. L. Rev., No. 2, Pp. 445-886, 2009. Pace Law Review 29 Pace L. Rev., No. 3, Spring, 2009. Quinnipiac Health Law Journal 12 Quinnipiac Health L.J., No. 2, Pp. 209-332, 2008-2009. Real Property, Trust and Estate Law Journal 44 Real Prop. Tr. & Est. L.J., No. 1, Spring, 2009. Rutgers Race and the Law Review 10 Rutgers Race & L. Rev., No. 2, Pp. 441-629, 2009. San Diego Law Review 46 San Diego L. Rev., No. 2, Spring, 2009. Seton Hall Law Review 39 Seton Hall L. Rev., No. 3, Pp. 725-1102, 2009. Southern California Interdisciplinary Law Journal 18 S. Cal. Interdisc. L.J., No. 3, Spring, 2009. Stanford Environmental Law Journal 28 Stan. Envtl. L.J., No. 3, July, 2009. Tulsa Law Review 44 Tulsa L. Rev., No. 2, Winter, 2008. UMKC Law Review 77 UMKC L. Rev., No. 4, Summer, 2009. Washburn Law Journal 48 Washburn L.J., No. 3, Spring, 2009. Washington University Global Studies Law Review 8 Wash. U. Global Stud. L. Rev., No. 3, Pp.451-617, 2009. Washington University Journal of Law & Policy 29 Wash. U. J.L. & Pol’y, Pp. 1-401, 2009. Washington University Law Review 86 Wash. U. L. Rev., No. 6, Pp. 1273-1521, 2009. William Mitchell Law Review 35 Wm. Mitchell L. Rev., No. 4, Pp. 1235-1609, 2009. Yale Journal of International Law 34 Yale J. Int’l L., No. 2, Summer, 2009. Yale Journal on Regulation 26 Yale J. on Reg., No. 2, Summer, 2009.

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Current index to legal periodicals

tions. Bootstrap has found many applications in engineering field, including artificial neural networks, biomedical engineering, environmental engineering, image processing, and radar and sonar signal processing. Basic concepts of the bootstrap are summarized in each section as a step-by-step algorithm for ease of implementation. Most of the applications are taken from the signal processing literature. The principles of the bootstrap are introduced in Chapter 2. Both the nonparametric and parametric bootstrap procedures are explained. Babu and Singh (1984) have demonstrated that in general, these two procedures behave similarly for pivotal (Studentized) statistics. The fact that the bootstrap is not the solution for all of the problems has been known to statistics community for a long time; however, this fact is rarely touched on in the manuscripts meant for practitioners. It was first observed by Babu (1984) that the bootstrap does not work in the infinite variance case. Bootstrap Techniques for Signal Processing explains the limitations of bootstrap method with an example. I especially liked the presentation style. The basic results are stated without proofs; however, the application of each result is presented as a simple step-by-step process, easy for nonstatisticians to follow. The bootstrap procedures, such as moving block bootstrap for dependent data, along with applications to autoregressive models and for estimation of power spectral density, are also presented in Chapter 2. Signal detection in the presence of noise is generally formulated as a testing of hypothesis problem. Chapter 3 introduces principles of bootstrap hypothesis testing. The topics are introduced with interesting real life examples. Flow charts, typical in engineering literature, are used to aid explanations of the bootstrap hypothesis testing procedures. The bootstrap leads to second-order correction due to pivoting; this improvement in the results due to pivoting is also explained. In the second part of Chapter 3, signal processing is treated as a regression problem. The performance of the bootstrap for matched filters as well as constant false-alarm rate matched filters is also illustrated. Chapters 2 and 3 focus on estimation problems. Chapter 4 introduces bootstrap methods used in model selection. Due to the inherent structure of the subject matter, this chapter may be difficult for nonstatisticians to follow. Chapter 5 is the most impressive chapter in the book, especially from the standpoint of statisticians. It provides real data bootstrap applications to illustrate the theory covered in the earlier chapters. These include applications to optimal sensor placement for knock detection and land-mine detection. The authors also provide a MATLAB toolbox comprising frequently used routines. Overall, this is a very useful handbook for engineers, especially those working in signal processing.

Probability and Random Processes

Serious games are computer-based games designed for training purposes. They are poised to expand their role in medical education. This systematic review, conducted in accordance with PRISMA guidelines, aimed to synthesize current serious gaming trends in health care training, especially those pertaining to developmental methodologies and game evaluation. PubMed, EMBASE, and Cochrane databases were queried for relevant documents published through December 2014. Of the 3737 publications identified, 48 of them, covering 42 serious games, were included. From 2007 to 2014, they demonstrate a growth from 2 games and 2 genres to 42 games and 8 genres. Overall, study design was heterogeneous and methodological quality by MERQSI score averaged 10.5/18, which is modest. Seventy-nine percent of serious games were evaluated for training outcomes. As the number of serious games for health care training continues to grow, having schemas that organize how educators approach their development and evaluation is essential for their success.

A Systematic Review of Serious Games in Training Health Care Professionals.

Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm/reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized, and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).

Predicting the Behavior of the Supreme Court of the United States: A General Approach.

Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.

/pdf/a-general-approach-for-predicting-the-behavior-of-the-3hnnz35ipw.pdf

A General Approach for Predicting the Behavior of the Supreme Court of the United States

Do I have a case? What is our likely exposure? How much is this going to cost? What will happen if we leave this particular provision out of this contract? How can we best staff this particular legal matter? These are core questions asked by sophisticated clients such as general counsels as well as consumers at the retail level. Whether generated by a mental model or a sophisticated algorithm, prediction is a core component of the guidance that lawyers offer. Indeed, it is by generating informed answers to these types of questions that many lawyers earn their respective wage. Every single day lawyers and law firms are providing predictions to their clients regarding their prospects in litigation and the cost associated with its pursuit (defense). How are these predictions being generated? Precisely what data or model is being leveraged? Could a subset of these predictions be improved by access to outcome data in a large number of 'similar' cases. Simply put, the answer is yes. Quantitative legal prediction already plays a significant role in certain practice areas and this role is likely increase as greater access to appropriate legal data becomes available. This article is dedicated to highlighting the coming age of Quantitative Legal Prediction with hopes that practicing lawyers, law students and law schools will take heed and prepare to survive (thrive) in this new ordering. Simply put, most lawyers, law schools and law students are going to have to do more to prepare for the data driven future of this industry. In other words, welcome to Law's Information Revolution and yeah - there is going to be math on the exam.

Quantitative Legal Prediction – or – How I Learned to Stop Worrying and Start Preparing for the Data Driven Future of the Legal Services Industry

Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court's overall affirm and reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized, and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).

Daniel Martin Katz

Papers

A Systematic Review of Serious Games in Training Health Care Professionals.

Predicting the Behavior of the Supreme Court of the United States: A General Approach.

A General Approach for Predicting the Behavior of the Supreme Court of the United States

Quantitative Legal Prediction – or – How I Learned to Stop Worrying and Start Preparing for the Data Driven Future of the Legal Services Industry

Predicting the Behavior of the Supreme Court of the United States: A General Approach