Pleading Patterns and the Role of Litigation as a Driver of Federal Climate Change Legislation

Based on a variant of the Elliott-Ackerman-Millian theory that variable, potentially inconsistent and costly litigation outcomes induce industry to seek federal preemptive legislation to reign in such costs, we collect data on climate change-related litigation to determine whether litigation might motivate major greenhouse gas emitters to accept a preemptive, though possibly carbon-restricting, legislative compromise. We conduct a spectral cluster analysis on 178 initial federal and state judicial filings to reveal the most relevant groupings among climate change-related suits and their underlying pleading patterns. Besides exposing the general content and structure of climate change-related filings, this study identifies major specific pleading trends, such as the low frequency of tort claim pleading and the high level of segregation of state and federal causes of action. These data also allow investigating how generally applicable litigation doctrines have influenced pleading patterns, even subduing the impact of the two major U.S. Supreme Court rulings in this area. These findings lead us to conclude that this type of litigation has not induced and is not likely to induce major emitters to embrace preemptive emissions legislation as a risk-reducing compromise

Pleading Patterns and the Role of Litigation as a Driver of Federal Climate Change Legislation

Based on a variant of the Elliott-Ackerman-Millian theory that variable, potentially inconsistent and costly litigation outcomes induce industry to seek federal preemptive legislation to reign in such costs, we collect data on climate change-related litigation to determine whether litigation might motivate major greenhouse gas emitters to accept a preemptive, though possibly carbon-restricting, legislative compromise. We conduct a spectral cluster analysis on 178 initial federal and state judicial filings to reveal the most relevant groupings among climate change-related suits and their underlying pleading patterns. Besides exposing the general content and structure of climate change-related filings, this study identifies major specific pleading trends, such as the low frequency of tort claim pleading and the high level of segregation of state and federal causes of action. These data also allow investigating how generally applicable litigation doctrines have influenced pleading patterns, even subduing the impact of the two major U.S. Supreme Court rulings in this area. These findings lead us to conclude that this type of litigation has not induced and is not likely to induce major emitters to embrace preemptive emissions legislation as a risk-reducing compromise

A Data Mining Approach for Optimization of Acute Inflammation Therapy

Abstract—Acute inflammation is a medical condition which occurs over seconds, minutes or hours and is characterized as a systemic inflammatory response to an infection. Delaying treatment by only one hour decreases patient chance of survival by about 7%. Therefore, there is a critical need for tools that can aid therapy optimization for this potentially fatal condition. Towards this objective we developed a data driven approach for therapy optimization where a predictive model for patients’ behavior is learned directly from historical data. As such, the predictive model is incorporated into a model predictive control optimization algorithm to find optimal therapy, which will lead the patient to a healthy state. To save on the cost of clinical trials and potential failure, we evaluated our model on a population of virtual patients capable of emulating the inflammatory response. Patients are treated with two drugs for which dosage and timing are critical for the outcome of the treatment. Our results show significant improvement in percentage of healthy outcomes comparing to previously proposed methods for acute inflammation treatment found in literature and in clinical practice. In particular, application of our method rescued 87 % of patients that would otherwise die within 168 hours due to septic or aseptic state. In contrast, the best method from literature rescued only 73 % of patients. Keywords-data mining; therapy optimization; acute inflammation. I

Continuous Conditional Random Fields for Efficient Regression in Large Fully Connected Graphs

When used for structured regression, powerful Conditional Random Fields (CRFs) are typically restricted to modeling effects of interactions among examples in local neighborhoods. Using more expressive representation would result in dense graphs, making these methods impractical for large-scale applications. To address this issue, we propose an effective CRF model with linear scale-up properties regarding approximate learning and inference for structured regression on large, fully connected graphs. The proposed method is validated on real-world large-scale problems of image de-noising and remote sensing. In conducted experiments, we demonstrated that dense connectivity provides an improvement in prediction accuracy. Inference time of less than ten seconds on graphs with millions of nodes and trillions of edges makes the proposed model an attractive tool for large-scale, structured regression problems

Building a Taxonomy of Litigation: Clusters of Causes of Action in Federal Complaints

This project empirically explores civil litigation from its inception by examining the content of civil complaints. We utilize spectral cluster analysis on a newly compiled federal district court data set of causes of action in complaints to illustrate the relationship of legal claims to one another, the broader composition of lawsuits in trial courts, and the breadth of pleading in individual complaints. Our results shed light not only on the networks of legal theories in civil litigation but also on how lawsuits are classified and the strategies that plaintiffs and their attorneys employ when commencing litigation. This approach permits us to lay the foundation for a more precise and useful taxonomy of federal litigation than has been previously available, one that, after the Supreme Court’s recent decisions in Bell Atlantic v. Twombly (2007) and Ashcroft v. Iqbal (2009), has also arguably never been more relevant than it is today. The idea of “a plain and short statement of the claim ” has not caught on. Few complaints follow the models in the Appendix of Forms. Plaintiffs ’ lawyers, knowing that some judges read a complaint as soon as it is filed in order to get a sense of the suit, hope by pleading facts to “educate ” (that is to say, influence) the judge with regard to the nature and probable merits of th

Regression Learning with Multiple Noisy Oracles

Abstract. In regression learning, it is often difficult to obtain the true values of the label variables, while multiple sources of noisy estimates of lower quality are readily available. To address this problem, we propose a new Bayesian approach that learns a regression model from data with noisy labels provided by multiple oracles. The proposed method provides closed form solution for model parameters and is applicable to both linear and nonlinear regression problems. In our experiments on synthetic and benchmark datasets this new regression model was consistently more accurate than a model trained with averaged estimates from multiple oracles as labels.

Regression learning with multiple noisy oracles

In regression learning, it is often difficult to obtain the true values of the label variables, while multiple sources of noisy estimates of lower quality are readily available. To address this problem, we propose a new Bayesian approach that learns a regression model from data with noisy labels provided by multiple oracles. The proposed method provides closed form solution for model parameters and is applicable to both linear and nonlinear regression problems. In our experiments on synthetic and benchmark datasets this new regression model was consistently more accurate than a model trained with averaged estimates from multiple oracles as labels