Email threading is a commonly used tool by legal practitioners to streamline document review and classification in legal proceedings. Threading organizes component pieces of an email thread together to effectively reduce a dataset. The most inclusive threads and their associated document attachments are maintained, and non-inclusive or duplicative thread components are set aside. However, as data volumes continue to grow and outpace deadlines for legal proceedings, practitioners often look to incorporate multiple cost-effective and defensible technology solutions to further reduce or otherwise accelerate document review and classification. One such technology is predictive modeling – known in the legal industry as ‘predictive coding’ or ‘Technology Assisted Review (TAR)’ – which is a popular tool used to augment a manual document review and classification process. Like email threading, predictive coding has become more commonplace recently for its proven ability to minimize manual document classification, thus reducing the time and cost associated with this aspect of legal proceedings.In this study, we explore the performance impacts of layering predictive modeling onto an email threading reduction workflow. Generally, a predictive model is established first, and email threading is layered onto the scored output to further reduce and streamline document review. Our research evaluates a reversed workflow, where a population is initially reduced to its inclusive email threads, and this limited dataset is used to train and apply a predictive model to the larger population of documents for classification. Using classified data from four real-world legal proceedings, we compare the performance impact of email threading on predictive modeling by 1) training a model using all positive and negative examples, 2) training a model using positive and negative examples from only inclusive email threads, and 3) training a model using positive examples from only inclusive email threads, but negative examples from all emails. The results of our research provide thoughtful, empirical insights for legal practitioners to review when exploring the deployment of both email threading and predictive modeling into a single, cohesive document classification strategy for their legal proceedings.

Predictive modeling - known in the legal industry as ‘predictive coding’ or ‘Technology Assisted Review (TAR)’ - is a popular tool used by legal professionals to augment a historically manual document review and classification process in responding to data requests for various legal proceedings. It has become more commonplace over the past decade due to its proven ability to minimize manual document classification, thus reducing the time and cost associated with this aspect of legal proceedings. There is significant research supporting the effectiveness of this technology that includes topics, such as identifying the most performant machine learning algorithms (e.g., logistic regression) or establishing the best methodologies for selecting representative training and testing data. Primarily, this research has been performed without a focus on multilanguage data sets because many legal proceedings typically involve one primary, dominant language. As acceptance of predictive modeling technology grows, legal practitioners have developed independent preferences for handling multiple languages in data sets. These preferences have been primarily based on anecdotal experience rather than empirical assessments and have resulted in two modeling approaches. The first approach uses a single model, which is less complex, and more cost effective than the next approach. The second develops multiple language-specific models, which can create workflow complexity that may impact the overall cost savings that predictive coding seeks to achieve. Proponents of the second approach believe that creating models per language results in better performance over a single model approach. In this study, we empirically explore the performance differences between the two approaches - single model and multiple language-specific models. We hypothesize that a single model approach performs similarly to a language-specific modeling approach when classifying documents for relevance. We evaluate both approaches by comparing their resulting precision-recall curves across real-world test data from legal document review projects. Excitingly, our results demonstrate that in most scenarios, a single model (trained with multiple languages) approach will perform as well, or sometimes better than the approach that uses a group of language-specific models. The results of our research provide a roadmap for legal practitioners and other parties to thoughtfully engage in dialogue around the most effective way to deploy predictive coding within a multilanguage data set. This research will change how predictive models are deployed for multilanguage data sets across the legal industry and enable significant cost savings for clients and legal practitioners by utilizing the most efficient modeling method.

The National Science Foundation's Large Facilities are major, multi-user research facilities that operate and manage sophisticated and diverse research instruments and platforms (e.g., large telescopes, interferometers, distributed sensor arrays) that serve a variety of scientific disciplines, from astronomy and physics to geology and biology and beyond. Large Facilities are increasingly dependent on advanced cyberinfrastructure (i.e., computing, data, and software systems; networking; and associated human capital) to enable the broad delivery and analysis of facility-generated data. These cyberinfrastructure tools enable scientists and the public to gain new insights into fundamental questions about the structure and history of the universe, the world we live in today, and how our environment may change in the coming decades. This paper describes a pilot project that aims to develop a model for a Cyberinfrastructure Center of Excellence (CI CoE) that facilitates community building and knowledge sharing and that disseminates and applies best practices and innovative solutions for facility CI.

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU) --- deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X -- 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X -- 80X higher. Moreover, using the CPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.