Resource Constrained Structured Prediction

We study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and natural language processing. Our approach seeks to adaptively generate computationally costly features during test-time in order to reduce the computational cost of prediction while maintaining prediction performance. We show that training the adaptive feature generation system can be reduced to a series of structured learning problems, resulting in efficient training using existing structured learning algorithms. This framework provides theoretical justification for several existing heuristic approaches found in literature. We evaluate our proposed adaptive system on two structured prediction tasks, optical character recognition (OCR) and dependency parsing and show strong performance in reduction of the feature costs without degrading accuracy

Efficient Non-deterministic Search in Structured Prediction: A Case Study on Syntactic Parsing

Non-determinism occurs naturally in many search-based machine learning and natural language processing (NLP) problems. For example, the goal of parsing is to construct the syntactic tree structure of a sentence given a grammar. Agenda-based parsing is a dynamic programming approach to find the most likely syntactic tree of a sentence according to a probabilistic grammar. A chart is used to maintain all the possible subtrees for different spans in the sentence and an agenda is used to rank all the constituents. The parser chooses only one constituent from the agenda per step. Non-determinism occurs naturally in agenda-based parsing since the new constituent is often built by combining items from a few steps earlier. Unfortunately, like most other problems in NLP, the size of the search space is huge and exhaustive search is impossible. However, users expect a fast and accurate system. In this dissertation, I focus on the question of ``Why, when, and how shall we take advantage of non-determinism?'' and show its efficacy to improve the parser in terms of speed and/or accuracy. Existing approaches like search-based imitation learning or reinforcement learning methods have different limitations when it comes to a large NLP system. The solution proposed in this dissertation is ``We should train the system non-deterministically and test it deterministically if possible.'' and I also show that ``it is better to learn with oracles than simple heuristics''. We start by solving a generic Markov Decision Process with a non-deterministic agent. We show its theoretical convergence guarantees and verify its efficiency on maze solving problems. Then we focus on agenda-based parsing. To re-prioritize the parser, we model a decoding problem as a Markov Decision Process with a large state/action space. We discuss the advantages/disadvantages of existing techniques and propose a hybrid reinforcement/apprenticeship learning algorithm to trade off speed and accuracy. We also propose to use a dynamic pruner with features that depend on the run-time status of the chart and agenda and analyze the importance of those features in the pruning classification. Our models show comparable results with respect to start-of-the-art strategies

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Historical Exploration - Learning Lessons from the Past to Inform the Future

This report examines a number of exploration campaigns that have taken place during the last 700 years, and considers them from a risk perspective. The explorations are those led by Christopher Columbus, Sir Walter Raleigh, John Franklin, Sir Ernest Shackleton, the Company of Scotland to Darien and the Apollo project undertaken by NASA. To provide a wider context for investigating the selected exploration campaigns, we seek ways of finding analogies at mission, programmatic and strategic levels and thereby to develop common themes. Ultimately, the purpose of the study is to understand how risk has shaped past explorations, in order to learn lessons for the future. From this, we begin to identify and develop tools for assessing strategic risk in future explorations. Figure 0.1 (see Page 6) summarizes the key inputs used to shape the study, the process and the results, and provides a graphical overview of the methodology used in the project. The first step was to identify the potential cases that could be assessed and to create criteria for selection. These criteria were collaboratively developed through discussion with a Business Historian. From this, six cases were identified as meeting our key criteria. Preliminary analysis of two of the cases allowed us to develop an evaluation framework that was used across all six cases to ensure consistency. This framework was revised and developed further as all six cases were analyzed. A narrative and summary statistics were created for each exploration case studied, in addition to a method for visualizing the important dimensions that capture major events. These Risk Experience Diagrams illustrate how the realizations of events, linked to different types of risks, have influenced the historical development of each exploration campaign. From these diagrams, we can begin to compare risks across each of the cases using a common framework. In addition, exploration risks were classified in terms of mission, program and strategic risks. From this, a Venn diagram and Belief Network were developed to identify how different exploration risks interacted. These diagrams allow us to quickly view the key risk drivers and their interactions in each of the historical cases. By looking at the context in which individual missions take place we have been able to observe the dynamics within an exploration campaign, and gain an understanding of how these interact with influences from stakeholders and competitors. A qualitative model has been created to capture how these factors interact, and are further challenged by unwanted events such as mission failures and competitor successes. This Dynamic Systemic Risk Model is generic and applies broadly to all the exploration ventures studied. This model is an amalgamation of a System Dynamics model, hence incorporating the natural feedback loops within each exploration mission, and a risk model, in order to ensure that the unforeseen events that may occur can be incorporated into the modeling. Finally, an overview is given of the motivational drivers and summaries are presented of the overall costs borne in each exploration venture. An important observation is that all the cases - with the exception of Apollo - were failures in terms of meeting their original objectives. However, despite this, several were strategic successes and indeed changed goals as needed in an entrepreneurial way. The Risk Experience Diagrams developed for each case were used to quantitatively assess which risks were realized most often during our case studies and to draw comparisons at mission, program and strategic levels. In addition, using the Risk Experience Diagrams and the narrative of each case, specific lessons for future exploration were identified. There are three key conclusions to this study: Analyses of historical cases have shown that there exists a set of generic risk classes. This set of risk classes cover mission, program and strategic levels, and includes all the risks encountered in the cases studied. At mission level these are Leadership Decisions, Internal Events and External Events; at program level these are Lack of Learning, Resourcing and Mission Failure; at Strategic Level they are Programmatic Failure, Stakeholder Perception and Goal Change. In addition there are two further risks that impact at all levels: Self-Interest of Actors, and False Model. There is no reason to believe that these risk classes will not be applicable to future exploration and colonization campaigns. We have deliberately selected a range of different exploration and colonization campaigns, taking place between the 15th Century and the 20th Century. The generic risk framework is able to describe the significant types of risk for these missions. Furthermore, many of these risks relate to how human beings interact and learn lessons to guide their future behavior. Although we are better schooled than our forebears and are technically further advanced, there is no reason to think we are fundamentally better at identifying, prioritizing and controlling these classes of risk. Modern risk modeling techniques are capable of addressing mission and program risk but are not as well suited to strategic risk. We have observed that strategic risks are prevalent throughout historic exploration and colonization campaigns. However, systematic approaches do not exist at the moment to analyze such risks. A risk-informed approach to understanding what happened in the past helps us guard against the danger of assuming that those events were inevitable, and highlights those chance events that produced the history that the world experienced. In turn, it allows us to learn more clearly from the past about the way our modern risk modeling techniques might help us to manage the future - and also bring to light those areas where they may not. This study has been retrospective. Based on this analysis, the potential for developing the work in a prospective way by applying the risk models to future campaigns is discussed. Follow on work from this study will focus on creating a portfolio of tools for assessing strategic and programmatic risk

CleanTX Analysis on the Smart Grid

The utility industry in the United States has an opportunity to revolutionize its electric grid system by utilizing emerging software, hardware and wireless technologies and renewable energy sources. As electricity generation in the U.S. increases by over 30% from today’s generation of 4,100 Terawatt hours per year to a production of 5,400 Terawatt hours per year by 2030, a new type of grid is necessary to ensure reliable and quality power. The projected U.S. population increase and economic growth will require a grid that can transmit and distribute significantly more power than it does today. Known as a Smart Grid, this system enables two- way transmission of electrons and information to create a demand-response system that will optimize electricity delivery to consumers. This paper outlines the issues with the current grid infrastructure, discusses the economic advantages of the Smart Grid for both consumers and utilities, and examines the emerging technologies that will enable cleaner, more efficient and cost- effective power transmission and consumption.IC2 Institut