Pushdown Control-Flow Analysis of Higher-Order Programs

Context-free approaches to static analysis gain precision over classical approaches by perfectly matching returns to call sites---a property that eliminates spurious interprocedural paths. Vardoulakis and Shivers's recent formulation of CFA2 showed that it is possible (if expensive) to apply context-free methods to higher-order languages and gain the same boost in precision achieved over first-order programs. To this young body of work on context-free analysis of higher-order programs, we contribute a pushdown control-flow analysis framework, which we derive as an abstract interpretation of a CESK machine with an unbounded stack. One instantiation of this framework marks the first polyvariant pushdown analysis of higher-order programs; another marks the first polynomial-time analysis. In the end, we arrive at a framework for control-flow analysis that can efficiently compute pushdown generalizations of classical control-flow analyses.Comment: The 2010 Workshop on Scheme and Functional Programmin

Introspective Pushdown Analysis of Higher-Order Programs

In the static analysis of functional programs, pushdown flow analysis and abstract garbage collection skirt just inside the boundaries of soundness and decidability. Alone, each method reduces analysis times and boosts precision by orders of magnitude. This work illuminates and conquers the theoretical challenges that stand in the way of combining the power of these techniques. The challenge in marrying these techniques is not subtle: computing the reachable control states of a pushdown system relies on limiting access during transition to the top of the stack; abstract garbage collection, on the other hand, needs full access to the entire stack to compute a root set, just as concrete collection does. \emph{Introspective} pushdown systems resolve this conflict. Introspective pushdown systems provide enough access to the stack to allow abstract garbage collection, but they remain restricted enough to compute control-state reachability, thereby enabling the sound and precise product of pushdown analysis and abstract garbage collection. Experiments reveal synergistic interplay between the techniques, and the fusion demonstrates "better-than-both-worlds" precision.Comment: Proceedings of the 17th ACM SIGPLAN International Conference on Functional Programming, 2012, AC

All My Circuits: Using Multiple Electrodes to Understand Functioning Neural Networks

Much of the work in systems neuroscience thus far has focused on the brain's parts studied individually. The past 20 years has seen the advent, rise, and application of multiple-electrode technology. This allows the study of the activity of many neurons simultaneously, which in turn has provided insight into how different neuron populations interact and collaborate to produce thought and action

Creating and Strengthening the Therapeutic System for Treatment Setting Serving Deaf Children

Treating deaf children with severe emotional and behavior problems, especially in residential and alternative school treatment settings, is a demanding undertaking. Finding and retaining skilled mental health paraprofessionals are continual challenges faced by mental health service providers. These challenges are even greater when the children served are deaf, and when staff members are expected to be multilingual, be knowledgeable about deaf culture, and be able to treat children with a multitude of severe mental health disturbances. Staff members face the dilemma of both raising and treating children in the same setting with limited resources. Limited funding and limited service models encourage the practice of hiring staff members with minimal skill requirements, at low-end payrates, and with little support or training. This article (a) identifies the issues which threaten the delivery of quality treatment for deaf children with severe emotional and behavioral problems; (b) describes the process that changes well-meaning staff into casualties of their work, and (c) makes recommendations for mental health service administrators, supervisors and agencies to help create and strengthen their therapeutic system

Automatically Deriving Spreadsheet Cell Values Using Natural Language

This disclosure describes techniques to populate spreadsheet cells that depend on, but aren’t numerically calculable from, other cells. Based on a natural language query, the empty cells of a partially populated sheet that are contextually dependent on the thus-far-filled cells are automatically filled. The techniques provide greater speed, accuracy, and scalability for datasets small and large, enabling users to efficiently explore data. The techniques obviate the need to manually populate each cell in the sheet, a time-consuming and error prone procedure that does not scale well. Automatically filling in values in a spreadsheet, as described herein, opens up possibilities for the user that don’t currently exist, e.g., answering hundreds or thousands of questions based on the context in a sheet using simple, templated, natural-language queries