610697
Marking EP Coursework Using Electronic Communication
SUMMARY This paper discusses experience of getting students undertaking EP coursework to submit their work electronically. This has a surprising number of advantages, beyond the obvious saving of paper, though there are disadvantages too.
1946418
A Cyclic Distributed Garbage Collector for Network Objects
This paper presents an algorithm for distributed garbage collection and outlines its implementation within the Network Objects system. The algorithm is based on a reference listing scheme, which is augmented by partial tracing in order to collect distributed garbage cycles. Processes may be dynamically organised into groups, according to appropriate heuristics, to reclaim distributed garbage cycles. The algorithm places no overhead on local collectors and suspends local mutators only briefly. Partial tracing of the distributed graph involves only objects thought to be part of a garbage cycle: no collaboration with other processes is required. The algorithm offers considerable flexibility, allowing expediency and fault-tolerance to be traded against completeness.
26305456
Tail Recursion without Space Leaks
The G-machine (Johnsson, 1987; Peyton Jones, 1987) is a compiled graph reduction machine for lazy functional languages. The G-machine compiler contains many optimizations to improve performance. One set of such optimizations is designed to improve the performance of tail recursive functions. Unfortunately, the abstract machine is subject to a space leak—objects are unnecessarily preserved by the garbage collector. This paper analyses why a particular form of space leak occurs in the G-machine, and presents some ideas for fixing this problem. This phenomena in other abstract machines is also examined briefly.
14277112
A Methodology for Large-Scale Hardware Verification
We present a formal verification methodology for datapath-dominated hardware. This provides a systematic but flexible framework within which to organize the activities undertaken in large-scale verification efforts and to structure the associated code and proof-script artifacts. The methodology deploys a combination of model checking and lightweight theorem proving in higher-order logic, tightly integrated within a general-purpose functional programming language that allows the framework to be easily customized and also serves as a specification language. We illustrate the methodology--which has has proved highly effective in large-scale industrial trials--with the verification of an IEEE-compliant, extended precision floating-point adder.
85258
Reducing Manual Abstraction in Formal Verification of Out-of-Order Execution
Several methods have recently been proposed for verifying processors with out-of-order execution. These methods use intermediate abstractions to decompose the verification process into smaller steps. Unfortunately, the process of manually creating intermediate abstractions is very laborious. We present an approach that dramatically reduces the need for an intermediate abstraction, so that only the scheduling logic of the implementation is abstracted. After the abstraction, we apply an enhanced incremental-flushing approach to verify the remaining circuitry by comparing the processor description against itself in a slightly simpler configuration. By induction, we demonstrate that any reachable configuration is equivalent to the simplest possible configuration. Finally, we prove correctness on the simplest configuration. The approach is illustrated with a simple example of an out-of-order execution core.
13080115
Building Minority Language Corpora by Learning to Generate Web Search Queries
The Web is a source of valuable information, but the process of collecting, organizing, and effectively utilizing the resources it contains is difficult. We describe CorpusBuilder, an approach for automatically generating Web search queries for collecting documents matching a minority concept. The concept used for this paper is that of text documents belonging to a minority natural language on the Web. Individual documents are automatically labeled as relevant or nonrelevant using a language filter, and the feedback is used to learn what query lengths and inclusion/exclusion term-selection methods are helpful for finding previously unseen documents in the target language. Our system learns to select good query terms using a variety of term scoring methods. Using odds ratio scores calculated over the documents acquired was one of the most consistently accurate query-generation methods. To reduce the number of estimated parameters, we parameterize the query length using a Gamma distribution and present empirical results with learning methods that vary the time horizon used when learning from the results of past queries. We find that our system performs well whether we initialize it with a whole document or with a handful of words elicited from a user. Experiments applying the same approach to multiple languages are also presented showing that our approach generalizes well across several languages regardless of the initial conditions.
915422
Experiments in Spoken Document Retrieval at CMU
Experience de recherche d'information sur un corpus representant 70 heures d'informations radiodiffusees (CMU= Carnegie Mellon University)
207159138
Generating query substitutions
We introduce the notion of query substitution, that is, generating a new query to replace a user's original search query. Our technique uses modifications based on typical substitutions web searchers make to their queries. In this way the new query is strongly related to the original query, containing terms closely related to all of the original terms. This contrasts with query expansion through pseudo-relevance feedback, which is costly and can lead to query drift. This also contrasts with query relaxation through boolean or TFIDF retrieval, which reduces the specificity of the query. We define a scale for evaluating query substitution, and show that our method performs well at generating new queries related to the original queries. We build a model for selecting between candidates, by using a number of features relating the query-candidate pair, and by fitting the model to human judgments of relevance of query suggestions. This further improves the quality of the candidates generated. Experiments show that our techniques significantly increase coverage and effectiveness in the setting of sponsored search.