This paper analyzes distributional properties that facilitate the categorization of words into lexical categories. First, word-context co-occurrence counts were collected using corpora of transcribed English child-directed speech. Then, an unsupervised k-nearest neighbor algorithm was used to categorize words into lexical categories. The categorization outcome was regressed over three main distributional predictors computed for each word, including frequency, contextual diversity, and average conditional probability given all the co-occurring contexts. Results show that both contextual diversity and frequency have a positive effect while the average conditional probability has a negative effect. This indicates that words are easier to categorize in the face of uncertainty: categorization works best for words which are frequent, diverse, and hard to predict given the co-occurring contexts. This shows how, in order for the learner to see an opportunity to form a category, there needs to be a certain degree of uncertainty in the co-occurrence pattern.

This paper presents both a calculus for stream processing, named Brooklet, and its realization as an intermediate language, named River. Because River is based on Brooklet, it has a formal semantics that enables reasoning about the correctness of source translations and optimizations. River builds on Brooklet by addressing the real-world details that the calculus elides. We evaluated our system by implementing front-ends for three streaming languages, and three important optimizations, and a back-end for the System S distributed streaming runtime. Overall, we significantly lower the barrier to entry for new stream-processing languages and thus grow the ecosystem of this crucial style of programming. Copyright © 2015 John Wiley & Sons, Ltd.

Programmers build large-scale systems with multiple languages to leverage legacy code and languages best suited to their problems. For instance, the same program may use Java for ease of programming and C to interface with the operating system. These programs pose significant debugging challenges, because programmers need to understand and control code across languages, which often execute in different environments. Unfortunately, traditional multilingual debuggers require a single execution environment. This paper presents a novel composition approach to building portable mixed-environment debuggers, in which an intermediate agent interposes on language transitions, controlling and reusing single-environment debuggers. We implement debugger composition in Blink, a debugger for Java, C, and the Jeannie programming language. We show that Blink is isimple: it requires modest amounts of new code; iiportable: it supports multiple Java virtual machines, C compilers, operating systems, and component debuggers; and iiipowerful: composition eases debugging, while supporting new mixed-language expression evaluation and Java native interface bug diagnostics. To demonstrate the generality of interposition, we build prototypes and demonstrate debugger language transitions with C for five of six other languages Caml, Common Lisp, C#, Perl 5, Python, and Ruby without modifications to their debuggers. Using real-world case studies, we show that diagnosing language interface errors require prior single-environment debuggers to restart execution multiple times, whereas Blink directly diagnoses them with one execution. Copyright © 2014 John Wiley & Sons, Ltd.

We introduce a new platform for annotating inferential phenomena in entailment data, buttressed by a formal semantic model and a proof-system that provide immediate verification of the coherency and completeness of the marked annotations. By integrating a web-based user interface, a formal lexicon, a lambda-calculus engine and an off-the-shelf theorem prover, the platform allows human annotators to mark linguistic phenomena in entailment data (pairs made up of a premise and a hypothesis) and to receive immediate feedback whether their annotations are substantiated: for positive entailment pairs, the system searches for a formal logical proof that the hypothesis follows from the premise; for negative pairs, the system verifies that a counter-model can be constructed. This novel approach facilitates the creation of textual entailment corpora with annotations that are sufficiently coherent and complete for recognizing the entailment relation or lack thereof. A corpus of several hundred annotated entailments is currently being compiled based on the platform and will be available for the research community in the foreseeable future.

Various research communities have independently arrived at stream processing as a programming model for efficient and parallel computing. These communities include digital signal processing, databases, operating systems, and complex event processing. Since each community faces applications with challenging performance requirements, each of them has developed some of the same optimizations, but often with conflicting terminology and unstated assumptions. This article presents a survey of optimizations for stream processing. It is aimed both at users who need to understand and guide the system’s optimizer and at implementers who need to make engineering tradeoffs. To consolidate terminology, this article is organized as a catalog, in a style similar to catalogs of design patterns or refactorings. To make assumptions explicit and help understand tradeoffs, each optimization is presented with its safety constraints (when does it preserve correctness?) and a profitability experiment (when does it improve performance?). We hope that this survey will help future streaming system builders to stand on the shoulders of giants from not just their own community.

Assaf Toledo, Stavroula Alexandropoulou, Sophie Chesney, Robert Grimm, Pepijn Kokke, Benno Kruit, Kyriaki Neophytou, Antony Nguyen, Yoad Winter. Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics. 2014.

Modern virtual machines, such as Java and Inferno, are emerging as network computing platforms. While these virtual machines provide higher-level abstractions and more sophisticated services than their predecessors from twenty years ago, their architecture has essentially remained unchanged. State of the art virtual machines are still monolithic, that is, they are comprised of closely-coupled service components, which are thus replicated over all computers in an organization. This crude replication of services forms one of the weakest points in today’s networked systems, as it creates widely acknowledged and well -publicized problems of security, manageabilit y and performance. We have designed and implemented a new system architecture for network computing based on distributed virtual machines. In our system, virtual machine services that perform rule checking and code transformation are factored out of clients and are located in enterprisewide network servers. The services operate by intercepting application code and modifying it on the fly to provide additional service functionality. This architecture reduces client resource demands and the size of the trusted computing base, establishes physical isolation between virtual machine services and creates a single point of administration. We demonstrate that such a distributed virtual machine architecture can provide substantially better integrity and manageabilit y than a monolithic architecture, scales well with increasing numbers of clients, and does not entail high overhead.

Developers increasingly use streaming languages to write applications that process large volumes of data with high throughput. Unfortunately, when picking which streaming language to use, they face a difficult choice. On the one hand, dynamically scheduled languages allow developers to write a wider range of applications, but cannot take advantage of many crucial optimizations. On the other hand, statically scheduled languages are extremely performant, but have difficulty expressing many important streaming applications. This paper presents the design of a hybrid scheduler for stream processing languages. The compiler partitions the streaming application into coarse-grained subgraphs separated by dynamic rate boundaries. It then applies static optimizations to those subgraphs. We have implemented this scheduler as an extension to the StreamIt compiler. To evaluate its performance, we compare it to three scheduling techniques used by dynamic systems (OS thread, demand, and no-op) on a combination of micro-benchmarks and real-world inspired synthetic benchmarks. Our scheduler not only allows the previously static version of StreamIt to run dynamic rate applications, but it outperforms the three dynamic alternatives. This demonstrates that our scheduler strikes the right balance between expressivity and performance for stream processing languages.

At one level, this paper is about River, a virtual execution environment for stream processing. Stream processing is a paradigm well-suited for many modern data processing systems that ingest high-volume data streams from the real world, such as audio/video streaming, high-frequency trading, and security monitoring. One attractive property of stream processing is that it lends itself to parallelization on multicores, and even to distribution on clusters when extreme scale is required. Stream processing has been co-evolved by several communities, leading to diverse languages with similar core concepts. Providing a common execution environment reduces language development effort and increases portability. We designed River as a practical realization of Brooklet, a calculus for stream processing. So at another level, this paper is about a journey from theory (the calculus) to practice (the execution environment). The challenge is that, by definition, a calculus abstracts away all but the most central concepts. Hence, there are several research questions in concretizing the missing parts, not to mention a significant engineering effort in implementing them. But the effort is well worth it, because using a calculus as a foundation yields clear semantics and proven correctness results.

Macros improve expressiveness, concision, abstraction, and language interoperability without changing the programming language itself. They are indispensable for building increasingly prevalent multilingual applications. Unfortunately, existing macro systems are well-encapsulated but unsafe (e.g., the C preprocessor) or are safe but tightly-integrated with the language implementation (e.g., Scheme macros). This paper introduces Marco, the first macro system that seeks both encapsulation and safety. Marco is based on the observation that the macro system need not know all the syntactic and semantic rules of the target language but must only directly enforce some rules, such as variable name binding. Using this observation, Marco off-loads most rule checking to unmodified target-language compilers and interpreters and thus becomes language-scalable. We describe the Marco language, its language-independent safety analysis, and how it uses two example target-language analysis plug-ins, one for C++ and one for SQL. This approach opens the door to safe and expressive macros for any language.

C tools, such as source browsers, bug finders, and automated refactorings, need to process two languages: C itself and the preprocessor. The latter improves expressivity through file includes, macros, and static conditionals. But it operates only on tokens, making it hard to even parse both languages. This paper presents a complete, performant solution to this problem. First, a configuration-preserving preprocessor resolves includes and macros yet leaves static conditionals intact, thus preserving a program's variability. To ensure completeness, we analyze all interactions between preprocessor features and identify techniques for correctly handling them. Second, a configuration-preserving parser generates a well-formed AST with static choice nodes for conditionals. It forks new subparsers when encountering static conditionals and merges them again after the conditionals. To ensure performance, we present a simple algorithm for table-driven Fork-Merge LR parsing and four novel optimizations. We demonstrate the effectiveness of our approach on the x86 Linux kernel.

Developers increasingly use streaming languages to write their data processing applications. While a variety of streaming languages exist, each targeting a particular application domain, they are all similar in that they represent a program as a graph of streams (i.e. sequences of data items) and operators (i.e. data transformers). They are also similar in that they must process large volumes of data with high throughput. To meet this requirement, compilers of streaming languages must provide a variety of streaming-specific optimizations, including automatic parallelization. Traditionally, when many languages share a set of optimizations, language implementors translate the source languages into a common representation called an intermediate language (IL). Because optimizations can modify the IL directly, they can be re-used by all of the source languages, reducing the overall engineering effort. However, traditional ILs and their associated optimizations target single-machine, single-process programs. In contrast, the kinds of optimizations that compilers must perform in the streaming domain are quite different, and often involve reasoning across multiple machines. Consequently, existing ILs are not suited to streaming languages. This thesis addresses the problem of how to provide a reusable infrastructure for stream processing languages. Central to the approach is the design of an intermediate language specifically for streaming languages and optimizations. The hypothesis is that an intermediate language designed to meet the requirements of stream processing can assure implementation correctness; reduce overall implementation effort; and serve as a common substrate for critical optimizations. In evidence, this thesis provides the following contributions: (1) a catalog of common streaming optimizations that helps define the requirements of a streaming IL; (2) a calculus that enables reasoning about the correctness of source language translation and streaming optimizations; and (3) an intermediate language that preserves the semantics of the calculus, while addressing the implementation issues omitted from the calculus. This work significantly reduces the effort it takes to develop stream processing languages by making optimizations reusable across languages, and jump-starts innovation in language and optimization design.

This talk is about making it easy to implement a distributed CQL. CQL is a continuous query language that extends SQL with a notion of windows over infinite streams of data. Programmers like using CQL, because its syntax is already familiar to them from their experience with other database-backed applications. CQL was originally designed to target a single node implementation of the Stanford Stream Data Management System. However, as streaming applications are expected to process larger and larger amounts of data at faster and faster rates, CQL will need to scale to run on clusters of machines.

Given the continuing popularity of C for building large-scale programs, such as Linux, Apache, and Bind, it is critical to provide effective tool support, including, for example, code browsing, bug finding, and automated refactoring. Common to all such tools is a need to parse C. But C programs contain not only the C language proper but also preprocessor invocations for file inclusion (#include), conditional compilation (#if, #ifdef, and so on), and macro definition/expansion (#define). Worse, the preprocessor is a textual substitution system, which is oblivious to C constructs and operates on individual tokens. At the same time, the preprocessor is indispensable for improving C’s expressivity, abstracting over software/hardware dependencies, and deriving variations from the same code base. The x86 version of the Linux kernel, for example, depends on about 7,600 header files for file inclusion, 7,000 configuration variables for conditional compilation, and 520,000 macros for code expansion. In this paper, we present a new tool for parsing all of C, including arbitrary preprocessor use. Our tool, which is called SuperC, is based on a systematic analysis of all interactions between lexing, preprocessing, and parsing to ensure completeness. It first lexes and preprocesses source code while preserving conditionals. It then parses the result using a novel variant of LR parsing, which automatically forks parsers when encountering a conditional and merges them again when reaching the same input in the same state. The result is a well-formed AST, containing static choice nodes for conditionals. While the parsing algorithm and engine are new, neither grammar nor LR parser table generator need to change. We discuss the results of our problem analysis, the parsing algorithm itself, the pragmatics of building a real-world tool, and a demonstration on the x86 version of the Linux kernel.

Programming language specifications mandate static and dynamic analyses to preclude syntactic and semantic errors. Although individual languages are usually well-specified, composing languages is not, and this poor specification is a source of many errors in multilingual programs. For example, virtually all Java programs compose Java and C using the Java Native Interface (JNI). Since JNI is informally specified, developers have difficulty using it correctly, and current Java compilers and virtual machines (VMs) inconsistently check only a subset of JNI constraints. This paper's most significant contribution is to show how to synthesize dynamic analyses from state machines to detect foreign function interface (FFI) violations. We identify three classes of FFI constraints encoded by eleven state machines that capture thousands of JNI and Python/C FFI rules. We use a mapping function to specify which state machines, transitions, and program entities (threads, objects, references) to check at each FFI call and return. From this function, we synthesize a context-specific dynamic analysis to find FFI bugs. We build bug detection tools for JNI and Python/C using this approach. For JNI, we dynamically and transparently interpose the analysis on Java and C language transitions through the JVM tools interface. The resulting tool, called Jinn, is compiler and virtual machine independent. It detects and diagnoses a wide variety of FFI bugs that other tools miss. This approach greatly reduces the annotation burden by exploiting common FFI constraints: whereas the generated Jinn code is 22,000+ lines, we wrote only 1,400 lines of state machine and mapping code. Overall, this paper lays the foundation for a more principled approach to developing correct multilingual software and a more concise and automated approach to FFI specification.