Determinism and looping in combinatory PDL

AbstractIn this paper some propositional modal logics of programs are considered, based on the system CPDL (Combinatory PDL)—an extension of PDL with proper names for states. These proper names are atomic formulae which are satisfied at exactly one state, in each model. Among other things (e.g., decidability and finite-model property results) a version of Streett's conjecture that his axioms do axiomatize the infinite repeating construct repeat is established with respect to CPDL

An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine

In-flight thrust determination on a real-time basis

A real time computer program was implemented on a F-15 jet fighter to monitor in-flight engine performance of a Digital Electronic Engine Controlled (DEES) F-100 engine. The application of two gas generator methods to calculate in-flight thrust real time is described. A comparison was made between the actual results and those predicted by an engine model simulation. The percent difference between the two methods was compared to the predicted uncertainty based on instrumentation and model uncertainty and agreed closely with the results found during altitude facility testing. Data was obtained from acceleration runs of various altitudes at maximum power settings with and without afterburner. Real time in-flight thrust measurement was a major advancement to flight test productivity and was accomplished with no loss in accuracy over previous post flight methods

Maximizing CNN Accelerator Efficiency Through Resource Partitioning

Convolutional neural networks (CNNs) are revolutionizing machine learning, but they present significant computational challenges. Recently, many FPGA-based accelerators have been proposed to improve the performance and efficiency of CNNs. Current approaches construct a single processor that computes the CNN layers one at a time; the processor is optimized to maximize the throughput at which the collection of layers is computed. However, this approach leads to inefficient designs because the same processor structure is used to compute CNN layers of radically varying dimensions. We present a new CNN accelerator paradigm and an accompanying automated design methodology that partitions the available FPGA resources into multiple processors, each of which is tailored for a different subset of the CNN convolutional layers. Using the same FPGA resources as a single large processor, multiple smaller specialized processors increase computational efficiency and lead to a higher overall throughput. Our design methodology achieves 3.8x higher throughput than the state-of-the-art approach on evaluating the popular AlexNet CNN on a Xilinx Virtex-7 FPGA. For the more recent SqueezeNet and GoogLeNet, the speedups are 2.2x and 2.0x

12th International Workshop on Termination (WST 2012) : WST 2012, February 19–23, 2012, Obergurgl, Austria / ed. by Georg Moser

This volume contains the proceedings of the 12th International Workshop on Termination (WST 2012), to be held February 19–23, 2012 in Obergurgl, Austria. The goal of the Workshop on Termination is to be a venue for presentation and discussion of all topics in and around termination. In this way, the workshop tries to bridge the gaps between different communities interested and active in research in and around termination. The 12th International Workshop on Termination in Obergurgl continues the successful workshops held in St. Andrews (1993), La Bresse (1995), Ede (1997), Dagstuhl (1999), Utrecht (2001), Valencia (2003), Aachen (2004), Seattle (2006), Paris (2007), Leipzig (2009), and Edinburgh (2010). The 12th International Workshop on Termination did welcome contributions on all aspects of termination and complexity analysis. Contributions from the imperative, constraint, functional, and logic programming communities, and papers investigating applications of complexity or termination (for example in program transformation or theorem proving) were particularly welcome. We did receive 18 submissions which all were accepted. Each paper was assigned two reviewers. In addition to these 18 contributed talks, WST 2012, hosts three invited talks by Alexander Krauss, Martin Hofmann, and Fausto Spoto

Rhythmically modulating neural entrainment during exposure to regularities influences statistical learning

The ability to discover regularities in the environment, such as syllable patterns in speech, is known as statistical learning. Previous studies have shown that statistical learning is accompanied by neural entrainment, in which neural activity temporally aligns with repeating patterns over time. However, it is unclear whether these rhythmic neural dynamics play a functional role in statistical learning, or whether they largely reflect the downstream consequences of learning, such as the enhanced perception of learned words in speech. To better understand this issue, we manipulated participants’ neural entrainment during statistical learning using continuous rhythmic visual stimulation. Participants were exposed to a speech stream of repeating nonsense words while viewing either (1) a visual stimulus with a “congruent” rhythm that aligned with the word structure, (2) a visual stimulus with an incongruent rhythm, or (3) a static visual stimulus. Statistical learning was subsequently measured using both an explicit and implicit test. Participants in the congruent condition showed a significant increase in neural entrainment over auditory regions at the relevant word frequency, over and above effects of passive volume conduction, indicating that visual stimulation successfully altered neural entrainment within relevant neural substrates. Critically, during the subsequent implicit test, participants in the congruent condition showed an enhanced ability to predict upcoming syllables and stronger neural phase synchronization to component words, suggesting that they had gained greater sensitivity to the statistical structure of the speech stream relative to the incongruent and static groups. This learning benefit could not be attributed to strategic processes, as participants were largely unaware of the contingencies between the visual stimulation and embedded words. These results indicate that manipulating neural entrainment during exposure to regularities influences statistical learning outcomes, suggesting that neural entrainment may functionally contribute to statistical learning. Our findings encourage future studies using non-invasive brain stimulation methods to further understand the role of entrainment in statistical learning

Textural and Timbral Ambiguities:Creating and Composing with Sound Groups in a Portfolio of Compositions

This practice-based research in music composition consists of a portfolio of original compositions and written commentary on the submitted works: two pieces for acoustic instruments and pre-recorded audio, two pieces for string duo, two pieces for sextet, a studio piece for toy accordion and a score for a contemporary dance performance.Through the practice of original composition, the research examines the creation of ambiguous textures using timbral ambiguity. Timbral ambiguity is achieved in the research through the integration of timbres. For example: the combining of solo clarinet and vibraphone sounds into an integrated sound. In the resulting integrated timbre, it is difficult to distinguish the clarinet and vibraphone. In ambiguous textures in the research, timbres of textural layers as foreground and background are difficult to distinguish from one another. These ambiguities emphasise sound groups over individual sounds.The research investigates the roles of timbral contrast, an antagonist of timbral ambiguity, in timbral and textural ambiguities. It suggests a timbral contrast spectrum, which can be consulted when composing with timbral and textural ambiguities. Further, it finds added value in these ambiguities, as increased interest and the enhancement of musical parameters as melody, harmony and rhythm. The music in the portfolio is influenced by minimalism in aesthetics and style and so, it is examined in this context.The research examines the mimicking of a music sequencing electronic/digital tool, the arpeggiator, in acoustic composition. It demonstrates the practice’s contributions to consistency, efficiency, novelty, and timbral and textural ambiguities.Context for the research stems from academic thought on ambiguity in language, literature, music and from musical works featuring timbral and textural ambiguities. Works by György Ligeti, Kaija Saariaho, Thomas Adès, John Adams and Michael Gordon are among those which informed the research, as did musicological work by composer Jonathan Harvey, Leonard Meyer and others.The subject of ambiguity has been widely discussed in academic literature, especially in the fields of language and the arts. Musical timbre has also received considerable attention as has texture, in practice and in academia. Timbral ambiguity as a term, tool and concept, has, however, been the subject of little focused academic research. Textural ambiguity has rarely been directly explored in academia. This thesis and accompanying portfolio aim to add to knowledge in these seldom discussed subjects.<br/