Providing Knowledge at the Click of a Mouse: Forestry and Natural Resources Desktop Reference Library

North Carolina State University Forestry Extension developed The Forestry and Natural Resources Desktop Reference Library (DRL) to help Extension county agents overcome a lack of training in forestry and natural resources. DRL is a Web-based, distance learning and information resource project that incorporates synchronized multimedia and the World Wide Web to provide up-to-date research-based information on forestry and natural resources. DRL is the result of collaboration between researchers and Extension faculty to provide a time- and space-independent model for information exchange between the university and Extension agents

Exploring resource/performance trade-offs for streaming applications on embedded multiprocessors

Embedded system design is challenged by the gap between the ever-increasing customer demands and the limited resource budgets. The tough competition demands ever-shortening time-to-market and product lifecycles. To solve or, at least to alleviate, the aforementioned issues, designers and manufacturers need model-based quantitative analysis techniques for early design-space exploration to study trade-offs of different implementation candidates. Moreover, modern embedded applications, especially the streaming applications addressed in this thesis, face more and more dynamic input contents, and the platforms that they are running on are more flexible and allow runtime configuration. Quantitative analysis techniques for embedded system design have to be able to handle such dynamic adaptable systems. This thesis has the following contributions: - A resource-aware extension to the Synchronous Dataflow (SDF) model of computation. - Trade-off analysis techniques, both in the time-domain and in the iterationdomain (i.e., on an SDF iteration basis), with support for resource sharing. - Bottleneck-driven design-space exploration techniques for resource-aware SDF. - A game-theoretic approach to controller synthesis, guaranteeing performance under dynamic input. As a first contribution, we propose a new model, as an extension of static synchronous dataflow graphs (SDF) that allows the explicit modeling of resources with consistency checking. The model is called resource-aware SDF (RASDF). The extension enables us to investigate resource sharing and to explore different scheduling options (ways to allocate the resources to the different tasks) using state-space exploration techniques. Consistent SDF and RASDF graphs have the property that an execution occurs in so-called iterations. An iteration typically corresponds to the processing of a meaningful piece of data, and it returns the graph to its initial state. On multiprocessor platforms, iterations may be executed in a pipelined fashion, which makes performance analysis challenging. As the second contribution, this thesis develops trade-off analysis techniques for RASDF, both in the time-domain and in the iteration-domain (i.e., on an SDF iteration basis), to dimension resources on platforms. The time-domain analysis allows interleaving of different iterations, but the size of the explored state space grows quickly. The iteration-based technique trades the potential of interleaving of iterations for a compact size of the iteration state space. An efficient bottleneck-driven designspace exploration technique for streaming applications, the third main contribution in this thesis, is derived from analysis of the critical cycle of the state space, to reveal bottleneck resources that are limiting the throughput. All techniques are based on state-based exploration. They enable system designers to tailor their platform to the required applications, based on their own specific performance requirements. Pruning techniques for efficient exploration of the state space have been developed. Pareto dominance in terms of performance and resource usage is used for exact pruning, and approximation techniques are used for heuristic pruning. Finally, the thesis investigates dynamic scheduling techniques to respond to dynamic changes in input streams. The fourth contribution in this thesis is a game-theoretic approach to tackle controller synthesis to select the appropriate schedules in response to dynamic inputs from the environment. The approach transforms the explored iteration state space of a scenario- and resource-aware SDF (SARA SDF) graph to a bipartite game graph, and maps the controller synthesis problem to the problem of finding a winning positional strategy in a classical mean payoff game. A winning strategy of the game can be used to synthesize the controller of schedules for the system that is guaranteed to satisfy the throughput requirement given by the designer

Symbolic model checking for one-resource RB±ATL

RB±ATL is an extension of ATL where it is possible to model consumption and production of several resources by a set of agents. The model-checking problem for RB±ATL is known to be decidable. However the only available model-checking algorithm for RB±ATL uses a forward search of the state space, and hence does not have an efficient symbolic implementation. In this paper, we consider a fragment of RB±ATL, 1RB±ATL, that allows only one resource type. We give a symbolic model-checking algorithm for this fragment of RB±ATL, and evaluate the performance of an MCMAS-based implementation of the algorithm on an example problem that can be scaled to large state spaces

Symbolic model checking for one-resource RB+-ATL

RB+-ATL is an extension of ATL where it is possible to model consumption and production of several resources by a set of agents. The modelchecking problem for RB+-ATL is known to be decidable. However the only available model checking algorithm for RB+-ATL uses a forward search of the state space, and hence does not have an efficient symbolic implementation. In this paper, we consider a fragment of RB+-ATL, 1RB+-ATL, that allows only one resource type. We give a symbolic model-checking algorithm for this fragment of RB+-ATL, and evaluate the performance of an MCMAS-based implementation of the algorithm on an example problem that can be scaled to large state spaces

Symbolic model checking for one-resource RB±ATL

RB±ATL is an extension of ATL where it is possible to model consumption and production of several resources by a set of agents. The model-checking problem for RB±ATL is known to be decidable. However the only available model-checking algorithm for RB±ATL uses a forward search of the state space, and hence does not have an efficient symbolic implementation. In this paper, we consider a fragment of RB±ATL, 1RB±ATL, that allows only one resource type. We give a symbolic model-checking algorithm for this fragment of RB±ATL, and evaluate the performance of an MCMAS-based implementation of the algorithm on an example problem that can be scaled to large state spaces

Elimination of spatial connectives in static spatial logics

AbstractThe recent interest for specification on resources yields so-called spatial logics, that is specification languages offering new forms of reasoning: the local reasoning through the separation of the resource space into two disjoint subspaces, and the contextual reasoning through hypothetical extension of the resource space.We consider two resource models and their related logics:•The static ambient model, proposed as an abstraction of semistructured data (Proc. ESOP’01, Lecture Notes in Computer Science, vol. 2028, Springer, Berlin, 2001, pp. 1–22 (invited paper)) with the static ambient logic (SAL) that was proposed as a request language, both obtained by restricting the mobile ambient calculus (Proc. FOSSACS’98, Lecture Notes in Computer Science, vol. 1378, Springer, Berlin, 1998, pp. 140–155) and logic (Proc. POPL’00, ACM Press, New York, 2000, pp. 365–377) to their purely static aspects.•The memory model and the assertion language of separation logic, both defined in Reynolds (Proc. LICS’02, 2002) for the purpose of the axiomatic semantic of imperative programs manipulating pointers.We raise the questions of the expressiveness and the minimality of these logics. Our main contribution is a minimalisation technique we may apply for these two logics. We moreover show some restrictions of this technique for the extension SAL∀ with universal quantification, and we establish the minimality of the adjunct-free fragment (SALint)

New Explanations to Law of Resource Utilization

Abstract. The serious problems of resources and environment arising in development of human society have important ties with the scientific understanding of mankind toward the law of resource utilization. The author has demonstrated, based on the division of human resource utilization in depth and breadth spaces, the material form and nature of the depth and breadth space resources; and put forward the time model of the depth and breadth space resource utilization--a time demarcation and analysis of human resource utilization, with which we can see in the other sphere the various states of the influences of resource utilization by mankind to the environment. The continuous extension and utilization of depth space resource at one point is the core content of new explanation to resource utilization in phenomenon of "work". The time model of the breadth space takes the carbon cycling as the example and divides the resource material cycling into three stages. The analysis by the author offers us an audio-visual and comparatively new "cross-section" in the research of depth and breadth space resource utilization, also a new and systematic explanation to the law of resource utilization

Speech Driven Expressive Animations

Current state of the art lip sync facial animation systems use vision ­based performance capture methods which are highly resource consuming. These techniques lack scalability and post hoc customizability whilst simpler and more automated alternatives often lack expressiveness. We propose an extension for a deep learning based speech driven lip sync facial synthesis system that allows for expressiveness and manual tweaking in the emotion space. Our model generates expressive animations by mapping recorded speech features into facial rig parameters. Our architecture consists of a conditional Variational Autoencoder conditioned on speech, whose latent space controls the facial expression during inference and is driven by predictions from a Speech Emotion Recognition module. This approach, to the extent of our knowledge, has not been tried before in the literature. The results show that our Speech Emotion Recognition (SER) model is able to make meaningful predictions and generalize to unseen game speech utterances. Our user study shows that participants significantly prefer our model animations when compared to animations generated from random emotions and a baseline neutral emotion model