541 research outputs found
An efficient algorithm for minimizing time granularity periodical representations
This paper addresses the technical problem of efficiently reducing the periodic representation of a time granularity to its minimal form. The minimization algorithm presented in the paper has an immediate practical application: it allows users to intuitively define granularities (and more generally, recurring events) with algebraic expressions that are then internally translated to mathematical characterizations in terms of minimal periodic sets. Minimality plays a crucial role, since the value of the recurring period has been shown to dominate the complexity when processing periodic sets.
Supporting Temporal Reasoning by Mapping Calendar Expressions to Minimal Periodic Sets
In the recent years several research efforts have focused on the concept of
time granularity and its applications. A first stream of research investigated
the mathematical models behind the notion of granularity and the algorithms to
manage temporal data based on those models. A second stream of research
investigated symbolic formalisms providing a set of algebraic operators to
define granularities in a compact and compositional way. However, only very
limited manipulation algorithms have been proposed to operate directly on the
algebraic representation making it unsuitable to use the symbolic formalisms in
applications that need manipulation of granularities.
This paper aims at filling the gap between the results from these two streams
of research, by providing an efficient conversion from the algebraic
representation to the equivalent low-level representation based on the
mathematical models. In addition, the conversion returns a minimal
representation in terms of period length. Our results have a major practical
impact: users can more easily define arbitrary granularities in terms of
algebraic operators, and then access granularity reasoning and other services
operating efficiently on the equivalent, minimal low-level representation. As
an example, we illustrate the application to temporal constraint reasoning with
multiple granularities.
From a technical point of view, we propose an hybrid algorithm that
interleaves the conversion of calendar subexpressions into periodical sets with
the minimization of the period length. The algorithm returns set-based
granularity representations having minimal period length, which is the most
relevant parameter for the performance of the considered reasoning services.
Extensive experimental work supports the techniques used in the algorithm, and
shows the efficiency and effectiveness of the algorithm
Cost-Based Optimization of Integration Flows
Integration flows are increasingly used to specify and execute data-intensive integration tasks between heterogeneous systems and applications. There are many different application areas such as real-time ETL and data synchronization between operational systems. For the reasons of an increasing amount of data, highly distributed IT infrastructures, and high requirements for data consistency and up-to-dateness of query results, many instances of integration flows are executed over time. Due to this high load and blocking synchronous source systems, the performance of the central integration platform is crucial for an IT infrastructure. To tackle these high performance requirements, we introduce the concept of cost-based optimization of imperative integration flows that relies on incremental statistics maintenance and inter-instance plan re-optimization. As a foundation, we introduce the concept of periodical re-optimization including novel cost-based optimization techniques that are tailor-made for integration flows. Furthermore, we refine the periodical re-optimization to on-demand re-optimization in order to overcome the problems of many unnecessary re-optimization steps and adaptation delays, where we miss optimization opportunities. This approach ensures low optimization overhead and fast workload adaptation
Programming agent-based demographic models with cross-state and message-exchange dependencies: A study with speculative PDES and automatic load-sharing
Agent-based modeling and simulation is a versatile and promising methodology to capture complex interactions among entities and their surrounding environment. A great advantage is its ability to model phenomena at a macro scale by exploiting simpler descriptions at a micro level. It has been proven effective in many fields, and it is rapidly becoming a de-facto standard in the study of population dynamics. In this article we study programmability and performance aspects of the last-generation ROOT-Sim speculative PDES environment for multi/many-core shared-memory architectures. ROOT-Sim transparently offers a programming model where interactions can be based on both explicit message passing and in-place state accesses. We introduce programming guidelines for systematic exploitation of these facilities in agent-based simulations, and we study the effects on performance of an innovative load-sharing policy targeting these types of dependencies. An experimental assessment with synthetic and real-world applications is provided, to assess the validity of our proposal
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
Spiking neural networks (SNNs) are emerging as an energy-efficient
alternative to traditional artificial neural networks (ANNs) due to their
unique spike-based event-driven nature. Coding is crucial in SNNs as it
converts external input stimuli into spatio-temporal feature sequences.
However, most existing deep SNNs rely on direct coding that generates powerless
spike representation and lacks the temporal dynamics inherent in human vision.
Hence, we introduce Gated Attention Coding (GAC), a plug-and-play module that
leverages the multi-dimensional gated attention unit to efficiently encode
inputs into powerful representations before feeding them into the SNN
architecture. GAC functions as a preprocessing layer that does not disrupt the
spike-driven nature of the SNN, making it amenable to efficient neuromorphic
hardware implementation with minimal modifications. Through an observer model
theoretical analysis, we demonstrate GAC's attention mechanism improves
temporal dynamics and coding efficiency. Experiments on CIFAR10/100 and
ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with
remarkable efficiency. Notably, we improve top-1 accuracy by 3.10\% on CIFAR100
with only 6-time steps and 1.07\% on ImageNet while reducing energy usage to
66.9\% of the previous works. To our best knowledge, it is the first time to
explore the attention-based dynamic coding scheme in deep SNNs, with
exceptional effectiveness and efficiency on large-scale datasets.Comment: 12 pages, 7 figure
Sorting Omega Networks Simulated with P Systems: Optimal Data Layouts
The paper introduces some sorting networks and their simulation with P
systems, in which each processor/membrane can hold more than one piece of data, and
perform operations on them internally. Several data layouts are discussed in this context,
and an optimal one is proposed, together with its implementation as a P system with
dynamic communication graphs
Multi-flow Optimization via Horizontal Message Queue Partitioning
Integration flows are increasingly used to specify and execute data-intensive integration tasks between heterogeneous systems and applications. There are many different application areas such as near real-time ETL and data synchronization between operational systems. For the reasons of an increasing amount of data, highly distributed IT infrastructures, as well as high requirements for up-to-dateness of analytical query results and data consistency, many instances of integration flows are executed over time. Due to this high load, the performance of the central integration platform is crucial for an IT infrastructure. With the aim of throughput maximization, we propose the concept of multi-flow optimization (MFO). In this approach, messages are collected during a waiting time and executed in batches to optimize sequences of plan instances of a single integration flow. We introduce a horizontal (value-based) partitioning approach for message batch creation and show how to compute the optimal waiting time. This approach significantly reduces the total execution time of a message sequence and hence, it maximizes the throughput, while accepting moderate latency time
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