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Neural simulation of a system that learns representations of sensory experience
The pyriform cortex forms stable representations of smells to allow their subsequent recognition. Clustering systems are shown to perform a similar function, so they provide a guide to understanding the operation of the pyriform. A neural model of a sample of pyriform cortex was built that adheres to most known biological constraints, including learning by long-term potentiation. Results of early simulations suggest some interesting properties. The effort has implications for the knowledge representations used in artificial intelligence work
A Flexible Shallow Approach to Text Generation
In order to support the efficient development of NL generation systems, two
orthogonal methods are currently pursued with emphasis: (1) reusable, general,
and linguistically motivated surface realization components, and (2) simple,
task-oriented template-based techniques. In this paper we argue that, from an
application-oriented perspective, the benefits of both are still limited. In
order to improve this situation, we suggest and evaluate shallow generation
methods associated with increased flexibility. We advise a close connection
between domain-motivated and linguistic ontologies that supports the quick
adaptation to new tasks and domains, rather than the reuse of general
resources. Our method is especially designed for generating reports with
limited linguistic variations.Comment: LaTeX, 10 page
Gate-Level Simulation of Quantum Circuits
While thousands of experimental physicists and chemists are currently trying
to build scalable quantum computers, it appears that simulation of quantum
computation will be at least as critical as circuit simulation in classical
VLSI design. However, since the work of Richard Feynman in the early 1980s
little progress was made in practical quantum simulation. Most researchers
focused on polynomial-time simulation of restricted types of quantum circuits
that fall short of the full power of quantum computation. Simulating quantum
computing devices and useful quantum algorithms on classical hardware now
requires excessive computational resources, making many important simulation
tasks infeasible. In this work we propose a new technique for gate-level
simulation of quantum circuits which greatly reduces the difficulty and cost of
such simulations. The proposed technique is implemented in a simulation tool
called the Quantum Information Decision Diagram (QuIDD) and evaluated by
simulating Grover's quantum search algorithm. The back-end of our package,
QuIDD Pro, is based on Binary Decision Diagrams, well-known for their ability
to efficiently represent many seemingly intractable combinatorial structures.
This reliance on a well-established area of research allows us to take
advantage of existing software for BDD manipulation and achieve unparalleled
empirical results for quantum simulation
BISMO: A Scalable Bit-Serial Matrix Multiplication Overlay for Reconfigurable Computing
Matrix-matrix multiplication is a key computational kernel for numerous
applications in science and engineering, with ample parallelism and data
locality that lends itself well to high-performance implementations. Many
matrix multiplication-dependent applications can use reduced-precision integer
or fixed-point representations to increase their performance and energy
efficiency while still offering adequate quality of results. However, precision
requirements may vary between different application phases or depend on input
data, rendering constant-precision solutions ineffective. We present BISMO, a
vectorized bit-serial matrix multiplication overlay for reconfigurable
computing. BISMO utilizes the excellent binary-operation performance of FPGAs
to offer a matrix multiplication performance that scales with required
precision and parallelism. We characterize the resource usage and performance
of BISMO across a range of parameters to build a hardware cost model, and
demonstrate a peak performance of 6.5 TOPS on the Xilinx PYNQ-Z1 board.Comment: To appear at FPL'1
CompendiumLD – a tool for effective, efficient and creative learning design
Developers and teachers go through a complex decision making process when designing new learning activities – working towards an effective pedagogical mix, combining resources, tools, student and tutor support. This paper describes CompendiumLD, a prototype tool we have built to support practitioners through the process of designing learning activities. We describe how the tool fits into our vision of a dynamic, interactive set of resources and system tools to support effective, efficient and creative learning design. It describes CompendiumLD's features and explains the rationale behind their development. It shows how the tool is intended to aid designers make choices, and plan developments, facilitating creativity and efficiency in the design process. In our conclusions we consider how such a system can support the design of effective learning activities
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