42,594 research outputs found
An Automated Design-flow for FPGA-based Sequential Simulation
In this paper we describe the automated design flow that will transform and map a given homogeneous or heterogeneous hardware design into an FPGA that performs a cycle accurate simulation. The flow replaces the required manually performed transformation and can be embedded in existing standard synthesis flows. Compared to the earlier manually translated designs, this automated flow resulted in a reduced number of FPGA hardware resources and higher simulation frequencies. The implementation of the complete design flow is work in progress.\u
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has
been widely used for synthesis of multi-domain medical images. The
domain-specific nonlinear deformations captured by CycleGAN make the
synthesized images difficult to be used for some applications, for example,
generating pseudo-CT for PET-MR attenuation correction. This paper presents a
deformation-invariant CycleGAN (DicycleGAN) method using deformable
convolutional layers and new cycle-consistency losses. Its robustness dealing
with data that suffer from domain-specific nonlinear deformations has been
evaluated through comparison experiments performed on a multi-sequence brain MR
dataset and a multi-modality abdominal dataset. Our method has displayed its
ability to generate synthesized data that is aligned with the source while
maintaining a proper quality of signal compared to CycleGAN-generated data. The
proposed model also obtained comparable performance with CycleGAN when data
from the source and target domains are alignable through simple affine
transformations
Three-dimensional surface codes: Transversal gates and fault-tolerant architectures
One of the leading quantum computing architectures is based on the
two-dimensional (2D) surface code. This code has many advantageous properties
such as a high error threshold and a planar layout of physical qubits where
each physical qubit need only interact with its nearest neighbours. However,
the transversal logical gates available in 2D surface codes are limited. This
means that an additional (resource intensive) procedure known as magic state
distillation is required to do universal quantum computing with 2D surface
codes. Here, we examine three-dimensional (3D) surface codes in the context of
quantum computation. We introduce a picture for visualizing 3D surface codes
which is useful for analysing stacks of three 3D surface codes. We use this
picture to prove that the and gates are transversal in 3D surface
codes. We also generalize the techniques of 2D surface code lattice surgery to
3D surface codes. We combine these results and propose two quantum computing
architectures based on 3D surface codes. Magic state distillation is not
required in either of our architectures. Finally, we show that a stack of three
3D surface codes can be transformed into a single 3D color code (another type
of quantum error-correcting code) using code concatenation.Comment: 23 pages, 24 figures, v2: published versio
Engineering model transformations with transML
The final publication is available at Springer via http://dx.doi.org/10.1007%2Fs10270-011-0211-2Model transformation is one of the pillars of model-driven engineering (MDE). The increasing complexity of systems and modelling languages has dramatically raised the complexity and size of model transformations as well. Even though many transformation languages and tools have been proposed in the last few years, most of them are directed to the implementation phase of transformation development. In this way, even though transformations should be built using sound engineering principles—just like any other kind of software—there is currently a lack of cohesive support for the other phases of the transformation development, like requirements, analysis, design and testing. In this paper, we propose a unified family of languages to cover the life cycle of transformation development enabling the engineering of transformations. Moreover, following an MDE approach, we provide tools to partially automate the progressive refinement of models between the different phases and the generation of code for several transformation implementation languages.This work has been sponsored by the Spanish Ministry of Science and Innovation with project METEORIC (TIN2008-02081), and by the R&D program of the Community of Madrid with projects “e-Madrid" (S2009/TIC-1650). Parts of this work were done during the research stays of Esther and Juan at the University of York, with financial support from the Spanish Ministry of Science and Innovation (grant refs. JC2009-00015, PR2009-0019 and PR2008-0185)
From source to target and back: symmetric bi-directional adaptive GAN
The effectiveness of generative adversarial approaches in producing images
according to a specific style or visual domain has recently opened new
directions to solve the unsupervised domain adaptation problem. It has been
shown that source labeled images can be modified to mimic target samples making
it possible to train directly a classifier in the target domain, despite the
original lack of annotated data. Inverse mappings from the target to the source
domain have also been evaluated but only passing through adapted feature
spaces, thus without new image generation. In this paper we propose to better
exploit the potential of generative adversarial networks for adaptation by
introducing a novel symmetric mapping among domains. We jointly optimize
bi-directional image transformations combining them with target self-labeling.
Moreover we define a new class consistency loss that aligns the generators in
the two directions imposing to conserve the class identity of an image passing
through both domain mappings. A detailed qualitative and quantitative analysis
of the reconstructed images confirm the power of our approach. By integrating
the two domain specific classifiers obtained with our bi-directional network we
exceed previous state-of-the-art unsupervised adaptation results on four
different benchmark datasets
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