167 research outputs found
Systematic Design Methods for Efficient Off-Chip DRAM Access
Typical design flows for digital hardware take, as their input, an abstract description
of computation and data transfer between logical memories. No existing commercial
high-level synthesis tool demonstrates the ability to map logical memory inferred from
a high level language to external memory resources. This thesis develops techniques for
doing this, specifically targeting off-chip dynamic memory (DRAM) devices. These are
a commodity technology in widespread use with standardised interfaces. In use, the
bandwidth of an external memory interface and the latency of memory requests asserted
on it may become the bottleneck limiting the performance of a hardware design. Careful
consideration of this is especially important when designing with DRAMs, whose latency
and bandwidth characteristics depend upon the sequence of memory requests issued by
a controller.
Throughout the work presented here, we pursue exact compile-time methods for designing
application-specific memory systems with a focus on guaranteeing predictable performance
through static analysis. This contrasts with much of the surveyed existing work,
which considers general purpose memory controllers and optimized policies which improve
performance in experiments run using simulation of suites of benchmark codes.
The work targets loop-nests within imperative source code, extracting a mathematical
representation of the loop-nest statements and their associated memory accesses, referred
to as the ‘Polytope Model’. We extend this mathematical representation to represent the
physical DRAM ‘row’ and ‘column’ structures accessed when performing memory transfers.
From this augmented representation, we can automatically derive DRAM controllers
which buffer data in on-chip memory and transfer data in an efficient order. Buffering
data and exploiting ‘reuse’ of data is shown to enable up to 50× reduction in the quantity
of data transferred to external memory. The reordering of memory transactions exploiting
knowledge of the physical layout of the DRAM device allowing to 4× improvement in
the efficiency of those data transfers
Electrically driven optical interferometry with spins in silicon carbide
Interfacing solid-state defect electron spins to other quantum systems is an
ongoing challenge. The ground-state spin's weak coupling to its environment
bestows excellent coherence properties, but also limits desired drive fields.
The excited-state orbitals of these electrons, however, can exhibit stronger
coupling to phononic and electric fields. Here, we demonstrate electrically
driven coherent quantum interference in the optical transition of single,
basally oriented divacancies in commercially available 4H silicon carbide. By
applying microwave frequency electric fields, we coherently drive the
divacancy's excited-state orbitals and induce Landau-Zener-Stuckelberg
interference fringes in the resonant optical absorption spectrum. Additionally,
we find remarkably coherent optical and spin subsystems enabled by the basal
divacancy's symmetry. These properties establish divacancies as strong
candidates for quantum communication and hybrid system applications, where
simultaneous control over optical and spin degrees of freedom is paramount.Comment: 17 pages, 4 figure
The promise of whole genome pathogen sequencing for the molecular epidemiology of emerging aquaculture pathogens
Aquaculture is the fastest growing food-producing sector, and the sustainability of this industry is critical both for global food security and economic welfare. The management of infectious disease represents a key challenge. Here, we discuss the opportunities afforded by whole genome sequencing of bacterial and viral pathogens of aquaculture to mitigate disease emergence and spread. We outline, by way of comparison, how sequencing technology is transforming the molecular epidemiology of pathogens of public health importance, emphasizing the importance of community-oriented databases and analysis tools
Strong Lens Models for 37 Clusters of Galaxies from the SDSS Giant Arcs Survey
We present strong gravitational lensing models for 37 galaxy clusters from
the SDSS Giant Arcs Survey. We combine data from multi-band Hubble Space
Telescope WFC3imaging, with ground-based imaging and spectroscopy from
Magellan, Gemini, APO, and MMT, in order to detect and spectroscopically
confirm new multiply-lensed background sources behind the clusters. We report
spectroscopic or photometric redshifts of sources in these fields, including
cluster galaxies and background sources. Based on all available lensing
evidence, we construct and present strong lensing mass models for these galaxy
clusters.Comment: 53 pages; submitted to ApJ
To have and to hold: embodied ownership is established in early childhood.
We investigated whether embodied ownership is evident in early childhood. To do so, we gifted a drinking bottle to children (aged 24–48 months) to use for 2 weeks. They returned to perform reach–grasp–lift–replace actions with their own or the experimenter’s bottle while we recorded their movements using motion capture. There were differences in motor interactions with self- vs experimenter-owned bottles, such that children positioned self-owned bottles significantly closer to themselves compared with the experimenter’s bottle. Age did not modulate the positioning of the self-owned bottle relative to the experimenter-owned bottle. In contrast, the pattern was not evident in children who selected one of the two bottles to keep only after the task was completed, and thus did not ‘own’ it during the task (Experiment 2). These results extend similar findings in adults, confirming the importance of ownership in determining self–other differences and provide novel evidence that object ownership influences sensorimotor processes from as early as 2 years of age
Bayesian identification of bacterial strains from sequencing data
Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an environmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate relative abundances of the target organisms from short sequence reads obtained from a sample. This task is particularly challenging when the set of interest includes very closely related organisms, such as different strains of pathogenic bacteria, which can vary considerably in terms of virulence, resistance and spread. Using advanced Bayesian statistical modelling and computation techniques we introduce a novel pipeline for bacterial identification that is shown to outperform the currently leading pipeline for this purpose. Our approach enables fast and accurate sequence-based identification of bacterial strains while using only modest computational resources. Hence it provides a useful tool for a wide spectrum of applications, including rapid clinical diagnostics to distinguish among closely related strains causing nosocomial infections. The software implementation is available at https://github.com/PROBIC/BIB.</p
Bayesian identification of bacterial strains from sequencing data
Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an environmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate relative abundances of the target organisms from short sequence reads obtained from a sample. This task is particularly challenging when the set of interest includes very closely related organisms, such as different strains of pathogenic bacteria, which can vary considerably in terms of virulence, resistance and spread. Using advanced Bayesian statistical modelling and computation techniques we introduce a novel pipeline for bacterial identification that is shown to outperform the currently leading pipeline for this purpose. Our approach enables fast and accurate sequence-based identification of bacterial strains while using only modest computational resources. Hence it provides a useful tool for a wide spectrum of applications, including rapid clinical diagnostics to distinguish among closely related strains causing nosocomial infections. The software implementation is available at https://github.com/PROBIC/BIB.Peer reviewe
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