13,040 research outputs found
Donor pretreatment with FLT-3 ligand augments anti-donor CTL, NK and LAK cell activities within liver allografts and alters the pattern of intragraft apoptotic activity
On the Performance Prediction of BLAS-based Tensor Contractions
Tensor operations are surging as the computational building blocks for a
variety of scientific simulations and the development of high-performance
kernels for such operations is known to be a challenging task. While for
operations on one- and two-dimensional tensors there exist standardized
interfaces and highly-optimized libraries (BLAS), for higher dimensional
tensors neither standards nor highly-tuned implementations exist yet. In this
paper, we consider contractions between two tensors of arbitrary dimensionality
and take on the challenge of generating high-performance implementations by
resorting to sequences of BLAS kernels. The approach consists in breaking the
contraction down into operations that only involve matrices or vectors. Since
in general there are many alternative ways of decomposing a contraction, we are
able to methodically derive a large family of algorithms. The main contribution
of this paper is a systematic methodology to accurately identify the fastest
algorithms in the bunch, without executing them. The goal is instead
accomplished with the help of a set of cache-aware micro-benchmarks for the
underlying BLAS kernels. The predictions we construct from such benchmarks
allow us to reliably single out the best-performing algorithms in a tiny
fraction of the time taken by the direct execution of the algorithms.Comment: Submitted to PMBS1
PS-FCN: A Flexible Learning Framework for Photometric Stereo
This paper addresses the problem of photometric stereo for non-Lambertian
surfaces. Existing approaches often adopt simplified reflectance models to make
the problem more tractable, but this greatly hinders their applications on
real-world objects. In this paper, we propose a deep fully convolutional
network, called PS-FCN, that takes an arbitrary number of images of a static
object captured under different light directions with a fixed camera as input,
and predicts a normal map of the object in a fast feed-forward pass. Unlike the
recently proposed learning based method, PS-FCN does not require a pre-defined
set of light directions during training and testing, and can handle multiple
images and light directions in an order-agnostic manner. Although we train
PS-FCN on synthetic data, it can generalize well on real datasets. We further
show that PS-FCN can be easily extended to handle the problem of uncalibrated
photometric stereo.Extensive experiments on public real datasets show that
PS-FCN outperforms existing approaches in calibrated photometric stereo, and
promising results are achieved in uncalibrated scenario, clearly demonstrating
its effectiveness.Comment: ECCV 2018: https://guanyingc.github.io/PS-FC
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An Overview of the Use of Neural Networks for Data Mining Tasks
In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks
The Dependency of Nematic and Twist-bend Mesophase Formation on Bend Angle
We have prepared and studied a family of cyanobiphenyl dimers with varying linking groups with a view to exploring how molecular structure dictates the stability of the nematic and twist-bend nematic mesophases. Using molecular modelling and 1D (1)H NOESY NMR spectroscopy, we determine the angle between the two aromatic core units for each dimer and find a strong dependency of the stability of both the nematic and twist-bend mesophases upon this angle, thereby satisfying earlier theoretical models
Improving ChIP-seq peak-calling for functional co-regulator binding by integrating multiple sources of biological information
Background: Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study genome-wide binding sites of transcription factors. There is an increasing interest in understanding the mechanism of action of co-regulator proteins, which do not bind DNA directly, but exert their effects by binding to transcription factors such as the estrogen receptor (ER). However, due to the nature of detecting indirect protein-DNA interaction, ChIP-seq signals from co-regulators can be relatively weak and thus biologically meaningful interactions remain difficult to identify
Single Step Solution Processed GaAs Thin Films from GaMe 3 and BuAsH 2 under Ambient Pressure
This article reports on the possibility of low-cost GaAs formed under ambient pressure via a single step solution processed route from only readily available precursors, tBuAsH2 and GaMe3. The thin films of GaAs on glass substrates were found to have good crystallinity with crystallites as large as 150 nm and low contamination with experimental results matching well with theoretical density of states calculations. These results open up a route to efficient and cost-effective scale up of GaAs thin films with high material properties for widespread industrial use. Confirmation of film quality was determined using XRD, Raman, EDX mapping, SEM, HRTEM, XPS, and SIMS
Behavioural stress responses predict environmental perception in European sea bass (Dicentrarchus labrax)
Individual variation in the response to environmental challenges depends partly on innate reaction norms, partly on experience-based cognitive/emotional evaluations that individuals make of the situation. The goal of this study was to investigate whether pre-existing differences in behaviour predict the outcome of such assessment of environmental cues, using a conditioned place preference/avoidance (CPP/CPA) paradigm. A comparative vertebrate model (European sea bass, Dicentrarchus labrax) was used, and ninety juvenile individuals were initially screened for behavioural reactivity using a net restraining test. Thereafter each individual was tested in a choice tank using net chasing as aversive stimulus or exposure to familiar conspecifics as appetitive stimulus in the preferred or non preferred side respectively (called hereafter stimulation side). Locomotor behaviour (i.e. time spent, distance travelled and swimming speed in each tank side) of each individual was recorded and analysed with video software. The results showed that fish which were previously exposed to appetitive stimulus increased significantly the time spent on the stimulation side, while aversive stimulus led to a strong decrease in time spent on the stimulation side. Moreover, this study showed clearly that proactive fish were characterised by a stronger preference for the social stimulus and when placed in a putative aversive environment showed a lower physiological stress responses than reactive fish. In conclusion, this study showed for the first time in sea bass, that the CPP/CPA paradigm can be used to assess the valence (positive vs. negative) that fish attribute to different stimuli and that individual behavioural traits is predictive of how stimuli are perceived and thus of the magnitude of preference or avoidance behaviour.European Commission [265957]; Portuguese Fundacao para a Ciencia e Tecnologia (FCT) [FRH/BPD/72952/2010]; FCT [SFRH/BD/80029/2011
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