123,020 research outputs found
Ultrafast processing of pixel detector data with machine learning frameworks
Modern photon science performed at high repetition rate free-electron laser
(FEL) facilities and beyond relies on 2D pixel detectors operating at
increasing frequencies (towards 100 kHz at LCLS-II) and producing rapidly
increasing amounts of data (towards TB/s). This data must be rapidly stored for
offline analysis and summarized in real time. While at LCLS all raw data has
been stored, at LCLS-II this would lead to a prohibitive cost; instead,
enabling real time processing of pixel detector raw data allows reducing the
size and cost of online processing, offline processing and storage by orders of
magnitude while preserving full photon information, by taking advantage of the
compressibility of sparse data typical for LCLS-II applications. We
investigated if recent developments in machine learning are useful in data
processing for high speed pixel detectors and found that typical deep learning
models and autoencoder architectures failed to yield useful noise reduction
while preserving full photon information, presumably because of the very
different statistics and feature sets between computer vision and radiation
imaging. However, we redesigned in Tensorflow mathematically equivalent
versions of the state-of-the-art, "classical" algorithms used at LCLS. The
novel Tensorflow models resulted in elegant, compact and hardware agnostic
code, gaining 1 to 2 orders of magnitude faster processing on an inexpensive
consumer GPU, reducing by 3 orders of magnitude the projected cost of online
analysis at LCLS-II. Computer vision a decade ago was dominated by hand-crafted
filters; their structure inspired the deep learning revolution resulting in
modern deep convolutional networks; similarly, our novel Tensorflow filters
provide inspiration for designing future deep learning architectures for
ultrafast and efficient processing and classification of pixel detector images
at FEL facilities.Comment: 9 pages, 9 figure
Detecting animals in African Savanna with UAVs and the crowds
Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife
monitoring, with several advantages over traditional field-based methods. They
have readily been used to count birds, marine mammals and large herbivores in
different environments, tasks which are routinely performed through manual
counting in large collections of images. In this paper, we propose a
semi-automatic system able to detect large mammals in semi-arid Savanna. It
relies on an animal-detection system based on machine learning, trained with
crowd-sourced annotations provided by volunteers who manually interpreted
sub-decimeter resolution color images. The system achieves a high recall rate
and a human operator can then eliminate false detections with limited effort.
Our system provides good perspectives for the development of data-driven
management practices in wildlife conservation. It shows that the detection of
large mammals in semi-arid Savanna can be approached by processing data
provided by standard RGB cameras mounted on affordable fixed wings UAVs
Performance Evaluation of Distributed Computing Environments with Hadoop and Spark Frameworks
Recently, due to rapid development of information and communication
technologies, the data are created and consumed in the avalanche way.
Distributed computing create preconditions for analyzing and processing such
Big Data by distributing the computations among a number of compute nodes. In
this work, performance of distributed computing environments on the basis of
Hadoop and Spark frameworks is estimated for real and virtual versions of
clusters. As a test task, we chose the classic use case of word counting in
texts of various sizes. It was found that the running times grow very fast with
the dataset size and faster than a power function even. As to the real and
virtual versions of cluster implementations, this tendency is the similar for
both Hadoop and Spark frameworks. Moreover, speedup values decrease
significantly with the growth of dataset size, especially for virtual version
of cluster configuration. The problem of growing data generated by IoT and
multimodal (visual, sound, tactile, neuro and brain-computing, muscle and eye
tracking, etc.) interaction channels is presented. In the context of this
problem, the current observations as to the running times and speedup on Hadoop
and Spark frameworks in real and virtual cluster configurations can be very
useful for the proper scaling-up and efficient job management, especially for
machine learning and Deep Learning applications, where Big Data are widely
present.Comment: 5 pages, 1 table, 2017 IEEE International Young Scientists Forum on
Applied Physics and Engineering (YSF-2017) (Lviv, Ukraine
Development of a mechatronic sorting system for removing contaminants from wool
Automated visual inspection (AVI) systems have been
extended to many fields, such as agriculture and the food, plastic
and textile industries. Generally, most visual systems only inspect
product defects, and then analyze and grade them due to the lack
of any sorting function. This main reason rests with the difficulty of
using the image data in real time. However, it is increasingly important
to either sort good products from bad or grade products into
separate groups usingAVI systems. This article describes the development
of a mechatronic sorting system and its integration with a
vision system for automatically removing contaminants from wool
in real time. The integration is implemented by a personal computer,
which continuously processes live images under the Windows
2000 operating system. The developed real-time sorting approach
is also applicable to many other AVI systems
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Motor expressions as creativity support: Exploring the potential for physical interaction
This research explores the effects of physical interactions designed on the basis of motor expressions to support creative ideation in creativity support technologies. The presented research looks into the effects on creative ideation of incompatibility between motor expressions and problem situations, and appraisals of (un)pleasantness. We report the results of a preliminary study which suggests that affective incompatibility between a problem situation and a motor expression benefits creative ideation, and that pleasantness motor expressions enhance task enjoyment, which in turn leads to a beneficial effect on the originality of ideas generated. Based on these results, we conclude with two new directions for the design of physical interactions with novel creativity support technologies
An instrument for assessing primary students' knowledge of information graphics in mathematics
Information graphics have become increasingly important in representing, organising and analysing information in a technological age. In classroom contexts, information graphics are typically associated with graphs, maps and number lines. However, all students need to become competent with the broad range of graphics that they will encounter in mathematical situations. This paper provides a rationale for creating a test to measure students’ knowledge of graphics. This instrument can be used in mass testing and individual (in-depth) situations. Our analysis of the utility of this instrument informs policy and practice. The results provide an appreciation of the relative difficulty of different information graphics; and provide the capacity to benchmark information about students’ knowledge of graphics. The implications for practice include the need to support the development of students’ knowledge of graphics, the existence of gender differences, the role of cross-curriculum applications in learning about graphics, and the need to explicate the links among graphics
Perceptual Grouping and Distance Estimates in Typical and Atypical Development: Comparing Performance across Perception, Drawing and Construction Tasks
Perceptual grouping is a pre-attentive process which serves to group local elements into global wholes, based on shared properties. One effect of perceptual grouping is to distort the ability to estimate the distance between two elements. In this study, biases in distance estimates, caused by four types of perceptual grouping, were measured across three tasks, a perception, a drawing and a construction task in both typical development (TD; Experiment 1) and in individuals with Williams syndrome (WS; Experiment 2). In Experiment 1, perceptual grouping distorted distance estimates across all three tasks. Interestingly, the effect of grouping by luminance was in the opposite direction to the effects of the remaining grouping types. We relate this to differences in the ability to inhibit perceptual grouping effects on distance estimates. Additive distorting influences were also observed in the drawing and the construction task, which are explained in terms of the points of reference employed in each task. Experiment 2 demonstrated that the above distortion effects are also observed in WS. Given the known deficit in the ability to use perceptual grouping in WS, this suggests a dissociation between the pre-attentive influence of and the attentive deployment of perceptual grouping in WS. The typical distortion in relation to drawing and construction points towards the presence of some typical location coding strategies in WS. The performance of the WS group differed from the TD participants on two counts. First, the pattern of overall distance estimates (averaged across interior and exterior distances) across the four perceptual grouping types, differed between groups. Second, the distorting influence of perceptual grouping was strongest for grouping by shape similarity in WS, which contrasts to a strength in grouping by proximity observed in the TD participants
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