222 research outputs found
Random Number Generation: Types and Techniques
What does it mean to have random numbers? Without understanding where a group of numbers came from, it is impossible to know if they were randomly generated. However, common sense claims that if the process to generate these numbers is truly understood, then the numbers could not be random. Methods that are able to let their internal workings be known without sacrificing random results are what this paper sets out to describe. Beginning with a study of what it really means for something to be random, this paper dives into the topic of random number generators and summarizes the key areas. It covers the two main groups of generators, true-random and pseudo-random, and gives practical examples of both. To make the information more applicable, real life examples of currently used and currently available generators are provided as well. Knowing the how and why of a number sequence without knowing the values that will come is possible, and this thesis explains how it is accomplished
Comparing state-of-the-art visual features on invariant object recognition tasks
Tolerance (āinvarianceā) to identity-preserving image variation (e.g. variation in position, scale, pose, illumination) is a fundamental problem that any visual object recognition system, biological or engineered, must solve. While standard natural image database benchmarks are useful for guiding progress in computer vision, they can fail to probe the ability of a recognition system to solve the invariance problem. Thus, to understand which computational approaches are making progress on solving the invariance problem, we compared and contrasted a variety of state-of-the-art visual representations using synthetic recognition tasks designed to systematically probe invariance. We successfully re-implemented a variety of state-of-the-art visual representations and confirmed their published performance on a natural image benchmark. We here report that most of these representations perform poorly on invariant recognition, but that one representation shows significant performance gains over two baseline representations. We also show how this approach can more deeply illuminate the strengths and weaknesses of different visual representations and thus guide progress on invariant object recognition
What response properties do individual neurons need to underlie position and clutter āinvariantā object recognition?
http://jn.physiology.org/content/102/1/360.abstractPrimates can easily identify visual objects over large changes in retinal positionāa property commonly referred to as position āinvariance.ā This ability is widely assumed to depend on neurons in inferior temporal cortex (IT) that can respond selectively to isolated visual objects over similarly large ranges of retinal position. However, in the real world, objects rarely appear in isolation, and the interplay between position invariance and the representation of multiple objects (i.e., clutter) remains unresolved. At the heart of this issue is the intuition that the representations of nearby objects can interfere with one another and that the large receptive fields needed for position invariance can exacerbate this problem by increasing the range over which interference acts. Indeed, most IT neurons' responses are strongly affected by the presence of clutter. While external mechanisms (such as attention) are often invoked as a way out of the problem, we show (using recorded neuronal data and simulations) that the intrinsic properties of IT population responses, by themselves, can support object recognition in the face of limited clutter. Furthermore, we carried out extensive simulations of hypothetical neuronal populations to identify the essential individual-neuron ingredients of a good population representation. These simulations show that the crucial neuronal property to support recognition in clutter is not preservation of response magnitude, but preservation of each neuron's rank-order object preference under identity-preserving image transformations (e.g., clutter). Because IT neuronal responses often exhibit that response property, while neurons in earlier visual areas (e.g., V1) do not, we suggest that preserving the rank-order object preference regardless of clutter, rather than the response magnitude, more precisely describes the goal of individual neurons at the top of the ventral visual stream.National Eye Institute (Grant R01-EY-014970)Pew Charitable TrustsMcKnight FoundationNational Eye Institute (NEI Integrative Training Grant for Vision)National Defense Science and Engineering Graduate FellowshipCharles A. King Trust Postdoctoral Fellowship ProgramCompagnia di San Paolo (Foundation
Why is Real-World Visual Object Recognition Hard?
Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain's anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, ānaturalā images have become popular in the study of vision and have been used to show apparently impressive progress in building such models. Here, we challenge the use of uncontrolled ānaturalā images in guiding that progress. In particular, we show that a simple V1-like modelāa neuroscientist's ānullā model, which should perform poorly at real-world visual object recognition tasksāoutperforms state-of-the-art object recognition systems (biologically inspired and otherwise) on a standard, ostensibly natural image recognition test. As a counterpoint, we designed a āsimplerā recognition test to better span the real-world variation in object pose, position, and scale, and we show that this test correctly exposes the inadequacy of the V1-like model. Taken together, these results demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially guiding progress in the wrong direction. Instead, we reexamine what it means for images to be natural and argue for a renewed focus on the core problem of object recognitionāreal-world image variation
Transcranial magnetic stimulation to assess motor neurophysiology after acute stroke in the United States: Feasibility, lessons learned, and values for future research
Transcranial magnetic stimulation (TMS) has been widely applied in both basic and clinical neuroscience since its introduction in 1985 . .
Next-Generation EU DataGrid Data Management Services
We describe the architecture and initial implementation of the
next-generation of Grid Data Management Middleware in the EU DataGrid (EDG)
project.
The new architecture stems out of our experience and the users requirements
gathered during the two years of running our initial set of Grid Data
Management Services. All of our new services are based on the Web Service
technology paradigm, very much in line with the emerging Open Grid Services
Architecture (OGSA). We have modularized our components and invested a great
amount of effort towards a secure, extensible and robust service, starting from
the design but also using a streamlined build and testing framework.
Our service components are: Replica Location Service, Replica Metadata
Service, Replica Optimization Service, Replica Subscription and high-level
replica management. The service security infrastructure is fully GSI-enabled,
hence compatible with the existing Globus Toolkit 2-based services; moreover,
it allows for fine-grained authorization mechanisms that can be adjusted
depending on the service semantics.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla,Ca, USA, March 2003 8 pages, LaTeX, the file contains all
LaTeX sources - figures are in the directory "figures
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