2,502 research outputs found
The Role of Interactivity in Local Differential Privacy
We study the power of interactivity in local differential privacy. First, we
focus on the difference between fully interactive and sequentially interactive
protocols. Sequentially interactive protocols may query users adaptively in
sequence, but they cannot return to previously queried users. The vast majority
of existing lower bounds for local differential privacy apply only to
sequentially interactive protocols, and before this paper it was not known
whether fully interactive protocols were more powerful. We resolve this
question. First, we classify locally private protocols by their
compositionality, the multiplicative factor by which the sum of a
protocol's single-round privacy parameters exceeds its overall privacy
guarantee. We then show how to efficiently transform any fully interactive
-compositional protocol into an equivalent sequentially interactive protocol
with an blowup in sample complexity. Next, we show that our reduction is
tight by exhibiting a family of problems such that for any , there is a
fully interactive -compositional protocol which solves the problem, while no
sequentially interactive protocol can solve the problem without at least an
factor more examples. We then turn our attention to
hypothesis testing problems. We show that for a large class of compound
hypothesis testing problems --- which include all simple hypothesis testing
problems as a special case --- a simple noninteractive test is optimal among
the class of all (possibly fully interactive) tests
What\u27s Ahead for the Auditors? AICPA Council Meeting, May 6, 1969
https://egrove.olemiss.edu/aicpa_assoc/2076/thumbnail.jp
Construction of asymptotically good low-rate error-correcting codes through pseudo-random graphs
A novel technique, based on the pseudo-random properties of certain graphs known as expanders, is used to obtain novel simple explicit constructions of asymptotically good codes. In one of the constructions, the expanders are used to enhance Justesen codes by replicating, shuffling, and then regrouping the code coordinates. For any fixed (small) rate, and for a sufficiently large alphabet, the codes thus obtained lie above the Zyablov bound. Using these codes as outer codes in a concatenated scheme, a second asymptotic good construction is obtained which applies to small alphabets (say, GF(2)) as well. Although these concatenated codes lie below the Zyablov bound, they are still superior to previously known explicit constructions in the zero-rate neighborhood
Time Data Sequential Processor /TDSP/
Time Data Sequential Processor /TDSP/ computer program provides preflight predictions for lunar trajectories from injection to impact, and for planetary escape trajectories for up to 100 hours from launch. One of the major options TDSP performs is the determination of tracking station view periods
Deep convolutional networks for automated detection of posterior-element fractures on spine CT
Injuries of the spine, and its posterior elements in particular, are a common
occurrence in trauma patients, with potentially devastating consequences.
Computer-aided detection (CADe) could assist in the detection and
classification of spine fractures. Furthermore, CAD could help assess the
stability and chronicity of fractures, as well as facilitate research into
optimization of treatment paradigms.
In this work, we apply deep convolutional networks (ConvNets) for the
automated detection of posterior element fractures of the spine. First, the
vertebra bodies of the spine with its posterior elements are segmented in spine
CT using multi-atlas label fusion. Then, edge maps of the posterior elements
are computed. These edge maps serve as candidate regions for predicting a set
of probabilities for fractures along the image edges using ConvNets in a 2.5D
fashion (three orthogonal patches in axial, coronal and sagittal planes). We
explore three different methods for training the ConvNet using 2.5D patches
along the edge maps of 'positive', i.e. fractured posterior-elements and
'negative', i.e. non-fractured elements.
An experienced radiologist retrospectively marked the location of 55
displaced posterior-element fractures in 18 trauma patients. We randomly split
the data into training and testing cases. In testing, we achieve an
area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at
5 or 10 false-positives per patient, respectively. Analysis of our set of
trauma patients demonstrates the feasibility of detecting posterior-element
fractures in spine CT images using computer vision techniques such as deep
convolutional networks.Comment: To be presented at SPIE Medical Imaging, 2016, San Dieg
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