8,983 research outputs found
The Sliding Window Protocol Revisited
We give a correctness proof of the sliding window protocol. Both safety and liveness properties are addressed. We show how faulty channels can be represented as nondeterministic programs. The correctness proof is given as a sequence of correctness-preserving transformations of a sequential program that satisfies the original specification, with the exception that it does not have any faulty channels. We work as long as possible with a sequential program, although the transformation steps are guided by the aim of going to a distributed program. The final transformation steps consist in distributing the actions of the sequential program over a number of processes
What makes for effective detection proposals?
Current top performing object detectors employ detection proposals to guide
the search for objects, thereby avoiding exhaustive sliding window search
across images. Despite the popularity and widespread use of detection
proposals, it is unclear which trade-offs are made when using them during
object detection. We provide an in-depth analysis of twelve proposal methods
along with four baselines regarding proposal repeatability, ground truth
annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM,
R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object
detection improving proposal localisation accuracy is as important as improving
recall. We introduce a novel metric, the average recall (AR), which rewards
both high recall and good localisation and correlates surprisingly well with
detection performance. Our findings show common strengths and weaknesses of
existing methods, and provide insights and metrics for selecting and tuning
proposal methods.Comment: TPAMI final version, duplicate proposals removed in experiment
Parameterized Concurrent Multi-Party Session Types
Session types have been proposed as a means of statically verifying
implementations of communication protocols. Although prior work has been
successful in verifying some classes of protocols, it does not cope well with
parameterized, multi-actor scenarios with inherent asynchrony. For example, the
sliding window protocol is inexpressible in previously proposed session type
systems. This paper describes System-A, a new typing language which overcomes
many of the expressiveness limitations of prior work. System-A explicitly
supports asynchrony and parallelism, as well as multiple forms of
parameterization. We define System-A and show how it can be used for the static
verification of a large class of asynchronous communication protocols.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432
Network coding meets TCP
We propose a mechanism that incorporates network coding into TCP with only
minor changes to the protocol stack, thereby allowing incremental deployment.
In our scheme, the source transmits random linear combinations of packets
currently in the congestion window. At the heart of our scheme is a new
interpretation of ACKs - the sink acknowledges every degree of freedom (i.e., a
linear combination that reveals one unit of new information) even if it does
not reveal an original packet immediately. Such ACKs enable a TCP-like
sliding-window approach to network coding. Our scheme has the nice property
that packet losses are essentially masked from the congestion control
algorithm. Our algorithm therefore reacts to packet drops in a smooth manner,
resulting in a novel and effective approach for congestion control over
networks involving lossy links such as wireless links. Our experiments show
that our algorithm achieves higher throughput compared to TCP in the presence
of lossy wireless links. We also establish the soundness and fairness
properties of our algorithm.Comment: 9 pages, 9 figures, submitted to IEEE INFOCOM 200
Particular object retrieval with integral max-pooling of CNN activations
Recently, image representation built upon Convolutional Neural Network (CNN)
has been shown to provide effective descriptors for image search, outperforming
pre-CNN features as short-vector representations. Yet such models are not
compatible with geometry-aware re-ranking methods and still outperformed, on
some particular object retrieval benchmarks, by traditional image search
systems relying on precise descriptor matching, geometric re-ranking, or query
expansion. This work revisits both retrieval stages, namely initial search and
re-ranking, by employing the same primitive information derived from the CNN.
We build compact feature vectors that encode several image regions without the
need to feed multiple inputs to the network. Furthermore, we extend integral
images to handle max-pooling on convolutional layer activations, allowing us to
efficiently localize matching objects. The resulting bounding box is finally
used for image re-ranking. As a result, this paper significantly improves
existing CNN-based recognition pipeline: We report for the first time results
competing with traditional methods on the challenging Oxford5k and Paris6k
datasets
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