39,097 research outputs found
Recursive Code Construction for Random Networks
A modification of Koetter-Kschischang codes for random networks is presented
(these codes were also studied by Wang et al. in the context of authentication
problems). The new codes have higher information rate, while maintaining the
same error-correcting capabilities. An efficient error-correcting algorithm is
proposed for these codes.Comment: Submitted to IEEE Transactions on Information Theor
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis
of 3D shapes, particularly their structures. Our key insight is that 3D shapes
are effectively characterized by their hierarchical organization of parts,
which reflects fundamental intra-shape relationships such as adjacency and
symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a
flat, unlabeled, arbitrary part layout to a compact code. The code effectively
captures hierarchical structures of man-made 3D objects of varying structural
complexities despite being fixed-dimensional: an associated decoder maps a code
back to a full hierarchy. The learned bidirectional mapping is further tuned
using an adversarial setup to yield a generative model of plausible structures,
from which novel structures can be sampled. Finally, our structure synthesis
framework is augmented by a second trained module that produces fine-grained
part geometry, conditioned on global and local structural context, leading to a
full generative pipeline for 3D shapes. We demonstrate that without
supervision, our network learns meaningful structural hierarchies adhering to
perceptual grouping principles, produces compact codes which enable
applications such as shape classification and partial matching, and supports
shape synthesis and interpolation with significant variations in topology and
geometry.Comment: Corresponding author: Kai Xu ([email protected]
Alignment based Network Coding for Two-Unicast-Z Networks
In this paper, we study the wireline two-unicast-Z communication network over
directed acyclic graphs. The two-unicast-Z network is a two-unicast network
where the destination intending to decode the second message has apriori side
information of the first message. We make three contributions in this paper:
1. We describe a new linear network coding algorithm for two-unicast-Z
networks over directed acyclic graphs. Our approach includes the idea of
interference alignment as one of its key ingredients. For graphs of a bounded
degree, our algorithm has linear complexity in terms of the number of vertices,
and polynomial complexity in terms of the number of edges.
2. We prove that our algorithm achieves the rate-pair (1, 1) whenever it is
feasible in the network. Our proof serves as an alternative, albeit restricted
to two-unicast-Z networks over directed acyclic graphs, to an earlier result of
Wang et al. which studied necessary and sufficient conditions for feasibility
of the rate pair (1, 1) in two-unicast networks.
3. We provide a new proof of the classical max-flow min-cut theorem for
directed acyclic graphs.Comment: The paper is an extended version of our earlier paper at ITW 201
Graph-based Semi-Supervised & Active Learning for Edge Flows
We present a graph-based semi-supervised learning (SSL) method for learning
edge flows defined on a graph. Specifically, given flow measurements on a
subset of edges, we want to predict the flows on the remaining edges. To this
end, we develop a computational framework that imposes certain constraints on
the overall flows, such as (approximate) flow conservation. These constraints
render our approach different from classical graph-based SSL for vertex labels,
which posits that tightly connected nodes share similar labels and leverages
the graph structure accordingly to extrapolate from a few vertex labels to the
unlabeled vertices. We derive bounds for our method's reconstruction error and
demonstrate its strong performance on synthetic and real-world flow networks
from transportation, physical infrastructure, and the Web. Furthermore, we
provide two active learning algorithms for selecting informative edges on which
to measure flow, which has applications for optimal sensor deployment. The
first strategy selects edges to minimize the reconstruction error bound and
works well on flows that are approximately divergence-free. The second approach
clusters the graph and selects bottleneck edges that cross cluster-boundaries,
which works well on flows with global trends
Modeling of the Acute Toxicity of Benzene Derivatives by Complementary QSAR Methods
A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for
fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure-
Activity Relationship (QSAR) models, either using or not using molecular descriptors,
respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the
molecular structure, described through an appropriate graphical tool (variable-size labeled
rooted ordered trees) by defining suitable representation rules. The input trees are encoded by
an adaptive process able to learn, by tuning its free parameters, from a given set of structureactivity
training examples. Owing to the use of a flexible encoding approach, the model is
target invariant and does not need a priori definition of molecular descriptors. The results
obtained in this study were analyzed together with those of a model based on molecular
descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression
selection of descriptors (CROMRsel). The comparison revealed interesting similarities that
could lead to the development of a combined approach, exploiting the complementary
characteristics of the two approaches
Sign-Compute-Resolve for Tree Splitting Random Access
We present a framework for random access that is based on three elements:
physical-layer network coding (PLNC), signature codes and tree splitting. In
presence of a collision, physical-layer network coding enables the receiver to
decode, i.e. compute, the sum of the packets that were transmitted by the
individual users. For each user, the packet consists of the user's signature,
as well as the data that the user wants to communicate. As long as no more than
K users collide, their identities can be recovered from the sum of their
signatures. This framework for creating and transmitting packets can be used as
a fundamental building block in random access algorithms, since it helps to
deal efficiently with the uncertainty of the set of contending terminals. In
this paper we show how to apply the framework in conjunction with a
tree-splitting algorithm, which is required to deal with the case that more
than K users collide. We demonstrate that our approach achieves throughput that
tends to 1 rapidly as K increases. We also present results on net data-rate of
the system, showing the impact of the overheads of the constituent elements of
the proposed protocol. We compare the performance of our scheme with an upper
bound that is obtained under the assumption that the active users are a priori
known. Also, we consider an upper bound on the net data-rate for any PLNC based
strategy in which one linear equation per slot is decoded. We show that already
at modest packet lengths, the net data-rate of our scheme becomes close to the
second upper bound, i.e. the overhead of the contention resolution algorithm
and the signature codes vanishes.Comment: This is an extended version of arXiv:1409.6902. Accepted for
publication in the IEEE Transactions on Information Theor
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