29,927 research outputs found
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Optical wireless communication (OWC) is a promising technology for future
wireless communications owing to its potentials for cost-effective network
deployment and high data rate. There are several implementation issues in the
OWC which have not been encountered in radio frequency wireless communications.
First, practical OWC transmitters need an illumination control on color,
intensity, and luminance, etc., which poses complicated modulation design
challenges. Furthermore, signal-dependent properties of optical channels raise
non-trivial challenges both in modulation and demodulation of the optical
signals. To tackle such difficulties, deep learning (DL) technologies can be
applied for optical wireless transceiver design. This article addresses recent
efforts on DL-based OWC system designs. A DL framework for emerging image
sensor communication is proposed and its feasibility is verified by simulation.
Finally, technical challenges and implementation issues for the DL-based
optical wireless technology are discussed.Comment: To appear in IEEE Communications Magazine, Special Issue on
Applications of Artificial Intelligence in Wireless Communication
Abduction-Based Explanations for Machine Learning Models
The growing range of applications of Machine Learning (ML) in a multitude of
settings motivates the ability of computing small explanations for predictions
made. Small explanations are generally accepted as easier for human decision
makers to understand. Most earlier work on computing explanations is based on
heuristic approaches, providing no guarantees of quality, in terms of how close
such solutions are from cardinality- or subset-minimal explanations. This paper
develops a constraint-agnostic solution for computing explanations for any ML
model. The proposed solution exploits abductive reasoning, and imposes the
requirement that the ML model can be represented as sets of constraints using
some target constraint reasoning system for which the decision problem can be
answered with some oracle. The experimental results, obtained on well-known
datasets, validate the scalability of the proposed approach as well as the
quality of the computed solutions
gMark: Schema-Driven Generation of Graphs and Queries
Massive graph data sets are pervasive in contemporary application domains.
Hence, graph database systems are becoming increasingly important. In the
experimental study of these systems, it is vital that the research community
has shared solutions for the generation of database instances and query
workloads having predictable and controllable properties. In this paper, we
present the design and engineering principles of gMark, a domain- and query
language-independent graph instance and query workload generator. A core
contribution of gMark is its ability to target and control the diversity of
properties of both the generated instances and the generated workloads coupled
to these instances. Further novelties include support for regular path queries,
a fundamental graph query paradigm, and schema-driven selectivity estimation of
queries, a key feature in controlling workload chokepoints. We illustrate the
flexibility and practical usability of gMark by showcasing the framework's
capabilities in generating high quality graphs and workloads, and its ability
to encode user-defined schemas across a variety of application domains.Comment: Accepted in November 2016. URL:
http://ieeexplore.ieee.org/document/7762945/. in IEEE Transactions on
Knowledge and Data Engineering 201
A Unified View of Piecewise Linear Neural Network Verification
The success of Deep Learning and its potential use in many safety-critical
applications has motivated research on formal verification of Neural Network
(NN) models. Despite the reputation of learned NN models to behave as black
boxes and the theoretical hardness of proving their properties, researchers
have been successful in verifying some classes of models by exploiting their
piecewise linear structure and taking insights from formal methods such as
Satisifiability Modulo Theory. These methods are however still far from scaling
to realistic neural networks. To facilitate progress on this crucial area, we
make two key contributions. First, we present a unified framework that
encompasses previous methods. This analysis results in the identification of
new methods that combine the strengths of multiple existing approaches,
accomplishing a speedup of two orders of magnitude compared to the previous
state of the art. Second, we propose a new data set of benchmarks which
includes a collection of previously released testcases. We use the benchmark to
provide the first experimental comparison of existing algorithms and identify
the factors impacting the hardness of verification problems.Comment: Updated version of "Piecewise Linear Neural Network verification: A
comparative study
Towards a Unified Framework for Declarative Structured Communications
We present a unified framework for the declarative analysis of structured
communications. By relying on a (timed) concurrent constraint programming
language, we show that in addition to the usual operational techniques from
process calculi, the analysis of structured communications can elegantly
exploit logic-based reasoning techniques. We introduce a declarative
interpretation of the language for structured communications proposed by Honda,
Vasconcelos, and Kubo. Distinguishing features of our approach are: the
possibility of including partial information (constraints) in the session
model; the use of explicit time for reasoning about session duration and
expiration; a tight correspondence with logic, which formally relates session
execution and linear-time temporal logic formulas
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