8,734 research outputs found
Thread-Modular Static Analysis for Relaxed Memory Models
We propose a memory-model-aware static program analysis method for accurately
analyzing the behavior of concurrent software running on processors with weak
consistency models such as x86-TSO, SPARC-PSO, and SPARC-RMO. At the center of
our method is a unified framework for deciding the feasibility of inter-thread
interferences to avoid propagating spurious data flows during static analysis
and thus boost the performance of the static analyzer. We formulate the
checking of interference feasibility as a set of Datalog rules which are both
efficiently solvable and general enough to capture a range of hardware-level
memory models. Compared to existing techniques, our method can significantly
reduce the number of bogus alarms as well as unsound proofs. We implemented the
method and evaluated it on a large set of multithreaded C programs. Our
experiments showthe method significantly outperforms state-of-the-art
techniques in terms of accuracy with only moderate run-time overhead.Comment: revised version of the ESEC/FSE 2017 pape
Research in constraint-based layout, visualization, CAD, and related topics : a bibliographical survey
The present work compiles numerous papers in the area of computer-aided design, graphics, layout configuration, and user interfaces in general. There is nearly no conference on graphics, multimedia, and user interfaces that does not include a section on constraint-based graphics; on the other hand most conferences on constraint processing favour applications in graphics. This work of bibliographical pointers may serve as a basis for a detailed and comprehensive survey of this important and challenging field in the intersection of constraint processing and graphics. In order to reach this ambitious aim, and also to keep this study up-to-date, the authors appreciate any comment and update information
3D freeform surfaces from planar sketches using neural networks
A novel intelligent approach into 3D freeform surface reconstruction from planar sketches is proposed. A multilayer perceptron (MLP) neural network is employed to induce 3D freeform surfaces from planar freehand curves. Planar curves were used to represent the boundaries of a freeform surface patch. The curves were varied iteratively and sampled to produce training data to train and test the neural network. The obtained results demonstrate that the network successfully learned the inverse-projection map and correctly inferred the respective surfaces from fresh curves
Set-based design of mechanical systems with design robustness integrated
This paper presents a method for parameter design of mechanical products based on a set-based approach. Set-based concurrent engineering emphasises on designing in a multi-stakeholder environment with concurrent involvement of the stakeholders in the design process. It also encourages flexibility in design through communication in terms of ranges instead of fixed point values and subsequent alternative solutions resulting from intersection of these ranges. These alternative solutions can then be refined and selected according to the designersâ preferences and clientsâ needs. This paper presents a model and tools for integrated flexible design that take into account the manufacturing variations as well as the design objectives for finding inherently robust solutions using QCSP transformation through interval analysis. In order to demonstrate the approach, an example of design of rigid flange coupling with a variable number of bolts and a choice of bolts from ISO M standard has been resolved and demonstrated
Towards Verifying Nonlinear Integer Arithmetic
We eliminate a key roadblock to efficient verification of nonlinear integer
arithmetic using CDCL SAT solvers, by showing how to construct short resolution
proofs for many properties of the most widely used multiplier circuits. Such
short proofs were conjectured not to exist. More precisely, we give n^{O(1)}
size regular resolution proofs for arbitrary degree 2 identities on array,
diagonal, and Booth multipliers and quasipolynomial- n^{O(\log n)} size proofs
for these identities on Wallace tree multipliers.Comment: Expanded and simplified with improved result
Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation
New communication standards need to deal with machine-to-machine
communications, in which users may start or stop transmitting at any time in an
asynchronous manner. Thus, the number of users is an unknown and time-varying
parameter that needs to be accurately estimated in order to properly recover
the symbols transmitted by all users in the system. In this paper, we address
the problem of joint channel parameter and data estimation in a multiuser
communication channel in which the number of transmitters is not known. For
that purpose, we develop the infinite factorial finite state machine model, a
Bayesian nonparametric model based on the Markov Indian buffet that allows for
an unbounded number of transmitters with arbitrary channel length. We propose
an inference algorithm that makes use of slice sampling and particle Gibbs with
ancestor sampling. Our approach is fully blind as it does not require a prior
channel estimation step, prior knowledge of the number of transmitters, or any
signaling information. Our experimental results, loosely based on the LTE
random access channel, show that the proposed approach can effectively recover
the data-generating process for a wide range of scenarios, with varying number
of transmitters, number of receivers, constellation order, channel length, and
signal-to-noise ratio.Comment: 15 pages, 15 figure
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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