87,960 research outputs found
Parallel Weighted Random Sampling
Data structures for efficient sampling from a set of weighted items are an important building block of many applications. However, few parallel solutions are known. We close many of these gaps both for shared-memory and distributed-memory machines. We give efficient, fast, and practicable algorithms for sampling single items, k items with/without replacement, permutations, subsets, and reservoirs. We also give improved sequential algorithms for alias table construction and for sampling with replacement. Experiments on shared-memory parallel machines with up to 158 threads show near linear speedups both for construction and queries
Sequential Circuit Design for Embedded Cryptographic Applications Resilient to Adversarial Faults
In the relatively young field of fault-tolerant cryptography, the main research effort has focused exclusively on the protection of the data path of cryptographic circuits. To date, however, we have not found any work that aims at protecting the control logic of these circuits against fault attacks, which thus remains the proverbial Achilles’ heel. Motivated by a hypothetical yet realistic fault analysis attack that, in principle, could be mounted against any modular exponentiation engine, even one with appropriate data path protection, we set out to close this remaining gap. In this paper, we present guidelines for the design of multifault-resilient sequential control logic based on standard Error-Detecting Codes (EDCs) with large minimum distance. We introduce a metric that measures the effectiveness of the error detection technique in terms of the effort the attacker has to make in relation to the area overhead spent in
implementing the EDC. Our comparison shows that the proposed EDC-based technique provides superior performance when compared against regular N-modular redundancy techniques. Furthermore, our technique scales well and does not affect the critical path delay
Adaptive Online Sequential ELM for Concept Drift Tackling
A machine learning method needs to adapt to over time changes in the
environment. Such changes are known as concept drift. In this paper, we propose
concept drift tackling method as an enhancement of Online Sequential Extreme
Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by
adding adaptive capability for classification and regression problem. The
scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme
that works well to handle real drift, virtual drift, and hybrid drift. The
AOS-ELM also works well for sudden drift and recurrent context change type. The
scheme is a simple unified method implemented in simple lines of code. We
evaluated AOS-ELM on regression and classification problem by using concept
drift public data set (SEA and STAGGER) and other public data sets such as
MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value
compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice
does not need hidden nodes increase, we address some issues related to the
increasing of the hidden nodes such as error condition and rank values. We
propose taking the rank of the pseudoinverse matrix as an indicator parameter
to detect underfitting condition.Comment: Hindawi Publishing. Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 8091267, 17 pages Received 29 January 2016,
Accepted 17 May 2016. Special Issue on "Advances in Neural Networks and
Hybrid-Metaheuristics: Theory, Algorithms, and Novel Engineering
Applications". Academic Editor: Stefan Hauf
The Potential of the Intel Xeon Phi for Supervised Deep Learning
Supervised learning of Convolutional Neural Networks (CNNs), also known as
supervised Deep Learning, is a computationally demanding process. To find the
most suitable parameters of a network for a given application, numerous
training sessions are required. Therefore, reducing the training time per
session is essential to fully utilize CNNs in practice. While numerous research
groups have addressed the training of CNNs using GPUs, so far not much
attention has been paid to the Intel Xeon Phi coprocessor. In this paper we
investigate empirically and theoretically the potential of the Intel Xeon Phi
for supervised learning of CNNs. We design and implement a parallelization
scheme named CHAOS that exploits both the thread- and SIMD-parallelism of the
coprocessor. Our approach is evaluated on the Intel Xeon Phi 7120P using the
MNIST dataset of handwritten digits for various thread counts and CNN
architectures. Results show a 103.5x speed up when training our large network
for 15 epochs using 244 threads, compared to one thread on the coprocessor.
Moreover, we develop a performance model and use it to assess our
implementation and answer what-if questions.Comment: The 17th IEEE International Conference on High Performance Computing
and Communications (HPCC 2015), Aug. 24 - 26, 2015, New York, US
Mechanisms for the generation and regulation of sequential behaviour
A critical aspect of much human behaviour is the generation and regulation of sequential activities. Such behaviour is seen in both naturalistic settings such as routine action and language production and laboratory tasks such as serial recall and many reaction time experiments. There are a variety of computational mechanisms that may support the generation and regulation of sequential behaviours, ranging from those underlying Turing machines to those employed by recurrent connectionist networks. This paper surveys a range of such mechanisms, together with a range of empirical phenomena related to human sequential behaviour. It is argued that the empirical phenomena pose difficulties for most sequencing mechanisms, but that converging evidence from behavioural flexibility, error data arising from when the system is stressed or when it is damaged following brain injury, and between-trial effects in reaction time tasks, point to a hybrid symbolic activation-based mechanism for the generation and regulation of sequential behaviour. Some implications of this view for the nature of mental computation are highlighted
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