Constant Memory Space Research Articles

How does adiabatic quantum computation fit into quantum automata theory?

Quantum computation has emerged as a powerful computational medium of our time, having demonstrated the remarkable efficiency in factoring a positive integer and searching databases faster than any currently known classical computing algorithm. Adiabatic evolution of quantum systems has been studied as a potential means that physically realizes quantum computation. Up to now, all the research on adiabatic quantum systems has dealt with polynomial time-bounded computation and little attention has been paid to, for instance, adiabatic quantum systems consuming only constant memory space. Such quantum systems can be modeled in a form similar to quantum finite automata. This exposition dares to ask a bold question of how to make adiabatic quantum computation fit into the rapidly progressing framework of quantum automata theory. As our answer to this eminent but profound question, we first lay out a fundamental platform to carry out adiabatic evolutionary quantum systems (AEQSs) with limited computational resources (in size, energy, spectral gap, etc.) and then demonstrate how to construct such AEQSs by operating suitable families of quantum finite automata. We further explore fundamental structural properties of decision problems (as well as promise problems) solved quickly by the appropriately constructed AEQSs.

Threaded ensembles of autoencoders for stream learning

AbstractAnomaly detection in streaming data is an important problem in numerous application domains. Most existing model‐based approaches to stream learning are based on decision trees due to their fast construction speed. This paper introduces streaming autoencoder (SA), a fast and novel anomaly detection algorithm based on ensembles of neural networks for evolving data streams. It is a one‐class learner, which only requires data from the positive class for training and is accurate even when anomalous training data are rare. It features an ensemble of threaded autoencoders with continuous learning capacity. Furthermore, the SA uses a 2‐step detection mechanism to ensure that real anomalies are detected with low false‐positive rates. The method is highly efficient because it processes data streams in parallel with multithreads and alternating buffers. Our analysis shows that SA has a linear runtime and requires constant memory space. Empirical comparisons to the state‐of‐the‐art methods on multiple benchmark data sets demonstrate that the proposed method detects anomalies efficiently with fewer false alarms.

A mobile architecture with dynamic message filtering for emergency response environments

This paper demonstrates that contemporary smartphones act as effective message filtering systems in high traffic environments such as emergency response organisations, without relying on central servers. We have prototyped a mobile messaging application for Android smartphones in the Erlang language. We implemented filtering rules based on message origin, importance, and temporal validity, and tested the filtering capabilities of a smartphone in a realistic setup, that simulates traffic of tens of thousands of messages per minute, as in a large scale emergency response operation. The conclusion is that careful coding of the messaging application so that it operates in constant memory space and judicious use of the available display area can provide an effective portable message filtering for real-time, high-volume traffic, and the potential to reduce information overload for the emergency responder.

A Linear-Time Constant-Space Algorithm for the Boundary Fill Problem

In this paper, we consider the problem of boundary fill of a 4 or 8-connected region in a graphic device having a color image frame-buffer memory. We provide an algorithm that solves the problem in a time linear in the number of pixels in the region and requiring only constant memory space in addition to the frame-buffer memory itself. We map this problem to a boundary fill problem in a general graph, and solve it using a novel depth first search-based algorithm.

Torus orientation

A torus is oriented if the processes have assigned their communication links North-East-South-West labels in a globally consistent manner. This paper presents an orientation protocol that is self-stabilizing, i.e., resilient to corruption of data stored in working memories and communication links. The protocol is randomized, uses constant memory space, and orients tori where the processes do not know the network size and have no identifiers; probabilistic stabilization is proved under a restricted form of asynchrony and composite atomicity.