Quantum Associative Memory

This paper combines quantum computation with classical neural network theory to produce a quantum computational learning algorithm. Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper combines two quantum computational algorithms to produce such a quantum associative memory. The result is an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network. The paper covers necessary high-level quantum mechanical and quantum computational ideas and introduces a quantum associative memory. Theoretical analysis proves the utility of the memory, and it is noted that a small version should be physically realizable in the near future

A Quantum Computational Learning Algorithm

An interesting classical result due to Jackson allows polynomial-time learning of the function class DNF using membership queries. Since in most practical learning situations access to a membership oracle is unrealistic, this paper explores the possibility that quantum computation might allow a learning algorithm for DNF that relies only on example queries. A natural extension of Fourier-based learning into the quantum domain is presented. The algorithm requires only an example oracle, and it runs in O(sqrt(2^n)) time, a result that appears to be classically impossible. The algorithm is unique among quantum algorithms in that it does not assume a priori knowledge of a function and does not operate on a superposition that includes all possible states.Comment: This is a reworked and improved version of a paper originally entitled "Quantum Harmonic Sieve: Learning DNF Using a Classical Example Oracle

Optically Simulating a Quantum Associative Memory

This paper discusses the realization of a quantum associative memory using linear integrated optics. An associative memory produces a full pattern of bits when presented with only a partial pattern. Quantum computers have the potential to store large numbers of patterns and hence have the ability to far surpass any classical neural-network realization of an associative memory. In this work two three-qubit associative memories will be discussed using linear integrated optics. In addition, corrupted, invented and degenerate memories are discussed

TreeBASIS Feature Descriptor and Its Hardware Implementation

This paper presents a novel feature descriptor called TreeBASIS that provides improvements in descriptor size, computation time, matching speed, and accuracy. This new descriptor uses a binary vocabulary tree that is computed using basis dictionary images and a test set of feature region images. To facilitate real-time implementation, a feature region image is binary quantized and the resulting quantized vector is passed into the BASIS vocabulary tree. A Hamming distance is then computed between the feature region image and the effectively descriptive basis dictionary image at a node to determine the branch taken and the path the feature region image takes is saved as a descriptor. The TreeBASIS feature descriptor is an excellent candidate for hardware implementation because of its reduced descriptor size and the fact that descriptors can be created and features matched without the use of floating point operations. The TreeBASIS descriptor is more computationally and space efficient than other descriptors such as BASIS, SIFT, and SURF. Moreover, it can be computed entirely in hardware without the support of a CPU for additional software-based computations. Experimental results and a hardware implementation show that the TreeBASIS descriptor compares well with other descriptors for frame-to-frame homography computation while requiring fewer hardware resources

Phylogenetic search through partial tree mixing.

BACKGROUND: Recent advances in sequencing technology have created large data sets upon which phylogenetic inference can be performed. Current research is limited by the prohibitive time necessary to perform tree search on a reasonable number of individuals. This research develops new phylogenetic algorithms that can operate on tens of thousands of species in a reasonable amount of time through several innovative search techniques. RESULTS: When compared to popular phylogenetic search algorithms, better trees are found much more quickly for large data sets. These algorithms are incorporated in the PSODA application available at http://dna.cs.byu.edu/psoda CONCLUSIONS: The use of Partial Tree Mixing in a partition based tree space allows the algorithm to quickly converge on near optimal tree regions. These regions can then be searched in a methodical way to determine the overall optimal phylogenetic solution

Towards Transformational Creation of Novel Songs

We study transformational computational creativity in the context of writing songs and describe an implemented system that is able to modify its own goals and operation. With this, we contribute to three aspects of computational creativity and song generation: (1) Application-wise, songs are an interesting and challenging target for creativity, as they require the production of complementary music and lyrics. (2) Technically, we approach the problem of creativity and song generation using constraint programming. We show how constraints can be used declaratively to define a search space of songs so that a standard constraint solver can then be used to generate songs. (3) Conceptually, we describe a concrete architecture for transformational creativity where the creative (song writing) system has some responsibility for setting its own search space and goals. In the proposed architecture, a meta-level control component does this transparently by manipulating the constraints at runtime based on self-reflection of the system. Empirical experiments suggest the system is able to create songs according to its own taste.Peer reviewe

Data Musicalization

Data musicalization is the process of automatically composing music based on given data, as an approach to perceptualizing information artistically. The aim of data musicalization is to evoke subjective experiences in relation to the information, rather than merely to convey unemotional information objectively. This paper is written as a tutorial for readers interested in data musicalization. We start by providing a systematic characterization of musicalization approaches, based on their inputs, methods and outputs. We then illustrate data musicalization techniques with examples from several applications: one that perceptualizes physical sleep data as music, several that artistically compose music inspired by the sleep data, one that musicalizes on-line chat conversations to provide a perceptualization of liveliness of a discussion, and one that uses musicalization in a game-like mobile application that allows its users to produce music. We additionally provide a number of electronic samples of music produced by the different musicalization applications.Peer reviewe

Ocean warming is the key filter for successful colonization of the migrant octocoral Melithaea erythraea (Ehrenberg, 1834) in the Eastern Mediterranean Sea

Climate, which sets broad limits for migrating species, is considered a key filter to species migration between contrasting marine environments. The Southeast Mediterranean Sea (SEMS) is one of the regions where ocean temperatures are rising the fastest under recent climate change. Also, it is the most vulnerable marine region to species introductions. Here, we explore the factors which enabled the colonization of the endemic Red Sea octocoral Melithaea erythraea (Ehrenberg, 1834) along the SEMS coast, using sclerite oxygen and carbon stable isotope composition (delta O-18(SC) and delta C-13(SC)), morphology, and crystallography. The unique conditions presented by the SEMS include a greater temperature range (similar to 15 degrees C) and ultra-oligotrophy, and these are reflected by the lower delta C-13(SC) values. This is indicative of a larger metabolic carbon intake during calcification, as well as an increase in crystal size, a decrease of octocoral wart density and thickness of the migrating octocoral sclerites compared to the Red Sea samples. This suggests increased stress conditions, affecting sclerite deposition of the SEMS migrating octocoral. The delta(OSC)-O-18 range of the migrating M. erythraea indicates a preference for warm water sclerite deposition, similar to the native depositional temperature range of 21-28 degrees C. These findings are associated with the observed increase of minimum temperatures in winter for this region, at a rate of 0.35 +/- 0.27 degrees C decade(-1) over the last 30 years, and thus the region is becoming more hospitable to the IndoPacific M. erythraea. This study shows a clear case study of "tropicalization" of the Mediterranean Sea due to recent warming