Synthesis and Optimization of Reversible Circuits - A Survey

Reversible logic circuits have been historically motivated by theoretical research in low-power electronics as well as practical improvement of bit-manipulation transforms in cryptography and computer graphics. Recently, reversible circuits have attracted interest as components of quantum algorithms, as well as in photonic and nano-computing technologies where some switching devices offer no signal gain. Research in generating reversible logic distinguishes between circuit synthesis, post-synthesis optimization, and technology mapping. In this survey, we review algorithmic paradigms --- search-based, cycle-based, transformation-based, and BDD-based --- as well as specific algorithms for reversible synthesis, both exact and heuristic. We conclude the survey by outlining key open challenges in synthesis of reversible and quantum logic, as well as most common misconceptions.Comment: 34 pages, 15 figures, 2 table

Synergistic degradation of lignocellulose by fungi and bacteria in boreal forest soil

Thesis (M.S.) University of Alaska Fairbanks, 2015Boreal forests contain an estimated 28% of the world's soil carbon, and currently act as a significant global carbon sink. Plant-derived lignocellulose is a major component of soil carbon, and its decomposition is dependent on soil bacteria and fungi. In order to predict the fate of this soil carbon and its potential feedbacks to climate change, the identities, activity, and interactions of soil microbial decomposer communities must be better understood. This study used stable isotope probing (SIP) with ¹³C-labeled lignocellulose and two of its constituents, cellulose and vanillin, to identify microbes responsible for the processing of lignocellulose-derived carbon and examine the specific roles that they perform. Results indicate that multiple taxa are involved in lignocellulose processing, and that certain taxa target specific portions of the lignocellulose macromolecule; specifically, fungi dominate the degradation of lignocellulose and cellulose macromolecules, while bacteria scavenge aromatic lignocellulose monomers. Major fungal taxa involved in lignocellulose degradation include Ceratobasidium, Geomyces, and Sebacina, among others. Bacterial taxa processing lignocellulose and cellulose included Cellvibrio and Mesorhizobium in high abundance relative to other taxa, although Burkholderia were the primary vanillin consumers. These results elucidate some of the major players in lignocellulose decomposition and their specific roles in boreal forest soil. This information provides knowledge of small-scale microbial processes that dictate ecosystem-level carbon cycling, and can assist in predictions of the fate of boreal forest carbon stocks

A Theorem on Preference Aggregation

I present a general theorem on preference aggregation. This theorem implies, as corollaries, Arrow's Impossibility Theorem, Wilson's extension of Arrow's to non-Paretian aggregation rules, the Gibbard-Satterthwaite Theorem and Sen's result on the Impossibility of a Paretian Liberal. The theorem shows that these classical results are not only similar, but actually share a common root. The theorem expresses a simple but deep fact that transcends each of its particular applications: it expresses the tension between decentralizing the choice of aggregate into partial choices based on preferences over pairs of alternatives, and the need for some coordination in these decisions, so as to avoid contradictory recommendations.NULL

Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences

Machine learning based Single Image Intrinsic Decomposition (SIID) methods decompose a captured scene into its albedo and shading images by using the knowledge of a large set of known and realistic ground truth decompositions. Collecting and annotating such a dataset is an approach that cannot scale to sufficient variety and realism. We free ourselves from this limitation by training on unannotated images. Our method leverages the observation that two images of the same scene but with different lighting provide useful information on their intrinsic properties: by definition, albedo is invariant to lighting conditions, and cross-combining the estimated albedo of a first image with the estimated shading of a second one should lead back to the second one's input image. We transcribe this relationship into a siamese training scheme for a deep convolutional neural network that decomposes a single image into albedo and shading. The siamese setting allows us to introduce a new loss function including such cross-combinations, and to train solely on (time-lapse) images, discarding the need for any ground truth annotations. As a result, our method has the good properties of i) taking advantage of the time-varying information of image sequences in the (pre-computed) training step, ii) not requiring ground truth data to train on, and iii) being able to decompose single images of unseen scenes at runtime. To demonstrate and evaluate our work, we additionally propose a new rendered dataset containing illumination-varying scenes and a set of quantitative metrics to evaluate SIID algorithms. Despite its unsupervised nature, our results compete with state of the art methods, including supervised and non data-driven methods.Comment: To appear in Pacific Graphics 201

Characterising gravitational wave stochastic background anisotropy with Pulsar Timing Arrays

Detecting a stochastic gravitational wave background, particularly radiation from individually unresolvable super-massive black hole binary systems, is one of the primary targets for Pulsar Timing Arrays. Increasingly more stringent upper limits are being set on these signals under the assumption that the background radiation is isotropic. However, some level of anisotropy may be present and the characterisation of the power at different angular scales carries important information. We show that the standard analysis for isotropic backgrounds can be generalised in a conceptually straightforward way to the case of generic anisotropic background radiation by decomposing the angular distribution of the gravitational wave power on the sky into multipole moments. We introduce the concept of generalised overlap reduction functions which characterise the effect of the anisotropy multipoles on the correlation of the timing residuals from the pulsars timed by a Pulsar Timing Array. In a search for a signal characterised by a generic anisotropy, the generalised overlap reduction functions play the role of the so-called Hellings and Downs curve used for isotropic radiation. We compute the generalised overlap reduction functions for a generic level of anisotropy and Pulsar Timing Array configuration. We also provide an order of magnitude estimate of the level of anisotropy that can be expected in the background generated by super-massive black hole binary systems.Comment: 12 pages plus 5 page Appendix. Accepted to PR