Automatic estimation of harmonic tension by distributed representation of chords

The buildup and release of a sense of tension is one of the most essential aspects of the process of listening to music. A veridical computational model of perceived musical tension would be an important ingredient for many music informatics applications. The present paper presents a new approach to modelling harmonic tension based on a distributed representation of chords. The starting hypothesis is that harmonic tension as perceived by human listeners is related, among other things, to the expectedness of harmonic units (chords) in their local harmonic context. We train a word2vec-type neural network to learn a vector space that captures contextual similarity and expectedness, and define a quantitative measure of harmonic tension on top of this. To assess the veridicality of the model, we compare its outputs on a number of well-defined chord classes and cadential contexts to results from pertinent empirical studies in music psychology. Statistical analysis shows that the model's predictions conform very well with empirical evidence obtained from human listeners.Comment: 12 pages, 4 figures. To appear in Proceedings of the 13th International Symposium on Computer Music Multidisciplinary Research (CMMR), Porto, Portuga

Estimating spatiotemporally continuous snow water equivalent from intermittent satellite observations: an evaluation using synthetic data

Accurate estimates of snow water equivalent (SWE) based on remote sensing have been elusive, particularly in mountain areas. However, there now appears to be some potential for direct satellite-based SWE observations along ground tracks that only cover a portion of a spatial domain (e.g., watershed). Fortunately, spatiotemporally continuous meteorological and surface variables could be leveraged to infer SWE in the gaps between satellite ground tracks. Here, we evaluate statistical and machine learning (ML) approaches to performing track-to-area (TTA) transformations of SWE observations in California's upper Tuolumne River watershed using synthetic data. The synthetic SWE measurements are designed to mimic a potential future P-band Signals of Opportunity (P-SoOP) satellite mission with a (along-track) spatial resolution of about 500 m. We construct relationships between multiple meteorological and surface variables and synthetic SWE observations along observation tracks, and we then extend these relationships to unobserved areas between ground tracks to estimate SWE over the entire watershed. Domain-wide, SWE inferred on 1 April using two synthetic satellite tracks (∼4.5 % basin coverage) led to percent errors of basin-averaged SWE (PEBAS) of 24.5 %, 4.5 % and 6.3 % in an extremely dry water year (WY2015), a normal water year (WY2008) and an extraordinarily wet water year (WY2017), respectively. Assuming a 10 d overpass interval, percent errors of basin-averaged SWE during both snow accumulation and snowmelt seasons were mostly less than 10 %. We employ a feature sensitivity analysis to overcome the black-box nature of ML methods and increase the explainability of the ML results. Our feature sensitivity analysis shows that precipitation is the dominant variable controlling the TTA SWE estimation, followed by net long-wave radiation (NetLong). We find that a modest increase in the accuracy of SWE estimation occurs when more than two ground tracks are leveraged. The accuracy of 1 April SWE estimation is only modestly improved for track repeats more often than about 15 d.</p

Light States in Chern-Simons Theory Coupled to Fundamental Matter

Motivated by developments in vectorlike holography, we study SU(N) Chern-Simons theory coupled to matter fields in the fundamental representation on various spatial manifolds. On the spatial torus T^2, we find light states at small `t Hooft coupling \lambda=N/k, where k is the Chern-Simons level, taken to be large. In the free scalar theory the gaps are of order \sqrt {\lambda}/N and in the critical scalar theory and the free fermion theory they are of order \lambda/N. The entropy of these states grows like N Log(k). We briefly consider spatial surfaces of higher genus. Based on results from pure Chern-Simons theory, it appears that there are light states with entropy that grows even faster, like N^2 Log(k). This is consistent with the log of the partition function on the three sphere S^3, which also behaves like N^2 Log(k). These light states require bulk dynamics beyond standard Vasiliev higher spin gravity to explain them.Comment: 58 pages, LaTeX, no figures, Minor error corrected, references added, The main results of the paper have not change

Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

Essential self-adjointness, generalized eigenforms, and spectra for the ∂ˉ\bar\partial-Neumann problem on GG-manifolds

Let $M$ be a strongly pseudoconvex complex manifold which is also the total space of a principal $G$-bundle with $G$ a Lie group and compact orbit space $\bar M/G$. Here we investigate the $\bar\partial$-Neumann Laplacian on $M$. We show that it is essentially self-adjoint on its restriction to compactly supported smooth forms. Moreover we relate its spectrum to the existence of generalized eigenforms: an energy belongs to $\sigma(\square)$ if there is a subexponentially bounded generalized eigenform for this energy. Vice versa, there is an expansion in terms of these well-behaved eigenforms so that, spectrally, almost every energy comes with such a generalized eigenform.Comment: 25 page

Symmetry structure in discrete models of biochemical systems : natural subsystems and the weak control hierarchy in a new model of computation driven by interactions

© 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.Interaction Computing (IC) is inspired by the observation that cell metabolic/regulatory systems construct order dynamically, through constrained interactions between their components and based on a wide range of possible inputs and environmental conditions. The goals of this work are (1) to identify and understand mathematically the natural subsystems and hierarchical relations in natural systems enabling this, and (2) to use the resulting insights to define a new model of computation based on interactions that is useful for both biology and computation. The dynamical characteristics of the cellular pathways studied in Systems Biology relate, mathematically, to the computational characteristics of automata derived from them, and their internal symmetry structures to computational power. Finite discrete automata models of biological systems such as the lac operon, Krebs cycle, and p53-mdm2 genetic regulation constructed from Systems Biology models have canonically associated algebraic structures { transformation semigroups. These contain permutation groups (local substructures exhibiting symmetry) that correspond to "pools of reversibility". These natural subsystems are related to one another in a hierarchical manner by the notion of "weak control ". We present natural subsystems arising from several biological examples and their weak control hierarchies in detail. Finite simple non-abelian groups (SNAGs) are found in biological examples and can be harnessed to realize nitary universal computation. This allows ensembles of cells to achieve any desired finitary computational transformation, depending on external inputs, via suitably constrained interactions. Based on this, interaction machines that grow and change their structure recursively are introduced and applied, providing a natural model of computation driven by interactions.Peer reviewe

Spin dynamics in semiconductors

This article reviews the current status of spin dynamics in semiconductors which has achieved a lot of progress in the past years due to the fast growing field of semiconductor spintronics. The primary focus is the theoretical and experimental developments of spin relaxation and dephasing in both spin precession in time domain and spin diffusion and transport in spacial domain. A fully microscopic many-body investigation on spin dynamics based on the kinetic spin Bloch equation approach is reviewed comprehensively.Comment: a review article with 193 pages and 1103 references. To be published in Physics Reports

The centrosome and spindle as a ribonucleoprotein complex

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Chromosome Research 19 (2011): 367-376, doi:10.1007/s10577-011-9186-7.The presence of nucleic acids in centrosomes and the spindle have been proposed, observed, and reported since the 1950s. Why did the subject remain, perhaps even until today, such a controversial issue? The explanation is manifold, and includes legitimate concern over contamination from other cellular compartments in biochemical preparations. With a typically high background of cytoplasmic ribosomes, even microscopic images of stained intact cells could be difficult to interpret. Also, evidence for RNA and DNA in centrosomes accumulated for approximately 40 years but was interspersed with contradictory studies, primarily regarding the presence of DNA (reviewed in Johnson and Rosenbaum, 1991; Marshall and Rosenbaum, 2000). Perhaps less tangible but still a likely cause for lingering controversy is that the presence of nucleic acids in the spindle or centrosomes will require us to look differently at these structures from a functional, and more to the point, evolutionary standpoint.This work was supported by grants from the NIH (GM088503) and NSF (MCB0843092) to MCA

Temporal mapping of photochemical reactions and molecular excited states with carbon specificity

Photochemical reactions are essential to a large number of important industrial and biological processes. A method for monitoring photochemical reaction kinetics and the dynamics of molecular excitations with spatial resolution within the active molecule would allow a rigorous exploration of the pathway and mechanism of photophysical and photochemical processes. Here we demonstrate that laser-excited muon pump-probe spin spectroscopy (photo-μSR) can temporally and spatially map these processes with a spatial resolution at the single-carbon level in a molecule with a pentacene backbone. The observed time-dependent light-induced changes of an avoided level crossing resonance demonstrate that the photochemical reactivity of a specific carbon atom is modified as a result of the presence of the excited state wavefunction. This demonstrates the sensitivity and potential of this technique in probing molecular excitations and photochemistry