Magneto-Optical Imaging of Superconductors for Liquid Hydrogen Applications

Restricted deposits of fossil fuels and ecological problems created by their extensive use require a transition to renewable energy resources and clean fuel free from emissions of CO2. This fuel is likely to be liquid hydrogen. An important feature of liquid hydrogen is that it allows wide use of superconductivity. Superconductors provide compactness, high efficiency, savings in energy and a range of new applications not possible with other materials. The benefits of superconductivity justify use of low temperatures and facilitate development of fossil-free energy economy. The widespread use of superconductors requires a simple and reliable technique to monitor their properties. Magneto-optical imaging (MOI) is currently the only direct technique allowing visualization of the superconducting properties of materials. We report the application of this technique to key superconducting materials suitable for the hydrogen economy: MgB2 and high temperature superconductors (HTS) in bulk and thin-film form. The study shows that the MOI technique is well suited to the study of these materials. It demonstrates the advantage of HTS at liquid hydrogen temperatures and emphasizes the benefits of MgB2, in particular. © 2012 Springer Science+Business Media New York.

A neurophysiologically plausible population code model for feature integration explains visual crowding.

An object in the peripheral visual field is more difficult to recognize when surrounded by other objects. This phenomenon is called "crowding". Crowding places a fundamental constraint on human vision that limits performance on numerous tasks. It has been suggested that crowding results from spatial feature integration necessary for object recognition. However, in the absence of convincing models, this theory has remained controversial. Here, we present a quantitative and physiologically plausible model for spatial integration of orientation signals, based on the principles of population coding. Using simulations, we demonstrate that this model coherently accounts for fundamental properties of crowding, including critical spacing, "compulsory averaging", and a foveal-peripheral anisotropy. Moreover, we show that the model predicts increased responses to correlated visual stimuli. Altogether, these results suggest that crowding has little immediate bearing on object recognition but is a by-product of a general, elementary integration mechanism in early vision aimed at improving signal quality.

A Maximum-Likelihood Interpretation for Slow Feature Analysis

The brain extracts useful features from a maelstrom of sensory information, and a fundamental goal of theoretical neuroscience is to work out how it does so. One proposed feature extraction strategy is motivated by the observation that the meaning of sensory data, such as the identity of a moving visual object, is often more persistent than the activation of any single sensory receptor. This notion is embodied in the slow feature analysis (SFA) algorithm, which uses “slowness” as an heuristic by which to extract semantic information from multi-dimensional time-series. Here, we develop a probabilistic interpretation of this algorithm showing that inference and learning in the limiting case of a suitable probabilistic model yield exactly the results of SFA. Similar equivalences have proved useful in interpreting and extending comparable algorithms such as independent component analysis. For SFA, we use the equivalent probabilistic model as a conceptual spring-board, with which to motivate several novel extensions to the algorithm.

On the relation of slow feature analysis and Laplacian eigenmaps

The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow feature analysis (SFA), a biologically inspired, unsupervised learning algorithm originally designed for learning invariant visual representations. We show that SFA can be interpreted as a function approximation of LEMs, where the topological neighborhoods required for LEMs are implicitly defined by the temporal structure of the data. Based on this relation, we propose a generalization of SFA to arbitrary neighborhood relations and demonstrate its applicability for spectral clustering. Finally, we review previous work with the goal of providing a unifying view on SFA and LEMs. © 2011 Massachusetts Institute of Technology.

A theory of slow feature analysis for transformation-based input signals with an application to complex cells

We develop a group-theoretical analysis of slow feature analysis for the case where the input data are generated by applying a set of continuous transformations to static templates. As an application of the theory, we analytically derive nonlinear visual receptive fields and show that their optimal stimuli, as well as the orientation and frequency tuning, are in good agreement with previous simulations of complex cells in primary visual cortex (Berkes and Wiskott, 2005). The theory suggests that side and end stopping can be interpreted as a weak breaking of translation invariance. Direction selectivity is also discussed. © 2011 Massachusetts Institute of Technology.

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary latent variables, and their values. The latent variables preserve neighbourhood structure of the data in a sense that objects in the same semantic concept have similar latent values, and objects in different concepts have dissimilar latent values. We formulate the supervised infinite latent variable problem based on an intuitive principle of pulling objects together if they are of the same type, and pushing them apart if they are not. We then combine this principle with a flexible Indian Buffet Process prior on the latent variables. We show that the inferred supervised latent variables can be directly used to perform a nearest neighbour search for the purpose of retrieval. We introduce a new application of dynamically extending hash codes, and show how to effectively couple the structure of the hash codes with continuously growing structure of the neighbourhood preserving infinite latent feature space.

Multilinear function factorisation for time series feature extraction

This work applies a variety of multilinear function factorisation techniques to extract appropriate features or attributes from high dimensional multivariate time series for classification. Recently, a great deal of work has centred around designing time series classifiers using more and more complex feature extraction and machine learning schemes. This paper argues that complex learners and domain specific feature extraction schemes of this type are not necessarily needed for time series classification, as excellent classification results can be obtained by simply applying a number of existing matrix factorisation or linear projection techniques, which are simple and computationally inexpensive. We highlight this using a geometric separability measure and classification accuracies obtained though experiments on four different high dimensional multivariate time series datasets. © 2013 IEEE.

Tandem system adaptation using multiple linear feature transforms

Adaptation to speaker and environment changes is an essential part of current automatic speech recognition (ASR) systems. In recent years the use of multi-layer percpetrons (MLPs) has become increasingly common in ASR systems. A standard approach to handling speaker differences when using MLPs is to apply a global speaker-specific constrained MLLR (CMLLR) transform to the features prior to training or using the MLP. This paper considers the situation when there are both speaker and channel, communication link, differences in the data. A more powerful transform, front-end CMLLR (FE-CMLLR), is applied to the inputs to the MLP to represent the channel differences. Though global, these FE-CMLLR transforms vary from time-instance to time-instance. Experiments on a channel distorted dialect Arabic conversational speech recognition task indicates the usefulness of adapting MLP features using both CMLLR and FE-CMLLR transforms. © 2013 IEEE.

The youngest split in sympatric schizothoracine fish (Cyprinidae) is shaped by ecological adaptations in a Tibetan Plateau glacier lake

Although new empirical evidence shows that sympatric speciation has occurred in some species, there are few indisputable model organisms for this process of speciation. The two subspecies (Gymnocypris eckloni eckloni and G. e. scoliostomus) of the schizothoracine Gymnocypris fish species complex from a small glacier lake in the Tibetan Plateau, Lake Sunmcuo, fit several of the key characteristics of the sympatric speciation model. We used combined mitochondrial control region sequences and the cytochrome b gene (1894 bp) to address the phylogenetics and population genetics of 232 specimens of G. e. eckloni and G. e. scoliostomus, as well as all of its closely related sister species. We found that: (i) a total of four old lineages were uncovered in the widespread G. e. eckloni, of which only one was shown to be shared with all G. e. scoliostomus individuals and (ii) the new subspecies (G. e. scoliostomus) evolved in Lake Sunmcuo from the ancestral G. e. eckloni population within approximately 0.057 Ma. These two taxa of the species complex are morphologically distinct, and reproductive isolation is further suggested. Ecological disruptive selection based on morphological traits (e.g. mouth cleft characters) and food utilization may be a mechanism of incipient speciation of two sympatric populations within Lake Sunmcuo. This study provides the first genetic evidence for sympatric speciation in the schizothoracine fish.

Diel isotopic fluctuation in surface seston and its physiological and ecological implications

We reported diet fluctuation in isotopic composition of surface seston from two connected lakes in China, oligotrophic Lake Fuxian and eutrophic Lake Xingyun. The decrease in nighttime and the increase in daytime of isotope signatures of seston might be attributed to the light-dependent balance between the photosynthesis and the respiration of phytoplankton and to the changes in the species composition and the relative abundance of phytoplankton functional groups at the water's surface in diel growth. The relatively high isotopic signatures and the large-extent diel fluctuation of phytoplankton in the eutrophic lake could be due to utilization of heavy-isotope-enriched inorganic sources and the high primary productivity. Extent of diel fluctuation in delta C-13 and delta N-15 of phytoplankton were relatively small compared with the isotopic enrichment per trophic transfer and thus might have negligible effect on the source identification and the trophic evaluation of consumers.

Response of macrozoobenthos communities to ecological engineering remediation in a hypertrophic urban lake

The relationship of macrozoobenthos communities with some environmental variables, and their response to the ongoing restoration measures were studied in a small hypertrophic urban lake near the Yangzte River, China. Twenty taxa including 9 oligochaetes, 7 insects, 2 mollusks and two other animals were found during March 2005 to May 2006. The reappearance of some indigenous macrozoobenthos species showed that the ecological engineering remediation carried out was helpful for the recovery of the macrozoobenthos communities. Through canonical correspondence analysis (CCA), it was detected that temperature (T), conductivity (COND), Secchi depth/deep (SD/Deep), TN, total suspended solids (SS) and chemical oxygen demand (CODcr) were significant environmental factors that influenced the pattern of macrozoobenthos. Limnodrilus hoffmeisteri, Tanypus sp. and Alocinma longicornis could be used as potential bio-indicators in monitoring the development of ecological restoration.

Progress in the evaluation of transgenic fish for possible ecological risk and its containment strategies

Genetically improved transgenic fish possess many beneficial economic traits; however, the commercial aquaculture of transgenic fish has not been performed till date. One of the major reasons for this is the possible ecological risk associated with the escape or release of the transgenic fish. Using a growth hormone transgenic fish with rapid growth characteristics as a subject, this paper analyzes the following: the essence of the potential ecological risks posed by transgenic fish; ecological risk in the current situation due to transgenic fish via one-factor phenotypic and fitness analysis, and mathematical model deduction. Then, it expounds new ideas and the latest findings using an artificially simulated ecosystem for the evaluation of the ecological risks posed by transgenic fish. Further, the study comments on the strategies and principles of controlling these ecological risks by using a triplold approach. Based on these results, we propose that ecological risk evaluation and prevention strategies are indispensable important components and should be accompanied with breeding research in order to provide enlightments for transgenic fish breeding, evaluation of the ecological risks posed by transgenic fish, and development of containment strategies against the risks.

Development of tolerance against toxic Microcystis aeruginosa in three cladocerans and the ecological implications

This is the first experimental study to compare difference in the development of tolerance against toxic Microcystis among multi-species of cladocerans (Daphnia, Moina and Ceriodaphnia) pre-exposed to two M. aeruginosa PCC7820 strains (MC-containing and MC-free). Zooplankton were divided into S population (fed Scenedesmus), M-F population (fed Scenedesmus + MC-free Microcystis), and M-C population (fed Scenedesmus + MC-containing Microcystis). M-F and M-C populations were pre-exposed to Microcystis strains for 4 weeks, and their newborns were collected for experiments. A pre-exposure to MC-containing or MC-free Microcystis increased tolerance against toxic Microcystis. The marked increases in survival rate and median lethal time (LT50, 100-194% increase) in the M-C population of Ceriodaphnia suggest that small-sized cladocerans may develop stronger tolerance against Microcystis than large-sized ones when both groups are exposed to toxic Microcystis. This may explain why dominant Daphnia is usually replaced by small-sized cladocerans when cyanobacteria bloomed in summer in eutrophic lakes. (c) 2005 Elsevier Ltd. All rights reserved.