A dependent partition-valued process for multitask clustering and time evolving network modelling

The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or time. One approach would be to use fragmentation-coagulation processes, but these, being Markov processes, are restricted to linear or tree structured covariate spaces. We define a partition-valued process on an arbitrary covariate space using Gaussian processes. We use the process to construct a multitask clustering model which partitions datapoints in a similar way across multiple data sources, and a time series model of network data which allows cluster assignments to vary over time. We describe sampling algorithms for inference and apply our method to defining cancer subtypes based on different types of cellular characteristics, finding regulatory modules from gene expression data from multiple human populations, and discovering time varying community structure in a social network.

Spin glass behaviour and spin-dependent scattering in la 0.7Ca 0.3Mn 0.9Cr 0.1O 3 perovskites

The magnetic, electrical and thermal transport properties of the perovskite La 0.7Ca 0.3Mn 0.9Cr 0.1O 3 have been investigated by measuring dc magnetization, ac susceptibility, the magnetoresistance and thermal conductivity in the temperature range of 5-300K. The spin glass behaviour with a spin freezing temperature of 70 K has been well confirmed for this compound, which demonstrates the coexistence and competition between ferromagnetic and antiferromagnetic clusters by the introduction of Cr. Colossal magnetoresistance has been observed over the temperature range investigated. The introduction of Cr causes the "double-bump" feature in electrical resistivity ρ(T). Anomalies on the susceptibility and the thermal conductivity associated with the double-bumps in ρ(T) are observed simultaneously. The imaginary part of ac susceptibility shows a sharp peak at the temperature of insulating-metallic transition where the first resistivity bump was observed, but it is a deep-set valley near the temperature where the second bump in ρ(T) emerges. The thermal conductivity shows an increase below the temperature of the insulating-metallic transition, but the phonon scattering is enhanced accompanying the appearance of the second peak of double-bumps in ρ(T). We relate those observed in magnetic and transport properties of La 0.7Ca 0.3Mn 0.9Cr 0.1O 3 to the spin-dependent scattering. The results reveal that the spin-phonon interaction may be of more significance than the electron (charge)-phonon interaction in the mixed perovskite system. © 2005 Chinese Physical Society and IOP Publishing Ltd.

Context-dependent human extinction memory is mediated by a ventromedial prefrontal and hippocampal network.

In fear extinction, an animal learns that a conditioned stimulus (CS) no longer predicts a noxious stimulus [unconditioned stimulus (UCS)] to which it had previously been associated, leading to inhibition of the conditioned response (CR). Extinction creates a new CS-noUCS memory trace, competing with the initial fear (CS-UCS) memory. Recall of extinction memory and, hence, CR inhibition at later CS encounters is facilitated by contextual stimuli present during extinction training. In line with theoretical predictions derived from animal studies, we show that, after extinction, a CS-evoked engagement of human ventromedial prefrontal cortex (VMPFC) and hippocampus is context dependent, being expressed in an extinction, but not a conditioning, context. Likewise, a positive correlation between VMPFC and hippocampal activity is extinction context dependent. Thus, a VMPFC-hippocampal network provides for context-dependent recall of human extinction memory, consistent with a view that hippocampus confers context dependence on VMPFC.

Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans.

Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.

Functional requirements for reward-modulated spike-timing-dependent plasticity.

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity is conditioned on a global modulatory factor signaling reward. We show that all learning rules in this class can be separated into a term that captures the covariance of neuronal firing and reward and a second term that presents the influence of unsupervised learning. The unsupervised term, which is, in general, detrimental for reward-based learning, can be suppressed if the neuromodulatory signal encodes the difference between the reward and the expected reward-but only if the expected reward is calculated for each task and stimulus separately. If several tasks are to be learned simultaneously, the nervous system needs an internal critic that is able to predict the expected reward for arbitrary stimuli. We show that, with a critic, reward-modulated spike-timing-dependent plasticity is capable of learning motor trajectories with a temporal resolution of tens of milliseconds. The relation to temporal difference learning, the relevance of block-based learning paradigms, and the limitations of learning with a critic are discussed.

Slowness: an objective for spike-timing-dependent plasticity?

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this "temporal stability" or "slowness" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the "trace rule." The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.

Context-dependent decay of motor memories during skill acquisition.

Current models of motor learning posit that skill acquisition involves both the formation and decay of multiple motor memories that can be engaged in different contexts. Memory formation is assumed to be context dependent, so that errors most strongly update motor memories associated with the current context. In contrast, memory decay is assumed to be context independent, so that movement in any context leads to uniform decay across all contexts. We demonstrate that for both object manipulation and force-field adaptation, contrary to previous models, memory decay is highly context dependent. We show that the decay of memory associated with a given context is greatest for movements made in that context, with more distant contexts showing markedly reduced decay. Thus, both memory formation and decay are strongest for the current context. We propose that this apparently paradoxical organization provides a mechanism for optimizing performance. While memory decay tends to reduce force output, memory formation can correct for any errors that arise, allowing the motor system to regulate force output so as to both minimize errors and avoid unnecessary energy expenditure. The motor commands for any given context thus result from a balance between memory formation and decay, while memories for other contexts are preserved.

Measurement of filling-factor-dependent magnetophonon resonances in graphene using Raman spectroscopy

We perform polarization-resolved Raman spectroscopy on graphene in magnetic fields up to 45 T. This reveals a filling-factor-dependent, multicomponent anticrossing structure of the Raman G peak, resulting from magnetophonon resonances between magnetoexcitons and E2g phonons. This is explained with a model of Raman scattering taking into account the effects of spatially inhomogeneous carrier densities and strain. Random fluctuations of strain-induced pseudomagnetic fields lead to increased scattering intensity inside the anticrossing gap, consistent with the experiments. © 2013 American Physical Society.

Stable multivariate rational interpolation for parameter-dependent aerospace models

A multivariate, robust, rational interpolation method for propagating uncertainties in several dimensions is presented. The algorithm for selecting numerator and denominator polynomial orders is based on recent work that uses a singular value decomposition approach. In this paper we extend this algorithm to higher dimensions and demonstrate its efficacy in terms of convergence and accuracy, both as a method for response suface generation and interpolation. To obtain stable approximants for continuous functions, we use an L2 error norm indicator to rank optimal numerator and denominator solutions. For discontinous functions, a second criterion setting an upper limit on the approximant value is employed. Analytical examples demonstrate that, for the same stencil, rational methods can yield more rapid convergence compared to pseudospectral or collocation approaches for certain problems. © 2012 AIAA.

Catalyst composition and impurity-dependent kinetics of nanowire heteroepitaxy.

The mechanisms and kinetics of axial Ge-Si nanowire heteroepitaxial growth based on the tailoring of the Au catalyst composition via Ga alloying are studied by environmental transmission electron microscopy combined with systematic ex situ CVD calibrations. The morphology of the Ge-Si heterojunction, in particular, the extent of a local, asymmetric increase in nanowire diameter, is found to depend on the Ga composition of the catalyst, on the TMGa precursor exposure temperature, and on the presence of dopants. To rationalize the findings, a general nucleation-based model for nanowire heteroepitaxy is established which is anticipated to be relevant to a wide range of material systems and device-enabling heterostructures.

Temperature-Dependent Growth and Life Cycle of Nemoura sichuanensis (Plecoptera: Nemouridae) in a Chinese Mountain Stream

Plecoptera constitute a numerically and ecologically significant component in mountain streams all over the world, but little is known of their life cycles in Asia. The life cycle of Nemoura sichuanensis and its relationship to water temperature was investigated during a 4-year study in a headwater stream (known as the Jiuchong torrent) of the Xiangxi River in Central China. Size structure histograms suggest that the life cycle was univoltine, and the relationships between the growth of Nemoura sichuanensis, physiological time, and effective accumulated water temperature were described using logistic regressions. The growth pattern was generally similar within year classes but growth rates did vary between year-classes. Our field data suggest a critical thermal threshold for emergence in Nemoura sichuanensis, that was close to 9 degrees C. The total number of physiological days required for completing larval development was 250 days. The effective accumulated water temperature was 2500 degree-days in the field. Development during the life cycle increased somewhat linearly with the physiological time and the effective accumulated water temperature, but some non-linear relationships were best developed by logistic equations.

Subcellular localization and inductive expression of the dsRNA-dependent protein kinase PKR from Japanese flounder, Paralichthys olivaceus

The double-stranded RNA (dsRNA)-dependent protein kinase (PKR) belongs to the eIF2 alpha kinase family and plays a critical role in interferon (IFN)-mediated antiviral response. Recently, in Japanese flounder (Paralichthys olivaceus), a PKR gene has been identified. In this study, we showed that PoPKR localized to the cytoplasm, and the dsRNA-binding motifs (dsRBMs) played a determinative role in protein localization. In cultured FEC cells, PoPKR was detected at a low level of constitutive expression but was highly induced after treatment with UV-inactivated grass carp hemorrhagic virus, active SMRV and Poly I:C although with different expression kinetics. In flounder, PoPKR was ubiquitously distributed in all tested tissues, and SMRV infection resulted in significant upregulation at mRNA and protein levels. In order to reveal the role of PoPKR in host antiviral response, its expression upon exposure to various inducers was characterized and further compared with that of PoHRI, which is another eIF2 alpha kinase of flounder. Interestingly, expression comparison revealed that all inducers stimulated upregulation of PoHRI in cultured flounder embryonic cells and fish, with a similar kinetics to PoPKR but to a less extent. These results suggest that, during antiviral immune response, both flounder eIF2 alpha kinases might play similar roles and that PoPKR is the predominant kinase. (C) 2009 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.