Community detection for correlation matrices

A challenging problem in the study of complex systems is that of resolving, without prior information, the emergent, mesoscopic organization determined by groups of units whose dynamical activity is more strongly correlated internally than with the rest of the system. The existing techniques to filter correlations are not explicitly oriented towards identifying such modules and can suffer from an unavoidable information loss. A promising alternative is that of employing community detection techniques developed in network theory. Unfortunately, this approach has focused predominantly on replacing network data with correlation matrices, a procedure that tends to be intrinsically biased due to its inconsistency with the null hypotheses underlying the existing algorithms. Here we introduce, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques. Our methods can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anti-correlated. We also implement multiresolution and multifrequency approaches revealing hierarchically nested sub-communities with `hard' cores and `soft' peripheries. We apply our techniques to several financial time series and identify mesoscopic groups of stocks which are irreducible to a standard, sectorial taxonomy, detect `soft stocks' that alternate between communities, and discuss implications for portfolio optimization and risk management.Comment: Final version, accepted for publication on PR

Neural replay in representation, learning and planning

Spontaneous neural activity is rarely the subject of investigation in cognitive neuroscience. This may be due to a dominant metaphor of cognition as the information processing unit, whereas internally generated thoughts are often considered as noise. Adopting a reinforcement learning (RL) framework, I consider cognition in terms of an agent trying to attain its internal goals. This framework motivated me to address in my thesis the role of spontaneous neural activity in human cognition. First, I developed a general method, called temporal delayed linear modelling (TDLM), to enable me to analyse this spontaneous activity. TDLM can be thought of as a domain general sequence detection method. It combines nonlinear classification and linear temporal modelling. This enables testing for statistical regularities in sequences of neural representations of a decoded state space. Although developed for use with human non- invasive neuroimaging data, the method can be extended to analyse rodent electrophysiological recordings. Next, I applied TDLM to study spontaneous neural activity during rest in humans. As in rodents, I found that spontaneously generated neural events tended to occur in structured sequences. These sequences are accelerated in time compared to those that related to actual experience (30 -50 ms state-to-state time lag). These sequences, termed replay, reverse their direction after reward receipt. Notably, this human replay is not a recapitulation of prior experience, but follows sequence implied by a learnt abstract structural knowledge, suggesting a factorized representation of structure and sensory information. Finally, I test the role of neural replay in model-based learning and planning in humans. Following reward receipt, I found significant backward replay of non-local experience with a 160 ms lag. This replay prioritises and facilitates the learning of action values. In a separate sequential planning task, I show these neural sequences go forward in direction, depicting the trajectory subjects about to take. The research presented in this thesis reveals a rich role of spontaneous neural activity in supporting internal computations that underpin planning and inference in human cognition

Parametric and non-parametric approaches for runoff and rainfall regionalization

The information on river flows is important for a number of reasons including; the construction of hydraulic structures for water management, for equitable distribution of water and for a number of environmental issues. The flow measurement devices are generally installed across the workspace at various locations to get data on river flows but due to a number of technical and accessibility issues, it is not always possible to get continuous data. The amount rainfall in a basin area also contributes towards the river flows and intense rainfall can cause flooding. The extended rainfall maps for the study areas to analyze these extreme events can be of great practical and theoretical interest. This thesis can be generally regarded as a work on catchment hydrology and mapping rainfall extremes to estimate certain hydrological variables that are not only useful for future research but also for practical designing and management issues. We analyzed a number of existing techniques available in literature to extend the hydrological information from gauged basin to ungauged basin; and suggested improvements. The three main frontiers of our work are: Monthly runoff regime regionalization, Flow duration curves (FDCs) regionalization and preparing rainfall hazardous maps. The proposed methods of regionalization for runoff regime and FDCs are tested for the basins located in northern Italy; whereas for rainfall extremes, the procedure is applied to the data points located in northern part of Pakistan

Metabolic Profiling of a Mapping Population Exposes New Insights in the Regulation of Seed Metabolism and Seed, Fruit, and Plant Relations

To investigate the regulation of seed metabolism and to estimate the degree of metabolic natural variability, metabolite profiling and network analysis were applied to a collection of 76 different homozygous tomato introgression lines (ILs) grown in the field in two consecutive harvest seasons. Factorial ANOVA confirmed the presence of 30 metabolite quantitative trait loci (mQTL). Amino acid contents displayed a high degree of variability across the population, with similar patterns across the two seasons, while sugars exhibited significant seasonal fluctuations. Upon integration of data for tomato pericarp metabolite profiling, factorial ANOVA identified the main factor for metabolic polymorphism to be the genotypic background rather than the environment or the tissue. Analysis of the coefficient of variance indicated greater phenotypic plasticity in the ILs than in the M82 tomato cultivar. Broad-sense estimate of heritability suggested that the mode of inheritance of metabolite traits in the seed differed from that in the fruit. Correlation-based metabolic network analysis comparing metabolite data for the seed with that for the pericarp showed that the seed network displayed tighter interdependence of metabolic processes than the fruit. Amino acids in the seed metabolic network were shown to play a central hub-like role in the topology of the network, maintaining high interactions with other metabolite categories, i.e., sugars and organic acids. Network analysis identified six exceptionally highly co-regulated amino acids, Gly, Ser, Thr, Ile, Val, and Pro. The strong interdependence of this group was confirmed by the mQTL mapping. Taken together these results (i) reflect the extensive redundancy of the regulation underlying seed metabolism, (ii) demonstrate the tight co-ordination of seed metabolism with respect to fruit metabolism, and (iii) emphasize the centrality of the amino acid module in the seed metabolic network. Finally, the study highlights the added value of integrating metabolic network analysis with mQTL mapping

Electric systems

Postprint (published version

Using a scenario-neutral framework to avoid potential maladaptation to future flood risk

This study develops a coherent framework to detect those catchment types associated with ahigh risk of maladaptation to futureflood risk. Using the“scenario‐neutral”approach to impactassessment the sensitivity of Irish catchments tofluvialflooding is examined in the context of nationalclimate change allowances. A predefined sensitivity domain is used to quantifyflood responses to +2 °Cmean annual temperature with incremental changes in the seasonality and mean of the annual precipitationcycle. The magnitude of the 20‐yearflood is simulated at each increment using two rainfall‐runoff models(GR4J, NAM), then concatenated as response surfaces for 35 sample catchments. A typology of catchmentsensitivity is developed using clustering and discriminant analysis of physical attributes. The same attributesare used to classify 215 ungauged/data‐sparse catchments. To address possible redundancies, the exposure ofdifferent catchment types to projected climate is established using an objectively selected subset of theCoupled Model Intercomparison Project Phase 5 ensemble. Hydrological model uncertainty is shown tosignificantly influence sensitivity and have a greater effect than ensemble bias. A nationalflood riskallowance of 20%, considering all 215 catchments is shown to afford protection against ~48% to 98% of theuncertainty in the Coupled Model Intercomparison Project Phase 5 subset (Representative ConcentrationPathway 8.5; 2070–2099), irrespective of hydrological model and catchment type. However, results indicatethat assuming a standard national or regional allowance could lead to local over/under adaptation. Herein,catchments with relatively less storage are sensitive to seasonal amplification in the annual cycle ofprecipitation and warrant special attention

Stream Temperature as a Tracer of Interactions Amongst Hydrological Processes, Atmospheric Exchange, and Human Activity

The water temperature of global river networks (also referred to as ‘stream temperature’ or ‘river temperature’) is an influential control on numerous aspects of water quality and riverine ecology, impacting rates of solute processing, dissolved oxygen content, and habitat viability for aquatic ecosystems. River water temperatures arise from the complex interplay of hydrological processes, atmospheric forcings, anthropogenic disturbances, making river thermal regimes challenging to understand and predict at the reach, regional, and global scale. In the absence of widespread water temperature observations, models are commonly used to simulate aspects of water temperature variability by integrating the influence of basin-specific controls and heat fluxes into and out of river systems. In addition to their role as a critical water quality parameter, water temperatures can also be leveraged as a practical tool to probe hydrologic interactions between stream channels and the underlying subsurface. This dissertation explores three diverse applications of water temperature modeling: 1) tracing groundwater-surface water interactions around stream restoration structures using water temperature observations; 2) leveraging machine learning to infer continental-scale drivers of river thermal behavior; and 3) predicting water temperatures at high spatial and temporal resolutions with coupled temperature-hydrologic models.The first chapter of this dissertation uses water temperature heat tracing methods, in combination with other field observations, to characterize hyporheic exchange induced by beaver dam analogue restoration structures. Beaver dam analogues are process-based restoration structures designed to mimic the effects of natural beaver dams and stabilize degraded and incised river reaches. Despite their frequent application, the influence of these structures on groundwater-surface water hydrology remains unclear. Vertical heat tracing, measurements of hydraulic head, and analyses of porewater biogeochemistry were used to investigate hydrologic behavior associated with three beaver dam analogues installed on Red Canyon Creek, WY, USA. These analyses demonstrated that while the restoration structures had a negligible effect on overall stream chemistry, beaver dam analogues were capable of producing heterogeneous and localized regions of hyporheic exchange. These results highlight the effectiveness of using water temperatures to trace vertical heat flow and related groundwater-surface water interactions in tandem with other field-based observations. Given the demonstrated impacts of water temperatures on river water quality, it is critical to better understand how the dominant controls on river thermal regimes vary in time and across broad spatial scales in order to design more effective watershed management strategies. Machine learning models are well suited to this objective, as they can generate accurate predictions of environmental processes while revealing key interactions between variables in large datasets. In the second chapter of this dissertation, a suite of random forest models was used to predict metrics of river temperature variability across the conterminous US using watershed characteristics extracted from a publicly-available dataset. Variable importance metrics were then interpreted to infer the underlying controls on river temperatures. Regional climate forcings tended to most closely control river temperature magnitude, though those forcings were mediated by the influence of hydrological processes, watershed characteristics, and anthropogenic disturbances. Results from the random forest models underscored the challenge in predicting aspects of water temperature variability at continental scales, particularly when river thermal regimes are disrupted by dams and reservoirs. The presented machine learning approach to river temperature prediction illustrates how large environmental datasets can be leveraged to provide discerning insight into the drivers of hydrologic and thermal processes. To supplement predictions of water temperatures at point locations along the river network, deterministic energy balance models are often applied to provide spatially distributed and temporally continuous water temperature simulations. Deterministic water temperature models function by quantifying radiative, turbulent, and advective heat fluxes into and out of a river at the air-water and water-streambed interfaces. While such water temperature models are often applied within single catchments, many watershed management applications require high resolution predictions of temperatures at a broader spatial extent. The third chapter of this dissertation focuses on the development of a coupled hydrological-water temperature energy balance model in a single test basin with the potential for expansion to the full conterminous US. Using forcings and outputs from the National Water Model, a continental-scale hydrologic model implemented by NOAA and NCAR, several water temperature model configurations of increasing complexity were tested to evaluate tradeoffs between performance and computational efficiency. Modeling efforts demonstrated that the National Water Model can be effectively leveraged to provide high-quality predictions of hourly water temperatures throughout a river network, though critical challenges remain in expanding coupled water temperature models to continental scales