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The increasing penetration of renewable energy sources has been leading to the progressive phase-out of synchronous generators, which constitute the main source of frequency stability for electric power systems. In the light of these changes, over the past years, some power systems started to exhibit an odd frequency distribution characterised by a bimodal behavior. This results in increased wear and tear of turbine governors and, in general, in degraded frequency performances. This is a cause of concern for grid operators, which have become increasingly interested in understanding the factors shaping frequency distribution. This paper explores the root causes of unwanted frequency distributions. The influence of some main aggregate system parameters on frequency distribution is detailed. The paper also shows that the implementation of the so-called synthetic inertia can lead to a robust unimodal frequency distribution.

As in many fields of dynamic modeling, the long runtime of hydrological models hinders Bayesian inference of model parameters from data. By replacing a model with an approximation of its output as a function of input and/or parameters, emulation allows us to complete this task by trading-off accuracy for speed. We combine (i) the use of a mechanistic emulator, (ii) low-discrepancy sampling of the parameter space, and (iii) iterative refinement of the design data set, to perform Bayesian inference with a very small design data set constructed with 128 model runs in a parameter space of up to eight dimensions. In our didactic example we use a model implemented with the hydrological simulator SWMM that allows us to compare our inference results against those derived with the full model. This comparison demonstrates that iterative improvements lead to reasonable results with a very small design data set.

Recently, a new concept for continuum robots capable of producing macro-scale and micro-scale motion has been presented. These robots achieve their multi-scale motion capabilities by coupling direct-actuation of push-pull back-bones for macro motion with indirect actuation whereby the equilibrium pose is altered to achieve micro-scale motion. This paper presents a first attempt at explaining the micro-motion capabilities of these robots from a modeling perspective. This paper presents the macro and micro motion kinematics of a single segment continuum robot by using statics coupling effects among its sub-segments. Experimental observations of the micro-scale motion demonstrate a turning point behavior which could not be explained well using the current modeling methods. We present a simplistic modeling approach that introduces two calibration parameters to calibrate the moment coupling effects among the sub segments of the robot. It is shown that these two parameters can reproduce the turning point behavior at the micro-scale. The instantaneous macro and micro scale kinematics Jacobians and the calibration parameters identification Jacobian are derived. The modeling approach is verified against experimental data showing that our simplistic modeling approach can capture the experimental motion data with RMS position error of 5.82 micrometers if one wishes to fit the entire motion profile with the turning point. If one chooses to exclude motions past the turning point, our model can fit the experimental data with an accuracy of 4.76 micrometers., Comment: In press for ASME Journal of Mechanisms and Robotics, 14 pages, 10 figures, 3 tables

Real-time coupling of cell cultures to neuromorphic circuits necessitates a neuromorphic network that replicates biological behaviour both on a per-neuron and on a population basis, with a network size comparable to the culture. We present a large neuromorphic system composed of 9 chips, with overall 2880 neurons and 144M conductance-based synapses. As they are realized in a robust switched-capacitor fashion, individual neurons and synapses can be configured to replicate with high fidelity a wide range of biologically realistic behaviour. In contrast to other exploration/heuristics-based approaches, we employ a theory-guided mesoscopic approach to configure the overall network to a range of bursting behaviours, thus replicating the statistics of our targeted in-vitro network. The mesoscopic approach has implications beyond our proposed biohybrid, as it allows a targeted exploration of the behavioural space, which is a non-trivial task especially in large, recurrent networks., Comment: 21 pages, 13 figures

Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW). Such dynamic diversity poses a challenge for producing efficient large-scale simulations that embody realistic metaphors of short- and long-range synaptic connectivity. In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates, which implies a wide variety of loads of local computation and communication. A balanced evaluation of simulation performance and robustness should therefore include tests of a variety of cortical dynamic states. Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which reflects the modular organization of the cortex. We explored networks up to 192x192 modules, each composed of 1250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying spatial decay constant. For the largest networks the total number of synapses was over 70 billion. The execution platform included up to 64 dual-socket nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz clock rates. Network initialization time, memory usage, and execution time showed good scaling performances from 1 to 1024 processes, implemented using the standard Message Passing Interface (MPI) protocol. We achieved simulation speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both cortical states in the explored range of inter-modular interconnections., Comment: 22 pages, 9 figures, 4 tables

Organic semiconductors are usually not thought to show outstanding performance in highly-integrated, sub 100 nm transistors. Consequently, single-crystalline materials such as SWCNTs, MoS2 or inorganic semiconductors are the material of choice at these nanoscopic dimensions. Here, we show that using a novel vertical field-effect transistor design with a channel length of only 40 nm and a footprint of 2 x 80 x 80 nm$^2$, high electrical performance with organic polymers can be realized when using electrolyte gating. Our organic transistors combine high on-state current densities of above 3 MA/cm$^2$, on/off current modulation ratios of up to 108 and large transconductances of up to 5000 S/m. Given the high on-state currents at yet large on/off ratios, our novel structures also show promise for use in artificial neural networks, where they could operate as memristive devices with sub 100 fJ energy usage.

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach., Comment: review article, 29 pages, 7 figures

In the binomial random graph $\mathcal{G}(n,p)$, when $p$ changes from $(1-\varepsilon)/n$ (subcritical case) to $1/n$ and then to $(1+\varepsilon)/n$ (supercritical case) for $\varepsilon>0$, with high probability the order of the largest component increases smoothly from $O(\varepsilon^{-2}\log(\varepsilon^3 n))$ to $\Theta(n^{2/3})$ and then to $(1 \pm o(1)) 2 \varepsilon n$. As a natural generalisation of random graphs and connectedness, we consider the binomial random $k$-uniform hypergraph $\mathcal{H}^k(n,p)$ (where each $k$-tuple of vertices is present as a hyperedge with probability $p$ independently) and the following notion of high-order connectedness. Given an integer $1 \leq j \leq k-1$, two sets of $j$ vertices are called \emph{$j$-connected} if there is a walk of hyperedges between them such that any two consecutive hyperedges intersect in at least $j$ vertices. A $j$-connected component is a maximal collection of pairwise $j$-connected $j$-tuples of vertices. Recently, the threshold for the appearance of the giant $j$-connected component in $\mathcal{H}^k(n,p)$ and its order were determined. In this article, we take a closer look at the subcritical random hypergraph. We determine the structure, order, and size of the largest $j$-connected components, with the help of a certain class of `hypertrees' and related objects. In our proofs, we combine various probabilistic and enumerative techniques, such as generating functions and couplings with branching processes. Our study will pave the way to establishing a symmetry between the subcritical random hypergraph and the hypergraph obtained from the supercritical random hypergraph by deleting its giant $j$-connected component., Comment: 27 pages

We consider $k$-dimensional random simplicial complexes that are generated from the binomial random $(k+1)$-uniform hypergraph by taking the downward-closure, where $k\geq 2$. For each $1\leq j \leq k-1$, we determine when all cohomology groups with coefficients in $\mathbb{F}_2$ from dimension one up to $j$ vanish and the zero-th cohomology group is isomorphic to $\mathbb{F}_2$. This property is not deterministically monotone for this model of random complexes, but nevertheless we show that it has a single sharp threshold. Moreover we prove a hitting time result, relating the vanishing of these cohomology groups to the disappearance of the last minimal obstruction. We also study the asymptotic distribution of the dimension of the $j$-th cohomology group inside the critical window. As a corollary, we deduce a hitting time result for a different model of random simplicial complexes introduced in [Linial and Meshulam, Combinatorica, 2006], a result which was previously only known for dimension two [Kahle and Pittel, Random Structures Algorithms, 2016]., Comment: 35 pages

The combination of new recording techniques in neuroscience and powerful inference methods recently held the promise to recover useful effective models, at the single neuron or network level, directly from observed data. The value of a model of course should critically depend on its ability to reproduce the dynamical behavior of the modeled system; however, few attempts have been made to inquire into the dynamics of inferred models in neuroscience, and none, to our knowledge, at the network level. Here we introduce a principled modification of a widely used generalized linear model (GLM), and learn its structural and dynamic parameters from ex-vivo spiking data. We show that the new model is able to capture the most prominent features of the highly non-stationary and non-linear dynamics displayed by the biological network, where the reference GLM largely fails. Two ingredients turn out to be key for success. The first one is a bounded transfer function that makes the single neuron able to respond to its input in a saturating fashion; beyond its biological plausibility such property, by limiting the capacity of the neuron to transfer information, makes the coding more robust in the face of the highly variable network activity, and noise. The second ingredient is a super-Poisson spikes generative probabilistic mechanism; this feature, that accounts for the fact that observations largely undersample the network, allows the model neuron to more flexibly incorporate the observed activity fluctuations. Taken together, the two ingredients, without increasing complexity, allow the model to capture the key dynamic elements. When left free to generate its spontaneous activity, the inferred model proved able to reproduce not only the non-stationary population dynamics of the network, but also part of the fine-grained structure of the dynamics at the single neuron level.

Individual cells exhibit specific proliferative responses to changes in microenvironmental conditions. Whether such potential is constrained by the cell density throughout the growth process is however unclear. Here, we identify a theoretical framework that captures how the information encoded in the initial density of cancer cell populations impacts their growth profile. By following the growth of hundreds of populations of cancer cells, we found that the time they need to adapt to the environment decreases as the initial cell density increases. Moreover, the population growth rate shows a maximum at intermediate initial densities. With the support of a mathematical model, we show that the observed interdependence of adaptation time and growth rate is significantly at odds both with standard logistic growth models and with the Monod-like function that governs the dependence of the growth rate on nutrient levels. Our results (i) uncover and quantify a previously unnoticed heterogeneity in the growth dynamics of cancer cell populations; (ii) unveil how population growth may be affected by single-cell adaptation times; (iii) contribute to our understanding of the clinically-observed dependence of the primary and metastatic tumor take rates on the initial density of implanted cancer cells., Comment: 25 pages (13 main text + 12 supplementary information), 16 figures (5 main text + 11 supplementary information). Comments welcome