Physics-inspired machine learning for power grid frequency modelling

The operation of power systems is affected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a non-autonomous system whose parameters vary strongly with time. However, the external driving factors are usually only available on coarse scales and the actual dependencies of the dynamic system parameters are generally unknown. Here, we propose a physics-inspired machine learning model that bridges the gap between large-scale drivers and short-term dynamics of the power system. Integrating stochastic differential equations and artificial neural networks, we construct a probabilistic model of the power grid frequency dynamics in Continental Europe. Its probabilistic prediction outperforms the daily average profile, which is an important benchmark. Using the integrated model, we identify and explain the parameters of the dynamical system from the data, which reveals their strong time-dependence and their relation to external drivers such as wind power feed-in and fast generation ramps. Finally, we generate synthetic time series from the model, which successfully reproduce central characteristics of the grid frequency such as their heavy-tailed distribution. All in all, our work emphasises the importance of modelling power system dynamics as a stochastic non-autonomous system with both intrinsic dynamics and external drivers.Comment: 21 pages, 5 figure

The barriers to nurturing and empowering long-term care experiments - lessons learnt to advance future healthcare projects

The objective of this study is to explore the barriers to nurturing and empowering subsidized long-term care experiments that try to deal with today's long-term care challenges such as an aging population and increasing healthcare costs. Nurturing is the process of planning, implementing, and learning from experiments. The empowerment process deals with stabilizing experiments into the existing long-term care system. This is a qualitative study of a network that nurtured and tried to empower three long-term care experiments, which were subsidized by a ministerial transition program (2009–2011) in the Netherlands. In total, 14 open-ended, semi-structured interviews were conducted. Further data were collected through participation, collecting documents, and pursuing a focus group. The findings revealed eight barriers to nurturing and empowering the experiments. During the planning of the experiments, top managers and consultants were (1) lacking time, (2) ignored the local context, and (3) did neither engage project managers nor professionals. At the start of the experimentation, project managers and professionals were lacking (4) motivation, (5) time, and (6) support while there was (7) no sense of urgency to experiment. Finally, there was (8) no commitment from the top managers during the empowerment of the experiments. In conclusion, future projects have to try to avoid these barriers. Otherwise, time, money, and energy are lost in overcoming these barriers, which are needed to deal with today's long-term care challenges

A joint time-dependent density-functional theory for excited states of electronic systems in solution

We present a novel joint time-dependent density-functional theory for the description of solute-solvent systems in time-dependent external potentials. Starting with the exact quantum-mechanical action functional for both electrons and nuclei, we systematically eliminate solvent degrees of freedom and thus arrive at coarse-grained action functionals which retain the highly accurate \emph{ab initio} description for the solute and are, in principle, exact. This procedure allows us to examine approximations underlying popular embedding theories for excited states. Finally, we introduce a novel approximate action functional for the solute-water system and compute the solvato-chromic shift of the lowest singlet excited state of formaldehyde in aqueous solution, which is in good agreement with experimental findings.Comment: 11 page

Signatures of hierarchical temporal processing in the mouse visual system

A core challenge for information processing in the brain is to integrate information across various timescales. This could be achieved by a hierarchical organization of temporal processing, as reported for primates; however, it is open whether this hierarchical organization generalizes to sensory processing across species. Here, we studied signatures of temporal processing along the anatomical hierarchy in the mouse visual system. We found that the intrinsic and information timescales of spiking activity, which serve as proxies for how long information is stored in neural activity, increased along the anatomical hierarchy. Using information theory, we also quantified the predictability of neural spiking. The predictability is expected to be higher for longer integration of past information, but low for redundancy reduction in an efficient code. We found that predictability decreases along the anatomical cortical hierarchy, which is in line with efficient coding, but in contrast to the expectation of higher predictability for areas with higher timescales. Mechanistically, we could explain these results in a basic network model, where the increase in timescales arises from increasing network recurrence, while recurrence also reduces predictability if the model's input is correlated. The model thus suggests that timescales are mainly a network-intrinsic effect, whereas information-theoretic predictability depends on other sources such as (correlated) sensory stimuli. This is supported by a comparison of experimental data from different stimulus conditions. Our results show a clear hierarchy across mouse visual cortex, and thus suggest that hierarchical temporal processing presents a general organization principle across mammals.Comment: 20 pages, 4 figure

Structural plasticity on an accelerated analog neuromorphic hardware system

In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their neural connectivity and synaptic capacity depends on their specific design choices, but is always intrinsically limited. Here, we present a strategy to achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- and gpostsynaptic partners while keeping the neuronal fan-in constant and the connectome sparse. In particular, we implemented this algorithm on the analog neuromorphic system BrainScaleS-2. It was executed on a custom embedded digital processor located on chip, accompanying the mixed-signal substrate of spiking neurons and synapse circuits. We evaluated our implementation in a simple supervised learning scenario, showing its ability to optimize the network topology with respect to the nature of its training data, as well as its overall computational efficiency

Facile Conversion of syn-[Fe-IV(O)(TMC)](2+) into the anti Isomer via Meunier's Oxo-Hydroxo Tautomerism Mechanism

The syn and anti isomers of [Fe-IV(O)(TMC)](2+) (TMC=tetramethylcyclam) represent the first isolated pair of synthetic non-heme oxoiron(IV) complexes with identical ligand topology, differing only in the position of the oxo unit bound to the iron center. Both isomers have previously been characterized. Reported here is that the syn isomer [Fe-IV(O-syn)(TMC)(NCMe)](2+) (2) converts into its anti form [Fe-IV(O-anti)(TMC)(NCMe)](2+) (1) in MeCN, an isomerization facilitated by water and monitored most readily by (HNMR)-H-1 and Raman spectroscopy. Indeed, when (H2O)-O-18 is introduced to 2, the nascent 1 becomes O-18-labeled. These results provide compelling evidence for a mechanism involving direct binding of a water molecule trans to the oxo atom in 2 with subsequent oxo-hydroxo tautomerism for its incorporation as the oxo atom of 1. The nonplanar nature of the TMC supporting ligand makes this isomerization an irreversible transformation, unlike for their planar heme counterparts

A Conserved GA Element in TATA-Less RNA Polymerase II Promoters

Initiation of RNA polymerase (Pol) II transcription requires assembly of the pre-initiation complex (PIC) at the promoter. In the classical view, PIC assembly starts with binding of the TATA box-binding protein (TBP) to the TATA box. However, a TATA box occurs in only 15% of promoters in the yeast Saccharomyces cerevisiae, posing the question how most yeast promoters nucleate PIC assembly. Here we show that one third of all yeast promoters contain a novel conserved DNA element, the GA element (GAE), that generally does not co-occur with the TATA box. The distance of the GAE to the transcription start site (TSS) resembles the distance of the TATA box to the TSS. The TATA-less TMT1 core promoter contains a GAE, recruits TBP, and supports formation of a TBP-TFIIB-DNA-complex. Mutation of the promoter region surrounding the GAE abolishes transcription in vivo and in vitro. A 32-nucleotide promoter region containing the GAE can functionally substitute for the TATA box in a TATA-containing promoter. This identifies the GAE as a conserved promoter element in TATA-less promoters