Infinite-dimensional reservoir computing.

Reservoir computing approximation and generalization bounds are proved for a new concept class of input/output systems that extends the so-called generalized Barron functionals to a dynamic context. This new class is characterized by the readouts with a certain integral representation built on infinite-dimensional state-space systems. It is shown that this class is very rich and possesses useful features and universal approximation properties. The reservoir architectures used for the approximation and estimation of elements in the new class are randomly generated echo state networks with either linear or ReLU activation functions. Their readouts are built using randomly generated neural networks in which only the output layer is trained (extreme learning machines or random feature neural networks). The results in the paper yield a recurrent neural network-based learning algorithm with provable convergence guarantees that do not suffer from the curse of dimensionality when learning input/output systems in the class of generalized Barron functionals and measuring the error in a mean-squared sense.

Transferability of a Bayesian Belief Network across diverse agricultural catchments using high-frequency hydrochemistry and land management data.

Biogeochemical catchment models are often developed for a single catchment and, as a result, often generalize poorly beyond this. Evaluating their transferability is an important step in improving their predictive power and application range. We assess the transferability of a recently developed Bayesian Belief Network (BBN) that simulated monthly stream phosphorus (P) concentrations in a poorly-drained grassland catchment through application to three further catchments with different hydrological regimes and agricultural land uses. In all catchments, flow and turbidity were measured sub-hourly from 2009 to 2016 and supplemented with 400-500 soil P test measurements. In addition to a previously parameterized BBN, five further model structures were implemented to incorporate in a stepwise way: in-stream P removal using expert elicitation, additional groundwater P stores and delivery, and the presence or absence of septic tank treatment, and, in one case, Sewage Treatment Works. Model performance was tested through comparison of predicted and observed total reactive P (TRP) concentrations and percentage bias (PBIAS). The original BBN accurately simulated the absolute values of observed flow and TRP concentrations in the poorly and moderately drained catchments (albeit with poor apparent percentage bias scores; 76 % ≤ PBIAS≤94 %) irrespective of the dominant land use, but performed less well in the groundwater-dominated catchments. However, including groundwater total dissolved P (TDP) and Sewage Treatment Works (STWs) inputs, and in-stream P uptake improved model performance (-5 % ≤ PBIAS≤18 %). A sensitivity analysis identified redundant variables further helping to streamline the model applications. An enhanced BBN model capable for wider application and generalisation resulted.

Test of lepton flavor universality inB±â†’K±µ+µ-andB±â†’K±e+e-decays in proton-proton collisions ats=13TeV.

A test of lepton flavor universality inB±â†’K±µ+µ-andB±â†’K±e+e-decays, as well as a measurement of differential and integrated branching fractions of a nonresonantB±â†’K±µ+µ-decay are presented. The analysis is made possible by a dedicated data set of proton-proton collisions ats=13TeVrecorded in 2018, by the CMS experiment at the LHC, using a special high-rate data stream designed for collecting about 10 billion unbiased b hadron decays. The ratio of the branching fractionsB(B±â†’K±µ+µ-)toB(B±â†’K±e+e-)is determined from the measured double ratioR(K)of these decays to the respective branching fractions of theB±â†’J/ψK±withJ/ψ→µ+µ-ande+e-decays, which allow for significant cancellation of systematic uncertainties. The ratioR(K)is measured in the range1.1

Toward a universal theory of consciousness.

While falsifiability has been broadly discussed as a desirable property of a theory of consciousness, in this paper, we introduce the meta-theoretic concept of "Universality" as an additional desirable property for a theory of consciousness. The concept of universality, often assumed in physics, posits that the fundamental laws of nature are consistent and apply equally everywhere in the universe and remain constant over time. This assumption is crucial in science, acting as a guiding principle for developing and testing theories. When applied to theories of consciousness, universality can be defined as the ability of a theory to determine whether any fully described dynamical system is conscious or non-conscious. Importantly, for a theory to be universal, the determinant of consciousness needs to be defined as an intrinsic property of a system as opposed to replying on the interpretation of the external observer. The importance of universality originates from the consideration that given that consciousness is a natural phenomenon, it could in principle manifest in any physical system that satisfies a certain set of conditions whether it is biological or non-biological. To date, apart from a few exceptions, most existing theories do not possess this property. Instead, they tend to make predictions as to the neural correlates of consciousness based on the interpretations of brain functions, which makes those theories only applicable to brain-centric systems. While current functionalist theories of consciousness tend to be heavily reliant on our interpretations of brain functions, we argue that functionalist theories could be converted to a universal theory by specifying mathematical formulations of the constituent concepts. While neurobiological and functionalist theories retain their utility in practice, we will eventually need a universal theory to fully explain why certain types of systems possess consciousness.

Spiking Neural Membrane Systems with Adaptive Synaptic Time Delay.

Spiking neural membrane systems (or spiking neural P systems, SNP systems) are a new type of computation model which have attracted the attention of plentiful scholars for parallelism, time encoding, interpretability and extensibility. The original SNP systems only consider the time delay caused by the execution of rules within neurons, but not caused by the transmission of spikes via synapses between neurons and its adaptive adjustment. In view of the importance of time delay for SNP systems, which are a time encoding computation model, this study proposes SNP systems with adaptive synaptic time delay (ADSNP systems) based on the dynamic regulation mechanism of synaptic transmission delay in neural systems. In ADSNP systems, besides neurons, astrocytes that can generate adenosine triphosphate (ATP) are introduced. After receiving spikes, astrocytes convert spikes into ATP and send ATP to the synapses controlled by them to change the synaptic time delays. The Turing universality of ADSNP systems in number generating and accepting modes is proved. In addition, a small universal ADSNP system using 93 neurons and astrocytes is given. The superiority of the ADSNP system is demonstrated by comparison with the six variants. Finally, an ADSNP system is constructed for credit card fraud detection, which verifies the feasibility of the ADSNP system for solving real-world problems. By considering the adaptive synaptic delay, ADSNP systems better restore the process of information transmission in biological neural networks, and enhance the adaptability of SNP systems, making the control of time more accurate.

Hypergraph-Based Numerical Spiking Neural Membrane Systems with Novel Repartition Protocols.

The classic spiking neural P (SN P) systems abstract the real biological neural network into a simple structure based on graphs, where neurons can only communicate on the plane. This study proposes the hypergraph-based numerical spiking neural membrane (HNSNM) systems with novel repartition protocols. Through the introduction of hypergraphs, the HNSNM systems can characterize the high-order relationships among neurons and extend the traditional neuron structure to high-dimensional nonlinear spaces. The HNSNM systems also abstract two biological mechanisms of synapse creation and pruning, and use plasticity rules with repartition protocols to achieve planar, hierarchical and spatial communications among neurons in hypergraph neuron structures. Through imitating register machines, the Turing universality of the HNSNM systems is proved by using them as number generating and accepting devices. A universal HNSNM system consisting of 41 neurons is constructed to compute arbitrary functions. By solving NP-complete problems using the subset sum problem as an example, the computational efficiency and effectiveness of HNSNM systems are verified.

Restoring the Fluctuation-Dissipation Theorem in Kardar-Parisi-Zhang Universality Class through a New Emergent Fractal Dimension.

The Kardar-Parisi-Zhang (KPZ) equation describes a wide range of growth-like phenomena, with applications in physics, chemistry and biology. There are three central questions in the study of KPZ growth: the determination of height probability distributions; the search for ever more precise universal growth exponents; and the apparent absence of a fluctuation-dissipation theorem (FDT) for spatial dimension d>1. Notably, these questions were answered exactly only for 1+1 dimensions. In this work, we propose a new FDT valid for the KPZ problem in d+1 dimensions. This is achieved by rearranging terms and identifying a new correlated noise which we argue to be characterized by a fractal dimension dn. We present relations between the KPZ exponents and two emergent fractal dimensions, namely df, of the rough interface, and dn. Also, we simulate KPZ growth to obtain values for transient versions of the roughness exponent α, the surface fractal dimension df and, through our relations, the noise fractal dimension dn. Our results indicate that KPZ may have at least two fractal dimensions and that, within this proposal, an FDT is restored. Finally, we provide new insights into the old question about the upper critical dimension of the KPZ universality class.

Influences of Faith on Rural Appalachian Older Adult Health: An Ethnonursing Study.

INTRODUCTION: Rural Appalachian older adults (RAOAs) constitute a vulnerable population and experience significant health disparities. The combination of age, poverty, rural residence, health care provider shortages, and limited transportation increases risks for poor health outcomes. Spirituality enhances older adult health; however, little is known about spirituality-health linkages of RAOAs. Therefore, the purpose of this study was to discover the influences of spirituality on RAOA health. METHODOLOGY: Culture Care Theory and ethnonursing method guided analysis of 32 RAOA interviews in community settings in East Tennessee. RESULTS: "Faith" is an integral component of RAOA culture and health. Three themes were extrapolated: (a) Relationship with God is personal; (b) faith beliefs and practices influence health, illness, death, and dying; and [the need to] (c) "Open the door" for spiritual care. DISCUSSION: Faith assessment and spiritual care recommendations contribute to culturally congruent care for RAOAs and may be transferable to care for other older adults.

Multilevel Gradient-Ordered Silicon Anode with Unprecedented Sodium Storage.

While cost-effective sodium-ion batteries (SIBs) with crystalline silicon anodes promise high theoretical capacities, they perform poorly because silicon stores sodium ineffectively (capacity <40 mAh g-1 ). To address this issue, herein an atomic-order structural-design tactic is adopted for obtaining unique multilevel gradient-ordered silicon (MGO-Si) by simple electrochemical reconstruction. In situ-formed short-range-, medium-range-, and long-range-ordered structures construct a stable MGO-Si, which contributes to favorable Na-Si interaction and fast ion diffusion channels. These characteristics afford a high reversible capacity (352.7 mAh g-1 at 50 mA g-1 ) and stable cycling performance (95.2% capacity retention after 4000 cycles), exhibiting record values among those reported for pure silicon electrodes. Sodium storage of MGO-Si involves an adsorption-intercalation mechanism, and a stepwise construction strategy of gradient-ordered structure further improves the specific capacity (339.5 mAh g-1 at 100 mA g-1 ). Reconstructed Si/C composites show a high reversible capacity of 449.5 mAh g-1 , significantly better than most carbonaceous anodes. The universality of this design principle is demonstrated for other inert or low-capacity materials (micro-Si, SiO2 , SiC, graphite, and TiO2 ), boosting their capacities by 1.5-6 times that of pristine materials, thereby providing new solutions to facilitate sodium storage capability for better-performing battery designs.

Universality in eye movements and reading: A replication with increased power.

Liversedge, Drieghe, Li, Yan, Bai and Hyönä (2016) reported an eye movement study that investigated reading in Chinese, Finnish and English (languages with markedly different orthographic characteristics). Analyses of the eye movement records showed robust differences in fine grained characteristics of eye movements between languages, however, overall sentence reading times did not differ. Liversedge et al. interpreted the entire set of results across languages as reflecting universal aspects of processing in reading. However, the study has been criticized as being statistically underpowered (Brysbaert, 2019) given that only 19-21 subjects were tested in each language. Also, given current best practice, the original statistical analyses can be considered to be somewhat weak (e.g., no inclusion of random slopes and no formal comparison of performance between the three languages). Finally, the original study did not include any formal statistical model to assess effects across all three languages simultaneously. To address these (and some other) concerns, we tested at least 80 new subjects in each language and conducted formal statistical modeling of our data across all three languages. To do this, we included an index that captured variability in visual complexity in each language. Unlike the original findings, the new analyses showed shorter total sentence reading times for Chinese relative to Finnish and English readers. The other main findings reported in the original study were consistent. We suggest that the faster reading times for Chinese subjects occurred due to cultural changes that have taken place in the decade or so that lapsed between when the original and current subjects were tested. We maintain our view that the results can be taken to reflect universality in aspects of reading and we evaluate the claims regarding a lack of statistical power that were levelled against the original article.

Universality and Multiplication of Gigahertz-Operated Silicon Pumps with Parts Per Million-Level Uncertainty.

The universality of physical phenomena is a pivotal concept underlying quantum standards. In this context, the realization of a quantum current standard using silicon single-electron pumps necessitates the verification of the equivalence across multiple devices. Herein, we experimentally investigate the universality of pumped currents from two different silicon single-electron devices which are placed inside the cryogen-free dilution refrigerator whose temperature (mixing chamber plate) was ∼150 mK under the operation of the pump devices. By direct comparison using an ultrastable current amplifier as a galvanometer, we confirm that two pumped currents are consistent with ∼1 ppm uncertainty. Furthermore, we realize quantum-current multiplication with a similar uncertainty by adding the currents of two different gigahertz (GHz)-operated silicon pumps, whose generated currents are confirmed to be identical. These results pave the way for realizing a quantum current standard in the nanoampere range and a quantum metrology triangle experiment using silicon pump devices.

Cluster Persistence for Weighted Graphs.

Persistent homology is a natural tool for probing the topological characteristics of weighted graphs, essentially focusing on their 0-dimensional homology. While this area has been thoroughly studied, we present a new approach to constructing a filtration for cluster analysis via persistent homology. The key advantages of the new filtration is that (a) it provides richer signatures for connected components by introducing non-trivial birth times, and (b) it is robust to outliers. The key idea is that nodes are ignored until they belong to sufficiently large clusters. We demonstrate the computational efficiency of our filtration, its practical effectiveness, and explore into its properties when applied to random graphs.

Quantitative Modelling in Stem Cell Biology and Beyond: How to Make Best Use of It.

Purpose of Review: This article gives a broad overview of quantitative modelling approaches in biology and provides guidance on how to employ them to boost stem cell research, by helping to answer biological questions and to predict the outcome of biological processes. Recent Findings: The twenty-first century has seen a steady increase in the proportion of cell biology publications employing mathematical modelling to aid experimental research. However, quantitative modelling is often used as a rather decorative element to confirm experimental findings, an approach which often yields only marginal added value, and is in many cases scientifically questionable. Summary: Quantitative modelling can boost biological research in manifold ways, but one has to take some careful considerations before embarking on a modelling campaign, in order to maximise its added value, to avoid pitfalls that may lead to wrong results, and to be aware of its fundamental limitations, imposed by the risks of over-fitting and "universality".

A universal approach to dual-metal-atom catalytic sites confined in carbon dots for various target reactions.

Here, a molecular-design and carbon dot-confinement coupling strategy through the pyrolysis of bimetallic complex of diethylenetriamine pentaacetic acid under low-temperature is proposed as a universal approach to dual-metal-atom sites in carbon dots (DMASs-CDs). CDs as the "carbon islands" could block the migration of DMASs across "islands" to achieve dynamic stability. More than twenty DMASs-CDs with specific compositions of DMASs (pairwise combinations among Fe, Co, Ni, Mn, Zn, Cu, and Mo) have been synthesized successfully. Thereafter, high intrinsic activity is observed for the probe reaction of urea oxidation on NiMn-CDs. In situ and ex situ spectroscopic characterization and first-principle calculations unveil that the synergistic effect in NiMn-DMASs could stretch the urea molecule and weaken the N-H bond, endowing NiMn-CDs with a low energy barrier for urea dehydrogenation. Moreover, DMASs-CDs for various target electrochemical reactions, including but not limited to urea oxidation, are realized by optimizing the specific DMAS combination in CDs.

Contingent universality: The epistemic politics of global mental health.

The field of global mental health (GMH) has undergone profound changes over the past decade. Outgrowing its earlier agenda it has performed a reflexive turn, broadened towards a social paradigm and developed new modes of knowledge production, all of which reshaped 'mental health' as a global object of knowledge and care, and the epistemic politics of the field. Drawing on long-term ethnographic fieldwork among GMH experts and recent agenda-setting publications, I discuss how GMH advocates and critical observers alike have created conceptual and practical middle-grounds between different forms of mental health knowledge - across culture, epistemic power, lived experience, policy platforms and academic disciplines - framing their dynamic encounters as dialogue, adaptation, participation, co-production or integration. Ultimately, I argue, GMH today is focusing less on establishing mental health as a universal problem than on managing its inherent multiplicity through alignment and integration across different bodies of knowledge. Global knowledge, so conceived, is fluid and malleable and produced in open-ended knowledge practices, governed by what I call 'contingent universality'. It is not new that the concepts and practices of the psy-disciplines are malleable and multiple, internally and externally contested, rapidly changing over time and not easily transferrable across space. What is new is that within the increasingly heterogenous epistemic space of GMH, these features have become assets rather than liabilities. GMH knowledge achieves both global reach and local relevance precisely because 'mental health' can be many things; it can be expressed in a wide range of idioms and concepts, and its problems and solutions align easily with others, at many scales. These fluid and integrative knowledge practices call for renewed empirical, critical and collaborative engagement.

Universal interpretations of vocal music.

Despite the variability of music across cultures, some types of human songs share acoustic characteristics. For example, dance songs tend to be loud and rhythmic, and lullabies tend to be quiet and melodious. Human perceptual sensitivity to the behavioral contexts of songs, based on these musical features, suggests that basic properties of music are mutually intelligible, independent of linguistic or cultural content. Whether these effects reflect universal interpretations of vocal music, however, is unclear because prior studies focus almost exclusively on English-speaking participants, a group that is not representative of humans. Here, we report shared intuitions concerning the behavioral contexts of unfamiliar songs produced in unfamiliar languages, in participants living in Internet-connected industrialized societies (n = 5,516 native speakers of 28 languages) or smaller-scale societies with limited access to global media (n = 116 native speakers of three non-English languages). Participants listened to songs randomly selected from a representative sample of human vocal music, originally used in four behavioral contexts, and rated the degree to which they believed the song was used for each context. Listeners in both industrialized and smaller-scale societies inferred the contexts of dance songs, lullabies, and healing songs, but not love songs. Within and across cohorts, inferences were mutually consistent. Further, increased linguistic or geographical proximity between listeners and singers only minimally increased the accuracy of the inferences. These results demonstrate that the behavioral contexts of three common forms of music are mutually intelligible cross-culturally and imply that musical diversity, shaped by cultural evolution, is nonetheless grounded in some universal perceptual phenomena.

Multifractality of Complex Networks Is Also Due to Geometry: A Geometric Sandbox Algorithm.

Over the past three decades, describing the reality surrounding us using the language of complex networks has become very useful and therefore popular. One of the most important features, especially of real networks, is their complexity, which often manifests itself in a fractal or even multifractal structure. As a generalization of fractal analysis, the multifractal analysis of complex networks is a useful tool for identifying and quantitatively describing the spatial hierarchy of both theoretical and numerical fractal patterns. Nowadays, there are many methods of multifractal analysis. However, all these methods take into account only the fact of connection between nodes (and eventually the weight of edges) and do not take into account the real positions (coordinates) of nodes in space. However, intuition suggests that the geometry of network nodes' position should have a significant impact on the true fractal structure. Many networks identified in nature (e.g., air connection networks, energy networks, social networks, mountain ridge networks, networks of neurones in the brain, and street networks) have their own often unique and characteristic geometry, which is not taken into account in the identification process of multifractality in commonly used methods. In this paper, we propose a multifractal network analysis method that takes into account both connections between nodes and the location coordinates of nodes (network geometry). We show the results for different geometrical variants of the same network and reveal that this method, contrary to the commonly used method, is sensitive to changes in network geometry. We also carry out tests for synthetic as well as real-world networks.

Universality of Benzoquinone-based Anodes toward Various Metal Cations in Aqueous Rechargeable Batteries.

The poly(2,5-dihydroxy-1,4-benzoquinone-3,6-methylene) (denoted as PDBM) capable of reversible coordination/uncoordination with both mono- and multivalent cations in aqueous electrolytes is desired to develop safe, sustainable, and cost-effective aqueous rechargeable batteries (ARBs). However, the comprehensive mechanism between the electrochemical performance of PDBM and properties of these metal cations is unclear. Herein, we initially demonstrate the universality of PDBM to reversibly coordinate/uncoordinate with various cations (Na+, Mg2+, Ca2+, Zn2+, Al3+, etc.) with high specific capacities (>200 mA h g-1), high rate capabilities (∼20 C), and long cycling life (5000 cycles). Additionally, an unprecedented ion-coordination mechanism is presented: the monovalent cations prefer to occupy the in-plane sites with respect to the benzene rings of PDBM during the electrochemical reduced process, while the multivalent cations with the larger charge density tend to occupy the out-of-plane sites, which can use more active sites in the PDBM molecule and deliver the higher specific capacities. Meanwhile, the redox potential of PDBM decreases with the decrease in the binding energy between metal cations and PDBM molecules. The universality of PDBM to numerous cations is beneficial to design high-safety, low-cost, and long-lifespan ARBs for large-scale energy storage systems by modulating the aqueous electrolytes.

Spiking neural P systems with lateral inhibition.

As a member of the third generation of artificial neural network models, spiking neural P systems (SN P systems) have gained a hot research spot in recent years. This work introduces the phenomenon of lateral inhibition in biological nervous systems into SN P systems, and proposes SN P systems with lateral inhibition (LISN P systems). LISN P systems add the property of synaptic length to portray the lateral distance between neurons, and adopt a new form of rules, lateral interaction rules, to describe the reception of spikes by postsynaptic neurons with different lateral distances from the presynaptic neuron. Specifically, an excited neuron produces lateral inhibition on surrounding postsynaptic neurons. Postsynaptic neurons close to the excited neuron, i.e., neurons with small lateral distances, are more susceptible to lateral inhibition and either receive a fewer number of spikes generated by the excited neuron or fail to receive spikes. As the lateral distance increases, the lateral inhibition weakens, and the number of spikes received by postsynaptic neurons increases. Based on the above mechanism, four specific LISN P systems are designed for generating arbitrary odd numbers, arbitrary even numbers, arbitrary natural numbers and arithmetic series, respectively, as examples. By designing working modules, LISN P systems provide equivalence in computational power to the universal register machines in both generating and accepting modes. This verifies the computational completeness of LISN P systems. A universal LISN P system using merely 65 neurons is devised for function computation. According to comparisons among several systems, universal LISN P systems require fewer computational resources.

Observation of magnetic structural universality and jamming transition with NMR.

Nuclear magnetic resonance (NMR) has been instrumental in deciphering the structure of proteins. Here we show that transverse NMR relaxation, through its time-dependent relaxation rate, is distinctly sensitive to the structure of complex materials or biological tissues at the mesoscopic scale, from micrometers to tens of micrometers. Based on the ideas of universality, we show analytically and numerically that the time-dependent transverse relaxation rate approaches its long-time limit in a power-law fashion, with the dynamical exponent reflecting the universality class of mesoscopic magnetic structure. The spectral line shape acquires the corresponding non-analytic power law singularity at zero frequency. We experimentally detect the change in the dynamical exponent as a result of the transition into maximally random jammed state characterized by hyperuniform correlations. The relation between relaxational dynamics and magnetic structure opens the way for noninvasive characterization of porous media, complex materials and biological tissues.