Search

The global outbreak of mpox in 2022 and subsequent sporadic outbreaks in 2023 highlighted the importance of nonpharmaceutical interventions such as case isolation. Individual variations in viral shedding dynamics may lead to either premature ending of isolation for infectious individuals, or unnecessarily prolonged isolation for those who are no longer infectious. Here, we developed a modeling framework to characterize heterogeneous mpox infectiousness profiles – specifically, when infected individuals cease to be infectious – based on viral load data. We examined the potential effectiveness of three different isolation rules: a symptom-based rule (the current guideline in many countries) and rules permitting individuals to stop isolating after either a fixed duration or following tests that indicate that they are no longer likely to be infectious. Our analysis suggests that the duration of viral shedding ranges from 23 to 50 days between individuals. The risk of infected individuals ending isolation too early was estimated to be 8.8% (95% CI: 6.7–10.5) after symptom clearance and 5.4% (95% CI: 4.1–6.7) after 3 weeks of isolation. While these results suggest that the current standard practice for ending isolation is effective, we found that unnecessary isolation following the infectious period could be reduced by adopting a testing-based rule. Current guidelines recommend that individuals exposed to mpox should isolate for three weeks, based on the average estimated incubation period. Here, the authors use a quantitative model to characterise mpox infectiousness profiles and quantify the impacts of different isolation rules. [ABSTRACT FROM AUTHOR]

Real systems showing regime shifts, such as ecosystems, are often composed of many dynamical elements interacting on a network. Various early warning signals have been proposed for anticipating regime shifts from observed data. However, it is unclear how one should combine early warning signals from different nodes for better performance. Based on theory of stochastic differential equations, we propose a method to optimize the node set from which to construct an early warning signal. The proposed method takes into account that uncertainty as well as the magnitude of the signal affects its predictive performance, that a large magnitude or small uncertainty of the signal in one situation does not imply the signal's high performance, and that combining early warning signals from different nodes is often but not always beneficial. The method performs well particularly when different nodes are subjected to different amounts of dynamical noise and stress. Early warning signals for rapid regime shifts in complex networks are of importance for ecology, climate and epidemics, where heterogeneities in network nodes and connectivity make construction of early warning signals challenging. The authors propose a method for selecting an optimal set of nodes from which a reliable early warning signal can be obtained. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) can efficiently process time-series data by mapping the input signal into a high-dimensional space via randomly connected recurrent neural networks (RNNs), which are referred to as a reservoir. The high-dimensional representation of time-series data in the reservoir simplifies subsequent learning tasks. Although this simple architecture allows fast learning and facile physical implementation, the learning performance is inferior to that of other state-of-the-art RNN models. In this study, to improve the learning ability of RC, we propose self-modulated RC (SM-RC) that extends RC by adding a self-modulation mechanism. SM-RC can perform attention tasks where input information is retained or discarded depending on the input signal. We find that a chaotic state can emerge as a result of learning in SM-RC. Furthermore, we demonstrate that SM-RC outperforms RC in NARMA and Lorenz model tasks. Because the SM-RC architecture only requires two additional gates, it is physically implementable as RC, thereby providing a direction for realizing edge artificial intelligence. Reservoir computing (RC) is energy-efficient due to its simple architecture, allowing physical implementation. The proposed self-modulated RC extends the capabilities of RC, exhibiting superior learning abilities and complex dynamics, including attention and chaos, while retaining the advantage of physical implementation. [ABSTRACT FROM AUTHOR]

The training of multilayer spiking neural networks (SNNs) using the error backpropagation algorithm has made significant progress in recent years. Among the various training schemes, the error backpropagation method that directly uses the firing time of neurons has attracted considerable attention because it can realize ideal temporal coding. This method uses time-to-first-spike (TTFS) coding, in which each neuron fires at most once, and this restriction on the number of firings enables information to be processed at a very low firing frequency. This low firing frequency increases the energy efficiency of information processing in SNNs. However, only an upper limit has been provided for TTFS-coded SNNs, and the information-processing capability of SNNs at lower firing frequencies has not been fully investigated. In this paper, we propose two spike-timing-based sparse-firing (SSR) regularization methods to further reduce the firing frequency of TTFS-coded SNNs. Both methods are characterized by the fact that they only require information about the firing timing and associated weights. The effects of these regularization methods were investigated on the MNIST, Fashion-MNIST, and CIFAR-10 datasets using multilayer perceptron networks and convolutional neural network structures. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we find that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher peak in viral load dynamics but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we show that a decreased incubation period and an increased proportion of asymptomatic infection are also positively selected for as SARS-CoV-2 mutated to adapt to human behavior (i.e., Omicron variants). The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution. The COVID-19 pandemic has seen the emergence of different variants. Analyzing the evolution from the Wuhan strain to the Delta variant, clinical data reveals a fivefold increase in peak viral load and a 1.5-fold faster time to peak. [ABSTRACT FROM AUTHOR]

The phenomenon of partial synchronization has attracted significant interest in the field of nonlinear dynamics, largely due to observations of related phenomena in real-world situations. In particular, the coexistence of synchronized and desynchronized phases, known as a chimera state, has been the subject of intense study. In this work, we experimentally demonstrate that networks of identical photonic spiking neurons based on coupled degenerate optical parametric oscillators can exhibit various chimera states in which, depending on their local synchronization and desynchronization, different kinds of spiking dynamics can develop in a self-organized manner. Even when only a static interaction is implemented, the spiking mode of photonic neurons can be spontaneously and adaptively changed between the Class-I and Class-II modes classified by A. L. Hodgkin. This spontaneous spiking-mode shift induces a significant change in the spiking frequency despite all neurons having the same natural spiking frequency, which encourages the generation of chimera states. Chimera states of coupled oscillators have been the subject of considerable interest in complex nonlinear systems. The authors experimentally observe chimera states in a photonic spiking neural network of identical neurons with homogeneous interactions, suggesting that the system with high controllability provides a novel platform to researching synchronous phenomena. [ABSTRACT FROM AUTHOR]

We propose a CMOS synapse circuit using a time-domain digital-to-analog converter (TDAC) for realizing spiking neural network hardware with on-chip learning. A TDAC has the advantages that (i) it can reproduce a post-synaptic potential and (ii) its number of analog components for DA conversion, such as current sources and capacitors, is independent of the bit width. We designed a synapse circuit using TSMC 40 nm technology, and the synaptic weight was updated by the remote supervised method (ReSuMe) or spike timing-dependent plasticity (STDP). The circuit simulation results of the designed circuit show that it can execute ReSuMe and generate the time-window function for STDP with high energy efficiency. [ABSTRACT FROM AUTHOR]

Herding is a deterministic algorithm used to generate data points regarded as random samples satisfying input moment conditions. This algorithm is based on a high-dimensional dynamical system and rooted in the maximum entropy principle of statistical inference. We propose an extension, entropic herding, which generates a sequence of distributions instead of points. We derived entropic herding from an optimization problem obtained using the maximum entropy principle. Using the proposed entropic herding algorithm as a framework, we discussed a closer connection between the herding and maximum entropy principle. Specifically, we interpreted the original herding algorithm as a tractable version of the entropic herding, the ideal output distribution of which is mathematically represented. We further discussed how the complex behavior of the herding algorithm contributes to optimization. We argued that the proposed entropic herding algorithm extends the herding to probabilistic modeling. In contrast to the original herding, the entropic herding can generate a smooth distribution such that both efficient probability density calculation and sample generation become possible. To demonstrate the viability of these arguments in this study, numerical experiments were conducted, including a comparison with other conventional methods, on both synthetic and real data. [ABSTRACT FROM AUTHOR]

Appropriate isolation guidelines for COVID-19 patients are warranted. Currently, isolating for fixed time is adopted in most countries. However, given the variability in viral dynamics between patients, some patients may no longer be infectious by the end of isolation, whereas others may still be infectious. Utilizing viral test results to determine isolation length would minimize both the risk of prematurely ending isolation of infectious patients and the unnecessary individual burden of redundant isolation of noninfectious patients. In this study, we develop a data-driven computational framework to compute the population-level risk and the burden of different isolation guidelines with rapid antigen tests (i.e., lateral flow tests). Here, we show that when the detection limit is higher than the infectiousness threshold values, additional consecutive negative results are needed to ascertain infectiousness status. Further, rapid antigen tests should be designed to have lower detection limits than infectiousness threshold values to minimize the length of prolonged isolation. Utilizing viral test results to determine isolation length would minimize both the risk of prematurely ending isolation of infectious patients and the unnecessary individual burden of redundant isolation of noninfectious patients. Here, the authors develop a framework to compute the population-level risk of different isolation guidelines with rapid antigen tests. [ABSTRACT FROM AUTHOR]

The development of physical simulators, called Ising machines, that sample from low energy states of the Ising Hamiltonian has the potential to transform our ability to understand and control complex systems. However, most of the physical implementations of such machines have been based on a similar concept that is closely related to relaxational dynamics such as in simulated, mean-field, chaotic, and quantum annealing. Here we show that dynamics that includes a nonrelaxational component and is associated with a finite positive Gibbs entropy production rate can accelerate the sampling of low energy states compared to that of conventional methods. By implementing such dynamics on field programmable gate array, we show that the addition of nonrelaxational dynamics that we propose, called chaotic amplitude control, exhibits exponents of the scaling with problem size of the time to find optimal solutions and its variance that are smaller than those of relaxational schemes recently implemented on Ising machines. Finding the ground state of a variety of complex systems can be formulated as the minimization of the total interaction energy of Ising machines, posing a challenge as computational cost increases exponentially with system size. In this paper, the authors propose an algorithm to find the ground states of Ising-type problems by destabilising non-trivial attractors in combinatorial optimisation solvers through a heuristic modulation of the target amplitude, and show that this provides an improved scaling with respect to several existing methods. [ABSTRACT FROM AUTHOR]

We propose a new digital-to-analog converter (DAC) for realizing a synapse circuit in mixed-signal spiking neural networks. We refer to this circuit as a "time-domain DAC (TDAC)". It produces weights for converting a digital input code into voltage using one current waveform. Therefore, the TDAC is more compact than a conventional DAC consisting of many current sources and resistors. Moreover, a TDAC with leak resistance reproduces biologically plausible synaptic responses expressed as alpha functions or dual exponential equations. We also present numerical analysis results for a TDAC and circuit simulation results for a circuit designed using the TSMC 40 nm CMOS process. [ABSTRACT FROM AUTHOR]

Nonlinear dynamics of spiking neural networks have recently attracted much interest as an approach to understand possible information processing in the brain and apply it to artificial intelligence. Since information can be processed by collective spiking dynamics of neurons, the fine control of spiking dynamics is desirable for neuromorphic devices. Here we show that photonic spiking neurons implemented with paired nonlinear optical oscillators can be controlled to generate two modes of bio-realistic spiking dynamics by changing optical-pump amplitude. When the photonic neurons are coupled in a network, the interaction between them induces an effective change in the pump amplitude depending on the order parameter that characterizes synchronization. The experimental results show that the effective change causes spontaneous modification of the spiking modes and firing rates of clustered neurons, and such collective dynamics can be utilized to realize efficient heuristics for solving NP-hard combinatorial optimization problems. Neuromorphic devices take inspiration from spiking dynamics of neurons in the brain. Here, the authors demonstrate synchronized spiking dynamics in 240 photonic artificial neurons, each of which is implemented with a pair of antisymmetrically coupled degenerate optical parametric oscillators. [ABSTRACT FROM AUTHOR]

The spread of intelligent transportation systems in urban cities has caused heavy computational loads, requiring a novel architecture for managing large-scale traffic. In this study, we develop a method for globally controlling traffic signals arranged on a square lattice by means of a quantum annealing machine, namely the D-Wave quantum annealer. We first formulate a signal optimization problem that minimizes the imbalance of traffic flows in two orthogonal directions. Then we reformulate this problem as an Ising Hamiltonian, which is compatible with quantum annealers. The new control method is compared with a conventional local control method for a large 50-by-50 city, and the results exhibit the superiority of our global control method in suppressing traffic imbalance over wide parameter ranges. Furthermore, the solutions to the global control method obtained with the quantum annealing machine are better than those obtained with conventional simulated annealing. In addition, we prove analytically that the local and the global control methods converge at the limit where cars have equal probabilities for turning and going straight. These results are verified with numerical experiments. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called "reservoirs." To implement RC in edge computing, it is highly important to reduce the amount of computational resources that RC requires. In this study, we propose methods that reduce the size of the reservoir by inputting the past or drifting states of the reservoir to the output layer at the current time step. To elucidate the mechanism of model-size reduction, the proposed methods are analyzed based on information processing capacity proposed by Dambre et al. (Sci Rep 2:514, 2012). In addition, we evaluate the effectiveness of the proposed methods on time-series prediction tasks: the generalized Hénon-map and NARMA. On these tasks, we found that the proposed methods were able to reduce the size of the reservoir up to one tenth without a substantial increase in regression error. [ABSTRACT FROM AUTHOR]

This paper presents a novel model-free method based on the dynamical network markers (DNM) to detect the traffic flow breakdown at an early transition stage in the context of the freeway connected with an on-ramp under a connected vehicle environment. In this method, the vehicle states are frequently observed at several cells or segments on each lane. By processing the observed data, the standard deviations and the correlation coefficients among the cells are analyzed to determine the dominant cells, the ones that are mostly influenced during the transition. Finally, the standard deviations and absolute values of the correlation coefficients of the dominant cells are combined to form a scalar warning signal, which provides a very strong indication when the traffic is at the critical state. The proposed method is evaluated through simulation on freeway traffic, whose flows are disturbed by the on-ramp merging vehicles. [ABSTRACT FROM AUTHOR]

Causality detection likely misidentifies indirect causations as direct ones, due to the effect of causation transitivity. Although several methods in traditional frameworks have been proposed to avoid such misinterpretations, there still is a lack of feasible methods for identifying direct causations from indirect ones in the challenging situation where the variables of the underlying dynamical system are non-separable and weakly or moderately interacting. Here, we solve this problem by developing a data-based, model-independent method of partial cross mapping based on an articulated integration of three tools from nonlinear dynamics and statistics: phase-space reconstruction, mutual cross mapping, and partial correlation. We demonstrate our method by using data from different representative models and real-world systems. As direct causations are keys to the fundamental underpinnings of a variety of complex dynamics, we anticipate our method to be indispensable in unlocking and deciphering the inner mechanisms of real systems in diverse disciplines from data. It is crucial yet challenging to identify cause-consequence relation in complex dynamical systems where direct causal links can mix with indirect ones. Leng et al. propose a data-driven model-independent method to distinguish direct from indirect causality and test its applicability to real-world data. [ABSTRACT FROM AUTHOR]

The energy efficiency of neural signal transmission is thought to be an important constraint in the nervous system. It is generally measured as the energy consumed per unit of information. Most of the previous studies have demonstrated this efficiency by focusing on single action potentials. However, neural information is more likely to be encoded by a spike train rather than by a single spike. To date, how the energy efficiency is dependent on patterns of spike trains is still unclear. In this study, we examined the energy efficiency of various firing patterns simulated by the Chay neuron model, including relatively high-frequency activities with massive spikes, medium-frequency activities with a moderate number of spikes, and low-frequency activities with rare spikes. Our results indicate that medium-frequency patterns are more energy efficient than both the high-frequency and low-frequency patterns. The most efficient medium-frequency pattern is a sparse burst firing (SBF) pattern because it consumes minimal energy and transmits an amount of neural information comparable to that of high-frequency patterns which consume much more energy. SBF patterns minimize energy consumption not only by producing fewer spikes than high-frequency patterns, but more importantly, also by consuming available energy sources, namely the potential energy stored in ionic concentration gradients, in a balanced way. Furthermore, with fewer spikes, the irregular spike trains of SBF patterns with short bursts and single spikes maximize the neural information that they carry, leading to higher energy efficiency. Thus, the sensory system may give priority to limiting energy costs over maximizing information to achieve greater energy efficiency. [ABSTRACT FROM AUTHOR]

Delay embedding—a method for reconstructing dynamical systems by delay coordinates—is widely used to forecast nonlinear time series as a model-free approach. When multivariate time series are observed, several existing frameworks can be applied to yield a single forecast combining multiple forecasts derived from various embeddings. However, the performance of these frameworks is not always satisfactory because they randomly select embeddings or use brute force and do not consider the diversity of the embeddings to combine. Herein, we develop a forecasting framework that overcomes these existing problems. The framework exploits various "suboptimal embeddings" obtained by minimizing the in-sample error via combinatorial optimization. The framework achieves the best results among existing frameworks for sample toy datasets and a real-world flood dataset. We show that the framework is applicable to a wide range of data lengths and dimensions. Therefore, the framework can be applied to various fields such as neuroscience, ecology, finance, fluid dynamics, weather, and disaster prevention. [ABSTRACT FROM AUTHOR]

The asymptotic attractors of a nonlinear dynamical system play a key role in the long-term physically observable behaviors of the system. The study of attractors and the search for distinct types of attractor have been a central task in nonlinear dynamics. In smooth dynamical systems, an attractor is often enclosed completely in its basin of attraction with a finite distance from the basin boundary. Recent works have uncovered that, in neuronal networks, unstable attractors with a remote basin can arise, where almost every point on the attractor is locally transversely repelling. Herewith we report our discovery of a class of attractors: partially unstable attractors, in pulse-coupled integrate-and-fire networks subject to a periodic forcing. The defining feature of such an attractor is that it can simultaneously possess locally stable and unstable sets, both of positive measure. Exploiting the structure of the key dynamical events in the network, we develop a symbolic analysis that can fully explain the emergence of the partially unstable attractors. To our knowledge, such exotic attractors have not been reported previously, and we expect them to arise commonly in biological networks whose dynamics are governed by pulse (or spike) generation. [ABSTRACT FROM AUTHOR]

The multiple depot vehicle scheduling problem (MDVSP) is a well-known and important NP-hard problem in transport scheduling. In this study, we first provide an original interpretation of the search space of the MDVSP. Next, we present a local search algorithm which utilizes pruning and deepening techniques in the variable depth search framework. Computational results using well-known test cases show that our method achieves better results than the second-best local search based method does by 8.6-30.1 %, and exhibits the best short-term performance among the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Although predicting sudden rapid changes of renewable energy outputs is useful for maintaining the stability of power grids with many renewable energy resources, the prediction is difficult so far. Here we list causes for the uncertainty for our prediction, quantify them, and forecast whether such sudden rapid changes are likely to happen or not by integrating their quantifications with a method of machine learning. We test the proposed forecast using a toy model and real datasets of solar irradiance and wind speed. [ABSTRACT FROM AUTHOR]

Intracellular biochemical networks fluctuate dynamically due to various internal and external sources of fluctuation. Dissecting the fluctuation into biologically relevant components is important for understanding how a cell controls and harnesses noise and how information is transferred over apparently noisy intracellular networks. While substantial theoretical and experimental advancement on the decomposition of fluctuation was achieved for feedforward networks without any loop, we still lack a theoretical basis that can consistently extend such advancement to feedback networks. The main obstacle that hampers is the circulative propagation of fluctuation by feedback loops. In order to define the relevant quantity for the impact of feedback loops for fluctuation, disentanglement of the causally interlocked influences between the components is required. In addition, we also lack an approach that enables us to infer non-perturbatively the influence of the feedback to fluctuation in the same way as the dual reporter system does in the feedforward networks. In this work, we address these problems by extending the work on the fluctuation decomposition and the dual reporter system. For a single-loop feedback network with two components, we define feedback loop gain as the feedback efficiency that is consistent with the fluctuation decomposition for feedforward networks. Then, we clarify the relation of the feedback efficiency with the fluctuation propagation in an open-looped FF network. Finally, by extending the dual reporter system, we propose a conjugate feedback and feedforward system for estimating the feedback efficiency non-perturbatively only from the statistics of the system. [ABSTRACT FROM AUTHOR]

This paper investigates the significance of a traffic signal control scheme that simultaneously adjusts all signal parameters, i.e., cycle time, split time and offset, in a road network. A novel framework of model predictive control (MPC) is designed that overcomes the limitations of other MPC based traffic signal control strategies, which are mostly restricted to control only split or green time in a fixed cycle ignoring signal offset. A simple macroscopic model of traffic tailored to MPC is formulated that describes traffic dynamics in the network at a short sampling interval. The proposed framework is demonstrated using a small road network with dynamically changing traffic flows. The parameters of the proposed model are calibrated by using data obtained from detailed microscopic simulation that yields realistic statistics. The model is transformed into a mixed logical dynamical system that is suitable to a finite horizon, and traffic signals are optimized using mixed integer linear programming (MILP) for a given performance index. The framework makes the signals flexibly turn to red and green by adapting quickly to any changes in traffic conditions. Results are also verified by microscopic traffic simulation and compared with other signal control schemes. [ABSTRACT FROM AUTHOR]

Temporal coherence among neural populations may contribute importantly to signal encoding, specifically by providing an optimal tradeoff between encoding reliability and efficiency. Here, we considered the possibility that learning modulates the temporal coherence among neural populations in association with well-characterized map plasticity. We previously demonstrated that, in appetitive operant conditioning tasks, the tone-responsive area globally expanded during the early stage of learning, but shrank during the late stage. The present study further showed that phase locking of the first spike to band-specific oscillations of local field potentials (LFPs) significantly increased during the early stage of learning but decreased during the late stage, suggesting that neurons in A1 were more synchronously activated during early learning, whereas they were more asynchronously activated once learning was completed. Furthermore, LFP amplitudes increased during early learning but decreased during later learning. These results suggest that, compared to naïve encoding, early-stage encoding is more reliable but energy-consumptive, whereas late-stage encoding is more energetically efficient. Such a learning-stage-dependent encoding strategy may underlie learning-induced, non-monotonic map plasticity. Accumulating evidence indicates that the cholinergic system is likely to be a shared neural substrate of the processes for perceptual learning and attention, both of which modulate neural encoding in an adaptive manner. Thus, a better understanding of the links between map plasticity and modulation of temporal coherence will likely lead to a more integrated view of learning and attention. [ABSTRACT FROM AUTHOR]

Quantifying causality between variables from observed time series data is of great importance in various disciplines but also a challenging task, especially when the observed data are short. Unlike the conventional methods, we find it possible to detect causality only with very short time series data, based on embedding theory of an attractor for nonlinear dynamics. Specifically, we first show that measuring the smoothness of a cross map between two observed variables can be used to detect a causal relation. Then, we provide a very effective algorithm to computationally evaluate the smoothness of the cross map, or "Cross Map Smoothness" (CMS), and thus to infer the causality, which can achieve high accuracy even with very short time series data. Analysis of both mathematical models from various benchmarks and real data from biological systems validates our method. [ABSTRACT FROM AUTHOR]