Dynamic Switching Research Articles

Exploring the origins of switching dynamics in a multifunctional reservoir computer.

The concept of multifunctionality has enabled reservoir computers (RCs), a type of dynamical system that is typically realized as an artificial neural network, to reconstruct multiple attractors simultaneously using the same set of trained weights. However, there are many additional phenomena that arise when training a RC to reconstruct more than one attractor. Previous studies have found that in certain cases, if the RC fails to reconstruct a coexistence of attractors, then it exhibits a form of metastability, whereby, without any external input, the state of the RC switches between different modes of behavior that resemble the properties of the attractors it failed to reconstruct. In this paper, we explore the origins of these switching dynamics in a paradigmatic setting via the "seeing double" problem.

Mechanisms of Defect-Mediated Memristive Behavior in MoS2 Monolayer.

The switching dynamics of a Au∥VS2@MoS2 atomristor is explored by first-principles computations of the atomic-configuration energy and electron transport. It is found that external bias can reduce the energy barrier between the two (high- and low-) conduction states, to achieve nonvolatile resistive switching. We find that the force acting on the switching atom is a combination of electrostatic force (while its charge is induced both electrostatically and chemically) and also by electron-wind, whose effect may hinder the writing process at larger bias. The analysis uncovers how the writing and reading processes of the atomristor depend on several factors: (i) atomic structure details of the Au tip; (ii) the space-gap distance between the tip and MoS2 layer; and (iii) tip metal choice. The fundamental understanding of switching events provides useful guidance for memristor design and possible limitations.

Time-varying stimuli that prolong IKK activation promote nuclear remodeling and mechanistic switching of NF-κB dynamics.

Temporal properties of molecules within signaling networks, such as sub-cellular changes in protein abundance, encode information that mediate cellular responses to stimuli. How dynamic signals relay and process information is a critical gap in understanding cellular behaviors. In this work, we investigate transmission of information about changing extracellular cytokine concentrations from receptor-level supramolecular assemblies of IκB kinases (IKK) downstream to the nuclear factor κB (NF-κB) transcription factor (TF). In a custom robot-controlled microfluidic cell culture, we simultaneously measure input-output (I/O) encoding of IKK-NF-κB in dual fluorescent-reporter cells. When compared with single cytokine pulses, dose-conserving pulse trains prolong IKK assemblies and lead to disproportionately enhanced retention of nuclear NF-κB. Using particle swarm optimization, we demonstrate that a mechanistic model does not recapitulate this emergent property. By contrast, invoking mechanisms for NF-κB-dependent chromatin remodeling to the model recapitulates experiments, showing how temporal dosing that prolongs IKK assemblies facilitates switching to permissive chromatin that sequesters nuclear NF-κB. Remarkably, using simulations to resolve single-cell receptor data accurately predicts same-cell NF-κB time courses for more than 80% of our single cell trajectories. Our data and simulations therefore suggest that cell-to-cell heterogeneity in cytokine responses are predominantly due to mechanisms at the level receptor-associated protein complexes.

Smart Photovoltaic Windows for Next-Generation Energy-Saving Buildings.

The global energy system transforming from fossil fuels to renewable green energy through the adaption of innovative and dynamic green technologies. Energy-saving buildings (ESBs) are attracting extensive attention as intelligent architectures capable of significantly reducing the energy consumption for heating, air-conditioning, and lighting. They provide comfortable working and living environment by regulating and harnessing solar energy. Smart photovoltaic windows (SPWs) offer a promising platform for designing ESBs due to their unique feature. They can modulate solar energy based on dynamic color switching behavior under external stimuli and generate electrical power by harvesting solar energy. In this review, the-state-of-art of strategies and technologies are summarized putting SPWs toward high-efficiency ESBs. The SPWs are systematically categorized according to the working principle and functional component. For each type of SPWs, material and architecture engineering are focused on to optimize operation mode, optical modulation capability, photovoltaic performance and durability for giving ESBs flexible manipulation, extraordinary energy-saving effect, and high electricity power. In addition, the challenges and opportunities in this cutting-edge research area are discussed, with the aim of promoting the development of advanced multifunctional SPWs and their application in high efficiency ESBs.

Isolation enhancement of a capacitively-fed MIMO antenna using a quasi-fractal parasitic element and defected ground structure

Vehicular Internet of Things (IoT) is facilitated by efficient RF front ends with suppressed mutual coupling for enhanced spatial diversity and increased channel capacity. This paper presents a mutual coupling suppressed MIMO antenna with a hybrid decoupling technique for Vehicle-to-Everything (V2X) communications, enabling IoT in automotive systems. The single elements consist of a radiating patch with a cleaving circular slot to introduce a capacitive effect on the radiating structure. Afterwards, the single-unit design is further extrapolated to a 2 × 2 MIMO antenna. The mutual coupling is suppressed between antenna elements by introducing a quasi-fractal parasitic element and a defected ground structure (DGS). The MIMO antenna is designed to conform to the requirements posed by V2X systems in Dedicated Short-Range Communications (DSRC) and Intelligent transportation system (ITS) scenarios. The proposed MIMO antenna offers measured |S11| < −10 dB of 200 MHz, ranging from 5.77 GHz to 5.97 GHz, fully covering the spectrum guided by the IEEE 802.11p standard. A physical prototype is fabricated and placed on a car roof to assess the congruency between measured and simulated results. The MIMO antenna exhibits exceptional diversity properties, such as enhanced isolation (>28 dB) between its individual elements, a diversity gain (DG) close to the ideal value of 10 dB (9.99 dB), peak realized gain of 6.5 dBi, an ECC below 0.001, and a beam coverage area of 180° in azimuthal and elevation plane by dynamic port switching. Thus, the proposed MIMO antenna module is a considerable candidate for future V2X communication paradigms.

Dynamic sampling of a surveillance state enables DNA proofreading by Cas9.

CRISPR-Cas9 has revolutionized genome engineering applications by programming its single-guide RNA, where high specificity is required. However, the precise molecular mechanism underscoring discrimination between on/off-target DNA sequences, relative to the guide RNA template, remains elusive. Here, using methyl-based NMR to study multiple holoenzymes assembled invitro, we elucidate a discrete protein conformational state which enables recognition of DNA mismatches at the protospacer adjacent motif (PAM)-distal end. Our results delineate an allosteric pathway connecting a dynamic conformational switch at the REC3 domain, with the sampling of a catalytically competent state by the HNH domain. Our NMR data show that HiFi Cas9 (R691A) increases the fidelity of DNA recognition by stabilizing this "surveillance state" for mismatched substrates, shifting the Cas9 conformational equilibrium away from the active state. These results establish a paradigm of substrate recognition through an allosteric protein-based switch, providing unique insights into the molecular mechanism which governs Cas9 selectivity.

Redundant Telemetry Link System for Uncrewed Aerial Vehicles

Thanks to their flexibility in research, prototyping, and development, Uncrewed Aerial Vehicles (UAV) are seeing increasing use in agriculture, healthcare, logistics, and other industries. With the increase in the use of UAV in civilian sectors, comes an increase in the possibility of errors in telemetry connections. Issues with the telemetry connection may be catastrophic and cause accidents and crashes and may also adversely affect the performance of the subsystems connected to the telemetry. In this study, a low-cost software-based redundant telemetry link system for UAV is proposed. The proposed system minimises the received packet loss using packet deduplication and is compared with a dynamic link switching method. In the proposed system, both fault tolerance and robustness are introduced into the UAV telemetry link. The analysis is performed using the ArduPilot UAV Simulation environment as a producer and a Golang implementation of the proposed methods.

In-Situ Gas Permeation-Driven Ionic Current Rectification of Heterogeneously Charged Nanopore Arrays.

Ionic diodes provide ionic current rectification (ICR), which is useful for micro-/nanofluidic devices for ionic current-mediated applications. However, the modulation of ICR is not fully developed, and current challenges include limited active control and localized modulation for further multiplexing of micro-/nanofluidic ionic diodes. Herein, a microfluidic device integrated with particle-assembly-based ionic diodes (PAIDs) and a gas-flow channel above them is presented. Exploiting in-situ gas permeation through a polymeric film, precise control over the physiochemical conditions of the nanopores within the PAIDs, leading to the modulation of ICR is demonstrated. The investigation not only characterizes the rectification properties of the PAIDs but also unveils their capacitor-like behavior and the ability to actively modulate ICR using various gas flows. Furthermore, the reversible modulation of ICR through dynamic switching of gas-dissolved solutions, enabling ion-signal amplification is showcased. This pioneering approach of in situ gas-permeation offers programmable manipulation of ion transport along PAIDs, thereby positioning ionic diodes as versatile nanofluidic components. Looking ahead, the development of multiplexed PAIDs in an addressable manner on a chip holds promise for practical applications across diverse fields, including ion signaling, ion-based logic, chemical reactors, and (bio)chemical sensing.

Complete Stability of Neural Networks With Extended Memristors.

The article considers a large class of delayed neural networks (NNs) with extended memristors obeying the Stanford model. This is a widely used and popular model that accurately describes the switching dynamics of real nonvolatile memristor devices implemented in nanotechnology. The article studies via the Lyapunov method complete stability (CS), i.e., convergence of trajectories in the presence of multiple equilibrium points (EPs), for delayed NNs with Stanford memristors. The obtained conditions for CS are robust with respect to variations of the interconnections and they hold for any value of the concentrated delay. Moreover, they can be checked either numerically, via a linear matrix inequality (LMI), or analytically, via the concept of Lyapunov diagonally stable (LDS) matrices. The conditions ensure that at the end of the transient capacitor voltages and NN power vanish. In turn, this leads to advantages in terms of power consumption. This notwithstanding, the nonvolatile memristors can retain the result of computation in accordance with the in-memory computing principle. The results are verified and illustrated via numerical simulations. From a methodological viewpoint, the article faces new challenges to prove CS since due to the presence of nonvolatile memristors the NNs possess a continuum of nonisolated EPs. Also, for physical reasons, the memristor state variables are constrained to lie in some given intervals so that the dynamics of the NNs need to be modeled via a class of differential inclusions named differential variational inequalities.

Asset prices, regime switches, and monetary policy: evidence from China

ABSTRACT This paper develops a regime-switching DSGE model that can capture time-varying volatilities in economic activity and monetary policy. The model is then used to study regime switches in macroeconomic dynamics and monetary policy in China. The results suggest a better fitness of the regime-switching model than conventional constant-parameter models. We also find notable differences in macroeconomic dynamics and monetary policy reactions between high and low volatility regimes. Further counterfactual analysis suggests that, at least in an environment with regime-switching volatilities, the optimal reaction of monetary policy involves responses to asset price variations. The findings of the paper shed new light on the regime-switching properties of the economy and the optimal choice of monetary policy within this context.

What can the psychoneuroimmunology of yoga teach us about depression's psychopathology?

Depression, the most prevailing mental health condition, remains untreated in over 30% of patients. This cluster presents with sub-clinical inflammation. Investigations trialling anti-inflammatory medications had mixed results. The lack of results may result from inflammation's complexity and targeting only a few of depression's abnormal pathways. Mind-body therapies' biological and neuro-imaging studies offer valuable insights into depression psychopathology. Interestingly, mind-body therapies, like yoga, reverse the aberrant pathways in depression. These aberrant pathways include decreased cognitive function, interoception, neuroplasticity, salience and default mode networks connectivity, parasympathetic tone, increased hypothalamic-pituitary-adrenal (HPA) axis activity, and metabolic hyper/hypofunction. Abundant evidence found yogic techniques improving self-reported depressive symptoms across various populations. Yoga may be more effective in treating depression in conjunction with pharmacological and cognitive therapies. Yoga's psychoneuroimmunology teaches us that reducing allostatic load is crucial in improving depressive symptoms. Mind-body therapies promote parasympathetic tone, downregulate the HPA axis, reduce inflammation and boost immunity. The reduced inflammation promotes neuroplasticity and, subsequently, neurogenesis. Improving interoception resolves the metabolic needs prediction error and restores homeostasis. Additionally, by improving functional connectivity within the salience network, they restore the dynamic switching between the default mode and central executive networks, reducing rumination and mind-wandering. Future investigations should engineer therapies targeting the mechanisms mentioned above. The creation of multi-disciplinary health teams offering a combination of pharmacological, gene, neurofeedback, behavioural, mind-body and psychological therapies may treat treatment-resistant depression.

Molecular switching by proton-coupled electron transport drives giant negative differential resistance

To develop new types of dynamic molecular devices with atomic-scale control over electronic function, new types of molecular switches are needed with time-dependent switching probabilities. We report such a molecular switch based on proton-coupled electron transfer (PCET) reaction with giant hysteric negative differential resistance (NDR) with peak-to-valley ratios of 120 ± 6.6 and memory on/off ratios of (2.4 ± 0.6) × 103. The switching dynamics probabilities are modulated by bias voltage sweep rate and can also be controlled by pH and relative humidity, confirmed by kinetic isotope effect measurements. The demonstrated dynamical and environment-specific modulation of giant NDR and memory effects provide new opportunities for bioelectronics and artificial neural networks.

Spectral period doubling and encoding of dissipative optical solitons via gain control

Period-doubling bifurcation, as an intermediate state between order and chaos, is ubiquitous in all disciplines of nonlinear science. However, previous experimental observations of period doubling in ultrafast fiber lasers are mainly restricted to self-sustained steady state, controllable manipulation and dynamic switching between period doubling and other intriguing dynamical states are still largely unexplored. Here, we propose to expand the vision of dissipative soliton periodic doubling, which we illustrate experimentally by reporting original spontaneous, collisional, and controllable spectral period doubling in a polarization-maintaining ultrafast fiber laser. Specifically, the spontaneous period doubling can be observed in both single- and double-pulses. The mechanism of the switchable state and periodic doubling was revealed by numerical simulation. Moreover, state transformation of individual solitons can be resolved during the collision of triple solitons involving stationary, oscillating, and period doubling. Further, controllable deterministic switching between period doubling and other dynamical states, as well as exemplifying the application of period-doubling-based digital encoding, is achieved under programmable pump modulation. Our results open a new window for unveiling complex Hopf bifurcation in dissipative systems and bring useful insights into nonlinear science and applications.

Dynamic Clock Switching Scheme for Enhanced Power Efficiency in System-on-Chip Architectures

High power consumption and thermal management are critical challenges in traditional System- on-Chip (SoC) architectures due to multiple active clock sources feeding individual IP blocks, even during idle states. This paper introduces a dynamic clock switching power-saving scheme that consolidates clock sources by dynamically switching to a single lower-frequency clock during low-power modes. A Clock Control Agent (CCA) monitors real-time operational status, power consumption, and performance needs, leveraging AI for predictive adjustments. Experimental results demonstrate a significant reduction in idle power consumption and improved thermal management, without compromising performance or data integrity. This scheme addresses inefficiencies in existing methods such as Dynamic Voltage and Frequency Scaling (DVFS) and clock gating, offering a robust and efficient solution for next-generation SoC designs.

Spatio-temporal breather dynamics in microcomb soliton crystals

Solitons, the distinct balance between nonlinearity and dispersion, provide a route toward ultrafast electromagnetic pulse shaping, high-harmonic generation, real-time image processing, and RF photonic communications. Here we uniquely explore and observe the spatio-temporal breather dynamics of optical soliton crystals in frequency microcombs, examining spatial breathers, chaos transitions, and dynamical deterministic switching – in nonlinear measurements and theory. To understand the breather solitons, we describe their dynamical routes and two example transitional maps of the ensemble spatial breathers, with and without chaos initiation. We elucidate the physical mechanisms of the breather dynamics in the soliton crystal microcombs, in the interaction plane limit cycles and in the domain-wall understanding with parity symmetry breaking from third-order dispersion. We present maps of the accessible nonlinear regions, the breather frequency dependences on third-order dispersion and avoided-mode crossing strengths, and the transition between the collective breather spatio-temporal states. Our range of measurements matches well with our first-principles theory and nonlinear modeling. To image these soliton ensembles and their breathers, we further constructed panoramic temporal imaging for simultaneous fast- and slow-axis two-dimensional mapping of the breathers. In the phase-differential sampling, we present two-dimensional evolution maps of soliton crystal breathers, including with defects, in both stable breathers and breathers with drift. Our fundamental studies contribute to the understanding of nonlinear dynamics in soliton crystal complexes, their spatio-temporal dependences, and their stability-existence zones.

An emergent attractor network in a passive resistive switching circuit

Resistive memory devices feature drastic conductance change and fast switching dynamics. Particularly, nonvolatile bipolar switching events (set and reset) can be regarded as a unique nonlinear activation function characteristic of a hysteretic loop. Upon simultaneous activation of multiple rows in a crosspoint array, state change of one device may contribute to the conditional switching of others, suggesting an interactive network existing in the circuit. Here, we prove that a passive resistive switching circuit is essentially an attractor network, where the binary memory devices are artificial neurons while the pairwise voltage differences define an anti-symmetric weight matrix. An energy function is successfully constructed for this network, showing that every switching in the circuit would decrease the energy. Due to the nonvolatile hysteretic function, the energy change for bit flip in this network is thresholded, which is different from the classic Hopfield network. It allows more stable states stored in the circuit, thus representing a highly compact and efficient solution for associative memory. Network dynamics (towards stable states) and their modulations by external voltages have been demonstrated in experiment by 3-neuron and 4-neuron circuits.

Anisotropic vanadium dioxide-based metasurfaces for polarization-multiplexed holograms in the terahertz region

Abstract Holography plays a significant role in optical research and has been utilized in numerous applications. Metasurface holograms are attracting more and more attention with the advancement of their efficient wavefront reshaping. However, the realization of multi-channel holograms and dynamic switching of them still remain challenging in the terahertz band. In this paper, anisotropic vanadium dioxide (VO2) metasurfaces are used to realize four-channel holograms at 1.5 THz. It is assembled by a set of VO2 meta-atoms with independent phase control for different channels. Depending on the polarization of incident wave and the state of VO2, four channels are independently selected. After optimization to eliminate crosstalk between top and bottom layers, two holograms are projected under x- and y-polarized incidences when VO2 is metallic. Similarly, two additional holograms are achieved as VO2 is insulating. As a novel solution to terahertz multi-channel holography, this work may be applied to compact optical system and high-volume optical encryption.&amp;#xD;

On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning

For over forty years, TCP has been the main protocol for transporting data on the Internet. To improve congestion control algorithms (CCAs), delay bounding algorithms such as Vegas, FAST, BBR, PCC, and Copa have been developed. However, despite being designed to ensure fairness between data flows, these CCAs can still lead to unfairness and, in some cases, even cause data flow starvation in WiFi networks under certain conditions. We propose a new CCA switching solution that works with existing TCP and WiFi standards. This solution is offline and uses Deep Reinforcement Learning (DRL) trained on features such as noncongestive delay variations to predict and prevent extreme unfairness and starvation. Our DRL-driven approach allows for dynamic and efficient CCA switching. We have tested our design preliminarily in realistic datasets, ensuring that they support both fairness and efficiency over WiFi networks, which requires further investigation and extensive evaluation before online deployment.

State transition learning with limited data for safe control of switched nonlinear systems

Switching dynamics are prevalent in real-world systems, arising from either intrinsic changes or responses to external influences, which can be appropriately modeled by switched systems. Control synthesis for switched systems, especially integrating safety constraints, is recognized as a significant and challenging topic. This study focuses on devising a learning-based control strategy for switched nonlinear systems operating under arbitrary switching law. It aims to maintain stability and uphold safety constraints despite limited system data. To achieve these goals, we employ the control barrier function method and Lyapunov theory to synthesize a controller that delivers both safety and stability performance. To overcome the difficulties associated with constructing the specific control barrier and Lyapunov function and take advantage of switching characteristics, we create a neural control barrier function and a neural Lyapunov function separately for control policies through a state transition learning approach. These neural barrier and Lyapunov functions facilitate the design of the safe controller. The corresponding control policy is governed by learning from two components: policy loss and forward state estimation. The effectiveness of the developing scheme is verified through simulation examples.

Base Station Energy Saving based on Imitation Learning in 5G Network

Abstract With the rapid development of communication technology, the large-scale deployment of base stations (BSs) has led to an increase in power consumption. To reduce power consumption, energy saving technologies for BSs have emerged, which are in line with the concept of green communications and can save operators’ costs. In this paper, our goal is to minimize the total power consumption of the base station by dynamically controlling the switching status of the base station. This article first proposes a dynamic base station switching framework based on deep reinforcement learning (DRL), which optimizes the power consumption of switching BSs. Then, considering the dynamic nature of the environment, this article proposes a dynamic BS switching algorithm that introduces the idea of imitation learning (IL) to update the policy by learning the expert’s action. The simulation results show that this scheme can effectively reduce power consumption.