Switching Probability Research Articles

Reversible switching and stability of the epigenetic memory system in bacteria.

In previous work, we have developed a DNA methylation-based epigenetic memory system that operates in Escherichia coli to detect environmental signals, trigger a phenotypic switch of the cells and store the information in DNA methylation. The system is based on the CcrM DNA methyltransferase and a synthetic zinc finger (ZnF4), which binds DNA in a CcrM methylation-dependent manner and functions as a repressor for a ccrM gene expressed together with an egfp reporter gene. Here, we developed a reversible reset for this memory system by adding an increased concentration of ZnSO4 to the bacterial cultivation medium and demonstrate that one bacterial culture could be reversibly switched ON and OFF in several cycles. We show that a previously developed differential equationmodel of the memory system can also describe the new data. Then, we studied the long-term stability of the ON-state of the system over approximately 100 cell divisions showing a gradual loss of ON-state signal starting after 4 days of cultivation that is caused by individual cells switching from an ON- into the OFF-state. Over time, the methylation of the ZnF4-binding sites is not fully maintained leading to an increased OFF switching probability of cells, because stronger binding of ZnF4 to partially demethylated operator sites leads to further reductions in the cellular concentrations of CcrM. These data will support future design to further stabilize the ON-state and enforce the binary switching behaviour of the system. Together with the development of a reversible OFF switch, our new findings strongly increase the capabilities of bacterial epigenetic biosensors.

Well under control: Control demand changes are sufficient for metacontrol.

Metacontrol arises from the efficient retrieval of cognitive control by environmental cues that are predictive of the upcoming control demands. Previous studies have demonstrated that proactive and reactive metacontrol can be indexed by a list-wide switch probability (LWSP) and an item-specific switch probability (ISSP) effect, respectively. However, what triggers metacontrol in the first place has not been clearly articulated. While a "mere-experience" hypothesis attributes metacontrol to changes in control demands, an "affective-signaling" hypothesis suggests that high control demands are aversive and aversiveness drives metacontrol. In two experiments, we adjudicated between these hypotheses by considering the modes of metacontrol (proactive vs. reactive) and temporal dynamics of background valence (sustained vs. transient and positive vs. negative). We induced metacontrol (proactive or reactive) in a task-switching paradigm and created background valence by using positive and negative images as stimuli. With valence being an irrelevant aspect of the task, the design allows us to test whether (task-irrelevant) background valence would modulate metacontrol. While we were able to replicate the LWSP effect in Experiment 1 and the ISSP effect in Experiment 2, we did not find valence modulating either effect, regardless of the background valence being a sustained (Experiment 1) or a transient one (Experiment 2). These findings together suggest that negative valence (i.e., aversiveness) does not necessarily benefit metacontrol, and control demand variations are sufficient to induce metacontrol.

The Account of the Effect of Switch Probability on Switch and Mixing Costs: An ERP Study in a Cued Task-switching Paradigm.

Whether the effect of switch probability on switch and mixing costs is explained by an activation or preparation account is unclear. To investigate the account of the effect of switch probability on switch and mixing costs. We used a cued task-switching paradigm with three switch probabilities (high, 75%; medium, 50%; and low, 25%) with 19 healthy young adults and recorded the cue- and target-locked event-related potentials (ERPs) and behavioral performance. The task included switch and stay trials under high, medium, and low switch conditions, as well as pure trials. There was no significant difference in reaction time (RT) in switch and mixing costs between the high and medium switch conditions. The RT in switch and mixing costs in the high and medium switch conditions was significantly less and more than in the low switch condition, respectively. The cue-locked ERPs revealed significant effects on mixing costs (stay - pure) that were cue early frontal positivity (260-300 ms) in the high and medium switch conditions, and on switch costs (switch - stay) that were cue early central positivity (240-260 ms) in the low switch condition. Moreover, the target-locked ERPs of the mixing costs revealed significant effects on mixing costs that were target P3b (440-540 ms) in all three switch conditions, and on switch costs that were target P3b in the medium and low switch conditions. The effect of switch probability on switch and mixing costs is explained by the activation account.

A combined search method based on a deep learning combined surrogate model for groundwater DNAPL contamination source identification

Ensemble Kalman filter (EnKF) and optimization methods are two mainstream methods in groundwater contamination source identification, but most researchers usually only use or improve on one method. However, each method has strengths and weaknesses, and using one method alone may cause inaccuracy of the identification results for complex situations. Therefore, it is necessary to explore the combination of methods. In this paper, for the optimization method, to further enhance the solution precision of the optimization model for groundwater dense non-aqueous phase liquid (DNAPL) contamination source identification (GDCSI), we first constructed an improved butterfly optimization algorithm by introducing the dynamic switching probability mechanism in the previous butterfly optimization algorithm. Next, the EnKF method and optimization method (based on the improved butterfly optimization algorithm) were used respectively for GDCSI, to assess the strengths and weaknesses of the methods. Then, according to the strengths and weaknesses of the EnKF and optimization methods, the two methods were merged to build a more robust and practical combined search method (CSM) for GDCSI, to further enhance the identification accuracy and effectiveness. In addition, we innovatively applied the EnKF method to combine two single deep learning surrogate models: the deep belief neural network surrogate model and the deep residual neural network surrogate model. And a deep learning combined surrogate model was established, which further enhanced the approximation precision and applicability to the groundwater DNAPL contamination multiphase flow numerical simulation model. Finally, the results of CSM were compared with those of two single methods to analyze the former's effectiveness. The results showed that the CSM significantly improved the identification accuracy and effectiveness of GDCSI compared with any single method.

Time-resolved detection of spin–orbit torque switching of magnetization and exchange bias

Current-induced magnetization switching driven by spin–orbit torques on sub-nanosecond timescales could be used to create fast and low-power spintronic devices. The time-resolved detection and analysis of switching trajectories in ferromagnet/antiferromagnet exchange-biased structures are the key to designing spin–orbit torque devices with high speed, but insight remains limited. Here we report the time-resolved detection of spin–orbit torque switching of the magnetization and exchange bias in platinum/cobalt/iridium–manganese heterostructures. Using time-resolved magneto-optical Kerr microscopy, combined with micromagnetic simulations, we show that the ferromagnets, as well as interfacial antiferromagnetic spins and exchange bias, can be partially switched by sub-nanosecond current pulses, which allows the switching probabilities to be flexibly controlled at multiple levels. We also show that the spin–orbit-torque-induced switching of the exchange bias, which intimately depends on the current density, can stabilize multilevelled magnetization switching within sub-nanosecond current pulses.

Regulation of the mammalian-brain V-ATPase through ultraslow mode-switching.

Vacuolar-type adenosine triphosphatases (V-ATPases)1-3 are electrogenic rotary mechanoenzymes structurally related to F-type ATP synthases4,5. They hydrolyse ATP to establish electrochemical proton gradients for a plethora of cellular processes1,3. In neurons, the loading of all neurotransmitters into synaptic vesicles is energized by about one V-ATPase molecule per synaptic vesicle6,7. To shed light on this bona fide single-molecule biological process, we investigated electrogenic proton-pumping by single mammalian-brain V-ATPases in single synaptic vesicles. Here we show that V-ATPases do not pump continuously in time, as suggested by observing the rotation of bacterial homologues8 and assuming strict ATP-proton coupling. Instead, they stochastically switch between three ultralong-lived modes: proton-pumping, inactive and proton-leaky. Notably, direct observation of pumping revealed that physiologically relevant concentrations of ATP do not regulate the intrinsic pumping rate. ATP regulates V-ATPase activity through the switching probability of the proton-pumping mode. By contrast, electrochemical proton gradients regulate the pumping rate and the switching of the pumping and inactive modes. A direct consequence of mode-switching is all-or-none stochastic fluctuations in the electrochemical gradient of synaptic vesicles that would be expected to introduce stochasticity in proton-driven secondary active loading of neurotransmitters and may thus have important implications for neurotransmission. This work reveals and emphasizes the mechanistic and biological importance of ultraslow mode-switching.

Meta-stable initial condition for improving the switching probability in azobenzene derivatives on surface

The trans isomer of azobenzene (AB) and its derivatives is the most abundant isomer at thermodynamic equilibrium and is known to switch between its trans and cis isomers when triggered by light, electrons/holes and electrical field in adlayers on surfaces. However, the equilibrium initial condition limits the operation of molecular switches on surface due to large activation barrier for switching (trans to cis), hence the switching probability is very low. We show in this article, condensation of meta-stable (non-equilibrium) cis state of azobenzene derivatives on graphite. When electrons/holes induced switching is performed on these molecular switches from the meta-stable initial condition, the switching probability is enhanced by several folds. The molecules are switching between two associated cis states, instead of trans–cis switching. The switching between the cis states has relatively low energy barrier as revealed by density functional theory (DFT) calculations is at the origin of the enhanced switching probability. We also observe that reversible trans–cis and cis–cis’ switching on graphite is mediated through a stable charged intermediate state, leading to a low threshold voltage for switching compared to neutral molecules.

Use of Ibrutinib in Real Life Settings in France: Results from a Retrospective Observational Study Using the Snds Database (OSIRIS)

BACKGROUND Ibrutinib is a Bruton's Tyrosine Kinase Inhibitor indicated in the treatment of several B-cell malignancies: Chronic Lymphocytic Leukemia (CLL), Mantle Cell Lymphoma (MCL) and Waldenström Macroglobulinemia (WM). The number and the characteristics of patients treated by Ibrutinib in France in real life settings are unknown and need to be determined, as well as treatment pattern in real-life settings. METHODS All patients affiliated with the general scheme (around 80% of the French population) with a first dispensing of ibrutinib from August 1, 2017 (date of reimbursement in France) to December 31, 2020 were identified in the French national health insurance database (SNDS). Indications for which ibrutinib was supposed to be used were defined by an algorithm based on ICD-10 codes to identify the disease (CLL, MCL or WM), and on codes of previous and concomitant dispensed drugs to understand treatment line (first line -L1- or second or later line -L2+). Patients’ characteristics were described overall and by disease. A discontinuation was defined as temporary if the duration of Ibrutinib discontinuation was 6 months or less, and as permanent otherwise. Time to permanent discontinuation of Ibrutinib (including death) and time to next treatment (including death) were estimated and plotted using Kaplan Meier curves, by disease. Hospitalizations for a cardiovascular (CV) or bleeding event (atrial fibrillation, hypertension, bleeding, cardiac arrest, paroxysmal tachycardia, heart failure) were described in patients with CLL. RESULTS The study population consisted of 6,083 patients who initiated ibrutinib from August 1, 2017 to December 31, 2020. Among them, 45.6% had CLL, 13.9% had MCL, 13.8% had WM and 26.7% were unclassified because of the absence of specific codes associated with CLL, MCL or WM. The median duration of follow-up of patients with CLL, MCL and WM was 16.2, 9.5 and 15.2 months, respectively. Patients were elderly (median age of 71.4 years; 64% of patients over 70 years old) and mostly men (63.0%). At ibrutinib initiation, 68.3% of patients had a Charlson comorbidity index of 5 or more. In fact, 47.6% of patients had at least one CV disease in the year before the initiation and 21.5% had at least one solid tumor. Most patients used Ibrutinib as a second or later line (80.2%, 94.2% and 94.5% of patients with CLL, MCL and WM, respectively). The probability of permanently discontinuing Ibrutinib or dying at 12 months was 27% (including 10.7% death), 52% (including 27.8% death) and 29% (including 10.7% death) in patients with CLL, MCL and WM, respectively. The median time to permanent discontinuation or death was 11.2 months in patients with MCL and was not reached for patients with CLL and WM. The probability of treatment switch or dying at 12 months was 21%, 45% and 21% in patients with CLL, MCL and WM, respectively. The median time to switch or death was 14.9 months in patients with MCL and was not reached for patients with CLL and WM (Figure 1). In the 2,771 patients with CLL, 8.0% (223) were hospitalized for a CV or bleeding event of interest over the follow-up (median time of onset of 6 months after ibrutinib initiation), among whom 60.1% (134) had another dispensing of Ibrutinib after this hospitalization (Table 1). The main reasons for hospitalization were atrial fibrillation (2.8%, 77), bleeding (2.7%, 75) and heart failure (2.6%, 72). CONCLUSION This is one of the largest cohorts of Ibrutinib patients in the world. This study provides interesting and useful data on the real-life use of Ibrutinib. As all claims database studies, clinical outcomes data are lacking. However, the size and the representativeness of the dataset facilitates reliable trends for results. Indeed, the characteristics of the population were consistent with those expected. This study supports the use of Ibrutinib in real-life setting in elderly patients with CV comorbidities. Moreover, this study highlights that few CLL patients experienced a hospitalization for CV or bleeding event of interest, among which more than half took again Ibrutinib after their hospitalization. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Test-retest reliability of time-varying patterns of brain activity across single band and multiband resting-state functional magnetic resonance imaging in healthy older adults.

Leading Eigenvector Dynamics Analysis (LEiDA) is an analytic approach that characterizes brain activity recorded with functional Magnetic Resonance Imaging (fMRI) as a succession of discrete phase-locking patterns, or states, that consistently recur over time across all participants. LEiDA allows for the extraction of three state-related measures which have previously been key to gaining a better understanding of brain dynamics in both healthy and clinical populations: the probability of occurrence of a given state, its lifetime and the probability of switching from one state to another. The degree to which test-retest reliability of the LEiDA measures may be affected by increasing MRI multiband (MB) factors in comparison with single band sequences is yet to be established. In this study, 24 healthy older adults were scanned over three sessions, on weeks 0, 1, and 4. On each visit, they underwent a conventional single band resting-state fMRI (rs-fMRI) scan and three different MB rs-fMRI scans, with MB factors of 4, with and without in-plane acceleration, and 6 without in-plane acceleration. We found test-retest reliability scores to be significantly higher with MB factor 4 with and without in-plane acceleration for most cortical networks. These findings will inform the choice of acquisition parameters for future studies and clinical trials.

Uncertain policy regimes and government spending effects

Money financing returns to policy debate as governments around the world adopted massive fiscal measures during the pandemic. Using a fully nonlinear New Keynesian model with endogenous policy regime switching, we show that a moderate inflation-driven switching probability to a debt-financing regime reduces money-financed spending multipliers. When interacted with high government debt, money-financed spending multipliers fall below one, similar to the size of debt-financed spending multipliers. This result holds at the zero lower bound, with long-term government debt, and under a wide range of key parameter values. Policy regime uncertainty, on the other hand, has little effect on debt-financed spending multipliers.

Shadowed Type-2 Fuzzy Sets in Dynamic Parameter Adaption in Cuckoo Search and Flower Pollination Algorithms for Optimal Design of Fuzzy Fault-Tolerant Controllers

In recent, various metaheuristic algorithms have shown significant results in control engineering problems; moreover, fuzzy sets (FSs) and theories were frequently used for dynamic parameter adaption in metaheuristic algorithms. The primary reason for this is that fuzzy inference system (FISs) can be designed using human knowledge, allowing for intelligent dynamic adaptations of metaheuristic parameters. To accomplish these tasks, we proposed shadowed type-2 fuzzy inference systems (ST2FISs) for two metaheuristic algorithms, namely cuckoo search (CS) and flower pollination (FP). Furthermore, with the advent of shadowed type-2 fuzzy logic, the abilities of uncertainty handling offer an appealing improved performance for dynamic parameter adaptation in metaheuristic methods; moreover, the use of ST2FISs has been shown in recent works to provide better results than type-1 fuzzy inference systems (T1FISs). As a result, ST2FISs are proposed for adjusting the Lèvy flight (P) and switching probability (P′) parameters in the original cuckoo search (CS) and flower pollination (FP) algorithms, respectively. Our approach investigated trapezoidal types of membership functions (MFs), such as ST2FSs. The proposed method was used to optimize the precursors and implications of a two-tank non-interacting conical frustum tank level (TTNCFTL) process using an interval type-2 fuzzy controller (IT2FLC). To ensure that the implementation is efficient compared with the original CS and FP algorithms, simulation results were obtained without and then with uncertainty in the main actuator (CV1) and system component (leak) at the bottom of frustum tank two of the TTNCFLT process. In addition, the statistical z-test and non-parametric Friedman test are performed to analyze and deliver the findings for the best metaheuristic algorithm. The reported findings highlight the benefits of employing this approach over traditional general type-2 fuzzy inference systems since we get superior performance in the majority of cases while using minimal computational resources.

Treatments for multi-system inflammatory syndrome in children - discharge, fever, and second-line therapies.

Scarce evidence exists about the best treatment for multi-system inflammatory syndrome (MIS-C). We analyzed the effects of steroids, intravenous immunoglobulin (IVIG), and their combination on the probability of discharge over time, the probability of switching to second-line treatment over time, and the persistence of fever 2 days after treatment. We did a retrospective study to investigate the effect of different treatments on children with MIS-C from 1 March 2020 to 1 June 2021. We estimated the time-to-event probability using a Cox model weighted by propensity score to balance the baseline characteristics. Thirty of 132 (22.7%) patients were initially treated with steroids alone, 29/132 (21.9%) with IVIG alone, and 73/132 (55%) with IVIG plus steroids. The probability of early discharge was higher with IVIG than with IVIG plus steroids (hazard ratio [HR] 1.65, 95% CI 1.11–2.45, p = 0.013), but with a higher probability of needing second-line therapy compared to IVIG plus steroids (HR 3.05, 95% CI 1.12–8.25, p = 0.028). Patients on IVIG had a higher likelihood of persistent fever than patients on steroids (odds ratio [OR] 4.23, 95% CI 1.43–13.5, p = 0.011) or on IVIG plus steroids (OR 4.4, 95% CI 2.05–9.82, p < 0.001). No differences were found for this endpoint between steroids or steroids plus IVIG. Conclusions: The benefits of each approach may vary depending on the outcome assessed. IVIG seemed to increase the probability of earlier discharge over time but also of needing second-line treatment over time. Steroids seemed to reduce persistent fever, and combination therapy reduced the need for escalating treatment. What is Known:• Steroids plus intravenous immunoglobulin, compared with intravenous immunoglobulin alone for multi-system inflammatory syndrome (MIS-C) might reduce the need for hemodynamic support and the duration of fever, but the certainty of the evidence is low. What is New:• Intravenous immunoglobulin, steroids, and their combination for MIS-C may have different outcomes.• In this study, intravenous immunoglobulin increased the probability of discharge over time, steroids reduced persistent fever, while combination therapy reduced the need for second-line treatments.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00431-022-04649-8.

Understanding the Paths and Patterns of App-Switching Experiences in Mobile Searches

Mobile searches have become the main channel for people to search for information, and mobile searches have received attention in the field of information-seeking behavior. Especially as users use various apps to search network information, the app-switching behaviors in mobile searches have also attracted scholars’ attention in recent years. Research on app-switching behaviors in mobile searches can help to further understand users’ search motivations, evaluate search results, and improve users’ mobile search experiences. This study recruited participants (n = 30) and conducted a 15-day user experiment. This study collected all participants’ mobile phone log data during the experiment and identified the app-switching behaviors in mobile searches through a log collection tool. This study aimed to discover the app-switching behavior paths and patterns in mobile searches. Firstly, the basic characteristics of app-switching behaviors in mobile searches were analyzed, as were the app-switching paths in mobile searches from the perspective of switching probability between apps. Then, the different behaviors in mobile search sessions were identified and app-switching behavior patterns were put forward. These behavior patterns summarize user behavior changes in mobile search sessions. This paper focused on analyzing app-switching behavior paths under different patterns and found apparent differences in app-switching behavior paths. This study examined mobile search behavior from the perspective of app-switching. The research of this paper can help to better understand the relationship between users’ mobile search behaviors and app interactions and is an excellent supplement to the analysis of mobile search behaviors.

HAMR switching dynamics and the magnetic recording quadrilemma

We investigate the dynamical switching process of Heat Assisted Magnetic Recording (HAMR) by numerical calculations of switching probability using an atomistic model. Calculations show that at the elevated write temperature of HAMR there is a loss of information arising from ’backswitching’: a thermodynamic phenomenon which comes into play when the ratio of the Zeeman energy to the thermal energy is insufficiently large to completely stabilise the switched direction. We consider the special case of Heated Dot Magnetic Recording, where a reduction of switching probability can be related to a bit error rate. We show that the backswitching becomes more pronounced at faster write times. Also, we show that in the case of current recording media, based on the binary alloy FePt, backswitching will be a more stringent limitation on recording density than the usually assumed thermal stability criterion.

METHODOLOGY FOR QUORUM CONTROL OF THE REDUNDANT IRS SIGNALS IN THE MAIN MODE OF THE CIVIL AIRCRAFT CONTROL SYSTEM

Modern civil aircraft control systems operate, as a rule, in two modes - main and standby. The main control mode provides full control functionality with appropriate limitation for the flight crew due to the presence of redundant signals from interacting systems (such as IRS, ADS etc.). The standby mode provides control of the main control surfaces, allowing to continued safe flight and landing, but loses most of the protective functions. The redundancy of signals from interacting systems and their monitoring makes it possible to avoid the loss of the main mode of the flight control system due to single failures. Monitoring of the signals of interacting three-channel systems is ensured mainly by voting quorum up to the second failure, which is not a practically improbable event in terms of CS-25. These methods include the method of calculating the average value and the method of calculating the median value of redundant signals. In this paper, we propose a technique for quoruming triple redundant systems that provides control up to the third failure. The technique consists in using a confidence parameter for each channel of the interacting system, depending directly on its previous values (ie, monitor by value history). The results show that the use of this methodology will reduce the probability of switching to a standby control mode by almost one order of magnitude (and becomes an almost improbable event), as well as improve economic performance by increasing the Minimum Equipment List.

BOT-MICS: Bounding Time Using Analytics in Mixed-Criticality Systems

An increasing trend for reducing cost, space, and weight leads to modern embedded systems that execute multiple tasks with different criticality levels on a common hardware platform while guaranteeing a safe operation. In such mixed-criticality (MC) systems, multiple worst case execution times (WCETs) are defined for each task, corresponding to the system operation mode to improve the MC system’s timing behavior at runtime. Determining the appropriate WCETs for lower criticality (LC) modes is nontrivial. On the one hand, considering a very low WCET for tasks can improve the processor utilization by scheduling more tasks in that mode, on the other hand, using a larger WCET ensures that the mode switches (which causes by task overrunning) are minimized, thereby improving the quality of service for all tasks, albeit at the cost of processor utilization. Hitherto, no analytical solutions are proposed to determine WCETs in LC modes. In this regard, we propose a scheme to determine WCETs by the Chebyshev theorem, to make a tradeoff between the number of scheduled tasks at design-time and the number of dropped low-criticality tasks at runtime as a result of frequent mode switches. To have a tight bound of execution times and mode switching probability, we also propose a distribution analytics-based scheme, in which the mode switching probability is obtained based on the cumulative distribution function. Our experimental results show that our scheme improves the utilization of state-of-the-art MC systems by up to 72.27%, while maintaining 24.28% mode switching probability in the worst case scenario. Besides, the results of running embedded real-time benchmarks on a real platform show that the distribution-based scheme can improve the utilization by 7.30% while bounding the mode switching probability by 4.85% more, compared to the Chebyshev-based scheme.

Magnetization switching probability in the dynamical switching regime driven by spin-transfer torque

We study magnetization switching in the dynamical regime for in-plane magnetized systems, useful for field-free spin-orbit torque switching devices. We derive a formula for the switching probability, characterized by the thermal stability factor, critical current density, and the linewidth of ferromagnetic resonance. The formula agrees well with numerical simulations based on the Landau-Lifshitz-Gilbert equation. To study the viability of the theory developed, the switching probability of an in-plane magnetized ferromagnet in three-terminal spin-orbit torque switching devices is measured. We find that the transition width of the switching probability versus current increases with decreasing pulse width. The shape of the probability density, the current derivative of the switching probability, changes with varying pulse width. These characteristics are in good agreement with the theory developed. From the analyses, we show that the dynamical and thermally activated switching regimes can be distinguished simply from the shape of the probability density. The formula therefore provides useful means to analyze the switching probability of two-terminal spin torque and three-terminal spin-orbit torque switching devices.

Uncovering the link between intra-individual heterogeneity and variety seeking: the case of new shared mobility

Preferences can vary both across respondents (i.e. inter-respondent preference heterogeneity) and across choice tasks within respondents (i.e. intra-respondent preference heterogeneity). Ignoring the existence of intra-respondent preference heterogeneity could bias preference elicitation and demand forecast. Thus far, most studies covering inter- and intra-respondent preference heterogeneity have applied the mixed multinomial logit model. Meanwhile, the behavioural explanations for such preference variations remain under-explored. This paper accommodates inter- and intra-respondent preference heterogeneity through a two-layer latent class modelling structure, where the continuous random distributions are replaced with discrete mixtures in both layers. A latent variable representing variety-seeking is included to explain class membership probabilities, offering additional behavioural insights concerning the source of preference heterogeneity both across and within respondents. Two aspects associated with variety-seeking are examined: novelty-seeking (i.e. the inclination to adopt new modes) and alternation (i.e. the tendency to vary one’s behaviour regularly by selecting different modes continuously). In the context of new shared mobility, this paper finds the role of both aspects in preference heterogeneity. Specifically, novelty seekers are found to be more likely to fall into the class with higher probabilities of switching from existing modes to the new air taxi service than novelty avoiders, and alternation seekers are more likely to belong to the class with higher probabilities to exhibit intra-respondent preference heterogeneity than alternation avoiders. This paper, therefore, provides empirical evidence to identify the target customers of the new air taxi service.

Binary weight network utilizing multi-functional spin-orbit torque device for image recognition

Spintronic devices driven by spin–orbit torque (SOT) have a lot of potential for future neuromorphic computing hardware platforms. Nevertheless, problems such as multistate loss, weight time-dependent variability, and output discontinuity ensue when the device size is reduced to the nanoscale, which are contradictory to the requirements of traditional high-precision neural networks. In this study, we propose a high-precision all-spin neural network based on magnetization switching of a simple nanoscale multi-functional SOT device. The all-spin neural network is implemented by the Binary Weight Network, in which the binary synapse is encoded by the two states formed by the device's deterministic switching and the practical neuron is realized by its stochastic switching probability. Furthermore, we use a difference derivation training algorithm for the general-purpose network in a novel way to be more compatible with the discontinuous neuron output. Using this strategy, our network can reach ∼81.73 % recognition accuracy on the universal CIFAR-10 dataset, paving the way for future practical hardware implementation of nanoscale SOT devices in high-precision compact neuromorphic computing.

Kinematics of persistent random walkers with two distinct modes of motion.

We study the stochastic motion of active particles that undergo spontaneous transitions between two distinct modes of motion. Each mode is characterized by a velocity distribution and an arbitrary (anti)persistence. We present an analytical formalism to provide a quantitative link between these two microscopic statistical properties of the trajectory and macroscopically observable transport quantities of interest. For exponentially distributed residence times in each state, we derive analytical expressions for the initial anomalous exponent, the characteristic crossover time to the asymptotic diffusive dynamics, and the long-term diffusion constant. We also obtain an exact expression for the time evolution of the mean square displacement over all timescales and provide a recipe to obtain higher displacement moments. Our approach enables us to disentangle the combined effects of velocity, persistence, and switching probabilities between the two states on the kinematics of particles in a wide range of stochastic active or passive processes and to optimize the transport quantities of interest with respect to any of the particle dynamics properties.