Non-zero Probability Research Articles

Resonant conversion of axion dark radiation into terahertz electromagnetic radiation in a neutron star magnetosphere

In the strong magnetic field of a neutron star’s magnetosphere, axions coupled to electromagnetism develop a nonzero probability to convert into photons. Past studies have revealed that the axion-photon conversion can be resonantly enhanced. We recognize that the axion-photon resonance admits two parametrically distinct resonant solutions, which we call the mass-matched resonance and the Euler-Heisenberg assisted resonance. The mass-matched resonance occurs at a point in the magnetosphere where the radially-varying plasma frequency crosses the axion mass ωpl≈ma. The Euler-Heisenberg assisted resonance occurs where the axion energy satisfies ω≈(2ωpl2/7gγγγγB¯2)1/2. This second resonance is made possible though the strong background magnetic field B¯, as well as the nonzero Euler-Heisenberg four-photon self-interaction, which has the coupling gγγγγ=8α2/45me4. We study the resonant conversion of relativistic axion dark radiation into photons via the Euler-Heisenberg assisted resonance, and we calculate the expected electromagnetic radiation assuming different values for the axion-photon coupling gaγγ and different amplitudes for the axion flux onto the neutron star Φa. We briefly discuss several possible sources of axion dark radiation. Achieving a sufficiently strong axion flux to induce a detectable electromagnetic signal seems unlikely. Published by the American Physical Society 2024

Merit-Based Sortition in Decentralized Systems

In decentralized systems, it is often necessary to select an 'active' subset of participants from the total participant pool, with the goal of satisfying computational limitations or optimizing resource efficiency. This selection can sometimes be made at random, mirroring the sortition practice invented in classical antiquity aimed at achieving a high degree of statistical representativeness. However, the recent emergence of specialized decentralized networks that solve concrete coordination problems and are characterized by measurable success metrics often requires prioritizing performance optimization over representativeness. We introduce a simple algorithm for 'merit-based sortition', in which the quality of each participant influences its probability of being drafted into the active set, while simultaneously retaining representativeness by allowing inactive participants an infinite number of chances to be drafted into the active set with non-zero probability. Using a suite of numerical experiments, we demonstrate that our algorithm boosts the quality metric describing the performance of the active set by >2 times the intrinsic stochasticity. This implies that merit-based sortition ensures a statistically significant performance boost to the drafted, 'active' set, while retaining the property of classical, random sortition that it enables upward mobility from a much larger 'inactive' set. This way, merit-based sortition fulfils a key requirement for decentralized systems in need of performance optimization.

RH-ECBS: enhanced conflict-based search for MRPP with region heuristics

Abstract This paper proposes a novel two-layer framework based on conflict-based search and regional divisions to improve the efficiency of multi-robot path planning. The high-level layer targets the reduction of conflicts and deadlocks, while the low-level layer is responsible for actual path planning. Distinct from previous dual-level search frameworks, the novelties of this work are (1) subdivision of planning regions for each robot to decrease the number of conflicts encountered during planning; (2) consideration of the number of robots in the region during planning in the node expansion stage of A*, and (3) formal proof demonstrating the nonzero probability of the proposed method in obtaining a solution, along with providing the upper bound of the solution in a special case. Experimental comparisons with Enhanced Conflict-Based Search demonstrate that the proposed method not only reduces the number of conflicts but also achieves a computation time reduction of over 30%.

VISUALIZATION OF THREATS IN CIVIL SECURITY USING THREE-DIMENSIONAL SPATIAL MODELING

The article analyzes the main concepts and methods of modeling used in civil security to solve the issues of analyzing the prerequisites for the occurrence of hazards, by forecasting, preventing and managing their components. On-site research, in particular, direct measurements, can only be carried out on an already existing object. Modeling today is one of the important methods, the main methodology of which is to learn the essence of natural and social phenomena of various levels of complexity. It provides an opportunity to demonstrate various systems related to civil security using methods of applied mathematics, mathematical statistics and optimization. The task of analysis and forecasting on any object can be reduced to building a model that reflects the behavior of a natural object. The main tasks of this article are to establish modeling principles and methods for their use in civil security using the example of modeling noise maps from various sources, one of which is construction in a dense residential area. The task of analysis and forecasting on almost any object can be reduced to building a model that adequately reflects the behavior of a natural object. The basic principles on which modeling is based are defined. The principle of information sufficiency – building its model is impossible in the complete absence of information about the object. Feasibility principle – the model should ensure the achievement of the research goal with a non-zero probability value. The principle of multiplicity of models - the model should reflect exactly those properties of a real object or system that are of interest to the researcher. The principle of aggregation - the system is better decomposed as a set of subsystems, for the description of which standard schemes can be used. Principle of parameterization – the model must be built in the form of a known system, the parameters of which are unknown. Crisis simulation training is a common organizational tool for improving civil security preparedness. Although today its general advantages cannot be denied, the conditions under which these simulations offer opportunities for improvement often remain unclear.

The spectrum of asymptotic Cayley trees

We characterize the spectrum of the transition matrix for simple random walk on graphs consisting of a finite graph with a finite number of infinite Cayley trees attached. We show that there is a continuous spectrum identical to that for a Cayley tree and, in general, a non-empty pure point spectrum. We apply our results to studying continuous time quantum walk on these graphs. If the pure point spectrum is nonempty the walk is in general confined with a nonzero probability.

The (Surprising) Sample Optimality of Greedy Procedures for Large-Scale Ranking and Selection

Ranking and selection (R&S) aims to select the best alternative with the largest mean performance from a finite set of alternatives. Recently, considerable attention has turned toward the large-scale R&S problem which involves a large number of alternatives. Ideal large-scale R&S procedures should be sample optimal; that is, the total sample size required to deliver an asymptotically nonzero probability of correct selection (PCS) grows at the minimal order (linear order) in the number of alternatives, k. Surprisingly, we discover that the naïve greedy procedure, which keeps sampling the alternative with the largest running average, performs strikingly well and appears sample optimal. To understand this discovery, we develop a new boundary-crossing perspective and prove that the greedy procedure is sample optimal for the scenarios where the best mean maintains at least a positive constant away from all other means as k increases. We further show that the derived PCS lower bound is asymptotically tight for the slippage configuration of means with a common variance. For other scenarios, we consider the probability of good selection and find that the result depends on the growth behavior of the number of good alternatives: if it remains bounded as k increases, the sample optimality still holds; otherwise, the result may change. Moreover, we propose the explore-first greedy procedures by adding an exploration phase to the greedy procedure. The procedures are proven to be sample optimal and consistent under the same assumptions. Last, we numerically investigate the performance of our greedy procedures in solving large-scale R&S problems. This paper was accepted by Baris Ata, stochastic models and simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72091211, 72071146, 72161160340]. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2023.00694 .

Nonparametric estimation of conditional incremental effects

Abstract Conditional effect estimation has great scientific and policy importance because interventions may impact subjects differently depending on their characteristics. Most research has focused on estimating the conditional average treatment effect (CATE). However, identification of the CATE requires that all subjects have a non-zero probability of receiving treatment, or positivity, which may be unrealistic in practice. Instead, we propose conditional effects based on incremental propensity score interventions, which are stochastic interventions where the odds of treatment are multiplied by some factor. These effects do not require positivity for identification and can be better suited for modeling scenarios in which people cannot be forced into treatment. We develop a projection approach and a flexible nonparametric estimator that can each estimate all the conditional effects we propose and derive model-agnostic error guarantees showing that both estimators satisfy a form of double robustness. Further, we propose a summary of treatment effect heterogeneity and a test for any effect heterogeneity based on the variance of a conditional derivative effect and derive a nonparametric estimator that also satisfies a form of double robustness. Finally, we demonstrate our estimators by analyzing the effect of intensive care unit admission on mortality using a dataset from the (SPOT)light study.

Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution

Determining background aerosol optical depth threshold value (BAOD) is critical to aerosol type identification and air pollution control. This study presents a statistical method to select the best BAOD threshold value using the VIIRS DB AOD products at 1 × 1 degree resolution from 2012 to 2019 as a major testbed. A series of multiple lognormal distributions with 1 to 5 peaks are firstly applied to fit the AOD histogram at each grid point, and the distribution with the highest correlation coefficient (R) gives preliminary estimations of BAOD, which is defined as either the intersection point of the first two normal distribution curves when having multiple peaks, or the midpoint between the peak AOD and the first AOD with non-zero probability when the mono peak is the best fit. Then, the lowest 1st to 100th percentile AOD distributions are compared with the preliminary BAOD distribution on a global scale. The final BAOD is obtained from the best cutoff percentile AOD distributions with the lowest bias compared with preliminary BAOD. Results show that the lowest 30th percentile AOD is the best estimation of BAOD for different AOD datasets and different seasons. Analysis of aerosol chemical information from MERRA-2 further supports this selection. Based on the BAOD, we updated the VIIRS aerosol type classification scheme, and the results show that the updated scheme is able to achieve reliable detection of aerosol type change in low aerosol loading conditions.

Crowding induced switching of polymer translocation by the amalgamation of entropy and osmotic pressure

The translocation of polymers is omnipresent in inherently crowded biological systems. We investigate the dynamics of polymer translocation through a pore in free and crowded environments using Langevin dynamics simulation. We observed a location-dependent translocation rate of monomers showcasing counterintuitive behavior in stark contrast to the bead velocity along the polymer backbone. The free energy calculation of asymmetrically placed polymers indicates a critical number of segments to direct receiver-side translocation. For one-sided crowding, we have identified a critical crowding size revealing a nonzero probability of translocation toward the crowded-side. Moreover, we have observed that shifting the polymer toward the crowded-side compensates for one-sided crowding, yielding an equal probability akin to a crowder-free system. In two-sided crowding, a slight variation in crowder size and packing fraction induces a polymer to switch its translocation direction. These conspicuous yet counter-intuitive phenomena are rationalized by minimalistic theoretical arguments based on osmotic pressure and radial entropic forces.

Increasing the probability of transfer GMDSS broadcast messages when transmitted over HF channel in case fading

Improving the quality of communication in the Global Maritime Distress and Safety System of navigation is one of the components of the development of the Arctic zone of the Russian Federation in terms of information support. At the same time, the most difficult is to ensure communication in the A3 and A4 areas, where a communication HF channel plays a not small role. The transfer of messages over HF channel in the broadcasting mode of SITOR-B is considered. To improve probabilistic characteristics in the work, a statistical analysis of the decisions of the demodulator is carried out. It is shown that the probability density functions are “noisy”, which is associated with the non-zero probability of a bit error. Models of Rayleigh, Rice and Nakagami distributions were used for statistical analysis. It is shown that in the case of low values of the signal-to-noise ratio, which is characteristic of high latitude tracks, a Nakagami model is preferable. The symbol error rate and message error rate in a fading communication channel, as well as the probability of receiving an erroneous message are shown. The results presented in this work can significantly improve the probabilistic characteristics of the HF radiolines when transmitting messages in SITOR-B mode.

New Yarkovsky drift detections using astrometric observations of NEAs

The Yarkovsky drift represents the semi-major axis variation of a celestial body due to the Yarkovsky effect. This thermodynamic effect acts more significantly on bodies with a diameter between ≈10m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 10 \\,\ ext {m}$$\\end{document} and ≈30km\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 30 \\,\ ext {km}$$\\end{document}. Therefore, the orbits of many minor bodies of the solar system are affected: knowing the value of the Yarkovsky drift can be crucial to accurately predict their positions, especially if the asteroids are Near Earth Asteroids (NEAs) and there may be a non-zero impact probability with the Earth. The direct computation of this effect is not easily achieved due to the scarce availability of NEAs physical information. Thus, the more promising method to estimate the Yarkovsky effect is through an orbital fit using seven parameters, the six orbital elements and a seventh parameter accounting for non-gravitational interactions. In this paper, we show the analysis of 1262 NEAs with Signal-to-Noise Ratio (SNR) greater or equal 2, of which 279 have the parameter S (absolute ratio between the Yarkovsky drift and its expected value) less than 1.5 and are therefore more reliable. Among these, 91 are not present in the literature, thus represent new Yarkovsky drift detections. Furthermore, we used our results to estimate the ratio of the retrograde over prograde rotators and to validate the dependence of the Yarkovsky drift from the diameter, da/dt ≈D-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx D^{-1}$$\\end{document}.

Visualizing non-adiabatic quantum dynamics and photo-excitation processes on Excel

We offer two Excel modules that students can use to view the real-time spatial evolution of a diatomic molecule's nuclei when two different potential energy curves governing the nucleus motion spatially cross one another or are coupled by a light field. Module 1 comprises three spreadsheets that can be used to view non-adiabatic nuclear dynamics on two crossing nuclear potential energy curves. These curves cross because there is a value of the spatial coordinate where the two curves have the same energy, which results in a non-zero probability of the probability density crossing from one potential curve to the other. Students can view non-adiabatic nuclear dynamics involving two unbounded nuclear potential curves, pre-dissociation with a bound “ionic state” to an unbound covalent state, and quantum tunneling dynamics between two bounded potential curves. Module 2 comprises two spreadsheets that can be used to view the dynamics of the photo-excitation process, i.e., where the absorption of light leads to transitions between two nuclear potentials. Students can model the light-induced transitions between nuclear potentials when the light is provided by both a continuous wave laser and a pulsed laser. These modules are included as the supplementary material and can be run on any computer that supports Excel.

Generating Task Reallocation Suggestions to Handle Contingencies in Human-Supervised Multi-Robot Missions

In a mission with significant uncertainty due to intermittent communications, delayed information flow, and robotic failures, the role of human supervisors is extremely challenging. As and when any new information arrives, humans must infer both the existing and predicted future states, identify potential contingencies, and update task assignments to robots rapidly. We propose methodologies for automated generation of task reallocation suggestions to humans to assist in the decision-making process. Our generated robot retasking plan minimizes a modified makespan of the mission, which incorporates task criticality and penalty for incomplete tasks. The plan considers the effects of potential mission contingencies on the tasks, the robots, and the future performance of the robots operating based on the previous task plans. Our method includes the incorporation of two optional tasks, i.e., relay and robot rescue, for performance improvement. The rescue task has probabilistic outcomes affecting the team size. One or more rescues are incorporated in a way that can minimize the expected value of the modified makespan overall possibilities of rescue outcomes. We have conducted performance evaluation using simulation, demonstrating the value of the optional tasks and performance enhancement using our method of incorporating them. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The work reported in this paper will be useful in applications where a team of agents is deployed to carry on a large-scale mission with communication constraints where the number of functional agents can change probabilistically. Typically, such uncertainty is encountered in applications that are challenging or dangerous in nature. Agents can have non-zero probabilities to fail while doing certain risky tasks and to get recovered by other agents. The proposed centralized multi-agent task reallocation method can help in proactively addressing potential contingencies in surveillance, search and rescue, or disaster relief to support resilient operations while having a supervisor in the higher chain of command.

Stopping times occurring simultaneously

Stopping times are used in applications to model random arrivals. A standard assumption in many models is that they are conditionally independent, given an underlying filtration. This is a widely useful assumption, but there are circumstances where it seems to be unnecessarily strong. We use a modified Cox construction along with the bivariate exponential introduced by Marshall and Olkin (1967) to create a family of stopping times, which are not necessarily conditionally independent, allowing for a positive probability for them to be equal. We show that our initial construction only allows for positive dependence between stopping times, but we also propose a joint distribution that allows for negative dependence while preserving the property of non-zero probability of equality. We indicate applications to modeling COVID-19 contagion (and epidemics in general), civil engineering, and to credit risk.

Stackelberg Security Game for Optimizing Cybersecurity Decisions in Cloud Computing

As it is difficult to cover all cybersecurity threats, an optimal defense strategy is one of the focal issues in cloud computing due to its dynamic abstraction and scalability. On this basis, Stackelberg security games (SSG) have received significant attention for their better deployment of limited security. To deal with uncertainty and incomplete information, we introduce a modified quantal response (Mod-QR) approach that incorporates bounded rationality and preference into the decision-making process. Formally, this can be done by using the quantal response equilibrium (QRE) framework to find a trade-off between the effectiveness and operating costs of cloud computing. In this case, the most effective countermeasures to defend the cloud can be viewed as a mixed strategy in which all the actions of the defender are played with a nonzero probability. This framework has been evaluated using an experimental study on MATLAB optimization toolbox to understand the behavioral aspects of cybersecurity actors and then to proactively protect cloud computing.

UNMANNED TRANSPORT AND THE REFORMATTING OF HUMAN NEEDS: NICHE AND EVOLUTIONARY SCENARIOS

There are quite a few phenomena in human civilisation that could be compared to transport in terms of the scale and irreversibility of their impact on the development of society. The domestication of the horse and the invention of the wheel are rightly regarded as some of mankind’s greatest achievements. The current stage of transport development claims to be a Revolution 2.0: the advent of the Global Navigation Satellite System (GPS) has made it possible not only to locate but also to remotely control unmanned vehicles. In the near future, this could radically change both the transportation system and human life as a whole.The purpose of the article is to consider niche and evolutionary scenarios for the development of unmanned vehicles. It is proved that unmanned transport involves large scale and very similar transportation of homogeneous products in order to cover higher investment costs through economies of scale. In addition, such transport requires extreme predictability in the entire chain: from the receipt of raw materials to the delivery of the finished product to the end user. The accident intolerance of unmanned vehicles can be solved by creating an isolated transportation system along closed circuits in which there is no room for a person.The article notes that there is a non-zero probability that unmanned vehicles will not be able to outperform traditional transport in terms of their characteristics and will therefore only operate in certain market niches.

Generating and detecting true ambiguity: a forgotten danger in DNN supervision testing

Deep Neural Networks (DNNs) are becoming a crucial component of modern software systems, but they are prone to fail under conditions that are different from the ones observed during training (out-of-distribution inputs) or on inputs that are truly ambiguous, i.e., inputs that admit multiple classes with nonzero probability in their labels. Recent work proposed DNN supervisors to detect high-uncertainty inputs before their possible misclassification leads to any harm. To test and compare the capabilities of DNN supervisors, researchers proposed test generation techniques, to focus the testing effort on high-uncertainty inputs that should be recognized as anomalous by supervisors. However, existing test generators aim to produce out-of-distribution inputs. No existing model- and supervisor independent technique targets the generation of truly ambiguous test inputs, i.e., inputs that admit multiple classes according to expert human judgment. In this paper, we propose a novel way to generate ambiguous inputs to test DNN supervisors and used it to empirically compare several existing supervisor techniques. In particular, we propose AmbiGuess to generate ambiguous samples for image classification problems. AmbiGuess is based on gradient-guided sampling in the latent space of a regularized adversarial autoencoder. Moreover, we conducted what is {-} to the best of our knowledge {-} the most extensive comparative study of DNN supervisors, considering their capabilities to detect 4 distinct types of high-uncertainty inputs, including truly ambiguous ones. We find that the tested supervisors’ capabilities are complementary: Those best suited to detect true ambiguity perform worse on invalid, out-of-distribution and adversarial inputs and vice-versa.

CheckShake: Passively Detecting Anomaly in Wi-Fi Security Handshake Using Gradient Boosting Based Ensemble Learning

Recently, a number of attacks have been demonstrated (like key reinstallation attack, called KRACK) on WPA2 protocol suite in Wi-Fi WLAN, for which a patching is often challenging. In this paper, we design and implement a system, called CheckShake, to passively detect anomalies in the handshake of Wi-Fi security protocols, in particular WPA2, between a client and an AP using COTS radios. Our proposed system works without decrypting any traffic and sniffing on multiple channels in parallel. It uses a state machine model for grouping Wi-Fi handshake packets and then perform deep packet inspection to identify the symptoms of the anomaly in specific stages of a handshake session. Our implementation of CheckShake does not require any modification to the firmware of the client or the AP or the COTS devices, it only requires to be physically placed within the range of the AP and its clients. We use both the publicly available dataset and our own data set for performance analysis of CheckShake. Using gradient boosting-based supervised machine learning (ML) models, we show that an accuracy around 98.50% with no false positive can be achieved using CheckShake in open sourced data that has non-zero probability of missing packets per group of packets.

Disingenuous Infallibilism

Abstract Some recent epistemologists propose that certainty is the norm of action and assertion. This proposal is subject to skeptical worries. If, as is usually supposed, certainty is very hard to come by, legitimate action and assertion will be rare. To remedy this, some have conjoined their certainty-norms with a context-sensitive semantics for ‘certainty’. For a proposition to be certain for you, you only need to be able to exclude relevant alternatives. I argue that, depending on what makes an alternative relevant, this kind of view is disingenuous. In particular, if an alternative can be made relevant by being relevant to rational action, it allows an escape from the skeptical consequences only by licensing David Lewis-style utterances of the form, “You know that p only if there is no probability, no matter how small, that not-p—Psst!—Unless that probability is really small.” While there are legitimate ways to exclude some possibilities from relevance, it is disingenuous, I argue, to exclude possibilities from relevance on the basis of the very characteristic—low but nonzero probability—that is claimed to be incompatible with certainty.

Improving estimates of waning immunity rates in stochastic SIRS models with a hierarchical framework

As most disease causing pathogens require transmission from an infectious individual to a susceptible individual, continued persistence of the pathogen within the population requires the replenishment of susceptibles through births, immigration, or waning immunity.Consider the introduction of an unknown infectious disease into a fully susceptible population where it is not known how long immunity is conferred once an individual recovers from infection. If, initially, the prevalence of disease increases (that is, the infection takes off), the number of infectives will usually decrease to a low level after the first major outbreak. During this post-outbreak period, the disease dynamics may be influenced by stochastic effects and there is a non-zero probability that the epidemic will die out. Die out in this period following the first major outbreak is known as an epidemic fade-out. If the disease does not die out, the susceptible population may be replenished by the waning of immunity, and a second wave may start.In this study, we investigate if the rate of waning immunity (and other epidemiological parameters) can be reliably estimated from multiple outbreak data, in which some outbreaks display epidemic fade-out and others do not. We generated synthetic outbreak data from independent simulations of stochastic SIRS models in multiple communities. Some outbreaks faded-out and some did not. We conducted Bayesian parameter estimation under two alternative approaches: independently on each outbreak and under a hierarchical framework. When conducting independent estimation, the waning immunity rate was poorly estimated and biased towards zero when an epidemic fade-out was observed. However, under a hierarchical approach, we obtained more accurate and precise posterior estimates for the rate of waning immunity and other epidemiological parameters. The greatest improvement in estimates was obtained for those communities in which epidemic fade-out was observed.Our findings demonstrate the feasibility and value of adopting a Bayesian hierarchical approach for parameter inference for stochastic epidemic models.