Probabilistic Scheme Research Articles

Evolutionary dynamics in networked trust games with diverse investment patterns

Most previous works study the evolution of trust by commonly assuming that investors adopt a deterministic investment strategy. In this work, we propose a mechanism of diverse investment in the trust game model on social networks, where each investor adopts a probabilistic strategy by considering the trustworthiness level in the local group to decide whether to invest or not. Extensive simulation results suggest that the proposed mechanism inhibits the untrustworthy behavior and limits its spread, thus stabilizing the cooperative cluster of investors and trustworthy trustees. Therefore, the trust level and global wealth are greatly enhanced comparing to the traditional setup with homogeneous investment pattern. The strong investment diversity can even eliminate untrustworthiness completely despite the fact that the severe temptation condition is disadvantageous to the evolution of trust. We also investigate the impact of investment diversity on trust game model embedding in different network structures with different initial conditions, where we observe similarly positive evolutionary outcomes. We hope these observations can provide valuable insights into further exploring the improvement of trust in real life.

Probabilistic resumable quantum teleportation for an arbitrary two-qubit entangled state with different quantum channels

We propose two probabilistic resumable quantum teleportation schemes for an arbitrary two-qubit entangled state with different quantum channels. The characteristic of our schemes is that the initial state in the sender’s hand can be recovered instead of being destroyed when teleportation fails, so we can improve the success probabilities by repeating the teleportation process more times. The analysis results show that the number of repetitions required for successful teleportation depends on the entanglement strength of the quantum channels, and even weak entanglement enables a high probability of success.

Probabilistic dischargeable time forecasting of power batteries via statistical characterization of future loading profiles

Accurately end-of-discharge prediction of lithium-ion batteries is of great significance for user convenience and optimal path-planning and energy-dispatching in various applications. However, accurate state-of-charge estimation, dynamic and stochastic operating conditions, and uncertainty quantification have remained major challenges. This paper thus proposes a probabilistic scheme for forecasting dischargeable time via a robust observer design and statistical characterization of future loading profiles. First, a robust observer with stability and convergence analysis using an equivalent-circuit model and Lyapunov theory is proposed to guarantee the robustness of state-of-charge estimations. Then, a power histogram is formulated to characterize future loading profiles. Next, a Gaussian process regression and a bootstrap sampling technique are combined to perform dischargeable time forecasting and uncertainty quantification. Finally, the proposed scheme is validated using experimental data acquired from battery cells at different temperatures and loading profiles. Experimental results show that the robust state-of-charge estimation can converge to the true value within an error of 3.50% against over 10% capacity biases. It also demonstrates that the proposed forecasting method can provide dischargeable time prediction within an error of 0.66h, about 20% of the total dischargeable time.

A hybrid ensemble deep reinforcement learning model for locomotive axle temperature using the deterministic and probabilistic strategy

Abstract This paper proposes a hybrid deep reinforcement learning framework for locomotive axle temperature by combining the wavelet packet decomposition (WPD), long short-term memory (LSTM), gated recurrent unit (GRU) reinforcement learning and generalized autoregressive conditional heteroskedasticity (GARCH) algorithms. The WPD is utilized to decompose the raw nonlinear series into subseries. Then the deep learning predictors LSTM and GRU are established to predict the future axle temperatures in each subseries. The Q-learning could generate optimal ensemble weights to integrate the predictors to finish the deterministic forecasting and GARCH is used to conduct the deterministic forecasting based on the deterministic forecasting residual. These parts of the hybrid ensemble structure contributed to optimal modelling accuracy and provided effective support in the real-time monitoring and fault diagnosis of transportation.

A sampling strategy for habitat selection, mapping, and abundance estimation of deer by pellet counts

AbstractWe propose a design‐based strategy to exploit pellet group counts performed within plots of a prefixed size using the clearance count technique with the purposes of analyzing habitat selection, mapping the pellet group presence throughout the study area, and estimating the abundance of deer populations. As is customary in design‐based inference, the strategy is free of model assumptions, and the precision and statistical consistency of the proposed estimators are determined by the probabilistic sampling scheme adopted to locate plots. The unique necessary assumptions are the absence of migratory movements during the survey period, the accurate recording of the number of pellet groups deposited within sample plots between the 2 visits, and a precise approximation of the daily defecation rate of the population. In addition to these assumptions, which can be attained by a suitable design of the survey, the statistical soundness of the strategy rests on the use of tessellation stratified sampling, a stratified sampling scheme that ensures an even distribution of plots throughout the study area. The scheme also allows for the estimation of the standard errors and the construction of confidence intervals without involving any other assumptions. We applied this strategy in summer 2019 in a protected area of a Mediterranean coastal region to estimate the density of a fallow deer (Dama dama) population. We estimated the corresponding standard error considering the uncertainty entailed by the estimation of the daily defecation rate, with the purpose of performing reliable monitoring. The proposed strategy provided precise estimates of deer abundance and is readily implementable in the field, standardized, and easily repeatable over time, thus allowing reliable monitoring and comparisons across time and space, which are fundamental attributes for management of deer populations.

Prevalence estimation and optimal classification methods to account for time dependence in antibody levels

Serology testing can identify past infection by quantifying the immune response of an infected individual providing important public health guidance. Individual immune responses are time-dependent, which is reflected in antibody measurements. Moreover, the probability of obtaining a particular measurement from a random sample changes due to changing prevalence (i.e., seroprevalence, or fraction of individuals exhibiting an immune response) of the disease in the population. Taking into account these personal and population-level effects, we develop a mathematical model that suggests a natural adaptive scheme for estimating prevalence as a function of time. We then combine the estimated prevalence with optimal decision theory to develop a time-dependent probabilistic classification scheme that minimizes the error associated with classifying a value as positive (history of infection) or negative (no such history) on a given day since the start of the pandemic. We validate this analysis by using a combination of real-world and synthetic SARS-CoV-2 data and discuss the type of longitudinal studies needed to execute this scheme in real-world settings.

A Nonbinary LDPC-Coded Probabilistic Shaping Scheme for a Rayleigh Fading Channel.

In this paper, a novel, nonbinary (NB) LDPC-coded probabilistic shaping (PS) scheme for a Rayleigh fading channel is proposed. For the NB LDPC-coded PS scheme in Rayleigh fading channel, the rotation angle of 16 quadrature amplitude modulation (QAM) constellations, 64QAM constellations and 256QAM constellations are optimized by the exhaustive search. The simulation results verify the information-theoretical analysis. Compared with the binary LDPC-coded PS scheme for Rayleigh fading channel, the proposed NB LDPC-coded PS scheme can improve error performance. In summary, the proposed NB LDPC-coded PS scheme for Rayleigh fading channel is reliable and thus suitable for future communication systems.

A learning scheme by sparse grids and Picard approximations for semilinear parabolic PDEs

Abstract Relying on the classical connection between backward stochastic differential equations and nonlinear parabolic partial differential equations (PDEs), we propose a new probabilistic learning scheme for solving high-dimensional semilinear parabolic PDEs. This scheme is inspired by the approach coming from machine learning and developed using deep neural networks in Han et al. (2018, Solving high-dimensional partial differential equations using deep learning. Proc. Natl. Acad. Sci., 115, 8505–8510. Our algorithm is based on a Picard iteration scheme in which a sequence of linear-quadratic optimization problem is solved by means of stochastic gradient descent algorithm. In the framework of a linear specification of the approximation space, we manage to prove a convergence result for our scheme, under some smallness condition. In practice, in order to be able to treat high-dimensional examples, we employ sparse-grid approximation spaces. In the case of periodic coefficients and using pre-wavelet basis functions, we obtain an upper bound on the global complexity of our method. It shows, in particular, that the curse of dimensionality is tamed in the sense that in order to achieve a root mean squared error of order $\varepsilon $, for a prescribed precision $\varepsilon $, the complexity of the Picard algorithm grows polynomially in $\varepsilon ^{-1}$ up to some logarithmic factor $|\!\log (\varepsilon )|$, whose exponent grows linearly with respect to the PDE dimension. Various numerical results are presented to validate the performance of our method, and to compare them with some recent machine learning schemes proposed in E et al. (2017, Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations. Commun. Math. Stat., 5, 349–380) and Huré et al. (2020, Deep backward schemes for high-dimensional nonlinear PDEs. Math. Comput., 89, 1547–1579).

Optimal Probabilistic Repetition for Massive MIMO-Aided Grant-Free Short-Packet Transmissions

In this letter, we propose a novel probabilistic repetition scheme for the uplink massive MIMO system with grant-free access. Compared with canonical K-repetition, the proposed scheme is more flexible and covers the K-repetition as a special case. With the consideration of multiple practical factors, including the sporadic arrivals, the short-packet transmissions, probabilistic repetition, and channel estimation error, the reliability and throughput of the proposed probabilistic repetition assisted grant-free access scheme are theoretically analyzed. It is also proved that the optimal transmission probability for reliability maximization exists and can be found through gradient descent method. Simulation results validate the accuracy of our theoretical analysis, and also show that the proposed Optimal Probabilistic Repetition assisted grant-free Access scheme (OPRA) is superior to the popular modern grant-free random access with repetition transmission, that is, irregular repetition slotted ALOHA (IRSA).

A hybrid caching strategy for information-centric satellite networks based on node classification and popular content awareness

Low Earth Orbit (LEO) satellite networks are inexpensive to deploy and have wide coverage, and are widely available for differentiated content distribution services. With the development of on-board storage and computing capabilities, in-network caching technology in information centric networking (ICN) has proven to be an effective way to increase the throughput and content distribution efficiency of satellite networks. However, traditional caching and distribution schemes, which do not take into account the high-speed motion of satellite nodes and dynamic changes in topology, are not applicable to satellite networks. To address these issues, a hybrid caching strategy for satellite networks based on node classification and popular content awareness (NCPCA) is proposed. Firstly, the time slot partitioning method based on interlayer similarity is proposed to transform the dynamically changing process into a set of time slots with stable topology. Next, the satellite nodes are dynamically divided into two categories by fully considering the changes in connection relationships and interaction order during the spatial–temporal evolution of the satellite nodes. Nodes with satellite topological and functional characteristics are screened out as core nodes with TOPSIS algorithm, and the remaining nodes are regarded as edge nodes. Finally, a probabilistic caching scheme based on content popularity is adopted with core nodes as caching nodes to ensure caching performance and promote the diversity of cached content. Simulation results show that this strategy can effectively improve cache hit rate, reduce user request delay and content fetching hops, and promote the stable operation of satellite networks compared with other caching strategies.

A Probabilistic Shaping Scheme for Bit-Interleaved Coded Modulation With Iterative Decoding

In this letter, a probabilistic shaping (PS) scheme for bit-interleaved coded modulation with iterative decoding (BICM-ID) between low density parity check (LDPC) decoder and demodulator is proposed. Compared to conventional LDPC-coded 8-quadrature amplitude modulation (QAM) system on additive white Gaussian noise (AWGN) channel, when spectral efficiency (SE) is 2.10 bits/s/Hz, the proposed PS-8QAM scheme for BICM-ID can obtain 0.73 dB theoretical gain, where the simulated error performance gain is 1.02 dB with frame error rate (FER) at 10−3. Compared to conventional LDPC-coded 32QAM system on AWGN channel, when SE is 3.75 bits/s/Hz, the proposed PS-32QAM scheme for BICM-ID can obtain 1.08 dB theoretical gain, where the simulated error performance gain is 1.10 dB with FER at 10−3. Both the information-theoretic analysis and simulated error performance show that the proposed PS scheme for BICM-ID is reliable, which is suitable for non gray mapped constellations.

Hamiltonian Monte Carlo Probabilistic Joint Inversion of 2D (2.75D) Gravity and Magnetic Data

AbstractTwo‐dimensional modeling of gravity and magnetic anomalies in terms of polygonal bodies is a popular approach to infer possible configurations of geological structures in the subsurface. Alternatively to the traditional trial‐and‐error manual fit of measured data, here we illustrate a probabilistic strategy to solve the inverse problem. First we derive a set of formulae for solving a 2.75‐dimensional forward model, where the polygonal bodies have a given finite lateral extent, and then we devise a Hamiltonian Monte Carlo algorithm to jointly invert gravity and magnetic data for the geometry and properties of the polygonal bodies. This probabilistic approach fully addresses the nonlinearity of the forward model and provides uncertainty estimation. The result of the inversion is a collection of models which represent the posterior distribution, analysis of which provides estimates of sought properties and may reveal different scenarios.

Few-shot learning for modeling cyber physical systems in non-stationary environments

This paper proposes a modeling scheme for cyber physical systems operating in non-stationary, small data environments. Unlike the traditional modeling logic, we introduce the few-shot learning paradigm, the operation of which is based on quantifying both similarities and dissimilarities. As such, we designed a suitable change detection mechanism able to reveal previously unknown operational states, which are incorporated in the dictionary online. We elaborate on spectrograms extracted from high-resolution ultrasound depth sensor timeseries, while the backbone of the proposed method is a Siamese Neural Network. The experimental scenario considers data representing liquid containers for fuel/water when the following five operational states are present: normal, accident, breakdown, sabotage, and cyber-attack. Thorough experiments were carried out assessing every aspect of the present framework and demonstrating its efficacy even when very few samples per class are available. In addition, we propose a probabilistic data selection scheme facilitating one-shot learning. Last but not least, responding to the wide requirement for interpretable AI, we explain the obtained predictions by examining the layer-wise activation maps.

A probabilistic public key encryption switching scheme for secure cloud storage

The high demand for user-centric applications such as secure cloud storage laid the foundation for the development of user-centric security schemes with multiple security features in recent years. But, the current state-of-art techniques primarily emphasized only one type of security feature i.e., either homomorphism or non-malleability. To fill this gap and provide a common platform for both homomorphic and non-malleable cloud applications, we have introduced a new public key-based probabilistic encryption switching method. The proposed scheme generates {mathcal {O}}(m+2 log N) bits of ciphertext where min {mathbb {N}} and m<n, nin {mathbb {N}} is the plaintext size, N is the RSA composite. The superior performance of the proposed scheme has been tested in comparison to existing methods and is reported in this paper.

Public Key Protocols over Skew Dihedral Group Rings

This paper introduces skew dihedral group rings and their applications for public-key cryptography. We present a specific skew group ring that is the underlying algebraic platform for our cryptographic constructions. We then build a two-party key exchange protocol and present an analysis of its security. We then exploit it to derive a group key agreement protocol, a probabilistic public-key scheme, and a key encapsulation mechanism. In addition to the security analysis of our cryptographic constructions, we present a proof-of-concept implementation.

Phylogenetic Analysis of Anti-CRISPR and Member Addition in the Families.

CRISPR-Cas is a widespread anti-viral adaptive immune system in the microorganisms. Viruses living in bacteria or some phages carry anti-CRISPR proteins to evade immunity by CRISPR-Cas. The anti-CRISPR proteins are prevalent in phages capable of lying dormant in a CRISPR-carrying host, while their orthologs frequently found in virulent phages. Here, we propose a probabilistic strategy of ancestral sequence reconstruction (ASR) and Hidden Markov Model (HMM) profile search to fish out sequences of anti-CRISPR proteins from environmental metagenomic, human microbiome metagenomic, human microbiome reference genome, and NCBI's non-redundant databases. Our results revealed that the metagenome database dark matter might contain anti-CRISPR encoding genes.

Revisiting the Ridgecrest Aftershock Catalog Using a Modified Source-Scanning Algorithm Applied to Multiple Dense Local Arrays

Abstract We develop and implement a modified source-scanning algorithm (SSA) method to estimate the absolute event locations in the presence of velocity model errors, commonly found around fault zones. We split the array into subarrays of clustered receivers, for which relative travel-time errors are smaller. We apply a conventional SSA using both P and S waves to each subarray and combine the estimations using a probabilistic scheme to yield locations that are robust to velocity model errors. We also compute uncertainty estimations for the locations. We apply the method to 688 aftershocks recorded by 197 short-period geophones deployed as part of the Ridgecrest dense array. The dense receiver deployment allows for the recording of spatially coherent seismic arrivals. We compare 339 locations to a relocated catalog built using a sparser regional array and the same 1D velocity model. In general, locations are consistent despite the different methodology and recorded data. We qualitatively compare location estimations using the alignment of time-shifted seismograms, utilizing the spatial coherency of the dense subarrays. Our locations yield, in most cases, better alignment and are 2 km deeper on average. For events in the northern part of the study area, our locations are shifted to the north-east. We discuss various potential causes for the differences between estimations and investigate the possibility of velocity-driven biases in our locations. We also attempt to approximate the scale of lateral velocity heterogeneity near the fault in the northern part of the region. Although our location method is tailored to the Ridgecrest dense array, it demonstrates that using dense arrays may help mitigate the effect of velocity model errors on the absolute locations.

Efficient generation of entangled multiphoton graph states from a single atom

The central technological appeal of quantum science resides in exploiting quantum effects, such as entanglement, for a variety of applications, including computing, communication and sensing1. The overarching challenge in these fields is to address, control and protect systems of many qubits against decoherence2. Against this backdrop, optical photons, naturally robust and easy to manipulate, represent ideal qubit carriers. However, the most successful technique so far for creating photonic entanglement3 is inherently probabilistic and, therefore, subject to severe scalability limitations. Here we report the implementation of a deterministic protocol4–6 for the creation of photonic entanglement with a single memory atom in a cavity7. We interleave controlled single-photon emissions with tailored atomic qubit rotations to efficiently grow Greenberger–Horne–Zeilinger (GHZ) states8 of up to 14 photons and linear cluster states9 of up to 12 photons with a fidelity lower bounded by 76(6)% and 56(4)%, respectively. Thanks to a source-to-detection efficiency of 43.18(7)% per photon, we measure these large states about once every minute, which is orders of magnitude faster than in any previous experiment3,10–13. In the future, this rate could be increased even further, the scheme could be extended to two atoms in a cavity14,15 or several sources could be quantum mechanically coupled16, to generate higher-dimensional cluster states17. Overcoming the limitations encountered by probabilistic schemes for photonic entanglement generation, our results may offer a way towards scalable measurement-based quantum computation18,19 and communication20,21.

The Use of SMAV Model for Computing Fragility Curves

Among the buildings with strategic role for civil protection purposes part of them were built before the introduction of modern seismic codes. Aiming to guarantee their operability in case of seismic events, they have to be assessed and, hopefully, seismically upgraded. This aspect arises the necessity of reliable low computational demanding numerical models and probabilistic strategies that can be easily adopted in case of the assessment of conspicuous building stock. Part of the authors proposed and validated, in previous works, a low-demanding seismic model calibrated on ambient vibrations (SMAV) that is engaged in this paper for calculating fragility curves. On the base of dynamic and geometric properties, the SMAV model simulates the dynamic response of buildings. The herein proposed procedure adopts the SMAV model and takes into account the direction of seismic input in a probabilistic manner. The use of the ASI scale as an Intensity Measure parameter is discussed and the results are compared to those obtained with others IM scales. Although SMAV implements an equivalent linear procedure the results are compared with those obtained by more sophisticated FEM models and nonlinear dynamic analyses with a satisfactory agreement. Finally, two real case studies are presented. The fragility curves for both the case studies are calculated and discussed. The results encourage the authors to the use of fragility curves based on SMAV model. Additionally, the fragility curves can be easily implemented in seismic risk assessment procedures at urban or territorial scale and, consequently, the proposed procedure may represent a key aspect in the reduction of seismic risk and rationalization of investments for the seismic upgrading of strategic buildings.

Sculpting bosonic states with arithmetic subtractions

Continuous-variable (CV) encoding allows information to be processed compactly and efficiently on quantum processors. Recently developed techniques such as controlled beam-splitter operations and the near deterministic phonon subtractions make trapped ion systems attractive for exploring CV quantum computing. Here we propose a probabilistic scheme based on the boson sculpting technique for generating multipartite highly entangled states of motional modes of trapped ion systems. We also investigate the effects of decoherence on the fidelity of the generated state by performing numerical simulations with realistic noise parameters. Our work is a step towards generating multipartite CV entanglement.