Maximum Probability Research Articles

Quantile Hedging in the complete financial market under the mixed fractional Brownian motion model and the liquidity constraint

This article proposes a pricing framework for European option that utilizes a Quantile hedging strategy in a complete financial market. The methodology involves applying the long memory Geometric Brownian motion model, utilizing the generalized mixed fractional Girsanov theorem, and incorporating relevant findings related to quasi-conditional expectation. The first step in this framework is to derive the option price formula using the equivalent martingale measure. This approach enables investors to use the entire option price without any restrictions to hedge their contingent claim. Then, we add the initial wealth condition to hedge and determine the option price with the Quantile hedging strategy. To do this, we find the maximum probability of success in complete risk hedging for different amounts of model parameters and then use the probability values to determine the option price. Moreover, we apply the minimum variance algorithms and the error correction method to find the hedge ratio. Finally, we illustrate the hedging strategy and provide numerical results that assist investors in determining the required capital for covering the option risk under market data. This calculation takes into account both the level of risk tolerance and the anticipated return on investment.

EEG-FRM: a neural network based familiar and unfamiliar face EEG recognition method.

Recognizing familiar faces holds great value in various fields such as medicine, criminal investigation, and lie detection. In this paper, we designed a Complex Trial Protocol-based familiar and unfamiliar face recognition experiment that using self-face information, and collected EEG data from 147 subjects. A novel neural network-based method, the EEG-based Face Recognition Model (EEG-FRM), is proposed in this paper for cross-subject familiar/unfamiliar face recognition, which combines a multi-scale convolutional classification network with the maximum probability mechanism to realize individual face recognition. The multi-scale convolutional neural network extracts temporal information and spatial features from the EEG data, the attention module and supervised contrastive learning module are employed to promote the classification performance. Experimental results on the dataset reveal that familiar face stimuli could evoke significant P300 responses, mainly concentrated in the parietal lobe and nearby regions. Our proposed model achieved impressive results, with a balanced accuracy of 85.64%, a true positive rate of 73.23%, and a false positive rate of 1.96% on the collected dataset, outperforming other compared methods. The experimental results demonstrate the effectiveness and superiority of our proposed model.

Quantitative assessment of building risks and loss ratios caused by storm surge disasters: A case study of Xiamen, China

China is severely affected by storm surge disasters, which result in substantial economic losses and casualties in coastal regions. Assessing the risk of storm surge disasters can provide valuable insights into the expected losses and severity of future impacts, offering critical foresight for disaster prevention and mitigation strategies. This study assesses the quantitative risk of storm surge disasters, focusing on coastal buildings since they are particularly susceptible to storm surges and frequently bear the brunt such disasters. Xiamen city, China, was used as case study. A high-precision numerical model, using Finite Volume Community Ocean Model (FVCOM), was developed to simulate inundation during storm surges. By referencing historical storm surge records, we defined key parameters for probable maximum typhoon-induced storm surge (PMTSS) in Xiamen. These parameters were used to calculate the corresponding inundation range and water depth distribution within the region. Subsequently, the results were integrated with vulnerability curves that represent the susceptibility of buildings in Xiamen to storm surge-induced damage, enabling the quantitative risk assessment for associated loss risks. The study findings offer valuable guidance for urban planning and functional layout design in coastal areas. Furthermore, the findings contribute to understanding storm surge disaster risks and facilitating informed decision-making processes, ultimately enhancing disaster preparedness and resilience in vulnerable coastal regions.

Trip Purpose Inference and Spatio-Temporal Characterization Based on Anonymized Trip Data: Empirical Study from Dockless Shared Bicycle Dataset in Xi’an, China

The rapid and widespread deployment and applications of the Internet of Things (IoT) and big data technology has generated massive personal travel data. Although most of the data are anonymized, they still provide possibilities for analyzing individual travel behavior. It is worthwhile to explore how to more accurately infer travel purposes based on trajectory data, smart card fare data, or shared bicycle data. In this paper, an improved research framework is proposed for travel purpose inference by applying the gravity model, Bayesian criterion, and spatial clustering method. The gravity model and Bayesian rule are used to calculate the probability of users traveling to nearby POIs (points of interest), and the clustering algorithm is used to identify the locations regularly visited by users. Through the identification of maximum probability POI and regular trips, different travel purposes represented by POI can be distinguished. The results showed that the identification of regular trips could verify and complement the recognition of trip purposes by POIs. Using Xi’an city of China as an example for the study, the results show that: 1) regular trips accounted for 32% of the week’s trips; 2) there are 10% of the trips reflect different trip purposes for the same POI; 3) it is also important to note that 70% of the users have a parking error of fewer than 100 m when going to the exact location. This distance can provide a reference for the study of bicycle commuting. Finally, we analyzed the spatial and temporal distribution characteristics of different trip purposes. The analysis of the spatial and temporal distribution can provide suggestions for the operation of bike-sharing enterprises and the management of regulators.

Deep Learning-based Ensemble Approach for Conventional Pap Smear Image Classification

Cervical cancer screening allows the early signs of precancerous abnormalities in the cervix before they develop into invasive cancer. The Pap Smear is a widely used screening for early detection and prevention of cervical cancer. In many remote areas, the number of cytologists available to interpret pap smear screening tests is insufficient. This lack of personnel makes the test interpretation very time-consuming. To address this, deep learning techniques have been employed to detect cervical cancer cells and support cytologists. Therefore, an integrative approach with deep learning models and the ensemble techniques such as the maximum occurrence and the maximum probability score of cervical cells was proposed. The multi-cell assessment of the Pap smear slide allowed aggregate predictions of single cervical cell images using the proposed method. The classification results between pre-trained deep learning models and the proposed method were compared. In the experimental results, the proposed method can achieve an accuracy score of more than 97%, while the best pre-trained deep learning model can attain an accuracy score of more than 85%. Hence, the proposed method may have the potential to assist physicians or cytologists in the classification of cervical cell types for Pap Smear images.

A Binary Object Detection Pattern Model to Assist the Visually Impaired in detecting Normal and Camouflaged Faces

This study presents a novel Binary Object Detection Pattern Model (BODPM) to detect objects with face key points and recognize them using the KERAS dataset. The proximity and accuracy of the recognized items were evaluated using computer vision techniques. The object recognition time interval and duration were recorded and stored permanently in a database, and the information was communicated to the visually impaired user as voice output. The normal face, without wearing a mask, was identified using binary patterns with proximity detection. Camouflaged objects were detected in a maximum probability range of 100%. The proposed method was tested, calculating accuracy and score, and compared with existing models, showcasing remarkable performance. The proposed method of normal and camouflage detection is a novel prediction with proximity analysis of objects in a frame.

Water Properties and Diffusive Convection in the Canada Basin

The aim of this study is to better understand diffusive convection (DC) and its role in the upper ocean dynamic environment and sea ice melting in the Canada Basin. Based on a moored dataset with 6737 profiles collected from August 2003 to August 2011 in the upper layer of the Canada Basin, DC between the warm and salty Atlantic Water (AW) and the colder and less salty Lower Halocline Water (LHW) were investigated. The moorings were designated at four stations: A, B, C, and D, located at the southwestern, southeastern, northeastern, and northwestern parts of the basin, respectively. During the observation period, the temperature, salinity, and depth of the AW and LHW exhibited unique temporal variations. The temperature and salinity of the AW varied among stations, with a decreasing trend from northwest to southeast, consistent with the propagation path of the AW in the Canada Basin. The temperature and salinity of the LHW were similar at all stations. The AW and LHW cores were located between depths of 320–500 m and 160–300 m, respectively, and both gradually deepened over time. Distinct DC staircase structures were observed between the AW and LHW, more pronounced at stations C and D than at stations A and B, which is speculated to be related to eddies at stations A and B during the observation period. The vertical heat fluxes through the DC staircase layer at stations C and D (FHc_C and FHc_D) were estimated using an empirical formula. FHc_C ranged from 0.05 to 0.94 W/m2, and FHc_D ranged from 0.05 to 0.6 W/m2, with the maximum probability value for both at approximately 0.2 W/m2. The effective diffusivities at these two stations (KT_C and KT_D) are similar, ranging from 2 × 10−6 to 3 × 10−5 m2/s, with the highest probability occurring at 6 × 10−6 m2/s. Both the probability density function of the heat flux and the effective diffusivity skewed towards larger values and obey a lognormal distribution, indicating turbulence intermittency of the DC staircase in the Canada Basin. These finding offers new insights into the heat transport and turbulence in the DC staircase, and then bring a deeper understanding of sea ice melting in the Canada Basin.

Influence of molecular orientation on the photodissociation process of LiH molecules

AbstractRegulation of photodissociation dynamics of oriented LiH molecules in different dissociation channels is proposed based on time dependent quantum wave packet theory. The enhancement of molecular orientation on the photodissociation of LiH is obvious with our theoretical scheme. The results show that the molecular orientation in the ground state has a great effect on the angular distributions of wave packets. By using the proper laser pulses and controlling the polarization direction of the laser pulses, the enhancement of the photodissociation could be realized. After the molecular orientation, an optimal dissociation channel is observed with an improved dissociation probability. Compared with the results without molecular orientation, the maximal dissociation probability is increased by 8.1% in the indirect dissociation channel and 30.7% in the direct dissociation channel. The enhancement effect is more obvious in the direct dissociation channel, which provides a possible method to manipulate the dissociation of LiH molecules experimentally. Additionally, the photodissociation process of LiH also relies on the electric intensity and delay time of two pump pulses.

Effect of lake waves on manoeuvring performance of a model ship

ABSTRACT In this study, we investigate how natural environmental conditions affect a training vessel using a ‘mathematical model of wind and wave’ specific to Lake Silm near Kamionka, Poland. Wind and wave measurements on the lake were conducted. We present the vessel’s trajectory under varying conditions: navigating in restricted waters simulating port conditions with speed readings, and during propeller operation. The control system of the nonlinear autonomous object employed two controllers: a linear matrix inequality controller and an overriding trajectory controller. We assess the impact of nonlinearities in the measured waves. It is shown that the maximum entropy probability distribution conforms more closely to the empirical frequency histogram than the Gaussian distribution. The wave height probability distributions diverge from Rayleigh distributions, indicating slight nonlinear effects. These results enhance the mathematical model of Silm Lake’s wave dynamics.

A probabilistic estimation model for seismic physical portfolio loss of a water supply pipeline system

Losses due to hazards are inevitable and numerical simulations for estimations are complex. This study proposes a model for estimating correlated seismic damages and losses of a water supply pipeline system as an alternative for numerical simulations. The common approach in other research shows average damage spots per mesh estimated statistically independent to one another. Spatially distributed lifeline systems, such as water supply pipelines, are interconnected, and seismic spatial variability affects the damages across the region; thus, spatial correlation of damage spots is an important factor in target areas for portfolio loss estimation. Generally, simulations are used to estimate possible losses; however, these assume each damage behaves independently and uncorrelated. This paper assumed that damages per mesh behave in a Poisson distribution to avoid over-dispersion and eliminate negative losses in estimations. The purpose of this study is to obtain a probabilistic portfolio loss model of an extensive water supply area. The proposed model was compared to the numerical simulation data with the correlated Poisson distribution. The application of the Normal To Anything (NORTA) obtained correlations for Poisson Distributions. The proposed probabilistic portfolio loss model, based on the generalized linear model and central limit theory, estimated the possible losses, such as the Probable Maximum Loss (PML, 90% non-exceedance) or Normal Expected Loss (NEL, 50 % non-exceedance). The proposed model can be used in other lifeline systems as well, though additional investigation is needed for confirmation. From the estimations, a seismic physical portfolio loss for the water supply system was presented. The portfolio was made to show possible outcomes for the system. The proposed method was tested and analyzed using an artificial field and a location-based scenario of a water supply pipeline system. This would aid in pre-disaster planning and would require only a few steps and time.

Systematic review using a spiral approach with machine learning

With the accelerating growth of the academic corpus, doubling every 9 years, machine learning is a promising avenue to make systematic review manageable. Though several notable advancements have already been made, the incorporation of machine learning is less than optimal, still relying on a sequential, staged process designed to accommodate a purely human approach, exemplified by PRISMA. Here, we test a spiral, alternating or oscillating approach, where full-text screening is done intermittently with title/abstract screening, which we examine in three datasets by simulation under 360 conditions comprised of different algorithmic classifiers, feature extractions, prioritization rules, data types, and information provided (e.g., title/abstract, full-text included). Overwhelmingly, the results favored a spiral processing approach with logistic regression, TF-IDF for vectorization, and maximum probability for prioritization. Results demonstrate up to a 90% improvement over traditional machine learning methodologies, especially for databases with fewer eligible articles. With these advancements, the screening component of most systematic reviews should remain functionally achievable for another one to two decades.

Updating probable maximum precipitation for Hong Kong under intensifying extreme precipitation events

Updating probable maximum precipitation for Hong Kong under intensifying extreme precipitation events

Load Profile Forecasting using a Time Series Model for Solar Rooftop and Integrated Carpark of a Public University in Malaysia

A precise and accurate load prediction is critical in a developing country like Malaysia to save the energy consumption. Power producers use load profile data and statistics to analyze and forecast the quantity of electricity required to be available at a given time. Load forecasting is the process of predicting future load requirements and this research focuses on the short-term load forecasting (STLF) for solar rooftop and integrated car park built at one of the public universities in Malaysia. Power system planners and demand controllers must ensure that there is enough generation to meet the increased demand. Load forecasting models that are accurate can lead to better budgeting, maintenance scheduling, and fuel management. This project seeks to anticipate the highest demand of power utilized by the consumer based on prior load profiles using a Multiple Regression Time Series model developed using MATLAB software. It consists of the following steps: data collection, clustering, series transformation using differentiation transform to remove trend and seasonal structure from the dataset, model identification using autocorrelation function (ACF) and partial correlation function (PCF), model estimation using ARIMA time series errors and maximum likelihood probability, and finally model forecasting using Auto Regression (AR), Moving Average. In this case, the goal is to ensure the power production equals electricity demand, and achieving the target will assure energy security, dependability, and the capacity to maximize profits while minimizing losses.

A Bayesian approach for constituent estimation in nucleic acid mixture models

Mass spectrometry (MS) is a powerful analytical method used for various purposes such as drug development, quality assurance, food inspection, and monitoring of pollutants in the environment. In recent years, with the active development of antibodies and nucleic acid-based drugs, impurities with various modifications are produced. These can lead to a decrease in drug stability, pharmacokinetics, and efficacy, making it crucial to differentiate these impurities. Previously, attempts have been made to estimate the monoisotopic mass and ion amounts in the spectrum generated by electrospray ionization (ESI). However, conventional methods could not explicitly estimate the number of constituents, and discrete state evaluations, such as the probability that the number of constituents is k or k+1, were not possible. We propose a method where, for each possible number of constituents in the sample, mass spectrometry is modeled using parameters like monoisotopic mass and ion counts. Using Simulated Annealing, NUTS, and stochastic variational inference, we determine the parameters for each constituent number model and the maximum posterior probability. Finally, by comparing the maximum posterior probabilities between models, we select the optimal number of constituents and estimate the monoisotopic mass and ion counts under that scenario.

Bayesian Spectral Decomposition for Efficient Modal Identification Using Ambient Vibration

Modal parameter identification via ambient vibration is popular but faces challenges from uncertainties due to unknown inputs and low signal‐to‐noise ratio. Bayesian methods are gaining increasing attention for operational modal identification due to their ability to quantify uncertainties. However, improvements in computational efficiency are needed, particularly when addressing numerous modes and degrees of freedom. To address this challenge, this study proposes an innovative approach, termed the “Bayesian spectral decomposition” method (BSD), employing the decompose‐and‐conquer strategy. This novel method, operating within the frequency domain, identifies each mode individually by exploiting their inherent separated modal characteristics. For each mode, the response spectrum matrix undergoes an eigenvalue decomposition, yielding crucial eigenvalues (incorporating frequency and damping information) and eigenvectors (containing mode shape information). Subsequently, statistical properties of the eigenvalues and eigenvectors are utilized to establish likelihood functions for Bayesian parameter identification. By combining prior information, the posterior probability distribution functions of modal parameters are derived. The optimal solution is then obtained by resolving the maximum posterior probability distribution function problem. To further quantify the uncertainty of modal parameters, Gaussian distributions are employed to approximate the posterior probability distribution functions. The adoption of the decomposition approach circumvents the joint identification of all modal parameters, substantially reducing the parameter dimensions for optimization. Consequently, this strategy leads to decreased computational complexity and significantly improved computational stability. The effectiveness of the BSD is confirmed through simulated data generated from an 8‐story shear building as well as measured data collected from both an experimental shear frame and the Canton Tower. The results demonstrate that the proposed method achieves high accuracy in identifying modal parameters, greatly improves computational efficiency, and effectively quantifies the uncertainties in modal parameters.

Asynchronous Control for Interval Type-2 Fuzzy Nonhomogeneous Markov Jump Systems Against Successive DoS Attacks.

This article is concerned with the problem of asynchronous control for Interval Type-2 (IT2) fuzzy nonhomogeneous Markov jump systems against successive denial-of-service (DoS) attacks. The system and the controller are assumed to be connected through a communication channel subject to malicious attacks. The maximum number and probability distribution of successive attacks are considered. Under the imperfect premise matching, a fuzzy asynchronous controller is constructed by the hidden Markov model. By means of the introduced transmission delay, a delay closed-loop system is constructed, where the stochastic description of the delay depends on the statistical characteristic of successive attacks. Then stability criteria together are derived in the form of linear matrix inequalities by the Lyapunov functional approach, as well as the condition on the existence of the fuzzy controller. Finally, the feasibility and effectiveness of the presented control scheme are demonstrated by simulation results.

Performance Analysis for Reconfigurable Intelligent Surface Assisted MIMO Systems

This paper investigates the maximal achievable rate for a given maximal error probability, and blocklength for the reconfigurable intelligent surface (RIS) assisted multiple-input and multiple-output (MIMO) system. The result consists of a finite blocklength and finite alphabet constraints channel coding achievability and converse bounds based on the Berry-Esseen theorem, the Mellin transform and the closed-form expression of the mutual information and the unconditional variance. The numerical evaluation shows a fast speed of convergence to the maximal achievable rate as the blocklength increases and also proves that the channel variance is a sound measurement of the backoff from the maximal achievable rate due to finite blocklength.

A Distributionally Robust Optimization Approach to Two-Sided Chance-Constrained Stochastic Model Predictive Control With Unknown Noise Distribution

In this work, we propose a distributionally robust stochastic model predictive control (DR-SMPC) algorithm to address the problem of multiple two-sided chance constrained discrete-time linear systems corrupted by additive noise. The prevalent mechanism to cope with two-sided chance constraints is the so-called risk allocation approach, which conservatively approximates the two-sided chance constraints with two single chance constraints by applying Bool's inequality. In this proposed DR-SMPC framework, an exact second-order cone approach is adopted to abstract the multiple two-sided chance constraints by considering the first and second moments of the noise. With the proposed DR-SMPC algorithm, the worst-case probability of violating safety constraints is guaranteed to be within a pre-specified maximum value. By flexibly adjusting this pre-specified maximum probability, the feasible region of the initial state can be increased for the SMPC problem. The recursive feasibility and convergence of the proposed DR-SMPC are rigorously established by introducing a binary initialization strategy for the nominal state. A simulation study of a single spring and double mass system was conducted to demonstrate the effectiveness of the proposed DR-SMPC algorithm.

Climate Change and Hydrological Risk Assessment: Probable Maximum Precipitation Analysis in the Sindh Basin

Climate Change and Hydrological Risk Assessment: Probable Maximum Precipitation Analysis in the Sindh Basin

Towards Codebook-Free Deep Probabilistic Quantization for Image Retrieval.

As a classical feature compression technique, quantization is usually coupled with inverted indices for scalable image retrieval. Most quantization methods explicitly divide feature space into Voronoi cells, and quantize feature vectors in each cell into the centroids learned from data distribution. However, Voronoi decomposition is difficult to achieve discriminative space partition for semantic image retrieval. In this paper, we explore semantic-aware feature space partition by deep neural network instead of Voronoi cells. To this end, we propose a new deep probabilistic quantization method, abbreviated as DeepIndex, which constructs inverted indices without explicit centroid learning. In our method, the deep neural network takes an image as input and outputs its probability of being put into each inverted index list. During training, we progressively quantize each image into the inverted lists with the top- T maximal probabilities, and calculate the reward of each trial based on retrieval accuracy. We optimize the deep neural network to maximize the probability of the inverted list with maximal reward. In this way, the retrieval performance is directly optimized, leading to a more semantically discriminative space partition than other quantization methods. The experiments on public image datasets demonstrate the effectiveness of our DeepIndex method on semantic image retrieval.