Bayesian Probabilistic Model Research Articles

Efficient in-situ image and video compression through probabilistic image representation

Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high-resolution images and videos. In this paper, we present an efficient in-situ method for multi-dimensional image and video compression called Compression via Adaptive Recursive Partitioning (CARP). CARP uses an optimal permutation of the image pixels inferred from a Bayesian probabilistic model on recursive partitions of the image to reduce its effective dimensionality, achieving a parsimonious representation that preserves information. Furthermore, it adopts a multi-layer Bayesian hierarchical model to achieve in-situ compression along with self-tuning and regularization, with just one single parameter to be specified by the user to achieve the desired compression rate. The properties of our proposed method include high reconstruction quality at a wide range of compression rates while preserving key local details, applicability to a variety of different image/video types and of different dimensions, computational scalability, progressive transmission and ease of tuning. Extensive numerical experiments using a variety of datasets including 2D still images, real-life YouTube videos, and surveillance videos show that CARP compares favorably to—and often uniformly outperforms—a wide range of popular image/video compression approaches, including JPEG, JPEG2000, AVI, BPG, MPEG4, HEVC, AV1, and three neural network-based methods.

Sea‐weeding: Manual removal of macroalgae facilitates rapid coral recovery

Abstract Coral reef ecosystems globally are under threat, leading to declining coral cover and macroalgal proliferation. Manually removing macroalgae (i.e. ‘sea‐weeding’) may promote local‐scale coral recovery by reducing a biological barrier, though the impact of removal on community composition of benthic reef organisms has not been quantified. In this three‐year study (2018–2021), fleshy macroalgae (predominantly Sargassum spp.) were periodically removed from 25 m2 experimental plots on two inshore fringing reefs of Yunbenun (Magnetic Island) in the central Great Barrier Reef. By the end of the study, coral cover in removal plots (n = 12 plots) assessed through in‐field transects increased by at least 47% (2019 mean: 25.5%, 2021 mean: 37.4%), and macroalgal cover decreased by more than half. In contrast, in control plots (n = 12 plots), there was no change in macroalgal cover while coral cover remained stable (2019 mean: 16.4%, 2021 mean: 13.6%). Changes in benthic cover were supported by photoquadrat data, with Bayesian probability modelling indicating a 100% likelihood that coral cover more than doubled in removal plots over the study period, compared to only a 29% chance of increased coral cover in control plots. Synthesis and applications. Manual macroalgal removal can provide rapid benefits and enhance inshore coral reef recovery. Through involvement of community groups and citizen scientists, larger scale removal of macroalgae is a low‐tech, high‐impact, and achievable method for local reef management.

Double Articulation Analyzer With Prosody for Unsupervised Word and Phone Discovery

Infants acquire words and phonemes from unsegmented speech signals using segmentation cues, such as distributional, prosodic, and co-occurrence cues. Many pre-existing computational models that represent the process tend to focus on distributional or prosodic cues. This paper proposes a nonparametric Bayesian probabilistic generative model called the prosodic hierarchical Dirichlet process-hidden language model (Prosodic HDP-HLM). Prosodic HDP-HLM, an extension of HDP-HLM, considers both prosodic and distributional cues within a single integrative generative model. We conducted three experiments on different types of datasets, and demonstrate the validity of the proposed method. The results show that the Prosodic DAA successfully uses prosodic cues and outperforms a method that solely uses distributional cues. The main contributions of this study are as follows: 1) We develop a probabilistic generative model for time series data including prosody that potentially has a double articulation structure; 2) We propose the Prosodic DAA by deriving the inference procedure for Prosodic HDP-HLM and show that Prosodic DAA can discover words directly from continuous human speech signals using statistical information and prosodic information in an unsupervised manner; 3) We show that prosodic cues contribute to word segmentation more in naturally distributed case words, i.e., they follow Zipf's law.

Mashup-Oriented Web API Recommendation Via Full-Text Semantic Mining of Developer Requirements

API recommendation differs from conventional recommendation systems in that it integrates the characteristics of development requirements, mashup, and API, where there are two primary challenges, i.e., how to effectively mine the personalized development requirements of developers and the sparseness of interaction data. This research proposes a novel model framework, called Bayesian Probabilistic Matrix Factorization Model with Text Similarity and Adversarial Training (SAMF), to address these two issues. Utilizing natural language processing technology to extract the full-text semantics of requirements documents, mashup descriptions, and API descriptions, fully mining personalized development requirements, and contextual information of mashups and APIs, and calculating text similarity, is our fundamental idea. Simultaneously, the collaborative filtering method is used to mine user needs, mashup, and API information from historical data, and the obtained text similarity is added to the training as auxiliary information. Furthermore, adversarial training is further incorporated to supplement data in order to minimize data sparsity and enhance model robustness and generalization. To assess the performance of SAMF, we run thorough experiments that illustrate the efficacy of each module of the model and explain the influence of hyperparameter settings. Specifically, compared to the baseline, the experimental findings demonstrate that SAMF can basically achieve better performance.

Associations of patient‐specific resting state variability with CSF and clinical biomarkers: Data from Alzheimer’s disease clinical trials

AbstractBackgroundChanges of brain functional connectivity assessed using MRI in patients with Alzheimer’s disease (AD) have been previously reported. However, factors contributing to the variability among patients have not been sufficiently investigated. Here, we investigated the association of patient‐specific resting state parameters with CSF markers and clinical measures.MethodWe analyzed the baseline rs‐fMRI of 158 patients (87 female, age 50‐89) with mild AD collected in the Marguerite RoAD trial of gantenerumab in mild AD (NCT02051608) and 132 cognitively unimpaired adults (88 female, age 56‐90) from ADNI. We investigated the within‐ and between‐subjects’ variability in five brain networks (Sensory Motor, Cerebellar, Visual, Default Mode and Cognitive Control), inferred from the two groups using a Bayesian probabilistic model (PROFUMO, Harrison‐Smith‐2015; Harrison‐Woolrich‐2020). We computed spatial maps, BOLD amplitude changes, and spatial (temporal) functional connectivity between‐ and within‐networks for each subject. Those metrics were compared between groups using T‐tests. In AD patients we used linear models to investigate associations of the extracted metrics with CSF markers (Amyloid Beta 42, p‐Tau217, pTAU/Abeta 1‐42 ratio and Neurogranin) and cognitive scores (ADAS‐Cog 13, MMSE and ADCS‐ADL), while controlling for age, sex and MRI‐scanner type. All results were corrected for multiplicity using FDR.ResultOn average the signal amplitude was lower in the Cognitive Control and the Sensory Motor (p < 0.0001) networks in AD patients compared to cognitively unimpaired adults. Interestingly, in AD patients the lower amplitude observed in the cognitive control network was associated with cognitive decline (β = 0.02, p = 0.01). Moreover, significant associations were observed in AD patients between lower signal amplitude and higher CSF levels of ABeta 42 (β = ‐0.02, p = 0.036) and cognitive decline (β = 0.02, p = 0.008) (Fig.2). AD group showed on average reduced functional connectivity compared to the healthy one. Furthermore, the within‐AD patients’ analysis showed weak between‐ and strong within‐networks connectivity (T‐test, q < 0.05, Fig.1).ConclusionIn patients with mild AD, the signal amplitude and connectivity in the cognitive control and DMN networks were associated with both CSF level of ABeta42 and cognitive scores.

Evaluating Bicycling Environments with Trajectory Data on Shared Bikes: A Case Study of Beijing

A detailed evaluation of the riding environment can help the government master the urban riding environment, identify problematic road sections, and improve riding quality. However, the current evaluation of riding environment is mainly subjective, lacking big data (e.g., shared bicycle trajectory data) as a data-driven objective evaluation system. The emergence of shared bicycle data has provided data support for data-driven riding environment evaluation, but there are few studies using shared bicycle data for riding evaluation at present. First, according to the characteristics of the data and the riding environment, a boxplot method and Bayesian probabilistic network model are used to exclude abnormal data and to match trajectories to road sections. Second, this paper proposes a data-driven evaluation framework based on riding influencing factors. An evaluation framework, which is composed of node-, link-, and block-level evaluation indicators, was constructed, using an evaluation model combining TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and KNN (K-Nearest Neighbors). The evaluation results can identify parking issues, intersection difficulties, and lane occupancy issues on bicycle sections, and visually reflect the riding environment. The significance of this article is to create an objective evaluation system based on a data-driven technology to accurately identify sections and causes of riding quality problems. The research results can be applied in the future to evaluate the cycling environment around the railway stations for bicycle parking planning and determine the foothold for traffic management.

Abstract 5714: Cell-free DNA fragmentation profiling for monitoring therapeutic response in metastatic colorectal cancer

Abstract BACKGROUND: Cell-free circulating tumor DNA (ctDNA) assays have been adopted to monitor therapeutic response in both early- and late-stage cancer. However, tests currently available require deep-targeted sequencing to detect cancer-specific mutations at low mutant allele frequency (MAF) levels in the circulation. Recently, we developed a tumor-agnostic approach called DELFI Tumor Fraction (DELFI-TF), a Bayesian probabilistic model designed to predict plasma tumor fractions based on genome-wide fragmentation-related features. METHODS: Overall, 692 longitudinal plasma samples collected from 153 patients with initially unresectable colorectal cancer (CRC) liver-only metastases participating in the phase III study CAIRO5 (NCT02162563) were split across a training (RAS/BRAF mutant) and a validation cohort (RAS/BRAF wildtype). For all cell-free DNA (cfDNA) samples of patients within the training cohort (n=312), the tumor burden was quantified as the MAF of the tumor-tissue-proven RAS/BRAF variant measured by digital droplet PCR (ddPCR). Using cfDNA fragment-sequencing statistics from low-coverage WGS data, a Bayesian regression model was trained against the MAF of the tumor-specific driver RAS/BRAF variant in all longitudinal timepoints in the training cohort. Changes in DELFI-TF scores during first-line FOLFOX/FOLFIRI and bevacizumab therapy were assessed to predict disease response. A tissue-informed approach was applied to detect minimal residual disease in the subgroup of patients treated with curative intent (complete resection of the primary tumor and liver metastases). RESULTS: In the training cohort, DELFI-TF scores strongly correlated with RAS/BRAF MAF measured by ddPCR (r=0.85, p<0.001). DELFI-TF captured plasma tumor fractions associated with copy number changes when ddPCR failed to detect RAS/BRAF mutations. Before treatment initiation, DELFI-TF correlated with the sum of the largest diameters on CT scans according to RECIST1.1 (r=0.49, p<0.001). Patients with negative and positive changes in DELFI-TF more often exhibited response and progressive disease based on consecutive RECIST assessments, respectively. Median progression-free survival was 9.9 and 12.8 months for patients with DELFI-TF low versus DELFI-TF high, respectively (p=0.007). Overall survival was 28.8 and 58.9 months for patients with DELFI-TF low versus DELFI-TF high, respectively (p=0.001). In patients treated with curative intent, tissue-informed focal and arm-level copy number changes were detected 4-12 weeks after metastasectomy. Most patients presenting molecular relapse were later diagnosed with clinical recurrence by conventional CT imaging. CONCLUSIONS: DELFI-TF demonstrates the ability to use cfDNA fragmentomes to estimate cfDNA tumor burden with a performance comparable to standard approaches for treatment response monitoring and clinical outcome prediction. Citation Format: Bahar Alipanahi, Iris van 't Erve, Keith Lumbard, Laurel K. Millberg, Zach Skidmore, Lorenzo Rinaldi, Jacob Carey, Jennifer Tom, Cornelis J. A. Punt, Nicholas C. Dracopoli, Gerrit A. Meijer, Robert B. Scharpf, Victor E. Velculescu, Remond Fijneman, Alessandro Leal. Cell-free DNA fragmentation profiling for monitoring therapeutic response in metastatic colorectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5714.

A new optimal sensor placement method for virtual sensing of composite laminate

Identifying modal coordinates from output-only data is a key link of virtual sensing technology based on the modal extension method. It is also one of the goals of optimal sensor placement (OSP). Traditional OSP methods are based on the underlying assumption that the estimated values of modal coordinates are unbiased estimates of real values. However, due to uncertainty and the characteristics of an inverse problem, the unbiased estimation obtained from the output-only data may seriously deviate from the true value. This study proposes a new OSP method for composite virtual strain sensing, which can obtain the global unbiased estimation of modal coordinates. First, a Bayesian probabilistic model for virtual sensing considering model uncertainty and measurement error is formulated. Then, the K-L divergence, which measures the reduction in utility by removing sensors from the full configuration, is used to obtain the unbiased estimation of modal parameters. Finally, using the regularization mechanism in the Bayesian method, a new variance determination method is proposed to improve the stability of the solution. Considering both unbiasedness and stability, NSGA-II multi-objective optimization algorithm and two OSP evaluation criteria are used to obtain the final optimal sensor placement. To illustrate the effectiveness of the proposed method, an example involving a laminate plate is considered, accompanied by comprehensive discussions.

Projections of future life expectancy in China up to 2035: a modelling study

To plan social and health services, future life expectancy projections are needed. The aim of this study was to forecast the future life expectancy for mainland China and its provinces. Following the same approach as the Global Burden of Disease Study, we used the largest compiled epidemiological and demographic datasets to estimate age-specific mortality and evaluate population data from 1990 to 2019. A total of 21 life expectancy forecasting models were combined by a probabilistic Bayesian model to forecast the life expectancy for mainland China and its provinces in 2035. The projected life expectancy at birth in mainland China in 2035 is 81·3 years (95% credible interval 79·2-85·0), and there is a high probability that the national goals of improving life expectancy will be achieved (79 years in 2030, and over 80 years in 2035). At the provincial level, women in Beijing have the highest projected life expectancy in 2035 with an 81% probability of reaching 90 years, followed by Guangdong, Zhejiang, and Shanghai, which all have more than a 50% probability of surpassing 90 years. Men in Shanghai are projected to have the highest life expectancy at birth in 2035, with a 77% probability of life expectancy being over 83 years, the highest provincial life expectancy in mainland China in 2019. The projected gains in life expectancy are mainly derived from older individuals (aged ≥65 years), except those in Xinjiang, Tibet, and Qinghai (for men), in which the main contributions come from younger (0-29 years) or middle-aged (30-64 years) individuals. Life expectancy in mainland China and its provinces has a high probability of continuing to increase through to 2035. Adequate policy planning of social and health services will be needed. China National Natural Science Foundation and Social Science Fund of Jiangsu Province.

Integrating Remote Sensing, Proximal Sensing, and Probabilistic Modeling to Support Agricultural Project Planning and Decision-Making for Waterlogged Fields

Waterlogging in agriculture poses severe threats to soil properties, crop yields, and farm profitability. Remote sensing data coupled with drainage systems offer solutions to monitor and manage waterlogging in agricultural systems. However, implementing agricultural projects such as drainage is associated with high uncertainty and risk, with substantial negative impacts on farm profitability if not well planned. Cost–benefit analyses can help allocate resources more effectively; however, data scarcity, high uncertainty, and risks in the agricultural sector make it difficult to use traditional approaches. Here, we combined a wide range of field and remote sensing data, unsupervised machine learning, and Bayesian probabilistic models to: (1) identify potential sites susceptible to waterlogging at the farm scale, and (2) test whether the installation of drainage systems would yield a positive benefit for the farmer. Using the K-means clustering algorithm on water and vegetation indices derived from Sentinel-2 multispectral imagery, we were able to detect potential waterlogging sites in the investigated field (elbow point = 2, silhouette coefficient = 0.46). Using a combination of the Bayesian statistical model and the A/B test, we show that the installation of a drainage system can increase farm profitability by 1.7 times per year compared to the existing farm management. The posterior effect size associated with yield, cropping area, and time (year) was 0.5, 1.5, and 1.9, respectively. Altogether, our results emphasize the importance of data-driven decision-making for agriculture project planning and resource management in the wake of smart agriculture for food security and adaptation to climate change.

Application of nursing technologies in complex therapy patients with ulcer

The article presents nursing technologies in the complex therapy of ulcer patients and the results of the application of these technologies. During the complex therapy of patients with peptic ulcer, their training according to a specially designed program has a positive clinical effect and contributes to reducing the frequency of exacerbations and complications of the disease. Purpose of the study. Improving the effectiveness of treatment and prevention of exacerbations of peptic ulcer through the use of modern nursing technologies in patient education. Results. The effectiveness of therapeutic education in the complex therapy of peptic ulcer was also confirmed by the method of multivariate statistics. Predicting the incidence of peptic ulcer complications by the Bayesian probabilistic modeling showed that improving hygiene literacy can reduce bleeding by 6.1 %, and perforation by 4.1 %, which ensures not only medical, but also socio-economic efficiency of treatment. Conclusion. The study is of practical importance for nurses of prevention departments and district nurses who are involved in conducting a school for ulcer patients. It has practical implications for the educational process in which students are trained in patient education skills.

Environmental and biotic filters interact to shape the coexistence of native and introduced bees in northern Patagonian forests

Native and introduced species can coexist in rich ecosystems where competition for resources is less intense; however, native species can be excluded from suitable habitats hosting a high abundance of introduced species. To test these hypotheses, we estimated the probability of introduced bees (buff-tailed bumblebees and honeybees) to co-occur and dominate giant bumblebees in forested landscapes of northern Patagonia. We evaluated the effects of 29 remote-sensing variables of forest ecosystems on bee abundance using data from 56 landscapes. Then, we used a probabilistic Bayesian model to estimate the effects of the most important predictors of bee abundance on their co-occurrence and dominance probabilities. Introduced and native bees exhibited heterogeneous and scale-dependent responses to environmental conditions, with only six variables affecting their co-occurrence and dominance probabilities. A high maximum temperature was associated with both a decreased co-occurrence and an increased dominance of introduced bumblebees, and the opposed pattern was found for vegetation continuous fields, an index of canopy continuity. Similar responses were found for the co-occurrence probability of honeybees and giant bumblebees. Thus, species dominance and co-occurrence changed differently along environmental gradients, with co-occurrence increasing as native and introduced species become more abundant. A high dominance of introduced bees in unsuitable habitats suggests interspecific competition is strengthened as resources become scarcer, thus reinforcing the idea that abiotic and biotic filters interact with each other.

On the interpretation of German <em>einige</em>. The effect of tense and cardinality

We present a study investigating the effect of tense (past vs. future) on the computation of scalar implicatures in connection with the German quantifier einige ‘some’ in an interactive experiment, which included a financial incentive for participants to consider whether another speaker would share their judgment. We tested the hypothesis that scalar implicatures are less frequently drawn in future tense than in past tense. In addition, we studied to what extent sets with various cardinalities are prototypical representatives of einige + N. We hypothesized that larger cardinalities are more prototypical representatives of the quantifier einige than smaller cardinalities (relative to the cardinality of the total set). We analyzed the experimental data with probabilistic Bayesian models with a linking hypothesis between participants’ responses and readings based on utility maximization in simple decision problems. In line with the hypotheses, we found that less scalar implicatures are drawn in future tense than in past tense, which replicates the results of previous research on English some, and that with an increase in set size acceptance of statements involving einige also increases.

SPRINT: Spectra Preserving Radiance Image Fusion Technique using holistic deep edge spatial attention and Minnaert guided Bayesian probabilistic model

Remote Sensing Image Fusion produces a high resolution multispectral image by merging panchromatic image with the corresponding low resolution multispectral counterpart. The limitation of the existing image fusion techniques is that it lacks in maintaining the spectral characteristics of the multispectral image in the fused output. The motivation of our proposed work is to develop balanced and robust image fusion method, named Spectra Preserving Radiance Image Fusion Technique (SPRINT). The technique developed extracts deep edge map from medoid intensity matched image using Holistic Nested Edge Detection (HNED). Minnaert function is applied on denoised panchromatic radiance image along with Digital Elevation Model (DEM) and solar angles’ computation to determine the surface topography. SPRINT’s core design emphasizes on holistic deep edges for spatial attention and terrain guidance using minnaert parameter at each image pixel in a Spectra Preserving Bayesian Probabilistic Model. The unique data pre-processing engine generates fusion ready representative datasets to trigger SPRINT processing workflow. The Indian Cartosat-1 panchromatic and Resourcesat-2/2A multispectral sensors’ datasets along with IKONOS and USGS-NASA’s Landsat-8 OLI images covering diverse landscapes are used for image fusion evaluation and assessment. It has been found that SPRINT’s fusion performance is superior to the state-of-the-art (SOTA) image fusion methods in terms of both visual effects and quantitative metrics. The Normalized Difference Vegetation Index (NDVI) using SPRINT’s fused radiance image tends to have negligible deviation at various classes with respect to reference NDVI. It has been observed that SPRINT derived surface reflectance values have close agreement with original reflectance measurements.

A platform trial approach to proof-of-concept (POC) studies in autism spectrum disorder: Autism spectrum POC initiative (ASPI)

BackgroundOver the past decade, autism spectrum disorder (ASD) research has blossomed, and multiple clinical trials have tested potential interventions, with varying results and no clear demonstration of efficacy. Lack of clarity concerning appropriate biological mechanisms to target and lack of sensitive, objective tools to identify subgroups and measure symptom changes have hampered the efforts to develop treatments. A platform trial for proof-of-concept studies in ASD could help address these issues. A major goal of a platform trial is to find the best treatment in the most expeditious manner, by simultaneously investigating multiple treatments, using specialized statistical tools for allocation and analysis. We describe the setup of a platform trial and perform simulations to evaluate the operating characteristics under several scenarios. We use the Autism Behavior Inventory (ABI), a psychometrically validated web-based rating scale to measure the change in ASD core and associated symptoms. MethodsDetailed description of the setup, conduct, and decision-making rules of a platform trial are explained. Simulations of a virtual platform trial for several scenarios are performed to compare operating characteristics. The success and futility criteria for treatments are based on a Bayesian posterior probability model. ResultsOverall, simulation results show the potential gain in terms of statistical properties especially for improved decision-making ability, while careful planning is needed due to the complexities of a platform trial. ConclusionsAutism research, shaped particularly by its heterogeneity, may benefit from the platform trial approach for POC clinical studies.

Characterization of natural fractures in tight sandstone reservoirs in Yuanba area, northern Sichuan Basin

Natural fractures are quite important for the high and stable production of tight sandstone gas reservoirs. However, it is commonly difficult to accurately characterize their distribution in tight sandstone gas reservoirs due to the strong heterogeneity. A comprehensive characterization of natural fractures in tight sandstone gas reservoirs of the Xujiahe Formation in the Yuanba area, Sichuan Basin, was carried out by multimethod of core description, log interpretation and seismic prediction. The results show that (1) the natural fractures in the Xujiahe Formation are mainly low-angle fractures, with a few medium- and high-angle fractures. The degree of fracture development is mainly controlled by the distance from the fault, the flexural degree of the strata and the thickness of the rock mechanical layer. (2) The fracture-sensitive seismic attributes such as the preferred maximum likelihood, structure entropy, illuminant-based symmetry and high-precision curvature are taken as the input, and the unsupervised cluster analysis algorithm based on Bayesian probability model is applied to predict the distribution of large-scale fractures at different dip angles; on the basis of narrow azimuthal pre-stack CMP gather optimization, the anisotropy detection technique of Hampson-Russell's ProAZ full CMP gather is used to characterize the distribution of small- and medium-scale fractures, and the predicted fracture development intensity matches the fracture interpretation results of the imaging logs by more than 85%. Moreover, the predicted fracture orientation is basically consistent with that of the imaging loggings. (3) The fault-genesis fractures of the Xujiahe Formation are mainly distributed near the fault on the southeast flank of the Jiulongshan anticline, and are distributed in a narrow strip in NE direction. The fold-genesis fractures are mainly developed in the Xu 3rd Member, and are located in the axis of the Jiulongshan anticline and its southeast flank where the strata are strongly flexed. This study provides a solid foundation for the development of tight sandstone gas reservoirs and extends the approaches for the accurate characterization of fractures.

Hole Inpainting Algorithm for Half-Organized Point Cloud Obtained by Structured-Light Section System

The advances in sensors and data processing technologies enrich the types of 3D point clouds acquirement, empowering numerous extensive and novel applications such as 3D reconstruction in various scenarios. However, the hole defects affect the accuracy and fidelity of the acquired point clouds, hindering further development and application of 3D point clouds. Aiming at the hole defects in point clouds, a Bayesian hole inpainting algorithm for the half-organized point cloud is proposed, where the point cloud is obtained by a structured-light section system. The algorithm establishes a Bayesian probability model in the hole region, which adopts specific distributions of the point cloud to estimate the maximum likelihood parameter. Simulation and experimental results show that the proposed approach outperforms other competing algorithms significantly in repairing various types of holes, both in objective and subjective qualities. In addition, the proposed algorithm has better scalabilities in the cases of wrong topology definition and self-intersection confusion. This is the first algorithm specially designed for hole inpainting in half-organized point clouds, which maximizes the comprehensive consideration of local features and global optimization, supplemented by targeted prior knowledge of density, Riemannian manifold, and discrete attributes.

Sentiment Analysis of Weibo Comments based on LDA Model

The essence of LDA (Latent Dirichlet Allocation) model is a generative Bayesian probability model that contains three layers of words, topics and corpus (sometimes called document set). Under the LDA algorithm theory, each document represents a probability distribution formed by some topics, and each topic represents a probability distribution formed by many words. Therefore, the model fitting results will present the core keywords and specific probabilities of each topic, and researchers can interpret the meaning of the document according to the model results. In this paper, we hope to use the LDA model as the basis for the emotional analysis of microblog comments.

An Improved Iterative Reweighted STAP Algorithm for Airborne Radar

In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples. Sparse Bayesian learning algorithms have received considerable attention for their remarkable and reliable performance. Its implementation in large-scale radar systems is however hindered by the overwhelming computational load and slow convergence speed. This paper aims to address these drawbacks by proposing an improved iterative reweighted sparse Bayesian learning algorithm based on expansion-compression variance-components (ExCoV-IIR-MSBL). Firstly, a modified Bayesian probabilistic model for SR-STAP is introduced. Exploiting the intrinsic sparsity prior of the clutter, we divide the space-time coefficients into two parts: the significant part with nontrivial coefficients and the irrelevant part with small or zero coefficients. Meanwhile, we only assign independent hyperparameters to the coefficients in the significant part, while the remaining coefficients share a common hyperparameter. Then the generalized maximum likelihood (GML) criterion is adopted to classify the coefficients, ensuring both accuracy and efficiency. Hence, the parameter space in Bayesian inference will be significantly reduced, and the computational efficiency can be considerably promoted. Both theoretical analysis and numerical experiments validate that the proposed algorithm achieves superior performance with considerably improved computational efficiency in sample shortage scenarios.

Game-Theory-Inspired Novel Mechanism for Assessing Healthcare Quality

Healthcare is the most pivotal domain of every nation. With the sudden upraise of the COVID-19 pandemic, there has been a major concern for the healthcare industry to provide quality medical services to the common people. Vulnerable healthcare conditions have proven to be fatal for the patients. Conspicuously, it has become indispensable to assess the quality of healthcare services provided by the hospitals. The current article focuses on analyzing healthcare service quality delivered by the hospitals and healthcare centers. Specifically, the presented framework utilizes Internet of Things (IoT) technology to acquire real-time ambient data inside smart hospitals. The quantification of the healthcare service is performed using the Probability of Health Grade (PoHG) to classify data segments using the probabilistic Bayesian belief model. Furthermore, the temporal data abstraction is performed for the numerical analysis of healthcare service quality in terms of the health quality index (HQI). Finally, a 2-player game theory-inspired decision modeling is performed to analyze healthcare quality in a time-sensitive manner. The proposed framework is assessed using a simulated environment where 225325 data segments are analyzed. Results are compared with state-of-the-art techniques in which enhanced performance measures are registered in terms of classification efficacy (93.74%), decision-making efficiency [coefficient of determination (95%), accuracy (97.53%), mean square error (2.01%), and root mean square error (1.95%)], temporal delay (96.62 s), and reliability (91.58%).