Retrieval Model Research Articles

MapReduce model for efficient image retrieval: a Hadoop-based framework

MapReduce model for efficient image retrieval: a Hadoop-based framework

Analyzing the impacts of Discharges from coastal outfalls on water quality of Kuwait Bay via Landsat monitoring

Monitoring water resources plays an important role in water pollution control. Taking Samples of the whole water body is limited in practice for many reasons such as time-consuming, cost constraints, and the situation of topography. So, the remote sensing method has been used as it provides a reasonable and feasible approach to providing water quality data. In this study, Nitrates and phosphates were assessed along Kuwait's territorial water using data from Bands reflection that was taken from remote sensing images to improve the prediction of Nitrate and Phosphate concentrations in waterbodies. The predicting algorithms have been subjected to calibration and validation by using the in-site data measured in the field from the KEPA Monitor stations. All the algorithms produced a good performance in estimating the concentration of nitrate and phosphate at the outfall sites, but the nitrate algorithms had superior performance over the phosphate algorithms. The Relative Error of the Nitrate and Phosphate Algorithm was 16.851% and 37.793%, respectively. One of the accuracy requirements for estimating the quality of water for the satellite imagery method is that the Relative Error should not be more than 30%, and the accuracy of the nitrate prediction algorithms is more accepted than phosphate prediction algorithms. At last, Nitrate and phosphate were estimated at the outfall’s sites using Landsat 8 OLI imagery. The retrieval models result showed that all the Nitrate and Phosphate concentrations were above the KEPA ambient seawater quality criteria standard values which are for the Nitrate (NO3-) is equal to 0.095 milligrams per liter, and equal to 0.035 milligrams per liter for the Phosphate (PO43-). Although this case study is a particular study, the modeling procedures related to the Nitrate and Phosphate retrieval model can provide others the ability when they use the technology of satellite imagery to predict Nitrate and Phosphate for identical or different water bodies.

Implementation of Aerosol Mie Scattering in POSEIDON with Application to the Hot Jupiter HD 189733 b’s Transmission, Emission, and Reflected Light Spectrum

Aerosols are a ubiquitous feature of planetary atmospheres and leave clear spectral imprints in exoplanet spectra. Pre-JWST, exoplanet retrieval frameworks mostly adopted simple parametric approximations. With JWST, we now have access to mid-infrared wavelengths where aerosols have detectable composition-specific resonance features. Here, we implement new features into the open-source atmospheric retrieval code POSEIDON to account for the complex scattering, reflection, and absorption properties of Mie-scattering aerosols. We provide an open-source database of these Mie-scattering cross sections and optical properties. We also extend the radiative transfer and retrieval functionality in POSEIDON to include multiple scattering reflection and emission spectroscopy. We demonstrate these new retrieval capabilities on archival Hubble and Spitzer transmission and secondary-eclipse spectra of the hot Jupiter HD 189733 b. We find that a high-altitude, low-density, and thin slab composed of submicron particles is necessary to fit HD 189733 b’s transmission spectrum, with multiple aerosol species providing a good fit. We additionally retrieve a subsolar H2O abundance, a subsolar K abundance, and do not detect CO2. Our joint thermal and reflection retrievals of HD 189733 b’s secondary-eclipse spectrum, however, finds no evidence of dayside aerosols, a subsolar dayside H2O abundance, enhanced CO2, or slightly subsolar alkali abundances. We additionally explore how retrieval model choices such as cloud parameterization, aerosol species and properties, and thermal structure parameterization affect retrieved atmospheric properties. Upcoming JWST data for hot Jupiters such as HD 189733 b will be well suited to enable deeper exploration of aerosol properties, allowing the formulation of a self-consistent, multidimensional picture of cloud formation processes.

Enhancing legal research through knowledge-infused information retrieval for Vietnamese labor law

<p>The role of intelligent information retrieval systems in legal research optimization has become increasingly recognized. There are many methods for exhibiting advance mentsin the proficient retrieval of legal documents. However, those methods fail to tackle the specific challenges encountered in real-world labor law searches. This research breaks new ground in Vietnamese labor law retrieval by leveraging a comprehensive dataset of 300,000 documents a cross diverse cat egories (20 document types and 27 legal fields) to train and evaluate retrieval models specifically designed for Vietnamese labor law. Unlike previous approaches, this work goes beyond simple information retrieval. It also constructs question & answer (Q&A) dataset specifically tailored to this legal domain. Be sides, this study introduces a novel approach of in corporating a legal ontology built from the data set itself. This knowledge in fusion significantly improves retrieval performance across legal search tasks, as demon strated through rigorous experimentation. These advancements empower intelligent systems to grasp the intricate semantic nuances of Vietnamese labor law.</p>

Adaptive CLIP for open-domain 3D model retrieval

In order to effectively enhance the practicality of 3D model retrieval, we adopt a single real image as the query sample for retrieving 3D models. However, the significant differences between 2D images and 3D models in terms of lighting conditions, textures and backgrounds, posing a great challenge for accurate retrieval. Existing work on 3D model retrieval mainly focuses on closed-domain research, while the open-domain condition where the category relationship between the query image and the 3D model is unknown is more in line with the needs of real scenarios. CLIP shows significant promise in comprehending open-world visual concepts, facilitating effective zero-shot image recognition. Based on this multimodal pre-training large language model, we introduce Adaptive Open-domain Semantic Nearest-neighbor Contrast (AOSNC), a method for learning and aligning multi-modal text, image, and 3D model. In order to solve the issue of inconsistent cross-domain categories and difficult sample correlation in open-domain, we construct a cross-modal bridge using CLIP. This model utilizes textual features to bridge the gap between 2D images and 3D model views. Additionally, we design an adaptive network layer to address the limitations of the pre-training model for 3D model views and enhance cross-modal alignment. We propose a mutual nearest-neighbor semantic alignment loss to address the challenge of aligning features from disparate modalities (text, images, and 3D models). This loss function enhances cross-modal learning by effectively associating and distinguishing features, improving retrieval accuracy. We conducted comprehensive experiments using the image-based 3D model retrieval dataset MI3DOR and the cross-domain 3D model retrieval dataset NTU-PSB to validate the superiority of the proposed method. Our results show significant improvements in several evaluation metrics, underscoring the efficacy of our method in augmenting cross-modal feature alignment and retrieval performance.

Combining UAV Multispectral Imaging and PROSAIL Model to Estimate LAI of Potato at Plot Scale

Accurate and rapid estimation of the leaf area index (LAI) is essential for assessing crop growth and nutritional status, guiding farm management, and providing valuable phenotyping data for plant breeding. Compared to the tedious and time-consuming manual measurements of the LAI, remote sensing has emerged as a valuable tool for rapid and accurate estimation of the LAI; however, the empirical inversion modeling methods face challenges of low efficiency for actual LAI measurements and poor model interpretability. The integration of radiative transfer models (RTMs) can overcome these problems to some extent. The aim of this study was to explore the potential of combining the PROSAIL model with high-resolution unmanned aerial vehicle (UAV) multispectral imaging to estimate the LAI across different growth stages at the plot scale. In this study, four inversion strategies for estimating the LAI were tested. Firstly, two types of lookup tables (LUTs) were built to estimate potato LAI of different varieties across different growth stages. Specifically, LUT1 was based on band reflectance, and LUT2 was based on vegetation index. Secondly, the hybrid models combining LUTs generated by PROSAIL and two machine learning algorithms (random forest (RF), Partial Least Squares Regression (PLSR)) are built to estimate potato LAI. The determination of coefficient (R2) of models for estimating LAI by LUTs ranged from 0.24 to 0.64. The hybrid method that integrates UAV multispectral, PROSAIL, and machine learning significantly improved the accuracy of LAI estimation. Compared to the results based on LUT2, the hybrid model achieved higher accuracy with the R2 of the inversion model improved by 30% to 263%. The LAI retrieval model using the PROSAIL model and PLSR achieved an R2 as high as 0.87, while the R2 using the RF algorithm ranged from 0.33 to 0.81. The proposed hybrid model, integrated with UAV multispectral data, PROSAIL, and PLSR can achieve approximate accuracy compared with the empirical inversion models, which can alleviate the labor-intensive process of handheld LAI measurements for building inversion models. Thus, the hybrid approach provides a feasible and efficient strategy for estimating the LAI of potato varieties across different growth stages at the plot scale.

Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations

Abstract. Cloud fraction as a vital component of meteorological satellite products plays an essential role in environmental monitoring, disaster detection, climate analysis and other research areas. Random forest (RF) and multilayer perceptron (MLP) algorithms were used in this paper to retrieve the cloud fraction of AGRI (Advanced Geosynchronous Radiation Imager) on board the Fengyun-4A (FY-4A) satellite based on its full-disk level-1 radiance observation. Corrections have been made subsequently to the retrieved cloud fraction in areas where solar glint occurs using a correction curve fitted with sunglint angle as weight. The algorithm includes two steps: the cloud detection is conducted firstly for each AGRI field of view to identify whether it is clear sky, partly cloudy or overcast within the observation field. Then, the cloud fraction is retrieved for the scene identified as partly cloudy. The 2B-CLDCLASS-lidar cloud fraction product from the CloudSat and CALIPSO active remote sensing satellite is employed as the truth to assess the accuracy of the retrieval algorithm. Comparison with the operational AGRI level-2 cloud fraction product is also conducted at the same time. The results indicate that both the RF and MLP cloud detection models achieved high accuracy, surpassing that of operational products. However, both algorithms demonstrated weaker discrimination capabilities for partly cloudy conditions compared to clear-sky and overcast situations. Specifically, they tended to misclassify fields of view with low cloud fractions (e.g., cloud fraction = 0.16) as clear sky and those with higher cloud fractions (e.g., cloud fraction = 0.83) as overcast. Between the two models, RF exhibited higher overall accuracy. Both RF and MLP models performed well in cloud fraction retrieval, showing lower mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) compared to operational products. The ME for both RF and MLP cloud fraction retrieval models was close to zero, while RF had slightly lower MAE and RMSE than MLP. During daytime, the high reflectance in sunglint areas led to larger retrieval errors for both RF and MLP algorithms. However, after correction, the retrieval accuracy in these regions improved significantly. At night, the absence of visible light observations from the AGRI instrument resulted in lower classification accuracy compared to daytime, leading to higher cloud fraction retrieval errors during nighttime.

Improvement of Space-Observation of Aerosol Chemical Composition by Synergizing a Chemical Transport Model and Ground-Based Network Data

Aerosol chemical components are critical parameters that influence the atmospheric environment, climate effects, and human health. Retrieving global columnar atmospheric aerosol components from satellite observations provides foundational data and practical value. This study develops a method for retrieving aerosol component composition from polarized satellite data by synergizing a chemical transport model with ground-based remote sensing data. The method enables the rapid acquisition of columnar mass concentrations for seven aerosol components on a global scale, including black carbon (BC), brown carbon (BrC), organic carbon (OC), ammonium sulfate (AS), aerosol water (AW), dust (DU), and sea salt (SS). We first establish a remote sensing model based on the multiple solution mixing mechanism (MSM2) to obtain aerosol chemical components using AERONET ground-based measurements. We then employ a cross-layer adaptive fusion (CAF)-Transformer model to learn the spatial distribution characteristics of aerosol components from the MERRA-2 model. Furthermore, we optimize the retrieval model by transfer learning from the ground-based composition data to achieve satellite remote sensing of aerosol components. Residual analysis indicates that the retrieval model exhibits robust generalization capabilities for components such as BC, OC, AS, and DU, achieving a coefficient of determination of 0.7. Moreover, transfer learning effectively enhances the consistency between satellite retrievals and ground-based remote sensing results, with an average improvement of 0.23 in the correlation coefficient. We present annual and seasonal means of global distributions of the retrieved aerosol component concentrations, with a major focus on the spatial and temporal variations of BC and DU. Additionally, we analyze three typical atmospheric environmental cases, wildfire, dust storm, and particulate pollution, by comparing our retrievals with model data and other datasets. This demonstrates the ability of satellite remote sensing to identify the location, intensity, and impact range of environmental pollution events. Satellite-retrieved aerosol component data offers high spatial resolution and efficiency, particularly providing significant advantages for near-real-time monitoring of regional atmospheric environmental events.

Knowledge enhanced edge-driven graph neural ranking for biomedical information retrieval

Neural networks used for information retrieval tend to capture textual matching signals between a query and a document. However, neural ranking models for biomedical information retrieval often struggle to semantically well match the query to the documents. The main reasons are that biomedical terms have many different representations and the fact description related to the query is non-consecutive and non-local in the documents. In this paper, we present an edge-driven graph neural ranking method for biomedical information retrieval by incorporating knowledge from medical databases. First, we propose to form an edge-driven graph by connecting some biomedical terms in the query and the document through different types of edges. Then, we design a novel way of knowledge integration to introduce knowledge related to biomedical terms into the graph and construct a knowledge-query-doc graph. Finally, a graph neural ranking model is applied to capture non-local and non-contiguous match signals between the query and the document. Experimental results show on the biomedical datasets that our method outperforms the advanced neural models. And further analysis shows that the knowledge integration method can well reduce the semantic gap between the query and the document, and our graph model can provide interpretation for matching between the query and the document.

Hypercube Pooling for Visual Semantic Embedding

Visual Semantic Embedding ( VSE ) is a primary model for cross-modal retrieval, wherein the global feature aggregator is a crucial component of the VSE model. In recent research, the General Pooling Operator ( GPO ) aggregator, which weighs the features reconstructed from the local feature set to aggregate, facilitates the related models to achieve good retrieval performance. However, the reason for the effectiveness remains to be explored. To enhance the rationality of aggregator designs, we analyze the reason from the perspective of feature space. Indeed, for each data, the local feature set forms a hypercube containing abundant data information, and the feature learned by GPO measures the hypercube, thereby representing the data. The geometric structure of the hypercube implies that the set containing all points within the hypercube is a convex set, so the feature learned by weighted aggregation is an interior point of the hypercube. However, using the interior point to measure the hypercube leads to some problems in feature representation and model optimization, as well as the reduction of retrieval efficiency caused by weight computation. For example, the related pair’s features may be far, while the unrelated ones may be close. To measure the hypercube more clearly and alleviate the problems mentioned above, we propose Hypercube Pooling ( HCP ) aggregator. Specifically, HCP concatenates the Max and Min Pooling features as the global features. This aggregation method has multiple advantages, e.g., the learned global feature represents all hyperplanes of the hypercube that contain critical information and hypercube geometric structure. Moreover, HCP adds normalization-before-concatenation and reduces the usual setting of margin in the loss function by half to avoid gradient loss caused by the difference in the feature value and dimensionality doubling. The experimental results on the Flickr30K and MSCOCO datasets show that the HCP model has excellent performance with high efficiency, confirming the correctness of the spatial analysis and the effectiveness of the HCP aggregator.

Retrieval of soil moisture in salinized farmland soil by multi-polarization SAR

ABSTRACT Synthetic Aperture Radar (SAR) is a potent instrument for estimating soil moisture across vast farmland areas. However, when soil salinity rises, existing SAR retrieval models for soil moisture become less accurate. Soluble salts in soil water alter the inherent correlation between the SAR backscattering coefficients and soil water, thus compromising the performance of retrieval models. The need to get surface roughness parameters from the field also limits the widespread application of the model. To address these issues, we developed a retrieval model for salty soil moisture assessment under small-scale roughness surfaces. First, we found a method to distinguish between slightly rough and rough surfaces using scattering entropy obtained from full-polarization decomposition. Then, we harnessed the co-polarization ratio to mitigate the effect of surface roughness in slightly rough conditions. Later, we developed a soil moisture retrieval model based on the co-polarization ratio, considering incidence angle, residual roughness, and soluble salt content. The validation of the model was verified using field surveying data, yielding an RMSE of 0.026 cm3 ·cm− 3 for the estimated soil moisture. This accuracy is comparable to or surpasses the level achieved by SAR for non-salinized soil moisture retrieval. Our findings showed that Scattering entropy is an effective parameter for distinguishing scattering mechanisms on farmland surfaces effectively. Specifically, when the scattering entropy of farmland is less than 0.5, the co-polarization ratio can mitigate some surface roughness effects. Additionally, when the soil-soluble salt content is incorporated, our model can accurately estimate the soil moisture of salty soil.

A context-based model of collaborative inhibition during memory search

Contrary to common intuition, a group of people recalling information together remembers less than the same number of individuals recalling alone (i.e., the collaborative inhibition effect). To understand this effect in a free recall task, we build a computational model of collaborative recall in groups, extended from the Context Maintenance and Retrieval (CMR) model, which captures how individuals recall information alone. We propose that in collaborative recall, one not only uses their previous recall as an internal retrieval cue, but one also listens to someone else’s recall and uses it as an external retrieval cue. Attending to this cue updates the listener’s context to be more similar to the context of someone else’s recall. Over an existing dataset of individual and collaborative recall in small and large groups, we show that our model successfully captures the difference in memory performance between individual recall and collaborative recall across different group sizes from 2 to 16, as well as additional recall patterns such as recency effects and semantic clustering effects. Our model further shows that collaborating individuals reach similar areas in the context space, whereby their contexts converge more than the contexts of individuals recalling alone. This convergence constrains their ability to search memories effectively and is negatively associated with recall performance. We discuss the contributions of our modeling results in relation to previous accounts of the collaborative inhibition effect.

The effect of economic growth level, trade openness, FDI on tax revenues in four low-income countries in Southeast Asia

The study aims to analyze the impact of economic growth level, trade openness, and FDI capital on tax revenue in four low-income Southeast Asian countries including Vietnam, Laos, Cambodia, and Myanmar. Secondary data was collected from the World Bank between 2000 and 2022. The data panel was analyzed using the Pooled OLS retrieval model. The research results indicate a significant impact of GDP per capita, trade openness, and FDI on tax revenue in these four nations. Consequently, this result aligns with previous studies. The article suggests recommendations to increase tax revenue for these countries.

An overview about hand exoskeleton actuation system and thumb motions analysis

Abstract. Stroke patients experience impaired hand function which significantly impacts their daily activities. Robot-assisted training is essential for restoring motor function, with hand exoskeletons serving as assistive devices to support, amplify, replace, or counteract body movements. Designing an exoskeleton that accurately mimics the natural movement of the thumb and provides adequate support is a key challenge in rehabilitation. This paper designs a retrieval model for hand rehabilitation exoskeletons from 2009 to 2023, analyzing the hand anatomy and thumb motion range. It discusses various driving mechanisms for hand exoskeletons, including cable, linkage, pneumatic, gear motor, and hybrid systems. While progress has been made in experimental stages, more research is needed for clinical applications. A universal hand exoskeleton actuation system that meets all patient preferences does not exist. Ongoing research on hybrid drive systems aims to enhance hand exoskeletons for better rehabilitation and daily use for patients with hand dysfunction.

Modelling of an Intelligent and Secured Image Retrieval Model by Employing Deep Belief Network

Modelling of an Intelligent and Secured Image Retrieval Model by Employing Deep Belief Network

StaPep: An Open-Source Toolkit for Structure Prediction, Feature Extraction, and Rational Design of Hydrocarbon-Stapled Peptides.

All-hydrocarbon stapled peptides, with their covalent side-chain constraints, provide enhanced proteolytic stability and membrane permeability, making them superior to linear peptides. However, tools for extracting structural and physicochemical descriptors to predict the properties of hydrocarbon-stapled peptides are lacking. To address this, we present StaPep, a Python-based toolkit for generating 3D structures and calculating 21 features for hydrocarbon-stapled peptides. StaPep supports peptides containing two non-standard amino acids (norleucine and 2-aminoisobutyric acid) and six non-natural anchoring residues (S3, S5, S8, R3, R5, and R8), with customization options for other non-standard amino acids. We showcase StaPep's utility through three case studies. The first generates 3D structures of these peptides with a mean RMSD of 1.62 ± 0.86, offering essential structural insights for drug design and biological activity prediction. The second develops machine learning models based on calculated molecular features to differentiate between membrane-permeable and non-permeable stapled peptides, achieving an AUC of 0.93. The third constructs regression models to predict the antimicrobial activity of stapled peptides against Escherichia coli, with a Pearson correlation of 0.84. StaPep's pipeline spans data retrieval, structure generation, feature calculation, and machine learning modeling for hydrocarbon-stapled peptides. The source codes and data set are freely available on Github: https://github.com/dahuilangda/stapep_package.

Biomedical Information Retrieval with Positive-Unlabeled Learning and Knowledge Graphs

The rapid growth of biomedical publications has presented significant challenges in the field of information retrieval. Most existing work focuses on document retrieval given explicit queries. However, in real applications such as curated biomedical database maintenance, explicit queries are missing. In this paper, we propose a two-step model for biomedical information retrieval in the case that only a small set of example documents is available without explicit queries. Initially, we extract keywords from the observed documents using large pre-trained language models and biomedical knowledge graphs. These keywords are then enriched with domain-specific entities. Information retrieval techniques can subsequently use the collected entities to rank the documents. Following this, we introduce an iterative Positive-Unlabeled learning method to classify all unlabeled documents. Experiments conducted on the PubMed dataset demonstrate that the proposed technique outperforms the state-of-the-art positive-unlabeled learning methods. The results underscore the effectiveness of integrating large language models and biomedical knowledge graphs in improving zero-shot information retrieval performance in the biomedical domain.

Spectral clustering and query expansion using embeddings on the graph-based extension of the set-based information retrieval model

Spectral clustering and query expansion using embeddings on the graph-based extension of the set-based information retrieval model

Cross-modal similar clinical case retrieval using a modular model based on contrastive learning and k-nearest neighbor search

ObjectiveElectronic health record systems have made it possible for clinicians to use previously encountered similar cases to support clinical decision-making. However, most studies for similar case retrieval were based on single-modal data. The existing studies on cross-modal clinical case retrieval were limited. We aimed to develop a CRoss-Modal Retrieval (CRMR) model to retrieve similar clinical cases recorded in different data modalities. Materials and methodsThe publically available Medical Information Mart for Intensive Care-Chest X-ray (MIMIC-CXR) dataset was used for model development and testing. The CRMR model was designed as a modular model containing two feature extraction models, two feature transformation models, one feature transformation optimization model, and one case retrieval model. The ability to retrieve similar clinical cases recorded in different data modalities was facilitated by the use of contrastive deep learning and k-nearest neighbor search. ResultsThe average retrieval precision, denoted as AP@k, of the developed CRMR model, were 76.9 %@5, 76.7 %@10, 76.5 %@20, 76.3 %@50, and 77.9 %@100, respectively. Here k is the number of similar cases returned after retrieval. The average retrieval time varied from 0.013 ms to 0.016 ms with k varying from 5 to 100. Moreover, the model can retrieve similar cases with the same multiple radiographic manifestations as the query case. DiscussionThe CRMR model has shown promising cross-modal retrieval performance in clinical case analysis, with the potential for future scalability and improvement in handling diverse disease types and data modalities. The CRMR model has promising potential to aid clinicians in making optimal and explainable clinical decisions.

Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods

Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood.