Retrieval Effectiveness Research Articles

How Psychedelics Modulate Multiple Memory Mechanisms in Posttraumatic Stress Disorder.

Posttraumatic stress disorder (PTSD) is a psychiatric disorder with defining abnormalities in memory, and psychedelics may be promising candidates for the treatment of PTSD given their effects on multiple memory systems. Most PTSD and psychedelic research has investigated memory with fear conditioning and extinction. While fruitful, conditioning and extinction provide a limited model of the complexity of PTSD and phenomenology of psychedelics, thereby limiting the refinement of therapies. In this review, we discuss abnormalities in fear conditioning and extinction in PTSD and review 25 studies testing psychedelics on these forms of memory. Perhaps the most reliable effect is that the acute effects of psychedelics can enhance extinction learning, which is impaired in PTSD. However, the post-acute effects may also enhance extinction learning, and the acute effects can also enhance fear conditioning. We then discuss abnormalities in episodic and semantic memory in PTSD and review current knowledge on how psychedelics impact these memory systems. Although PTSD and psychedelics acutely impair the formation of hippocampal-dependent episodic memories, psychedelics may acutely enhance cortical-dependent learning of semantic memories that could facilitate the integration of trauma memories and disrupt maladaptive beliefs. More research is needed on the acute effects of psychedelics on episodic memory consolidation, retrieval, and reconsolidation and post-acute effects of psychedelics on all phases of episodic memory. We conclude by discussing how targeting multiple memory mechanisms could improve upon the current psychedelic therapy paradigm for PTSD, thereby necessitating a greater emphasis on assessing diverse measures of memory in translational PTSD and psychedelic research.

Learning to match patients to clinical trials using large language models

Objective:This study investigates the use of Large Language Models (LLMs) for matching patients to clinical trials (CTs) within an information retrieval pipeline. Our objective is to enhance the process of patient-trial matching by leveraging the semantic processing capabilities of LLMs, thereby improving the effectiveness of patient recruitment for clinical trials. Methods:We employed a multi-stage retrieval pipeline integrating various methodologies, including BM25 and Transformer-based rankers, along with LLM-based methods. Our primary datasets were the TREC Clinical Trials 2021–23 track collections. We compared LLM-based approaches, focusing on methods that leverage LLMs in query formulation, filtering, relevance ranking, and re-ranking of CTs. Results:Our results indicate that LLM-based systems, particularly those involving re-ranking with a fine-tuned LLM, outperform traditional methods in terms of nDCG and Precision measures. The study demonstrates that fine-tuning LLMs enhances their ability to find eligible trials. Moreover, our LLM-based approach is competitive with state-of-the-art systems in the TREC challenges.The study shows the effectiveness of LLMs in CT matching, highlighting their potential in handling complex semantic analysis and improving patient-trial matching. However, the use of LLMs increases the computational cost and reduces efficiency. We provide a detailed analysis of effectiveness-efficiency trade-offs. Conclusion:This research demonstrates the promising role of LLMs in enhancing the patient-to-clinical trial matching process, offering a significant advancement in the automation of patient recruitment. Future work should explore optimising the balance between computational cost and retrieval effectiveness in practical applications.

Efektivitas Kegiatan Shelving terhadap Temu Kembali Informasi Pemustaka di Dinas Kearsipan dan Perpustakaan Provinsi Sumatera Barat

This research aims to eval_uate the effectiveness of shelving activities on user information retrieval_ at the Archives and Library Service of West Sumatra Province. Using descriptive methods, this research involved 99 respondents who were randomly selected using random sampling techniques. Data was collected through a questionnaire consisting of 32 statements. The research results show that the effectiveness of shelving has a positive and significant effect on user information retrieval_. The shelving variable obtained a percentage value of 71.78% (good category), while the information retrieval_ variable reached 76.18% (good category). The effectiveness of shelving has a significant effect on information retrieval_ by 52.5%. Although the overall assessment was positive, the research identified several areas requiring improvement, particularly in the aspects of accuracy of collection organization and shelving activities. Improvements in these aspects are expected to further increase user satisfaction and the effectiveness of information retrieval_ in the library.

Nonlinear difference subspace method of motor imagery EEG classification in brain-computer interface

Noninvasive Motor Imagery (MI) electroencephalography (EEG) based brain-computer interface (BCI) systems are in high demand in both the medical and consumer sectors. However, the low signal-to-noise ratio of EEG and its nonlinear dynamics present challenges in processing. The effectiveness of information retrieval is directly related to the complexity of the algorithms, making BCI impractical for real-time applications. To enhance performance, this study introduced classification frameworks using nonlinear subspace and difference subspace methods, employing manifold learning techniques such as Kernel principal component analysis (KPCA) and Isometric feature mapping (Isomap). The optimal dimensions for the subspaces were selected using overlapping criteria in conjunction with the breadth-first-search (BFS) and depth-first-search (DFS) graph search methods. The proposed methods were evaluated on the BCI competition III and IV datasets using metrics including classification accuracy, Cohen's kappa coefficient, and F1-score. By exploiting the nonlinear characteristics of EEG and employing the geodesic distance between all pairs of trials, the modified Isomap called supervised discriminant Isomap projection (SD-ISoP) outperformed the KPCA-based methods and other recently proposed state-of-the-art methods. The results demonstrssate that the SD-ISoP-based difference subspace method achieved an average classification accuracy of 86.25% and 85.44% for BCI competition III and IV datasets, respectively.

Efficient Algorithms for Range Mode Queries in the Big Data Era

The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem expands upon this concept by preprocessing an array A containing n natural numbers. This allows for the swift determination of the mode within any subarray A[a..b], thus optimizing the computation of the mode for a multitude of range queries. The efficacy of this process bears considerable importance in data analytics and retrieval across diverse platforms, including but not limited to online shopping experiences and financial auditing systems. This study is dedicated to exploring and benchmarking different algorithms and data structures designed to tackle the RMQ problem. The goal is to not only address the theoretical aspects of RMQ but also to provide practical solutions that can be applied in real-world scenarios, such as the optimization of an online shopping platform’s understanding of customer preferences, enhancing the efficiency and effectiveness of data retrieval in large datasets.

Efficient Document Retrieval System using Locality Sensitive Hashing Nearest Neighbor Algorithm and Weighted Jaccard Distance for Retrieving Closest Personalities

The process of retrieving significant documents based on the search key from a corpus has been a vital research problem in the information retrieval field. This paper proposes an efficient way to retrieve documents related to different personalities extracted from Wikipedia. The proposed method utilizes the Locality Sensitive Hashing Nearest Neighbor algorithm combined with Weighted Jaccard Distance to measure document similarity with enhanced precision. This document retrieval system demonstrates competitive performance compared to existing methods in the Personality Identification domain. The introduction of a document centroid normalization technique significantly improves the effectiveness of information retrieval by enabling better discrimination between documents. The personality document search results were compared for different distance measures using performance metrics like Normalized Discounted Cumulative Gain and Mean Average Precision. The results presented in this paper show that the TF-IDF scheme with Locality Sensitive Hashing Nearest Neighbor Algorithm using the Weighted Jaccard Distance can yield superior retrieval efficiency when contrasted with alternative approaches found in the existing literature.

The audiovisual competition effect induced by temporal asynchronous encoding weakened the visual dominance in working memory retrieval

ABSTRACT Converging evidence suggests a facilitation effect of multisensory interactions on memory performance, reflected in higher accuracy or faster response time under a bimodal encoding condition than a unimodal condition. However, relatively little attention has been given to the effect of multisensory competition on memory. The present study adopted an adaptive staircase test to measure the point of subjective simultaneity (PSS), combined with a delayed matched-to-sample (DMS) task to probe the effect of audiovisual competition during the encoding stage on subsequent unisensory retrieval. The results showed that there was a robust visual dominance effect and multisensory interference effect in WM retrieval, regardless of the subjective synchronous or subjective asynchronous audiovisual presentation. However, a weakened visual dominance effect was observed when the auditory stimulus was presented before the visual stimulus in the encoding period, particularly in the semantically incongruent case. These findings revealed that the prior-entry of sensory information in the early perceptual stage could affect the processing in the late cognitive stage to some extent, and supported the evidence that there is a persistent advantage for visuospatial sketchpad in multisensory WM.

Cobalt Oxide Nanoparticle Synthesis by Cell-Surface-Engineered Recombinant Escherichia coli and Potential Application for Anticancer Treatment.

Cell surface display engineering facilitated the development of a cobalt-binding hybrid Escherichia coli. OmpC served as the molecular anchor for showcasing the cobalt-binding peptides (CBPs), creating the structural model of the hybrid OmpC-CBPs (OmpC-CP, OmpC-CF). Subsequently, the recombinant peptide's cobalt adsorption and retrieval effectiveness were evaluated at various concentrations. When subjected to a pH of 7 and a concentration of 2 mM, OmpC-CF exhibited a significantly higher cobalt recovery rate (2183.87 mol/g DCW) than OmpC-CP. The strain with bioadsorbed cobalt underwent thermal treatment at varying temperatures (400 °C, 500 °C, 600 °C, and 700 °C) and morphological characterization of the thermally decomposed cobalt nanoparticle oxides using diverse spectroscopy techniques. The analysis showed that nanoparticles confined themselves to metal ions, and EDS mapping detected the presence of cobalt on the cell surface. Finally, the nanoparticles' anticancer potential was assessed by subjecting them to heating at 500 °C in a furnace; they demonstrated noteworthy cytotoxicity, as evidenced by IC50 values of 59 μg/mL. These findings suggest that these nanoparticles hold promise as potential anticancer agents. Overall, this study successfully engineered a recombinant E. coli capable of efficiently binding to cobalt, producing nanoparticles with anticancer properties. The results of this investigation could have significant implications for advancing novel cancer therapies.

Cling together, swing together? Assessing indirect retrieval of stimulus-response bindings for associated stimuli.

When a stimulus is paired with a response, a stimulus-response (SR) binding (or event file) is formed. Subsequent stimulus repetition retrieves the SR binding from memory, which facilitates (impedes) performance when the same (a different) response is required. We aimed to explore whether indirect retrieval of SR bindings by a newly learnt associated stimulus is possible. Participants first went through a learning task to acquire novel stimulus-stimulus associations. The same stimulus pairs were then presented in a prime-probe task to assess direct and indirect retrieval effects. Participants responded by classifying word color in prime and probe trials. Probe words were either identical to prime words (test for direct retrieval) or corresponded to the associated stimulus (test for indirect retrieval) or were unrelated words (baseline). Independently of word relation, response relation (repetition vs. change) across prime and probe trials was manipulated. In two highly powered preregistered studies (total N = 260) using different types of stimulus associations, we obtained evidence for direct retrieval due to identical word repetition in the probe. Crucially, evidence for indirect retrieval upon presentation of an associated probe word was absent. Controlling for memory of each stimulus-stimulus association did not alter the findings. Our results show that indirect retrieval through newly acquired associations does not occur at the level of SR bindings, at least not for recently acquired stimulus-stimulus associations. Our study illustrates the scope of binding principles and highlights boundary conditions for the stimulus properties that can elicit automatic response retrieval.

Memory retrieval effects as a function of differences in phenomenal experience.

Conscious experience and perception are restricted to a single perspective. Although evidence to suggest differences in phenomenal experience can produce observable differences in behavior, it is not well understood how these differences might influence memory. We used fMRI to scan n = 49 participants while they encoded and performed a recognition memory test for faces and words. We calculated a cognitive bias score reflecting individual participants' propensity toward either Visual Imagery or Internal Verbalization based on their responses to the Internal Representations Questionnaire (IRQ). Neither visual imagery nor internal verbalization scores were significantly correlated with memory performance. In the fMRI data, there were typical patterns of activation differences between words and faces during both encoding and retrieval. There was no effect of internal representation bias on fMRI activation during encoding. At retrieval, however, a bias toward visualization was positively correlated with memory-related activation for both words and faces in inferior occipital gyri. Further, there was a crossover interaction in a network of brain regions such that visualization bias was associated with greater activation for words and verbalization bias was associated with greater activation for faces, consistent with increased effort for non-preferred stimulus retrieval. These findings suggest that individual differences in cognitive representations affect neural activation across different types of stimuli, potentially affecting memory retrieval performance.

An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models

With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval models using a bi-encoder to encode each query or document into a dense vector, the recently proposed late-interaction multi-vector models (i.e., ColBERT and COIL) achieve state-of-the-art retrieval effectiveness by using all token embeddings to represent documents and queries and modeling their relevance with a sum-of-max operation. However, these fine-grained representations may cause unacceptable storage overhead for practical search systems. In this study, we systematically analyze the matching mechanism of these late-interaction models and show that the sum-of-max operation heavily relies on the co-occurrence signals and some important words in the document. Based on these findings, we then propose several simple document pruning methods to reduce the storage overhead and compare the effectiveness of different pruning methods on different late-interaction models. We also leverage query pruning methods to further reduce the retrieval latency. We conduct extensive experiments on both in-domain and out-domain datasets and show that some of the used pruning methods can significantly improve the efficiency of these late-interaction models without substantially hurting their retrieval effectiveness.

A model of the relationship between the variations of effectiveness and fairness in information retrieval

The requirement that, for fair document retrieval, the documents should be ranked in the order to equally expose authors and organizations has been studied for some years. The fair exposure of a ranking, however, undermines the optimality of the Probability Ranking Principle and as a consequence retrieval effectiveness. It is shown how the variations of fairness and effectiveness can be related by a model. To this end, the paper introduces a fairness measure inspired in Gini’s index of mutability for non-ordinal variables and relates it to a general enough measure of effectiveness, thus modeling the connection between these two dimensions of Information Retrieval. The paper also introduces the measurement of the statistical significance of the fairness measure. An empirical study completes the paper.

A Novel Semantic Content-Based Retrieval System for Hyperspectral Remote Sensing Imagery

With the growing use of hyperspectral remote sensing payloads, there has been a significant increase in the number of hyperspectral remote sensing image archives, leading to a massive amount of collected data. This highlights the need for an efficient content-based hyperspectral image retrieval (CBHIR) system to manage and enable better use of hyperspectral remote-sensing image archives. Conventional CBHIR systems characterize each image by a set of endmembers and then perform image retrieval based on pairwise distance measures. Such an approach significantly increases the computational complexity of the retrieval, mainly when the diversity of materials is high. Those systems also have difficulties in retrieving images containing particular materials with extremely low abundance compared to other materials, which leads to describing image content with inappropriate and/or insufficient spectral features. In this article, a novel CBHIR system to define global hyperspectral image representations based on a semantic approach to differentiate foreground and background image content for different retrieval scenarios is introduced to address these issues. The experiments conducted on a new benchmark archive of multi-label hyperspectral images, which is first introduced in this study, validate the retrieval accuracy and effectiveness of the proposed system. Comparative performance analysis with the state-of-the-art CBHIR systems demonstrates that modeling hyperspectral image content with foreground and background vocabularies has a positive effect on retrieval performance.

Impact of Channel Selection with Different Bandwidths on Retrieval at 50–60 GHz

Microwave hyperspectral instruments represent one of the main atmospheric sounders of China’s next-generation Fengyun meteorological satellites. In order to better apply microwave hyperspectral observations in the fields of atmospheric parameter retrieval and data assimilation, this paper analyzes the sensitivity of trace gases to five selected bandwidth channels using a radiative transfer model based on the simulated data of microwave hyperspectral radiances at 50–60 GHz. This method uses information entropy and a weighting function to select channels and analyze the impact of this on the retrieval accuracy of atmospheric profiles before and after channel selection. The experimental results show that channel selection can reduce the number of channels by approximately 74.05% while maintaining a large amount of information content, and this retrieval effect is significantly better than that of MWTS-III. After channel selection, the 10 MHz, 30 MHz, and 50 MHz bandwidths have the best retrieval results in the stratosphere, whole atmosphere, and troposphere, respectively. When considering the number of channels, computational scale, and retrieval results comprehensively, the channel selection method is effective.

Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest

Precipitation is the basic component of the Earth’s water cycle. Obtaining high-resolution and high-precision precipitation data is of great significance. This paper establishes a precipitation retrieval model based on a random forest classification and regression model during the day and at night with FY-4B/AGRI Level1 data on China from July to August 2022. To evaluate the retrieval effect of the model, the GPM IMERG product is used as a reference, and the retrieval results are compared against those of the FY-4B/AGRI operational precipitation product. In addition, the retrieval results are analyzed according to different underlying surfaces. The results showed that compared with the FY-4B/AGRI operational precipitation product, the retrieval model can better identify precipitation and capture precipitation areas of light rain, moderate rain, heavy rain and torrential rain. Among them, the probability of detection (POD) of the day model increased from 0.328 to 0.680, and the equitable threat score (ETS) increased from 0.252 to 0.432. The POD of the night model increased from 0.337 to 0.639, and the ETS score increased from 0.239 to 0.369. Meanwhile, the precipitation estimation accuracy of the day model increased by 38.98% and that of the night model increased by 40.85%. Our results also showed that due to the surface uniformity of the ocean, the model can identify precipitation better on the ocean than on the land. Our findings also indicated that for the different underlying surfaces of the land, there is no significant difference in each evaluation index of the model. This is a strong argument for the universal applicability of the model. Notably, the results showed that, especially for more vegetated areas and areas covered by water, the model is capable of estimating precipitation. In conclusion, the precipitation retrieval model that is proposed herein can better determine precipitation regions and estimate precipitation intensities compared with the FY-4B/AGRI operational precipitation product. It can provide some reference value for future precipitation retrieval research on FY-4B/AGRI.

Towards Efficient and Effective Text-to-Video Retrieval with Coarse-to-Fine Visual Representation Learning

In recent years, text-to-video retrieval methods based on CLIP have experienced rapid development. The primary direction of evolution is to exploit the much wider gamut of visual and textual cues to achieve alignment. Concretely, those methods with impressive performance often design a heavy fusion block for sentence (words)-video (frames) interaction, regardless of the prohibitive computation complexity. Nevertheless, these approaches are not optimal in terms of feature utilization and retrieval efficiency. To address this issue, we adopt multi-granularity visual feature learning, ensuring the model's comprehensiveness in capturing visual content features spanning from abstract to detailed levels during the training phase. To better leverage the multi-granularity features, we devise a two-stage retrieval architecture in the retrieval phase. This solution ingeniously balances the coarse and fine granularity of retrieval content. Moreover, it also strikes a harmonious equilibrium between retrieval effectiveness and efficiency. Specifically, in training phase, we design a parameter-free text-gated interaction block (TIB) for fine-grained video representation learning and embed an extra Pearson Constraint to optimize cross-modal representation learning. In retrieval phase, we use coarse-grained video representations for fast recall of top-k candidates, which are then reranked by fine-grained video representations. Extensive experiments on four benchmarks demonstrate the efficiency and effectiveness. Notably, our method achieves comparable performance with the current state-of-the-art methods while being nearly 50 times faster.

LISR: Learning Linear 3D Implicit Surface Representation Using Compactly Supported Radial Basis Functions

Implicit 3D surface reconstruction of an object from its partial and noisy 3D point cloud scan is the classical geometry processing and 3D computer vision problem. In the literature, various 3D shape representations have been developed, differing in memory efficiency and shape retrieval effectiveness, such as volumetric, parametric, and implicit surfaces. Radial basis functions provide memory-efficient parameterization of the implicit surface. However, we show that training a neural network using the mean squared error between the ground-truth implicit surface and the linear basis-based implicit surfaces does not converge to the global solution. In this work, we propose locally supported compact radial basis functions for a linear representation of the implicit surface. This representation enables us to generate 3D shapes with arbitrary topologies at any resolution due to their continuous nature. We then propose a neural network architecture for learning the linear implicit shape representation of the 3D surface of an object. We learn linear implicit shapes within a supervised learning framework using ground truth Signed-Distance Field (SDF) data for guidance. The classical strategies face difficulties in finding linear implicit shapes from a given 3D point cloud due to numerical issues (requires solving inverse of a large matrix) in basis and query point selection. The proposed approach achieves better Chamfer distance and comparable F-score than the state-of-the-art approach on the benchmark dataset. We also show the effectiveness of the proposed approach by using it for the 3D shape completion task.

Abstract 3755: Impact of antigen retrieval and other parameters on detection of FGFR2b in various solid tumors on a BenchMark ULTRA automated staining platform: Development of a new IHC assay for FGFR2b

Abstract Background: Fibroblast growth factor receptor 2b (FGFR2b) is part of the FGFR transmembrane tyrosine kinase receptor family. This protein has gained more attention in recent years as a tumor progression biomarker in numerous solid tumors. The detection of FGFR2b by immunohistochemistry (IHC) has been used recently in immunotherapy clinical trials with multiple tumor types. During IHC assay development on formalin-fixed paraffin embedded tissue, a thorough study of antigen retrieval through cell conditioning or enzyme incubation, primary antibody incubation, and detection parameters is critical to evaluate staining performance. Design: A study of an investigational anti-FGFR2b (FPR2-D) assay developed on a BenchMark ULTRA automated staining platform explored the effect of antigen retrieval and detection parameters on staining performance. VENTANA System Software was used to modify steps in the IHC staining protocol resulting in 42 permutations. Ten different solid tumors were stained with these modifications to the assay and subsequently evaluated for FGFR2b expression. Result:s The results demonstrate that the staining clarity, intensity, and dynamic range within tissue cases of FGFR2b staining can vary widely between different solid tumor types. While most permutations of the initial assay parameters had minimal impact on the staining performance and readability by a pathologist, the order of the enzyme treatment and the cell conditioning steps (in addition to increased incubation time of primary antibody) had a significant impact when comparing across different solid tumors. Detection of FGFR2b seemed to have a greater susceptibility by tumor type to the antigen retrieval process than has traditionally been observed in other biomarkers. Conclusion: The overall results of this investigation show the importance of an extensive parameter screening approach when evaluating new antibody targets and indication combinations in IHC to ensure optimal staining performance. This is critical to downstream testing and the potential use of new assays in the context of in vitro diagnostics. Citation Format: Autumn Sky Watson, Catherine Le, Anne M. Wertheimer, Spencer Escobedo, Lisa So, Bhavani Bagevalu Siddegowda, John D. Palting, Susan Marion, Steven P. Stratton, Birte Aggeler. Impact of antigen retrieval and other parameters on detection of FGFR2b in various solid tumors on a BenchMark ULTRA automated staining platform: Development of a new IHC assay for FGFR2b [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3755.

Time series retrieval of Multi-wavelength Aerosol optical depth by adapting Transformer (TMAT) using Himawari-8 AHI data

Accurate aerosol optical depth (AOD) with high temporal resolution is required for dynamic monitoring changes of aerosol properties. The Himawari-8 Advanced Himawari imager (AHI) data with 10-min temporal resolution has been widely used for AOD retrieval but usually involves radiative transfer models and assumptions on surface reflection and aerosol properties that are hard to satisfy. This paper introduces a data-driven algorithm without any priori assumptions on surface or aerosol properties called Time series retrieval of Multi-wavelength AOD by adapting Transformer (TMAT). TMAT directly retrieves AOD at eight-wavelengths every 10-min throughout the day, using AHI top of atmosphere (TOA) reflectances in 6 wavebands and ancillary data. TMAT explores diurnal temporal change patterns of AOD and surface reflection using time series data. TMAT was trained and evaluated using samples derived by matching a year (2019) of AHI full-disk data and AERONET/SONET observations with 12,768 diurnal time series, each of which includes up to 81 10-min 6-band AHI TOA observations and 81 (time) × 8 (wavelength) AOD values per day. There are up to 273,413 (<12,768 × 81) TOA observations in 6 wavebands and up to 1,420,716 (<12,768 × 81 × 8) AOD values in all the time series samples, with filled values (−9999) to represent no data due to cloud contamination and incomplete wavelength configurations of AOD stations. The masking mechanism in Transformer is leveraged to exclude the contribution of the AHI filled values in time series AOD retrieval and exclude the contribution of AOD filled values in training. TMAT was trained with a physical constraint that the retrieved AOD decreases toward longer wavelengths. TMAT AOD was evaluated by separating training and validation samples using site-specific leave-one-station-out validation. TMAT AOD shows extremely high accuracy at all wavelengths, with RMSE = 0.101, R = 0.949 and 81.69% of retrievals within ±0.05+15%∗AODsites for the AOD at 550 nm. We also validated the accuracy of the NASA multiangle implementation of atmospheric correction (MAIAC) AHI AOD over these sites with RMSE = 0.186, R = 0.832 and 52.10% of retrievals within ±0.05+15%∗AODsites. The Ångström exponent (AE) derived from the multi-wavelength TMAT AOD also shows good agreement with AERONET/SONET AE, with a correlation coefficient of 0.620 and an RMSE of 0.291. The TMAT effectiveness to capture diurnal temporal change patterns of surface reflection and AOD was evidenced by the significant reduction of AOD accuracy when using a non-time series deep learning method. The effectiveness of multi-wavelength joint retrieval was evidenced by the significant reduction of AE accuracy when using single wavelength retrieval models. Evidence shows the training and evaluation split methods have a large impact on evaluation accuracy and this study further used ground network independent training and evaluation split to confirm the leave-one-station-out validation accuracy. Despite this, future study will focus on more spatially independent validation and more comprehensive comparison between the MAIAC and TMAT results.

On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation

On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation